diff --git a/CMakeLists.txt b/CMakeLists.txt index c473e2c0..250cba63 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -73,17 +73,20 @@ set(headers src/sceneStructs.h src/preview.h src/utilities.h + src/ImGui/imconfig.h - - src/ImGui/imgui.h + src/ImGui/imgui.h src/ImGui/imconfig.h src/ImGui/imgui_impl_glfw.h - src/ImGui/imgui_impl_opengl3.h - src/ImGui/imgui_impl_opengl3_loader.h - src/ImGui/imgui_internal.h - src/ImGui/imstb_rectpack.h - src/ImGui/imstb_textedit.h - src/ImGui/imstb_truetype.h + src/ImGui/imgui_impl_opengl3.h + src/ImGui/imgui_impl_opengl3_loader.h + src/ImGui/imgui_internal.h + src/ImGui/imstb_rectpack.h + src/ImGui/imstb_textedit.h + src/ImGui/imstb_truetype.h + + src/tinygltf/tiny_gltf.h + src/tinygltf/json.hpp ) set(sources @@ -97,12 +100,12 @@ set(sources src/utilities.cpp src/ImGui/imgui.cpp - src/ImGui/imgui_demo.cpp - src/ImGui/imgui_draw.cpp - src/ImGui/imgui_impl_glfw.cpp - src/ImGui/imgui_impl_opengl3.cpp - src/ImGui/imgui_tables.cpp - src/ImGui/imgui_widgets.cpp + src/ImGui/imgui_demo.cpp + src/ImGui/imgui_draw.cpp + src/ImGui/imgui_impl_glfw.cpp + src/ImGui/imgui_impl_opengl3.cpp + src/ImGui/imgui_tables.cpp + src/ImGui/imgui_widgets.cpp ) list(SORT headers) diff --git a/README.md b/README.md index 110697ce..de3d67a0 100644 --- a/README.md +++ b/README.md @@ -3,11 +3,226 @@ CUDA Path Tracer **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 3** -* (TODO) YOUR NAME HERE -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Alan Qiao +* Tested on: Windows 11 22H2, Intel Xeon W-2145 @ 3.70GHz 64GB, RTX 3070 8GB (Dorm Workstation) -### (TODO: Your README) + -*DO NOT* leave the README to the last minute! It is a crucial part of the -project, and we will not be able to grade you without a good README. +# Introduction +This project demonstrates a relatively basic implementation of a CUDA path tracer where the final image is displayed through OpenGL. The project offers a naive path tracer that implements the Light Transport Equation for Lambertian diffuse materials, specular transmission, specular reflection, and Fresnel Dielectrics. + +The camera model simulates a pinhole camera, with support for adjustable depth of field and stochastic antialiasing. The camera can be moved using the middle mouse button, rotated with the left mouse button, and zoomed in and out with the right mouse button. + +Objects in the scene can be specified in two ways. +1. Basic geometries. + 1. Currently, only cubes and sphere are supported. + 2. Each geometry can be supplied with a different transformation. + 3. Geometry materials support diffuse coloring, a different specular color for optional reflection or refraction with custom index of refraction, and any combination of these. Colors cannot have alpha channel. +2. GLTF Meshes + 1. Currently, only `.gltf` and `.glb` files with embedded textures are supported. + 2. The mesh as a whole can be transformed by specifying a custom transformation. + 3. Meshes must consist only of traingles as primitives. Vertex position, normal, and uv information is required for correct rendering. Only diffuse shading with textures is implemented. Transparency in textures is supported. + +To execute the path tracer, call the built executable in the command line with the commandline argument `address/to/your/scene_name.txt`. Examples of the expected format for scene files can be found in the included demos under `scenes/`. + +# Background +Consider taking a picture of a park with a camera. Light from a light source, such as the sun, shines on objects like trees, flowers or grasses. Some light is reflected by these objects into the cameras lens, and the camera captures the light it detected pixel by pixel into a picture. + +Path tracing is a computer graphics rendering technique that tries to simulate this idea. +Path tracing tries to get an acccurate representation of how light would illuminate objects, both directly and indirectly, by tracing paths of light as they travel through a scene. Instead of following light rays from the light source, rays are traced backwards from the camera into the scene. + +For each pixel in the rendered image: + +A ray ("camera ray") is shot from the camera into the scene. +If this ray intersects a surface, the intersection point becomes a new starting point. +From this point, a new ray ("bounce ray" or "scatter ray") is shot in a random direction based on the material's reflection properties. +This process is repeated, bouncing off surfaces, until a predefined depth or until a light source is hit. +Each bounce gathers light (or color) from the objects it interacts with. The gathered light values are combined to calculate the final color of the pixel. + +### Naive Path Tracing +In "naive" path tracing, every time a ray hits a surface, a single random bounce direction is chosen. This is the simplest method that would capture shadows and colored colored reflections from other objects in the scene. Because it only traces one path per ray, each iteration of naive path tracing can be rather noisy. This noise can be reduced by averaging the results of multiple samples per pixel, but there can still be some artifacts that are difficult to eliminate even with many samples. + +#### Advantages: + +1. Physically Accurate: Path tracing can produce images with photorealistic qualities, capturing complex lighting interactions. +2. Unified Model: Instead of separate algorithms for shadows, reflections, and refractions, path tracing offers a unified approach to handle all these effects. + +#### Limitations: + +1. Noise: Naive path tracing can produce noisy images, especially in areas with indirect light. +2. Computationally Intensive: Achieving noise-free images requires many samples, making it computationally expensive. +3. Poor parallelization: Even though rendering is embarassingly parallel, it is difficult to leverage the full potential of GPU rendering enabled by CUDA programming as naive path tracing reuses the same gemometry data across threads and iterations, but these buffers are often too big to fit into shared memory. Furthermore, there are a lot of kernel calls, but each call tends to do relatively little work. Lastly, in a complex scene, there can be a lot of branching that results from the unified nature of the model, resulting in many idle threads. + +# Features +The base path tracing loop is exactly as described in the background section. For a specified depth and number of iterations, the path tracing keeps sampling up until the specified depth and averaging the results until the total number of iterations are reached. +In a single cycle, the path tracer first generates rays from the camera, one per pixel. Next, each ray is tested against the entire scene for intersection with the closest object. Then, for each intersection, the color contribution from that surface is accumulated and a new ray is generated based on the material properties. The process of computing intersections and scaterring the rays repeats until either a light is hit, at which point the ray returns a final color for coloring this pixel, or the max depth is reached, in which case black is returned as the color for this pixel. These returned colors are then accumulated into the current rendered image, average for the number of iterations, and the final image displayed to screen. +To leverage parallel computing capabilities, this loop is split into four kernel functions: generate camera rays, compute intersections, shade and scatter rays, and gather the ray data and update the image buffer. This way, kernels of different sizes taking different data can be launched to more optimally compute for the distinctly different tasks. + +![](img/basic_cornell.5000samp.png) +This is what the base path tracer produces for a cornell scene with a single square light and diffuse sphere. There's a shadow of the sphere on the floor, slight imprints of the colors of the walls and a sheen from the light. + +## Path Tracing Loop Optimizations +The first of optimizations that could be made are to the loop itself. Splitting up into 4 kernels allows for some work in between the kernels to reduce the number of threads needed at the next stage. + +For this section, we will be experimenting with the following two scenes: + + +On the left is the open box demo. It consists of 5 diffuse cubes for walls, 1 Fresnel Dielectric glass cube, 1 reflective mirror cube, 2 diffuse spheres, 1 emissive cube as the light, and a Seele mesh with 23079 triangles. There are a total of 30 materials, consisting of 6 geometry materials and 24 texture materials. + +On the right is the closed box demo. It has all the same components as the open box demo exept that there is one extra diffuse blue cube wall behind the camera. + +By default, both scenes are rendered at 800x800 resolution with trace depth of 8 for a total of 10,000 sample iterations. + +### Early Ray Termination +If a camera doesn't intersect with the scene. That ray will simply return the color black. Similarly, any rays that failed to reach a light after `MAX_DEPTH` bounces should also return black. The last kind of rays that would terminate early are those that hit a light before max depth is reached. All of these early terminated rays can be easily distinguished by setting `remainingBounces = 0`. This means we can launch the kernels in the next iteration without these rays as they all already have their final colors. We can't simply discard these rays from memory as we still need to accumulate their color. +The solution is to partition the rays buffer by the condition `remainingBounces > 0`. By placing all rays that have terminated at the back, we can launch a kernel with `num_active_paths` threads. + +![](img/stream_compaction_depth.png) +Figure 1: Number of paths remaining with stream compaction for early ray termination with respect to path tracing depth. Two cornell scenes that are identical except one box has an open face while the other is fully closed are used. Number of active rays hits 0 on the 21st path in both scenes as the max depth is 20. + +As seen in this graph, the effect of stream compaction with partitioning is most prominent after a few bounces with diminishing returns. In the open box case, this makes sense because as the rays bounce around more, there is a greater chance that it bounces out the open face into void and thus get terminated for no intersection. In the closed box case, the effect of stream compaction is much smaller as rays are trapped in a box and will certainly collide with a surface. Notice, however, that there is a nearly linear trend in the number of rays terminating in the closed box case, suggesting that about an equal number of rays hit the light in a given pass. Another interesting observation is that most rays actually fail to terminate by the 20th bounce and so nearly 90% of the rays shot are actually wasted in the closed box case. + +### First Bounce Cache +In a simple model where the camera always generates the same rays for each pixel, that is, a ray extending outwards from the center of each pixel in the camera's viewport, the first bounce of any sample iteration would always hit the same objects. As a result, we can cache this ray to save one iteration of intersection computation. + +![](img/first_bounce_cache_on_rays.png) +Figure 2: Time required to render each frame in the closed box scene with respect to the number of rays generated by the camera. The lower the Time/Frame the better. + +It turns out that first bounce cache creates a negligible improvement even as the number of rays that are cached increases. One possible explanation for this is that the cost of computing intersections in a scene with relatively few geometries is small enough that it is not much greater than the cost of copying a cached buffer to the intersections buffer in memory. Perhaps with an even more complex scene there would be a more significant effect, but for the closed box demo, the improvement is negligible. + +Furthermore, this technique inherent conflicts with other camera based features like stoachastic antialiasing and depth of field adjustment. Thus, this technique was not used in the final implementation. + +### Material Sorting +As there is a single shading kernel that is responsible for shading all the different materials, there is inevitably a lot of branching that occurs as a result of threads calling different device functions to shade for different materials. Since CUDA schedules threads in warps, it may be possible to reduce the idle time from branching by sorting the paths by material so that there is a higher chance an entire warp would have threads all calling the same branch. + +![](img/sort_by_material_on_scenes.png) +Figure 3: The effect of sorting rays by material before shading in rendering the closed box demo and the open box demo. The lower the Time/Frame the better. + +It is clear from the graph that the cost of sorting rays by material significantly outweighs the benefit of branching reduction. This makes sense as modern GPUs have much better performance with some branching and even if threads are idle, the time of computing a new ray direction and shading in each branch is so small that the idle is still significantly smaller than sorting, which tends to be a very expensive operation even with GPU parallelization. + +## Base Naive Path Tracing +For basic path tracing, how a ray is colored and where it is scattered next is dependent on the properties of the material it intersects, and this is simulated by the light transport equation. The three most basic forms are diffusion, specular reflection, and specular transmission. The following images showcases all these shading scenarios. + +![](img/shading_cornell.10000samp.png) + +The leftmost cuboid features both specular reflection and specular transmission with an index of refraction of 1.55, intended to simulate glass. The middle cube has only specular transmission with an index of refraction of 1.55. The rightmost cube has only specular reflection, and is intended to simulate a mirror. The top sphere is a diffuse sphere with a grayish-white diffuse color. + +### Diffusion +A diffusive material diffuses light in all directions with equal probaility. This creates the matte looking texture that we see in most rougher opaque objects. The color of light diffused depends on the color of the material, and to some extend also the influence of reflected light from other nearby materials. +This is most obvious on the diffuse sphere in the middle, where reflections from the walls and the light can be seen lightly on the edges of the sphere, while the base color of the sphere, a grayish white, is still visible overall. + +### Specular Reflection +Specular reflection describes the mirror-like reflection of light. That is, when light hits the surface, it is reflected in the exact opposite angle relative to the surface normal. Since light coming from the same direction always bounces off in the same manner, we get a consistent reflection of incoming rays, and hence the mirroring proprety. + + In code, this is simply implemented as the reflection of the incoming ray across the surface normal, and the color is simply the color of the incoming ray influenced by the color of the material itself. (Yes, you can make a blue mirror here by specifying a specular color) + + The rightmost cube is a mirror with a white specular color, so it just reflects light coming to it. On the faces visible, we see the influence of the floor, the back wall, and the green wall. The bit of black reflects the lack of light from the void outside the box. We can also see some light reflected on the floor to the left face of the mirror cube that comes from the mirror reflecting direct light from the light source. + +### Specular Transmission +Specular transmission essentially describes refraction through a uniform material. When light enters a medium that is denser, it bends slightly towards the surface normal of the incident surface separating the mediums, and when it enters a medium that is less dense, it bends away from the surface normal. This results in the bending effect we see on a stick that is half-submerged in water. + +For this implementation, specular transmission is simulated using snell's law to calcualte the bending of the ray. Unlike reflection, transmitted rays pass into the object and then out from the other side, hence the name transmission. The incident ray color is influenced by the specular color of the transmissive material similar to specular reflection. + +This can be seen in the middle cube, where we can clearly see the floor and wall behind it, but there is some slightly distortion to the position of the wall corner. We also see some red light from the left wall that passes through the cube, showing on both the left and right face. + +### Fresnel Dielectric +In reality, when light strikes a specular surface, some of it is reflected and some of it is refracted. The Fresnel equations describes this ratio of reflected light to refracted light. The full set of equations describes much more than just transmission to reflection ratio, but for this purpose we are only interested in teh transmission and reflection coefficients. + +In this implementation, when a ray hits a dielectric surface, there is a 50% chance that it will be sampled using specular reflection, and 50% chance that it will be sampled using specular transmission. The color, or intensity, of the new resultant ray is then attenuated by the corresponding coefficient to account for the correct ratio of influence. However, since on any iteration only either of reflection or refraction is sampled, the effect of each ray is doubled so that when averaged out across samples, the effect would be the same as taking both a reflection and refraction sample on each iteration. + +This can be seen on the left glass cuboid. While it is slightly difficult to see the different between this glass cuboid and the center transmissive cube, the light reflected on the red wall gives a more obvious indication that reflection is also happening. + +### Further Improvements +The additional performance cost of implementing these different shaders is relatively small as each sampling function takes about the same amount of computation, and the branching time from four brief branches isn't too noticeable. Compared to the most basic cornell box with one diffuse sphere, the extra geometries introduced a mere 6ms additional rendering time per frame. + +One major limitation of this implementation though is that the exact sampling code can also run on the CPU, which means that the parallel architecture of the GPU is not leveraged at all beyond the parallelization benefits from the Path Tracing Loop. In fact, there is little room for improvement to the sampling functions themselves as they are just solving a few simple equations. A more thorough, but definitely more useful change, especially with a sufficiently complex scene, would be to separate the shading kernel into several dedicated kernels, one for each type of material. This will resolve the impact of branching. However, it is also unclear how effective this would be given the expensive cost of material sorting discussed earlier. + +In terms of additional sampling methods, two methods that would increase the photorealisticness of the simulation greatly are microfacet sampling and subsurface scattering. Microfacet sampling allows for the simulation of materials with various roughness, which would allow the simulation of a much greater variety of real-life materials. Subsurface scattering on the other hand would improve the realisticness of materials like skin, where some light can pass through and be visible as a change to the surface color of the material. + +## Camera Effects +While pinhole camera with an infinitely small aperture is not possible in the real world, we can simulate some realistic effects as well as improve the quality of our render by tweaking the way we generate rays from the camera. These changes are often computationally inexpensive but can make dramatic differences in the output. + +### Stochastic Antialiasing +Aliasing describes the jagged edges that result from the fact that pixels have area and thus can't perfectly represent an edge between two surfaces. This can result in the diagonal stairscase outlines you sometimes see in certain video games, or, in cases where the primitives sampled are small enough relative to the pixel size, the complete omission of some detail or even gaps. This is caused by the camera always shooting out rays form the center of a pixel, causing the region in between two pixel centers to be missed. Take a look at the closed box demo rendered without antialiasing below on the left. + + + +There are rough edges at corners of the walls and around the contour of Seele. These jagged edges are the result of these pixel samples only select either of the two intersecting materials as the color of the entire pixel. + +Another interesting and jarringly artifact is the gap straight down the middle of Seele's face. This particular model of Seele has a rather abrubt height change towards the center line of the face to represent the protrusion from the nose arch. It is clear from the wireframe on the right that from a front view, there are only a few very narrow traingles that account for most of the elevation change. This can result in a very unique situation where the sampled ray intersects with her face exactly on the edge between the triangles that define the two sides of her nose bridge. It happens that neither triangle considers the ray as in bounds and thus it passes straight through the mesh to the back wall, and returns the blue color of the wall as the pixel color. + +All of these artifacts can be solved by stochastic antialiasing, which is a very simple technique that jiggers the positions within a pixel from which a ray is generated between different samples. Using a uniform jigger, we will sample the entirety of the pixel with approximately equal weighting given enough samples. This way, the averaged pixel color would be a blend of the colors of all surfaces within the pixel, resulting in a smoother transition and elimination of a rays unexpectedly tunneling through meshes. Below is a side by side comparison of the render without aliasing from above and a render with antialiasing to illustrate the difference. + + + +The additional computational cost per ray of stochastic antialiasing is just taking two uniform samples between [0, 1] and adding it to the position of the ray. Again, this cost is negligible compared to the other more expensive parts of the cycle, and performs similarly to a CPU implementation. (The parallelization of ray generation on GPU is certainly faster than sequential generation on CPU, but this difference is not a result of antialiasing implementation) + +### Depth of Field +Real cameras have lenses that focus light through a finite-sized aperture on the the receptors on the camera's film plane. Lenses can only focus on a single plane, and as a result objects that are further away from the focus plane will appear blurrier. This bigger the aperture, the greater this effect. In practice, objects do not have to be exactly on the plane of focus to appear in sharp focus. There is actually a region of focus, which is called the lens' depth of field. + +For this implemention, this effect is simulated using the thin-lens approximation, which specifies a simple set of equations to calculate where a light ray would focus on the focal plane based on the angle and position at which it passes through the lens. For each ray generated, we simply transform it by how it would bend given where it would pass through the imaginary lens. The refractive properties of the thin less would effective create a cone that pentrates through the plane of focus, where a circle of rays coming from beyond the plane of focus would focus onto a single point on the lens, causing all their rays to blend into one, resulting in the blurry effect. + +Below we demonstrate some renders with different combinations of focal distance and lens radius. + + + + +The computational cost of depth-of-field is a bit more noticeable. For the closed box demo, it adds on average 4ms per frame. This cost is independent of the focal distance and lens radius used. Perhaps the primary cause of this noticeable computational increase is that the thin lens equation involves divisions, and divisions are notoriously expensive arithmetic operations. Unfortunately, this division value cannot be cached as each ray uses a different divisor. Similar to antialiasing, the CPU implementation of this function would not be different. + +## Mesh Rendering +While spheres and cubes are great primitives to start with, they are very tedious to work with for constructing more complex models. Mesh based models, which are commonly used in CAD software for modeling pretty much anything from people to objects, are constructed from many interconnected triangles. The primary benefits of using triangles are that they are small to store, can approximate a smooth surface very well given enough suffiently small triangles, and for any point inside a triangle, its position, normals, and uvs can easily be interpolated using just the three vertices. Thus, any renderer isn't complete without support for mesh rendering. Since mesh rendeirng is much more difficult to implement and debug, this project only supports one rather particular format. + +### GLTF Mesh Loading +GLTF is a newer standard for storing meshes that can conveniently hold vertex, normals, uvs, and even material and texture data in one file. It also has support for animation, armatures, and other features commonly used in 3D animations, but those are not used in this implementation. + +Mesh loading is done on the GPU side with help from the TinyGLTF library for decoding the binary `.glb` format and json `.gltf` format. For each model in the file, we traverse iteratively through the models in every node and load its primitives into memory as a buffer of triangles. Each vertex of the triangles stores its position, normal, and uv. Once a mesh is fully loaded, it is linked to a geometry by specifying a starting index for its primitives in the buffer, and a count of the number of primitives to indicate the end of its occupied segment in the buffer. The path tracer can then interact with these triangle primitives by checking for intersection and then shading through its normal pipeline. The cost of checking mesh intersection is enormous. More details on this will be discussed in the Bounding Volume Hiearchy section. + +### Albedo texture mapping + +For more intricate models, it doesn't make sense to just specify a single material for each primitive. Within the space of a primitive, there can be a blend of colours or heterogeneous speckles, like realistic human skin. To faciliate this level of detail, a commonly used technique is to create a square image known as a texture and sample from the texture for the color to apply for a given mesh-ray intersection. Each vertex in a traingle records a 2D vector called uv, which is a normalized coordinate for sampling a texture. Furthermore, using barycentric interpolation, the exact point to sample on a texture can be computed for any point on a triangular primitive. + +In this implementation, each Triangle struct contains the three vertices and their data as well as an index to the relevant texture to sample. When a triangle is selected as the intersecting object, the shader then will look for its uv to extract the relevant color from the referenced texture, and shade it using diffuse shading. + +The outcome of GLTF mesh loading and albedo texture shading are demonstrated below in two scenes involving one and three Seele models respectively. Both scenes have two 4800K light sources acting as diffuse lights for portrait photography and a white diffuse box on the bottom for flooring. + +![](img/seele_scene.10000samp.png) +![](img/seele_portrait.10000samp.png) + +In terms of performance, most of the time is spent on the overhead cost of loading the textures from GLTF file, which is done on the CPU side by looping through the set of materials and associated textures to load them into memory buffers. The textures are then copied into GPU memory using CUDA dedicated texture objects. These objects are optimized for hardware level texture sampling, which makes the cost of sampling a texture for diffuse shade not too different from directly passing a diffuse color vector from global memory. Thus, while the start time has increased, the actual rendering time per frame does not change significantly between texture sampling and direct shading. + +### Bounding Volume Hierarchy +While naively looping through all the geometries in the scene works for simple scenes like the cornell boxes, this approach no longer works once meshes are introduced. The Seele model in open box and closed box demo as well as the single Seele demo consists of 23079 triangles. Naively iterating through all these traingles to test for intersection would take an extremely long time compared to checking 5 - 10 basic geometries in the basic cornell scenes. This clearly does not scale well as the three Seele scene, which is still a relatively simple scene, has 73788 triangles, and thus 73788 checks per ray generated. + +To reduce the number of checks required for finding an intersection, one option is to use a Bounding Volume hierarchy. The basic concept behind this is that each mesh is contained within an axis-aligned bounding box, which is then recursively subdivided into smaller bounding boxes containing less primitives. Each subdivision occurs along one axis so that all child bounding boxes remain axis-aligned. Axis-alignment allows a box to be specified with just two coordinates of opposing corners, thus minimizing the memory footprint required to define this structure. Depending on the BVH construction method, the number of checks required for finding an intersection between a mesh and a ray can be reduce to at least $\log(n)$, where $n$ is the number of primitives in the mesh. + +In this implementation, the surface area heuristic is used to determine the cost of making a division. For each subdivision, the axis is split into 8 bins, thus performing 7 checks for potential split and picking the one with the least cost. This implementation typically results in a shallower hierarchy, thus reducing the number of bounding box checks, but in turn may increase the number of primitive intersection checks in a leaf bounding box depending how sparsely the primitives are distributed. + +The construction of the BVH was done on the CPU because recursive subdivision does not work well on the GPU. While it is technically possible to implement parallel recursive construction using stacks and some careful controls, the complexity of avoiding undesired concurrent writing to the same memory buffer slot, maintaining the recursion stack, and recurisve sub-kernel launching makes the speed improvement insufficient to justify the extra implementation time. In fact the BVH construction for three Seeles took approximately 2 seconds, which is acceptable considering that this is a one-time initialization cost. + +For BVH traversal on the GPU side, this is implemented as a pseudo-recursive traversal using a stack. Each bounding box that the ray intersects is pushed into the stack and then its children boxes checked until a triangle is intersected or all intersecting bounding boxes have been tested. + +![](img/BVH_render_time.png) +Figure 4: The render time per frame for the three Seele scene and a single Seele scene with and without BVH. Both scenes have the same number of lights and non-mesh geometries for equal comparison. The lower Time/Frame the better. + +It is clear from the graph that the performance improvement from using BVH is significant, and the impact becomes even more prominent as the number of meshes increase. In both the single and triple Seele scene, the render time per frame for the BVH implementation was about 30ms, but the naive intersection algorithm scales linearly with the number primitives, taking about 500ms for one Seele mesh and about 1550ms for three Seele meshes. Compared to a CPU implementation of BVH, the per ray traversal performance would not be dramatically different as the same recursive process would be performed to check for intersection. + +### Further Improvements +The current support for mesh rendering is limited to a very particular format. For GLTF based models, more general support can be achieved by setting up the option to read material properties and thus shade a mesh with a uniform material with varying roughness, specular properties etc. Another additional feature would be to support importing textures directly from image files. This would allow support for gltf files without embedded textures and open up the option for supporting other file formats that do not support embedded textures like classic obj files. Texture mapping could also be expanded to support other kinds of maps, such as normal maps. + +In terms of performance, a stackless implementation of BVH could increase performance even further for even more complex scenes. The BVH could also be expanded to take other geometries so that sufficiently complex scenes consisting of other basic geometries could also benefit from less intersection tests. + +Furthermore, the memory footprint of the current mesh implementation can be improved by storing a separate buffer of vertices and have triangles store pointers to these vertices instead of copies. This can potentially reduce the memory footprint of a mesh dramatically as each vertex in a connected mesh can be shared by multiple triangles. For example, most vertices in the wireframe of Seele's face shown in the Antialiasing section are shared by 4 or 8 triangles, which means the memory footprint can be decreased by approximately 6 times. + + +## References +* Physically Based Rendering 3rd Edition [🔗](https://pbr-book.org/3ed-2018/contents) +* "How to build a BVH" - JBIKKER [🔗](https://jacco.ompf2.com/2022/04/13/how-to-build-a-bvh-part-1-basics/) +* TinyGLTF [🔗](https://github.com/syoyo/tinygltf) + +## Model Attributions +* Seele - Stygian Nymphs: 神帝宇/Mihoyo [🔗](https://www.aplaybox.com/details/model/lUlYznLigW7V) +* Seele - Stygian Nymphs New Years Outfit: 神帝宇/Mihoyo [🔗](https://www.aplaybox.com/u/359396473) +* Seele - Herrscher of Rebirth: 神帝宇/Mihoyo [🔗](https://www.aplaybox.com/details/model/4a70Pb6y7VaC) +* GLTF Box: Cesium [🔗](https://github.com/KhronosGroup/glTF-Sample-Models/tree/master/2.0/BoxTextured) +* Monkey Textured: Three.js Tutorials [🔗](https://sbcode.net/threejs/textured-gltf/) \ No newline at end of file diff --git a/external/include/stb_image.h b/external/include/stb_image.h index b9b265fa..323a5ec4 100644 --- a/external/include/stb_image.h +++ b/external/include/stb_image.h @@ -1,5 +1,5 @@ -/* stb_image - v2.06 - public domain image loader - http://nothings.org/stb_image.h - no warranty implied; use at your own risk +/* stb_image - v2.28 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk Do this: #define STB_IMAGE_IMPLEMENTATION @@ -21,17 +21,20 @@ avoid problematic images and only need the trivial interface JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) - PNG 1/2/4/8-bit-per-channel (16 bpc not supported) + PNG 1/2/4/8/16-bit-per-channel TGA (not sure what subset, if a subset) BMP non-1bpp, non-RLE - PSD (composited view only, no extra channels) + PSD (composited view only, no extra channels, 8/16 bit-per-channel) GIF (*comp always reports as 4-channel) HDR (radiance rgbE format) PIC (Softimage PIC) PNM (PPM and PGM binary only) + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) - decode from arbitrary I/O callbacks - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) @@ -39,177 +42,86 @@ Full documentation under "DOCUMENTATION" below. - Revision 2.00 release notes: - - - Progressive JPEG is now supported. - - - PPM and PGM binary formats are now supported, thanks to Ken Miller. - - - x86 platforms now make use of SSE2 SIMD instructions for - JPEG decoding, and ARM platforms can use NEON SIMD if requested. - This work was done by Fabian "ryg" Giesen. SSE2 is used by - default, but NEON must be enabled explicitly; see docs. - - With other JPEG optimizations included in this version, we see - 2x speedup on a JPEG on an x86 machine, and a 1.5x speedup - on a JPEG on an ARM machine, relative to previous versions of this - library. The same results will not obtain for all JPGs and for all - x86/ARM machines. (Note that progressive JPEGs are significantly - slower to decode than regular JPEGs.) This doesn't mean that this - is the fastest JPEG decoder in the land; rather, it brings it - closer to parity with standard libraries. If you want the fastest - decode, look elsewhere. (See "Philosophy" section of docs below.) - - See final bullet items below for more info on SIMD. - - - Added STBI_MALLOC, STBI_REALLOC, and STBI_FREE macros for replacing - the memory allocator. Unlike other STBI libraries, these macros don't - support a context parameter, so if you need to pass a context in to - the allocator, you'll have to store it in a global or a thread-local - variable. - - - Split existing STBI_NO_HDR flag into two flags, STBI_NO_HDR and - STBI_NO_LINEAR. - STBI_NO_HDR: suppress implementation of .hdr reader format - STBI_NO_LINEAR: suppress high-dynamic-range light-linear float API - - - You can suppress implementation of any of the decoders to reduce - your code footprint by #defining one or more of the following - symbols before creating the implementation. - - STBI_NO_JPEG - STBI_NO_PNG - STBI_NO_BMP - STBI_NO_PSD - STBI_NO_TGA - STBI_NO_GIF - STBI_NO_HDR - STBI_NO_PIC - STBI_NO_PNM (.ppm and .pgm) - - - You can request *only* certain decoders and suppress all other ones - (this will be more forward-compatible, as addition of new decoders - doesn't require you to disable them explicitly): - - STBI_ONLY_JPEG - STBI_ONLY_PNG - STBI_ONLY_BMP - STBI_ONLY_PSD - STBI_ONLY_TGA - STBI_ONLY_GIF - STBI_ONLY_HDR - STBI_ONLY_PIC - STBI_ONLY_PNM (.ppm and .pgm) - - Note that you can define multiples of these, and you will get all - of them ("only x" and "only y" is interpreted to mean "only x&y"). - - - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still - want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB - - - Compilation of all SIMD code can be suppressed with - #define STBI_NO_SIMD - It should not be necessary to disable SIMD unless you have issues - compiling (e.g. using an x86 compiler which doesn't support SSE - intrinsics or that doesn't support the method used to detect - SSE2 support at run-time), and even those can be reported as - bugs so I can refine the built-in compile-time checking to be - smarter. - - - The old STBI_SIMD system which allowed installing a user-defined - IDCT etc. has been removed. If you need this, don't upgrade. My - assumption is that almost nobody was doing this, and those who - were will find the built-in SIMD more satisfactory anyway. - - - RGB values computed for JPEG images are slightly different from - previous versions of stb_image. (This is due to using less - integer precision in SIMD.) The C code has been adjusted so - that the same RGB values will be computed regardless of whether - SIMD support is available, so your app should always produce - consistent results. But these results are slightly different from - previous versions. (Specifically, about 3% of available YCbCr values - will compute different RGB results from pre-1.49 versions by +-1; - most of the deviating values are one smaller in the G channel.) - - - If you must produce consistent results with previous versions of - stb_image, #define STBI_JPEG_OLD and you will get the same results - you used to; however, you will not get the SIMD speedups for - the YCbCr-to-RGB conversion step (although you should still see - significant JPEG speedup from the other changes). - - Please note that STBI_JPEG_OLD is a temporary feature; it will be - removed in future versions of the library. It is only intended for - near-term back-compatibility use. - - - Latest revision history: - 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value - 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning - 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit - 2.03 (2015-04-12) additional corruption checking - stbi_set_flip_vertically_on_load - fix NEON support; fix mingw support - 2.02 (2015-01-19) fix incorrect assert, fix warning - 2.01 (2015-01-17) fix various warnings - 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG - 2.00 (2014-12-25) optimize JPEG, including x86 SSE2 & ARM NEON SIMD - progressive JPEG - PGM/PPM support - STBI_MALLOC,STBI_REALLOC,STBI_FREE - STBI_NO_*, STBI_ONLY_* - GIF bugfix - 1.48 (2014-12-14) fix incorrectly-named assert() - 1.47 (2014-12-14) 1/2/4-bit PNG support (both grayscale and paletted) - optimize PNG - fix bug in interlaced PNG with user-specified channel count +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.28 (2023-01-29) many error fixes, security errors, just tons of stuff + 2.27 (2021-07-11) document stbi_info better, 16-bit PNM support, bug fixes + 2.26 (2020-07-13) many minor fixes + 2.25 (2020-02-02) fix warnings + 2.24 (2020-02-02) fix warnings; thread-local failure_reason and flip_vertically + 2.23 (2019-08-11) fix clang static analysis warning + 2.22 (2019-03-04) gif fixes, fix warnings + 2.21 (2019-02-25) fix typo in comment + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED See end of file for full revision history. ============================ Contributors ========================= - Image formats Bug fixes & warning fixes - Sean Barrett (jpeg, png, bmp) Marc LeBlanc - Nicolas Schulz (hdr, psd) Christpher Lloyd - Jonathan Dummer (tga) Dave Moore - Jean-Marc Lienher (gif) Won Chun - Tom Seddon (pic) the Horde3D community - Thatcher Ulrich (psd) Janez Zemva - Ken Miller (pgm, ppm) Jonathan Blow - Laurent Gomila - Aruelien Pocheville - Extensions, features Ryamond Barbiero - Jetro Lauha (stbi_info) David Woo - Martin "SpartanJ" Golini (stbi_info) Martin Golini - James "moose2000" Brown (iPhone PNG) Roy Eltham - Ben "Disch" Wenger (io callbacks) Luke Graham - Omar Cornut (1/2/4-bit PNG) Thomas Ruf - Nicolas Guillemot (vertical flip) John Bartholomew - Ken Hamada - Optimizations & bugfixes Cort Stratton - Fabian "ryg" Giesen Blazej Dariusz Roszkowski - Arseny Kapoulkine Thibault Reuille - Paul Du Bois - Guillaume George - If your name should be here but Jerry Jansson - isn't, let Sean know. Hayaki Saito - Johan Duparc - Ronny Chevalier - Michal Cichon - Tero Hanninen - Sergio Gonzalez - Cass Everitt - Engin Manap - Martins Mozeiko - Joseph Thomson - Phil Jordan - -LICENSE - -This software is in the public domain. Where that dedication is not -recognized, you are granted a perpetual, irrevocable license to copy, -distribute, and modify this file as you see fit. - + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine Simon Breuss (16-bit PNM) + John-Mark Allen + Carmelo J Fdez-Aguera + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Blazej Dariusz Roszkowski + Phil Jordan Dave Moore Roy Eltham + Hayaki Saito Nathan Reed Won Chun + Luke Graham Johan Duparc Nick Verigakis the Horde3D community + Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Eugene Golushkov Laurent Gomila Cort Stratton github:snagar + Aruelien Pocheville Sergio Gonzalez Thibault Reuille github:Zelex + Cass Everitt Ryamond Barbiero github:grim210 + Paul Du Bois Engin Manap Aldo Culquicondor github:sammyhw + Philipp Wiesemann Dale Weiler Oriol Ferrer Mesia github:phprus + Josh Tobin Neil Bickford Matthew Gregan github:poppolopoppo + Julian Raschke Gregory Mullen Christian Floisand github:darealshinji + Baldur Karlsson Kevin Schmidt JR Smith github:Michaelangel007 + Brad Weinberger Matvey Cherevko github:mosra + Luca Sas Alexander Veselov Zack Middleton [reserved] + Ryan C. Gordon [reserved] [reserved] + DO NOT ADD YOUR NAME HERE + + Jacko Dirks + + To add your name to the credits, pick a random blank space in the middle and fill it. + 80% of merge conflicts on stb PRs are due to people adding their name at the end + of the credits. */ #ifndef STBI_INCLUDE_STB_IMAGE_H @@ -218,10 +130,8 @@ distribute, and modify this file as you see fit. // DOCUMENTATION // // Limitations: -// - no 16-bit-per-channel PNG // - no 12-bit-per-channel JPEG // - no JPEGs with arithmetic coding -// - no 1-bit BMP // - GIF always returns *comp=4 // // Basic usage (see HDR discussion below for HDR usage): @@ -231,13 +141,13 @@ distribute, and modify this file as you see fit. // // ... x = width, y = height, n = # 8-bit components per pixel ... // // ... replace '0' with '1'..'4' to force that many components per pixel // // ... but 'n' will always be the number that it would have been if you said 0 -// stbi_image_free(data) +// stbi_image_free(data); // // Standard parameters: -// int *x -- outputs image width in pixels -// int *y -- outputs image height in pixels -// int *comp -- outputs # of image components in image file -// int req_comp -- if non-zero, # of image components requested in result +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result // // The return value from an image loader is an 'unsigned char *' which points // to the pixel data, or NULL on an allocation failure or if the image is @@ -245,11 +155,12 @@ distribute, and modify this file as you see fit. // with each pixel consisting of N interleaved 8-bit components; the first // pixel pointed to is top-left-most in the image. There is no padding between // image scanlines or between pixels, regardless of format. The number of -// components N is 'req_comp' if req_comp is non-zero, or *comp otherwise. -// If req_comp is non-zero, *comp has the number of components that _would_ -// have been output otherwise. E.g. if you set req_comp to 4, you will always -// get RGBA output, but you can check *comp to see if it's trivially opaque -// because e.g. there were only 3 channels in the source image. +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. // // An output image with N components has the following components interleaved // in this order in each pixel: @@ -261,14 +172,50 @@ distribute, and modify this file as you see fit. // 4 red, green, blue, alpha // // If image loading fails for any reason, the return value will be NULL, -// and *x, *y, *comp will be unchanged. The function stbi_failure_reason() -// can be queried for an extremely brief, end-user unfriendly explanation -// of why the load failed. Define STBI_NO_FAILURE_STRINGS to avoid -// compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly // more user-friendly ones. // // Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. // +// To query the width, height and component count of an image without having to +// decode the full file, you can use the stbi_info family of functions: +// +// int x,y,n,ok; +// ok = stbi_info(filename, &x, &y, &n); +// // returns ok=1 and sets x, y, n if image is a supported format, +// // 0 otherwise. +// +// Note that stb_image pervasively uses ints in its public API for sizes, +// including sizes of memory buffers. This is now part of the API and thus +// hard to change without causing breakage. As a result, the various image +// loaders all have certain limits on image size; these differ somewhat +// by format but generally boil down to either just under 2GB or just under +// 1GB. When the decoded image would be larger than this, stb_image decoding +// will fail. +// +// Additionally, stb_image will reject image files that have any of their +// dimensions set to a larger value than the configurable STBI_MAX_DIMENSIONS, +// which defaults to 2**24 = 16777216 pixels. Due to the above memory limit, +// the only way to have an image with such dimensions load correctly +// is for it to have a rather extreme aspect ratio. Either way, the +// assumption here is that such larger images are likely to be malformed +// or malicious. If you do need to load an image with individual dimensions +// larger than that, and it still fits in the overall size limit, you can +// #define STBI_MAX_DIMENSIONS on your own to be something larger. +// +// =========================================================================== +// +// UNICODE: +// +// If compiling for Windows and you wish to use Unicode filenames, compile +// with +// #define STBI_WINDOWS_UTF8 +// and pass utf8-encoded filenames. Call stbi_convert_wchar_to_utf8 to convert +// Windows wchar_t filenames to utf8. +// // =========================================================================== // // Philosophy @@ -281,15 +228,15 @@ distribute, and modify this file as you see fit. // // Sometimes I let "good performance" creep up in priority over "easy to maintain", // and for best performance I may provide less-easy-to-use APIs that give higher -// performance, in addition to the easy to use ones. Nevertheless, it's important +// performance, in addition to the easy-to-use ones. Nevertheless, it's important // to keep in mind that from the standpoint of you, a client of this library, -// all you care about is #1 and #3, and stb libraries do not emphasize #3 above all. +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. // // Some secondary priorities arise directly from the first two, some of which -// make more explicit reasons why performance can't be emphasized. +// provide more explicit reasons why performance can't be emphasized. // // - Portable ("ease of use") -// - Small footprint ("easy to maintain") +// - Small source code footprint ("easy to maintain") // - No dependencies ("ease of use") // // =========================================================================== @@ -321,13 +268,6 @@ distribute, and modify this file as you see fit. // (at least this is true for iOS and Android). Therefore, the NEON support is // toggled by a build flag: define STBI_NEON to get NEON loops. // -// The output of the JPEG decoder is slightly different from versions where -// SIMD support was introduced (that is, for versions before 1.49). The -// difference is only +-1 in the 8-bit RGB channels, and only on a small -// fraction of pixels. You can force the pre-1.49 behavior by defining -// STBI_JPEG_OLD, but this will disable some of the SIMD decoding path -// and hence cost some performance. -// // If for some reason you do not want to use any of SIMD code, or if // you have issues compiling it, you can disable it entirely by // defining STBI_NO_SIMD. @@ -336,11 +276,10 @@ distribute, and modify this file as you see fit. // // HDR image support (disable by defining STBI_NO_HDR) // -// stb_image now supports loading HDR images in general, and currently -// the Radiance .HDR file format, although the support is provided -// generically. You can still load any file through the existing interface; -// if you attempt to load an HDR file, it will be automatically remapped to -// LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; +// stb_image supports loading HDR images in general, and currently the Radiance +// .HDR file format specifically. You can still load any file through the existing +// interface; if you attempt to load an HDR file, it will be automatically remapped +// to LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; // both of these constants can be reconfigured through this interface: // // stbi_hdr_to_ldr_gamma(2.2f); @@ -372,18 +311,59 @@ distribute, and modify this file as you see fit. // // iPhone PNG support: // -// By default we convert iphone-formatted PNGs back to RGB, even though -// they are internally encoded differently. You can disable this conversion -// by by calling stbi_convert_iphone_png_to_rgb(0), in which case -// you will always just get the native iphone "format" through (which -// is BGR stored in RGB). +// We optionally support converting iPhone-formatted PNGs (which store +// premultiplied BGRA) back to RGB, even though they're internally encoded +// differently. To enable this conversion, call +// stbi_convert_iphone_png_to_rgb(1). // // Call stbi_set_unpremultiply_on_load(1) as well to force a divide per // pixel to remove any premultiplied alpha *only* if the image file explicitly // says there's premultiplied data (currently only happens in iPhone images, // and only if iPhone convert-to-rgb processing is on). // - +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// +// - If you define STBI_MAX_DIMENSIONS, stb_image will reject images greater +// than that size (in either width or height) without further processing. +// This is to let programs in the wild set an upper bound to prevent +// denial-of-service attacks on untrusted data, as one could generate a +// valid image of gigantic dimensions and force stb_image to allocate a +// huge block of memory and spend disproportionate time decoding it. By +// default this is set to (1 << 24), which is 16777216, but that's still +// very big. #ifndef STBI_NO_STDIO #include @@ -393,120 +373,164 @@ distribute, and modify this file as you see fit. enum { - STBI_default = 0, // only used for req_comp + STBI_default = 0, // only used for desired_channels - STBI_grey = 1, - STBI_grey_alpha = 2, - STBI_rgb = 3, - STBI_rgb_alpha = 4 + STBI_grey = 1, + STBI_grey_alpha = 2, + STBI_rgb = 3, + STBI_rgb_alpha = 4 }; +#include typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; #ifdef __cplusplus extern "C" { #endif +#ifndef STBIDEF #ifdef STB_IMAGE_STATIC #define STBIDEF static #else #define STBIDEF extern +#endif #endif -////////////////////////////////////////////////////////////////////////////// -// -// PRIMARY API - works on images of any type -// + ////////////////////////////////////////////////////////////////////////////// + // + // PRIMARY API - works on images of any type + // -// -// load image by filename, open file, or memory buffer -// + // + // load image by filename, open file, or memory buffer + // -typedef struct -{ - int (*read) (void *user,char *data,int size); // fill 'data' with 'size' bytes. return number of bytes actually read - void (*skip) (void *user,int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative - int (*eof) (void *user); // returns nonzero if we are at end of file/data -} stbi_io_callbacks; + typedef struct + { + int (*read) (void* user, char* data, int size); // fill 'data' with 'size' bytes. return number of bytes actually read + void (*skip) (void* user, int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative + int (*eof) (void* user); // returns nonzero if we are at end of file/data + } stbi_io_callbacks; + + //////////////////////////////////// + // + // 8-bits-per-channel interface + // -STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *comp, int req_comp); -STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *comp, int req_comp); -STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *comp, int req_comp); + STBIDEF stbi_uc* stbi_load_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* channels_in_file, int desired_channels); + STBIDEF stbi_uc* stbi_load_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* channels_in_file, int desired_channels); #ifndef STBI_NO_STDIO -STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *comp, int req_comp); -// for stbi_load_from_file, file pointer is left pointing immediately after image + STBIDEF stbi_uc* stbi_load(char const* filename, int* x, int* y, int* channels_in_file, int desired_channels); + STBIDEF stbi_uc* stbi_load_from_file(FILE* f, int* x, int* y, int* channels_in_file, int desired_channels); + // for stbi_load_from_file, file pointer is left pointing immediately after image #endif -#ifndef STBI_NO_LINEAR - STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *comp, int req_comp); - STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp); - STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp); +#ifndef STBI_NO_GIF + STBIDEF stbi_uc* stbi_load_gif_from_memory(stbi_uc const* buffer, int len, int** delays, int* x, int* y, int* z, int* comp, int req_comp); +#endif - #ifndef STBI_NO_STDIO - STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *comp, int req_comp); - #endif +#ifdef STBI_WINDOWS_UTF8 + STBIDEF int stbi_convert_wchar_to_utf8(char* buffer, size_t bufferlen, const wchar_t* input); #endif -#ifndef STBI_NO_HDR - STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); - STBIDEF void stbi_hdr_to_ldr_scale(float scale); + //////////////////////////////////// + // + // 16-bits-per-channel interface + // + + STBIDEF stbi_us* stbi_load_16_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* channels_in_file, int desired_channels); + STBIDEF stbi_us* stbi_load_16_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO + STBIDEF stbi_us* stbi_load_16(char const* filename, int* x, int* y, int* channels_in_file, int desired_channels); + STBIDEF stbi_us* stbi_load_from_file_16(FILE* f, int* x, int* y, int* channels_in_file, int desired_channels); #endif + //////////////////////////////////// + // + // float-per-channel interface + // #ifndef STBI_NO_LINEAR - STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); - STBIDEF void stbi_ldr_to_hdr_scale(float scale); + STBIDEF float* stbi_loadf_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* channels_in_file, int desired_channels); + STBIDEF float* stbi_loadf_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO + STBIDEF float* stbi_loadf(char const* filename, int* x, int* y, int* channels_in_file, int desired_channels); + STBIDEF float* stbi_loadf_from_file(FILE* f, int* x, int* y, int* channels_in_file, int desired_channels); +#endif +#endif + +#ifndef STBI_NO_HDR + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); + STBIDEF void stbi_hdr_to_ldr_scale(float scale); #endif // STBI_NO_HDR -// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR -STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); -STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len); +#ifndef STBI_NO_LINEAR + STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); + STBIDEF void stbi_ldr_to_hdr_scale(float scale); +#endif // STBI_NO_LINEAR + + // stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR + STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const* clbk, void* user); + STBIDEF int stbi_is_hdr_from_memory(stbi_uc const* buffer, int len); #ifndef STBI_NO_STDIO -STBIDEF int stbi_is_hdr (char const *filename); -STBIDEF int stbi_is_hdr_from_file(FILE *f); + STBIDEF int stbi_is_hdr(char const* filename); + STBIDEF int stbi_is_hdr_from_file(FILE* f); #endif // STBI_NO_STDIO -// get a VERY brief reason for failure -// NOT THREADSAFE -STBIDEF const char *stbi_failure_reason (void); + // get a VERY brief reason for failure + // on most compilers (and ALL modern mainstream compilers) this is threadsafe + STBIDEF const char* stbi_failure_reason(void); -// free the loaded image -- this is just free() -STBIDEF void stbi_image_free (void *retval_from_stbi_load); + // free the loaded image -- this is just free() + STBIDEF void stbi_image_free(void* retval_from_stbi_load); -// get image dimensions & components without fully decoding -STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); -STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); + // get image dimensions & components without fully decoding + STBIDEF int stbi_info_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* comp); + STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* comp); + STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const* buffer, int len); + STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const* clbk, void* user); #ifndef STBI_NO_STDIO -STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); -STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); - + STBIDEF int stbi_info(char const* filename, int* x, int* y, int* comp); + STBIDEF int stbi_info_from_file(FILE* f, int* x, int* y, int* comp); + STBIDEF int stbi_is_16_bit(char const* filename); + STBIDEF int stbi_is_16_bit_from_file(FILE* f); #endif -// for image formats that explicitly notate that they have premultiplied alpha, -// we just return the colors as stored in the file. set this flag to force -// unpremultiplication. results are undefined if the unpremultiply overflow. -STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply); + // for image formats that explicitly notate that they have premultiplied alpha, + // we just return the colors as stored in the file. set this flag to force + // unpremultiplication. results are undefined if the unpremultiply overflow. + STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply); -// indicate whether we should process iphone images back to canonical format, -// or just pass them through "as-is" -STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); + // indicate whether we should process iphone images back to canonical format, + // or just pass them through "as-is" + STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); -// flip the image vertically, so the first pixel in the output array is the bottom left -STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); + // flip the image vertically, so the first pixel in the output array is the bottom left + STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); -// ZLIB client - used by PNG, available for other purposes + // as above, but only applies to images loaded on the thread that calls the function + // this function is only available if your compiler supports thread-local variables; + // calling it will fail to link if your compiler doesn't + STBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply); + STBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert); + STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip); -STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); -STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header); -STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen); -STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + // ZLIB client - used by PNG, available for other purposes -STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen); -STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + STBIDEF char* stbi_zlib_decode_malloc_guesssize(const char* buffer, int len, int initial_size, int* outlen); + STBIDEF char* stbi_zlib_decode_malloc_guesssize_headerflag(const char* buffer, int len, int initial_size, int* outlen, int parse_header); + STBIDEF char* stbi_zlib_decode_malloc(const char* buffer, int len, int* outlen); + STBIDEF int stbi_zlib_decode_buffer(char* obuffer, int olen, const char* ibuffer, int ilen); + + STBIDEF char* stbi_zlib_decode_noheader_malloc(const char* buffer, int len, int* outlen); + STBIDEF int stbi_zlib_decode_noheader_buffer(char* obuffer, int olen, const char* ibuffer, int ilen); #ifdef __cplusplus @@ -524,33 +548,33 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \ || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \ || defined(STBI_ONLY_ZLIB) - #ifndef STBI_ONLY_JPEG - #define STBI_NO_JPEG - #endif - #ifndef STBI_ONLY_PNG - #define STBI_NO_PNG - #endif - #ifndef STBI_ONLY_BMP - #define STBI_NO_BMP - #endif - #ifndef STBI_ONLY_PSD - #define STBI_NO_PSD - #endif - #ifndef STBI_ONLY_TGA - #define STBI_NO_TGA - #endif - #ifndef STBI_ONLY_GIF - #define STBI_NO_GIF - #endif - #ifndef STBI_ONLY_HDR - #define STBI_NO_HDR - #endif - #ifndef STBI_ONLY_PIC - #define STBI_NO_PIC - #endif - #ifndef STBI_ONLY_PNM - #define STBI_NO_PNM - #endif +#ifndef STBI_ONLY_JPEG +#define STBI_NO_JPEG +#endif +#ifndef STBI_ONLY_PNG +#define STBI_NO_PNG +#endif +#ifndef STBI_ONLY_BMP +#define STBI_NO_BMP +#endif +#ifndef STBI_ONLY_PSD +#define STBI_NO_PSD +#endif +#ifndef STBI_ONLY_TGA +#define STBI_NO_TGA +#endif +#ifndef STBI_ONLY_GIF +#define STBI_NO_GIF +#endif +#ifndef STBI_ONLY_HDR +#define STBI_NO_HDR +#endif +#ifndef STBI_ONLY_PIC +#define STBI_NO_PIC +#endif +#ifndef STBI_ONLY_PNM +#define STBI_NO_PNM +#endif #endif #if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB) @@ -562,9 +586,10 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #include // ptrdiff_t on osx #include #include +#include #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) -#include // ldexp +#include // ldexp, pow #endif #ifndef STBI_NO_STDIO @@ -576,19 +601,42 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const ch #define STBI_ASSERT(x) assert(x) #endif +#ifdef __cplusplus +#define STBI_EXTERN extern "C" +#else +#define STBI_EXTERN extern +#endif + #ifndef _MSC_VER - #ifdef __cplusplus - #define stbi_inline inline - #else - #define stbi_inline - #endif +#ifdef __cplusplus +#define stbi_inline inline +#else +#define stbi_inline +#endif #else - #define stbi_inline __forceinline +#define stbi_inline __forceinline #endif +#ifndef STBI_NO_THREAD_LOCALS +#if defined(__cplusplus) && __cplusplus >= 201103L +#define STBI_THREAD_LOCAL thread_local +#elif defined(__GNUC__) && __GNUC__ < 5 +#define STBI_THREAD_LOCAL __thread +#elif defined(_MSC_VER) +#define STBI_THREAD_LOCAL __declspec(thread) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L && !defined(__STDC_NO_THREADS__) +#define STBI_THREAD_LOCAL _Thread_local +#endif -#ifdef _MSC_VER +#ifndef STBI_THREAD_LOCAL +#if defined(__GNUC__) +#define STBI_THREAD_LOCAL __thread +#endif +#endif +#endif + +#if defined(_MSC_VER) || defined(__SYMBIAN32__) typedef unsigned short stbi__uint16; typedef signed short stbi__int16; typedef unsigned int stbi__uint32; @@ -602,7 +650,7 @@ typedef int32_t stbi__int32; #endif // should produce compiler error if size is wrong -typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; +typedef unsigned char validate_uint32[sizeof(stbi__uint32) == 4 ? 1 : -1]; #ifdef _MSC_VER #define STBI_NOTUSED(v) (void)(v) @@ -615,23 +663,27 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #endif #ifdef STBI_HAS_LROTL - #define stbi_lrot(x,y) _lrotl(x,y) +#define stbi_lrot(x,y) _lrotl(x,y) #else - #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) +#define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (-(y) & 31))) #endif -#if defined(STBI_MALLOC) && defined(STBI_FREE) && defined(STBI_REALLOC) +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) // ok -#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) // ok #else -#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC." +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." #endif #ifndef STBI_MALLOC -#define STBI_MALLOC(sz) malloc(sz) -#define STBI_REALLOC(p,sz) realloc(p,sz) -#define STBI_FREE(p) free(p) +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) #endif // x86/x64 detection @@ -641,12 +693,14 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define STBI__X86_TARGET #endif -#if defined(__GNUC__) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) -// NOTE: not clear do we actually need this for the 64-bit path? +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) // gcc doesn't support sse2 intrinsics unless you compile with -msse2, -// (but compiling with -msse2 allows the compiler to use SSE2 everywhere; -// this is just broken and gcc are jerks for not fixing it properly -// http://www.virtualdub.org/blog/pivot/entry.php?id=363 ) +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. #define STBI_NO_SIMD #endif @@ -665,7 +719,7 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #define STBI_NO_SIMD #endif -#if !defined(STBI_NO_SIMD) && defined(STBI__X86_TARGET) +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) #define STBI_SSE2 #include @@ -675,44 +729,46 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #include // __cpuid static int stbi__cpuid3(void) { - int info[4]; - __cpuid(info,1); - return info[3]; + int info[4]; + __cpuid(info, 1); + return info[3]; } #else static int stbi__cpuid3(void) { - int res; - __asm { - mov eax,1 - cpuid - mov res,edx - } - return res; + int res; + __asm { + mov eax, 1 + cpuid + mov res, edx + } + return res; } #endif #define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name -static int stbi__sse2_available() +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) { - int info3 = stbi__cpuid3(); - return ((info3 >> 26) & 1) != 0; + int info3 = stbi__cpuid3(); + return ((info3 >> 26) & 1) != 0; } +#endif + #else // assume GCC-style if not VC++ #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) -static int stbi__sse2_available() +#if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) +static int stbi__sse2_available(void) { -#if defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__) >= 408 // GCC 4.8 or later - // GCC 4.8+ has a nice way to do this - return __builtin_cpu_supports("sse2"); -#else - // portable way to do this, preferably without using GCC inline ASM? - // just bail for now. - return 0; -#endif + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; } +#endif + #endif #endif @@ -723,14 +779,21 @@ static int stbi__sse2_available() #ifdef STBI_NEON #include -// assume GCC or Clang on ARM targets +#ifdef _MSC_VER +#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name +#else #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) #endif +#endif #ifndef STBI_SIMD_ALIGN #define STBI_SIMD_ALIGN(type, name) type name #endif +#ifndef STBI_MAX_DIMENSIONS +#define STBI_MAX_DIMENSIONS (1 << 24) +#endif + /////////////////////////////////////////////// // // stbi__context struct and start_xxx functions @@ -739,58 +802,67 @@ static int stbi__sse2_available() // contains all the IO context, plus some basic image information typedef struct { - stbi__uint32 img_x, img_y; - int img_n, img_out_n; + stbi__uint32 img_x, img_y; + int img_n, img_out_n; - stbi_io_callbacks io; - void *io_user_data; + stbi_io_callbacks io; + void* io_user_data; - int read_from_callbacks; - int buflen; - stbi_uc buffer_start[128]; + int read_from_callbacks; + int buflen; + stbi_uc buffer_start[128]; + int callback_already_read; - stbi_uc *img_buffer, *img_buffer_end; - stbi_uc *img_buffer_original; + stbi_uc* img_buffer, * img_buffer_end; + stbi_uc* img_buffer_original, * img_buffer_original_end; } stbi__context; -static void stbi__refill_buffer(stbi__context *s); +static void stbi__refill_buffer(stbi__context* s); // initialize a memory-decode context -static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) +static void stbi__start_mem(stbi__context* s, stbi_uc const* buffer, int len) { - s->io.read = NULL; - s->read_from_callbacks = 0; - s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; - s->img_buffer_end = (stbi_uc *) buffer+len; + s->io.read = NULL; + s->read_from_callbacks = 0; + s->callback_already_read = 0; + s->img_buffer = s->img_buffer_original = (stbi_uc*)buffer; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc*)buffer + len; } // initialize a callback-based context -static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user) +static void stbi__start_callbacks(stbi__context* s, stbi_io_callbacks* c, void* user) { - s->io = *c; - s->io_user_data = user; - s->buflen = sizeof(s->buffer_start); - s->read_from_callbacks = 1; - s->img_buffer_original = s->buffer_start; - stbi__refill_buffer(s); + s->io = *c; + s->io_user_data = user; + s->buflen = sizeof(s->buffer_start); + s->read_from_callbacks = 1; + s->callback_already_read = 0; + s->img_buffer = s->img_buffer_original = s->buffer_start; + stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; } #ifndef STBI_NO_STDIO -static int stbi__stdio_read(void *user, char *data, int size) +static int stbi__stdio_read(void* user, char* data, int size) { - return (int) fread(data,1,size,(FILE*) user); + return (int)fread(data, 1, size, (FILE*)user); } -static void stbi__stdio_skip(void *user, int n) +static void stbi__stdio_skip(void* user, int n) { - fseek((FILE*) user, n, SEEK_CUR); + int ch; + fseek((FILE*)user, n, SEEK_CUR); + ch = fgetc((FILE*)user); /* have to read a byte to reset feof()'s flag */ + if (ch != EOF) { + ungetc(ch, (FILE*)user); /* push byte back onto stream if valid. */ + } } -static int stbi__stdio_eof(void *user) +static int stbi__stdio_eof(void* user) { - return feof((FILE*) user); + return feof((FILE*)user) || ferror((FILE*)user); } static stbi_io_callbacks stbi__stdio_callbacks = @@ -800,507 +872,866 @@ static stbi_io_callbacks stbi__stdio_callbacks = stbi__stdio_eof, }; -static void stbi__start_file(stbi__context *s, FILE *f) +static void stbi__start_file(stbi__context* s, FILE* f) { - stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f); + stbi__start_callbacks(s, &stbi__stdio_callbacks, (void*)f); } //static void stop_file(stbi__context *s) { } #endif // !STBI_NO_STDIO -static void stbi__rewind(stbi__context *s) +static void stbi__rewind(stbi__context* s) { - // conceptually rewind SHOULD rewind to the beginning of the stream, - // but we just rewind to the beginning of the initial buffer, because - // we only use it after doing 'test', which only ever looks at at most 92 bytes - s->img_buffer = s->img_buffer_original; + // conceptually rewind SHOULD rewind to the beginning of the stream, + // but we just rewind to the beginning of the initial buffer, because + // we only use it after doing 'test', which only ever looks at at most 92 bytes + s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; } +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + #ifndef STBI_NO_JPEG -static int stbi__jpeg_test(stbi__context *s); -static stbi_uc *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__jpeg_test(stbi__context* s); +static void* stbi__jpeg_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__jpeg_info(stbi__context* s, int* x, int* y, int* comp); #endif #ifndef STBI_NO_PNG -static int stbi__png_test(stbi__context *s); -static stbi_uc *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_test(stbi__context* s); +static void* stbi__png_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__png_info(stbi__context* s, int* x, int* y, int* comp); +static int stbi__png_is16(stbi__context* s); #endif #ifndef STBI_NO_BMP -static int stbi__bmp_test(stbi__context *s); -static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__bmp_test(stbi__context* s); +static void* stbi__bmp_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__bmp_info(stbi__context* s, int* x, int* y, int* comp); #endif #ifndef STBI_NO_TGA -static int stbi__tga_test(stbi__context *s); -static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__tga_test(stbi__context* s); +static void* stbi__tga_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__tga_info(stbi__context* s, int* x, int* y, int* comp); #endif #ifndef STBI_NO_PSD -static int stbi__psd_test(stbi__context *s); -static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_test(stbi__context* s); +static void* stbi__psd_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri, int bpc); +static int stbi__psd_info(stbi__context* s, int* x, int* y, int* comp); +static int stbi__psd_is16(stbi__context* s); #endif #ifndef STBI_NO_HDR -static int stbi__hdr_test(stbi__context *s); -static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__hdr_test(stbi__context* s); +static float* stbi__hdr_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__hdr_info(stbi__context* s, int* x, int* y, int* comp); #endif #ifndef STBI_NO_PIC -static int stbi__pic_test(stbi__context *s); -static stbi_uc *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__pic_test(stbi__context* s); +static void* stbi__pic_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__pic_info(stbi__context* s, int* x, int* y, int* comp); #endif #ifndef STBI_NO_GIF -static int stbi__gif_test(stbi__context *s); -static stbi_uc *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__gif_test(stbi__context* s); +static void* stbi__gif_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static void* stbi__load_gif_main(stbi__context* s, int** delays, int* x, int* y, int* z, int* comp, int req_comp); +static int stbi__gif_info(stbi__context* s, int* x, int* y, int* comp); #endif #ifndef STBI_NO_PNM -static int stbi__pnm_test(stbi__context *s); -static stbi_uc *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp); -static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__pnm_test(stbi__context* s); +static void* stbi__pnm_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri); +static int stbi__pnm_info(stbi__context* s, int* x, int* y, int* comp); +static int stbi__pnm_is16(stbi__context* s); #endif -// this is not threadsafe -static const char *stbi__g_failure_reason; +static +#ifdef STBI_THREAD_LOCAL +STBI_THREAD_LOCAL +#endif +const char* stbi__g_failure_reason; -STBIDEF const char *stbi_failure_reason(void) +STBIDEF const char* stbi_failure_reason(void) { - return stbi__g_failure_reason; + return stbi__g_failure_reason; } -static int stbi__err(const char *str) +#ifndef STBI_NO_FAILURE_STRINGS +static int stbi__err(const char* str) { - stbi__g_failure_reason = str; - return 0; + stbi__g_failure_reason = str; + return 0; } +#endif -static void *stbi__malloc(size_t size) +static void* stbi__malloc(size_t size) { return STBI_MALLOC(size); } -// stbi__err - error -// stbi__errpf - error returning pointer to float -// stbi__errpuc - error returning pointer to unsigned char - -#ifdef STBI_NO_FAILURE_STRINGS - #define stbi__err(x,y) 0 -#elif defined(STBI_FAILURE_USERMSG) - #define stbi__err(x,y) stbi__err(y) -#else - #define stbi__err(x,y) stbi__err(x) -#endif - -#define stbi__errpf(x,y) ((float *) (stbi__err(x,y)?NULL:NULL)) -#define stbi__errpuc(x,y) ((unsigned char *) (stbi__err(x,y)?NULL:NULL)) +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. -STBIDEF void stbi_image_free(void *retval_from_stbi_load) +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) { - STBI_FREE(retval_from_stbi_load); + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; } -#ifndef STBI_NO_LINEAR -static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); -#endif - -#ifndef STBI_NO_HDR -static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); -#endif - -static int stbi__vertically_flip_on_load = 0; - -STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) { - stbi__vertically_flip_on_load = flag_true_if_should_flip; -} - -static unsigned char *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) -{ - #ifndef STBI_NO_JPEG - if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp); - #endif - #ifndef STBI_NO_PNG - if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp); - #endif - #ifndef STBI_NO_BMP - if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp); - #endif - #ifndef STBI_NO_GIF - if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp); - #endif - #ifndef STBI_NO_PSD - if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp); - #endif - #ifndef STBI_NO_PIC - if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp); - #endif - #ifndef STBI_NO_PNM - if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp); - #endif - - #ifndef STBI_NO_HDR - if (stbi__hdr_test(s)) { - float *hdr = stbi__hdr_load(s, x,y,comp,req_comp); - return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); - } - #endif - - #ifndef STBI_NO_TGA - // test tga last because it's a crappy test! - if (stbi__tga_test(s)) - return stbi__tga_load(s,x,y,comp,req_comp); - #endif - - return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX / b; } -static unsigned char *stbi__load_flip(stbi__context *s, int *x, int *y, int *comp, int req_comp) +#if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR) +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) { - unsigned char *result = stbi__load_main(s, x, y, comp, req_comp); - - if (stbi__vertically_flip_on_load && result != NULL) { - int w = *x, h = *y; - int depth = req_comp ? req_comp : *comp; - int row,col,z; - stbi_uc temp; - - // @OPTIMIZE: use a bigger temp buffer and memcpy multiple pixels at once - for (row = 0; row < (h>>1); row++) { - for (col = 0; col < w; col++) { - for (z = 0; z < depth; z++) { - temp = result[(row * w + col) * depth + z]; - result[(row * w + col) * depth + z] = result[((h - row - 1) * w + col) * depth + z]; - result[((h - row - 1) * w + col) * depth + z] = temp; - } - } - } - } - - return result; + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a * b, add); } +#endif -static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) { - if (stbi__vertically_flip_on_load && result != NULL) { - int w = *x, h = *y; - int depth = req_comp ? req_comp : *comp; - int row,col,z; - float temp; - - // @OPTIMIZE: use a bigger temp buffer and memcpy multiple pixels at once - for (row = 0; row < (h>>1); row++) { - for (col = 0; col < w; col++) { - for (z = 0; z < depth; z++) { - temp = result[(row * w + col) * depth + z]; - result[(row * w + col) * depth + z] = result[((h - row - 1) * w + col) * depth + z]; - result[((h - row - 1) * w + col) * depth + z] = temp; - } - } - } - } + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a * b, c) && + stbi__addsizes_valid(a * b * c, add); } - -#ifndef STBI_NO_STDIO - -static FILE *stbi__fopen(char const *filename, char const *mode) +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) { - FILE *f; -#if defined(_MSC_VER) && _MSC_VER >= 1400 - if (0 != fopen_s(&f, filename, mode)) - f=0; -#else - f = fopen(filename, mode); -#endif - return f; + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a * b, c) && + stbi__mul2sizes_valid(a * b * c, d) && stbi__addsizes_valid(a * b * c * d, add); } +#endif - -STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp) +#if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR) +// mallocs with size overflow checking +static void* stbi__malloc_mad2(int a, int b, int add) { - FILE *f = stbi__fopen(filename, "rb"); - unsigned char *result; - if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); - result = stbi_load_from_file(f,x,y,comp,req_comp); - fclose(f); - return result; + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a * b + add); } +#endif -STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +static void* stbi__malloc_mad3(int a, int b, int c, int add) { - unsigned char *result; - stbi__context s; - stbi__start_file(&s,f); - result = stbi__load_flip(&s,x,y,comp,req_comp); - if (result) { - // need to 'unget' all the characters in the IO buffer - fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); - } - return result; + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a * b * c + add); } -#endif //!STBI_NO_STDIO -STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM) +static void* stbi__malloc_mad4(int a, int b, int c, int d, int add) { - stbi__context s; - stbi__start_mem(&s,buffer,len); - return stbi__load_flip(&s,x,y,comp,req_comp); + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a * b * c * d + add); } +#endif -STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +// returns 1 if the sum of two signed ints is valid (between -2^31 and 2^31-1 inclusive), 0 on overflow. +static int stbi__addints_valid(int a, int b) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); - return stbi__load_flip(&s,x,y,comp,req_comp); -} - -#ifndef STBI_NO_LINEAR -static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) -{ - unsigned char *data; - #ifndef STBI_NO_HDR - if (stbi__hdr_test(s)) { - float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp); - if (hdr_data) - stbi__float_postprocess(hdr_data,x,y,comp,req_comp); - return hdr_data; - } - #endif - data = stbi__load_flip(s, x, y, comp, req_comp); - if (data) - return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); - return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); + if ((a >= 0) != (b >= 0)) return 1; // a and b have different signs, so no overflow + if (a < 0 && b < 0) return a >= INT_MIN - b; // same as a + b >= INT_MIN; INT_MIN - b cannot overflow since b < 0. + return a <= INT_MAX - b; } -STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +// returns 1 if the product of two signed shorts is valid, 0 on overflow. +static int stbi__mul2shorts_valid(short a, short b) { - stbi__context s; - stbi__start_mem(&s,buffer,len); - return stbi__loadf_main(&s,x,y,comp,req_comp); + if (b == 0 || b == -1) return 1; // multiplication by 0 is always 0; check for -1 so SHRT_MIN/b doesn't overflow + if ((a >= 0) == (b >= 0)) return a <= SHRT_MAX / b; // product is positive, so similar to mul2sizes_valid + if (b < 0) return a <= SHRT_MIN / b; // same as a * b >= SHRT_MIN + return a >= SHRT_MIN / b; } -STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) -{ - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); - return stbi__loadf_main(&s,x,y,comp,req_comp); -} +// stbi__err - error +// stbi__errpf - error returning pointer to float +// stbi__errpuc - error returning pointer to unsigned char -#ifndef STBI_NO_STDIO -STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp) -{ - float *result; - FILE *f = stbi__fopen(filename, "rb"); - if (!f) return stbi__errpf("can't fopen", "Unable to open file"); - result = stbi_loadf_from_file(f,x,y,comp,req_comp); - fclose(f); - return result; -} +#ifdef STBI_NO_FAILURE_STRINGS +#define stbi__err(x,y) 0 +#elif defined(STBI_FAILURE_USERMSG) +#define stbi__err(x,y) stbi__err(y) +#else +#define stbi__err(x,y) stbi__err(x) +#endif + +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) -STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +STBIDEF void stbi_image_free(void* retval_from_stbi_load) { - stbi__context s; - stbi__start_file(&s,f); - return stbi__loadf_main(&s,x,y,comp,req_comp); + STBI_FREE(retval_from_stbi_load); } -#endif // !STBI_NO_STDIO -#endif // !STBI_NO_LINEAR +#ifndef STBI_NO_LINEAR +static float* stbi__ldr_to_hdr(stbi_uc* data, int x, int y, int comp); +#endif -// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is -// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always -// reports false! +#ifndef STBI_NO_HDR +static stbi_uc* stbi__hdr_to_ldr(float* data, int x, int y, int comp); +#endif -STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len) -{ - #ifndef STBI_NO_HDR - stbi__context s; - stbi__start_mem(&s,buffer,len); - return stbi__hdr_test(&s); - #else - STBI_NOTUSED(buffer); - STBI_NOTUSED(len); - return 0; - #endif -} +static int stbi__vertically_flip_on_load_global = 0; -#ifndef STBI_NO_STDIO -STBIDEF int stbi_is_hdr (char const *filename) +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) { - FILE *f = stbi__fopen(filename, "rb"); - int result=0; - if (f) { - result = stbi_is_hdr_from_file(f); - fclose(f); - } - return result; + stbi__vertically_flip_on_load_global = flag_true_if_should_flip; } -STBIDEF int stbi_is_hdr_from_file(FILE *f) +#ifndef STBI_THREAD_LOCAL +#define stbi__vertically_flip_on_load stbi__vertically_flip_on_load_global +#else +static STBI_THREAD_LOCAL int stbi__vertically_flip_on_load_local, stbi__vertically_flip_on_load_set; + +STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip) { - #ifndef STBI_NO_HDR - stbi__context s; - stbi__start_file(&s,f); - return stbi__hdr_test(&s); - #else - return 0; - #endif + stbi__vertically_flip_on_load_local = flag_true_if_should_flip; + stbi__vertically_flip_on_load_set = 1; } -#endif // !STBI_NO_STDIO -STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user) +#define stbi__vertically_flip_on_load (stbi__vertically_flip_on_load_set \ + ? stbi__vertically_flip_on_load_local \ + : stbi__vertically_flip_on_load_global) +#endif // STBI_THREAD_LOCAL + +static void* stbi__load_main(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri, int bpc) { - #ifndef STBI_NO_HDR - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); - return stbi__hdr_test(&s); - #else - return 0; - #endif -} + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; -static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; -static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; + // test the formats with a very explicit header first (at least a FOURCC + // or distinctive magic number first) +#ifndef STBI_NO_PNG + if (stbi__png_test(s)) return stbi__png_load(s, x, y, comp, req_comp, ri); +#endif +#ifndef STBI_NO_BMP + if (stbi__bmp_test(s)) return stbi__bmp_load(s, x, y, comp, req_comp, ri); +#endif +#ifndef STBI_NO_GIF + if (stbi__gif_test(s)) return stbi__gif_load(s, x, y, comp, req_comp, ri); +#endif +#ifndef STBI_NO_PSD + if (stbi__psd_test(s)) return stbi__psd_load(s, x, y, comp, req_comp, ri, bpc); +#else + STBI_NOTUSED(bpc); +#endif +#ifndef STBI_NO_PIC + if (stbi__pic_test(s)) return stbi__pic_load(s, x, y, comp, req_comp, ri); +#endif -#ifndef STBI_NO_LINEAR -STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } -STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } + // then the formats that can end up attempting to load with just 1 or 2 + // bytes matching expectations; these are prone to false positives, so + // try them later +#ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s, x, y, comp, req_comp, ri); +#endif +#ifndef STBI_NO_PNM + if (stbi__pnm_test(s)) return stbi__pnm_load(s, x, y, comp, req_comp, ri); #endif -STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } -STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } +#ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + float* hdr = stbi__hdr_load(s, x, y, comp, req_comp, ri); + return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); + } +#endif +#ifndef STBI_NO_TGA + // test tga last because it's a crappy test! + if (stbi__tga_test(s)) + return stbi__tga_load(s, x, y, comp, req_comp, ri); +#endif -////////////////////////////////////////////////////////////////////////////// -// -// Common code used by all image loaders -// + return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); +} -enum +static stbi_uc* stbi__convert_16_to_8(stbi__uint16* orig, int w, int h, int channels) { - STBI__SCAN_load=0, - STBI__SCAN_type, - STBI__SCAN_header -}; + int i; + int img_len = w * h * channels; + stbi_uc* reduced; -static void stbi__refill_buffer(stbi__context *s) -{ - int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); - if (n == 0) { - // at end of file, treat same as if from memory, but need to handle case - // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file - s->read_from_callbacks = 0; - s->img_buffer = s->buffer_start; - s->img_buffer_end = s->buffer_start+1; - *s->img_buffer = 0; - } else { - s->img_buffer = s->buffer_start; - s->img_buffer_end = s->buffer_start + n; - } + reduced = (stbi_uc*)stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; } -stbi_inline static stbi_uc stbi__get8(stbi__context *s) +static stbi__uint16* stbi__convert_8_to_16(stbi_uc* orig, int w, int h, int channels) { - if (s->img_buffer < s->img_buffer_end) - return *s->img_buffer++; - if (s->read_from_callbacks) { - stbi__refill_buffer(s); - return *s->img_buffer++; - } - return 0; + int i; + int img_len = w * h * channels; + stbi__uint16* enlarged; + + enlarged = (stbi__uint16*)stbi__malloc(img_len * 2); + if (enlarged == NULL) return (stbi__uint16*)stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; } -stbi_inline static int stbi__at_eof(stbi__context *s) +static void stbi__vertical_flip(void* image, int w, int h, int bytes_per_pixel) { - if (s->io.read) { - if (!(s->io.eof)(s->io_user_data)) return 0; - // if feof() is true, check if buffer = end - // special case: we've only got the special 0 character at the end - if (s->read_from_callbacks == 0) return 1; - } + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc* bytes = (stbi_uc*)image; - return s->img_buffer >= s->img_buffer_end; + for (row = 0; row < (h >> 1); row++) { + stbi_uc* row0 = bytes + row * bytes_per_row; + stbi_uc* row1 = bytes + (h - row - 1) * bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; + } + } } -static void stbi__skip(stbi__context *s, int n) +#ifndef STBI_NO_GIF +static void stbi__vertical_flip_slices(void* image, int w, int h, int z, int bytes_per_pixel) { - if (n < 0) { - s->img_buffer = s->img_buffer_end; - return; - } - if (s->io.read) { - int blen = (int) (s->img_buffer_end - s->img_buffer); - if (blen < n) { - s->img_buffer = s->img_buffer_end; - (s->io.skip)(s->io_user_data, n - blen); - return; - } - } - s->img_buffer += n; + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc* bytes = (stbi_uc*)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } } +#endif -static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) +static unsigned char* stbi__load_and_postprocess_8bit(stbi__context* s, int* x, int* y, int* comp, int req_comp) { - if (s->io.read) { - int blen = (int) (s->img_buffer_end - s->img_buffer); - if (blen < n) { - int res, count; + stbi__result_info ri; + void* result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); - memcpy(buffer, s->img_buffer, blen); + if (result == NULL) + return NULL; - count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen); - res = (count == (n-blen)); - s->img_buffer = s->img_buffer_end; - return res; - } - } + // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. + STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); - if (s->img_buffer+n <= s->img_buffer_end) { - memcpy(buffer, s->img_buffer, n); - s->img_buffer += n; - return 1; - } else - return 0; -} + if (ri.bits_per_channel != 8) { + result = stbi__convert_16_to_8((stbi__uint16*)result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } -static int stbi__get16be(stbi__context *s) -{ - int z = stbi__get8(s); - return (z << 8) + stbi__get8(s); -} + // @TODO: move stbi__convert_format to here -static stbi__uint32 stbi__get32be(stbi__context *s) -{ - stbi__uint32 z = stbi__get16be(s); - return (z << 16) + stbi__get16be(s); + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char*)result; } -static int stbi__get16le(stbi__context *s) +static stbi__uint16* stbi__load_and_postprocess_16bit(stbi__context* s, int* x, int* y, int* comp, int req_comp) { - int z = stbi__get8(s); - return z + (stbi__get8(s) << 8); + stbi__result_info ri; + void* result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. + STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); + + if (ri.bits_per_channel != 16) { + result = stbi__convert_8_to_16((stbi_uc*)result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16*)result; } -static stbi__uint32 stbi__get32le(stbi__context *s) +#if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR) +static void stbi__float_postprocess(float* result, int* x, int* y, int* comp, int req_comp) { - stbi__uint32 z = stbi__get16le(s); - return z + (stbi__get16le(s) << 16); + if (stbi__vertically_flip_on_load && result != NULL) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); + } } +#endif -#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings +#ifndef STBI_NO_STDIO + +#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) +STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char* str, int cbmb, wchar_t* widestr, int cchwide); +STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t* widestr, int cchwide, char* str, int cbmb, const char* defchar, int* used_default); +#endif + +#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) +STBIDEF int stbi_convert_wchar_to_utf8(char* buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int)bufferlen, NULL, NULL); +} +#endif + +static FILE* stbi__fopen(char const* filename, char const* mode) +{ + FILE* f; +#if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename) / sizeof(*wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode) / sizeof(*wMode))) + return 0; + +#if defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f = 0; +#else + f = fopen(filename, mode); +#endif + return f; +} + + +STBIDEF stbi_uc* stbi_load(char const* filename, int* x, int* y, int* comp, int req_comp) +{ + FILE* f = stbi__fopen(filename, "rb"); + unsigned char* result; + if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file(f, x, y, comp, req_comp); + fclose(f); + return result; +} + +STBIDEF stbi_uc* stbi_load_from_file(FILE* f, int* x, int* y, int* comp, int req_comp) +{ + unsigned char* result; + stbi__context s; + stbi__start_file(&s, f); + result = stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, -(int)(s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi__uint16* stbi_load_from_file_16(FILE* f, int* x, int* y, int* comp, int req_comp) +{ + stbi__uint16* result; + stbi__context s; + stbi__start_file(&s, f); + result = stbi__load_and_postprocess_16bit(&s, x, y, comp, req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, -(int)(s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us* stbi_load_16(char const* filename, int* x, int* y, int* comp, int req_comp) +{ + FILE* f = stbi__fopen(filename, "rb"); + stbi__uint16* result; + if (!f) return (stbi_us*)stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f, x, y, comp, req_comp); + fclose(f); + return result; +} + + +#endif //!STBI_NO_STDIO + +STBIDEF stbi_us* stbi_load_16_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s, buffer, len); + return stbi__load_and_postprocess_16bit(&s, x, y, channels_in_file, desired_channels); +} + +STBIDEF stbi_us* stbi_load_16_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks*)clbk, user); + return stbi__load_and_postprocess_16bit(&s, x, y, channels_in_file, desired_channels); +} + +STBIDEF stbi_uc* stbi_load_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s, buffer, len); + return stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp); +} + +STBIDEF stbi_uc* stbi_load_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks*)clbk, user); + return stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc* stbi_load_gif_from_memory(stbi_uc const* buffer, int len, int** delays, int* x, int* y, int* z, int* comp, int req_comp) +{ + unsigned char* result; + stbi__context s; + stbi__start_mem(&s, buffer, len); + + result = (unsigned char*)stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices(result, *x, *y, *z, *comp); + } + + return result; +} +#endif + +#ifndef STBI_NO_LINEAR +static float* stbi__loadf_main(stbi__context* s, int* x, int* y, int* comp, int req_comp) +{ + unsigned char* data; +#ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + stbi__result_info ri; + float* hdr_data = stbi__hdr_load(s, x, y, comp, req_comp, &ri); + if (hdr_data) + stbi__float_postprocess(hdr_data, x, y, comp, req_comp); + return hdr_data; + } +#endif + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); + if (data) + return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); + return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); +} + +STBIDEF float* stbi_loadf_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s, buffer, len); + return stbi__loadf_main(&s, x, y, comp, req_comp); +} + +STBIDEF float* stbi_loadf_from_callbacks(stbi_io_callbacks const* clbk, void* user, int* x, int* y, int* comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks*)clbk, user); + return stbi__loadf_main(&s, x, y, comp, req_comp); +} + +#ifndef STBI_NO_STDIO +STBIDEF float* stbi_loadf(char const* filename, int* x, int* y, int* comp, int req_comp) +{ + float* result; + FILE* f = stbi__fopen(filename, "rb"); + if (!f) return stbi__errpf("can't fopen", "Unable to open file"); + result = stbi_loadf_from_file(f, x, y, comp, req_comp); + fclose(f); + return result; +} + +STBIDEF float* stbi_loadf_from_file(FILE* f, int* x, int* y, int* comp, int req_comp) +{ + stbi__context s; + stbi__start_file(&s, f); + return stbi__loadf_main(&s, x, y, comp, req_comp); +} +#endif // !STBI_NO_STDIO + +#endif // !STBI_NO_LINEAR + +// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is +// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always +// reports false! + +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const* buffer, int len) +{ +#ifndef STBI_NO_HDR + stbi__context s; + stbi__start_mem(&s, buffer, len); + return stbi__hdr_test(&s); +#else + STBI_NOTUSED(buffer); + STBI_NOTUSED(len); + return 0; +#endif +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr(char const* filename) +{ + FILE* f = stbi__fopen(filename, "rb"); + int result = 0; + if (f) { + result = stbi_is_hdr_from_file(f); + fclose(f); + } + return result; +} + +STBIDEF int stbi_is_hdr_from_file(FILE* f) +{ +#ifndef STBI_NO_HDR + long pos = ftell(f); + int res; + stbi__context s; + stbi__start_file(&s, f); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; +#else + STBI_NOTUSED(f); + return 0; +#endif +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const* clbk, void* user) +{ +#ifndef STBI_NO_HDR + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks*)clbk, user); + return stbi__hdr_test(&s); +#else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); + return 0; +#endif +} + +#ifndef STBI_NO_LINEAR +static float stbi__l2h_gamma = 2.2f, stbi__l2h_scale = 1.0f; + +STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } +STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } +#endif + +static float stbi__h2l_gamma_i = 1.0f / 2.2f, stbi__h2l_scale_i = 1.0f; + +STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1 / gamma; } +STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1 / scale; } +////////////////////////////////////////////////////////////////////////////// +// +// Common code used by all image loaders +// + +enum +{ + STBI__SCAN_load = 0, + STBI__SCAN_type, + STBI__SCAN_header +}; + +static void stbi__refill_buffer(stbi__context* s) +{ + int n = (s->io.read)(s->io_user_data, (char*)s->buffer_start, s->buflen); + s->callback_already_read += (int)(s->img_buffer - s->img_buffer_original); + if (n == 0) { + // at end of file, treat same as if from memory, but need to handle case + // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file + s->read_from_callbacks = 0; + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + 1; + *s->img_buffer = 0; + } + else { + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + n; + } +} + +stbi_inline static stbi_uc stbi__get8(stbi__context* s) +{ + if (s->img_buffer < s->img_buffer_end) + return *s->img_buffer++; + if (s->read_from_callbacks) { + stbi__refill_buffer(s); + return *s->img_buffer++; + } + return 0; +} + +#if defined(STBI_NO_JPEG) && defined(STBI_NO_HDR) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +// nothing +#else +stbi_inline static int stbi__at_eof(stbi__context* s) +{ + if (s->io.read) { + if (!(s->io.eof)(s->io_user_data)) return 0; + // if feof() is true, check if buffer = end + // special case: we've only got the special 0 character at the end + if (s->read_from_callbacks == 0) return 1; + } + + return s->img_buffer >= s->img_buffer_end; +} +#endif + +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) +// nothing +#else +static void stbi__skip(stbi__context* s, int n) +{ + if (n == 0) return; // already there! + if (n < 0) { + s->img_buffer = s->img_buffer_end; + return; + } + if (s->io.read) { + int blen = (int)(s->img_buffer_end - s->img_buffer); + if (blen < n) { + s->img_buffer = s->img_buffer_end; + (s->io.skip)(s->io_user_data, n - blen); + return; + } + } + s->img_buffer += n; +} +#endif + +#if defined(STBI_NO_PNG) && defined(STBI_NO_TGA) && defined(STBI_NO_HDR) && defined(STBI_NO_PNM) +// nothing +#else +static int stbi__getn(stbi__context* s, stbi_uc* buffer, int n) +{ + if (s->io.read) { + int blen = (int)(s->img_buffer_end - s->img_buffer); + if (blen < n) { + int res, count; + + memcpy(buffer, s->img_buffer, blen); + + count = (s->io.read)(s->io_user_data, (char*)buffer + blen, n - blen); + res = (count == (n - blen)); + s->img_buffer = s->img_buffer_end; + return res; + } + } + + if (s->img_buffer + n <= s->img_buffer_end) { + memcpy(buffer, s->img_buffer, n); + s->img_buffer += n; + return 1; + } + else + return 0; +} +#endif + +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC) +// nothing +#else +static int stbi__get16be(stbi__context* s) +{ + int z = stbi__get8(s); + return (z << 8) + stbi__get8(s); +} +#endif + +#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC) +// nothing +#else +static stbi__uint32 stbi__get32be(stbi__context* s) +{ + stbi__uint32 z = stbi__get16be(s); + return (z << 16) + stbi__get16be(s); +} +#endif + +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else +static int stbi__get16le(stbi__context* s) +{ + int z = stbi__get8(s); + return z + (stbi__get8(s) << 8); +} +#endif + +#ifndef STBI_NO_BMP +static stbi__uint32 stbi__get32le(stbi__context* s) +{ + stbi__uint32 z = stbi__get16le(s); + z += (stbi__uint32)stbi__get16le(s) << 16; + return z; +} +#endif + +#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings + +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +// nothing +#else ////////////////////////////////////////////////////////////////////////////// // // generic converter from built-in img_n to req_comp @@ -1314,97 +1745,167 @@ static stbi__uint32 stbi__get32le(stbi__context *s) static stbi_uc stbi__compute_y(int r, int g, int b) { - return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); + return (stbi_uc)(((r * 77) + (g * 150) + (29 * b)) >> 8); +} +#endif + +#if defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +// nothing +#else +static unsigned char* stbi__convert_format(unsigned char* data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i, j; + unsigned char* good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (unsigned char*)stbi__malloc_mad3(req_comp, x, y, 0); + if (good == NULL) { + STBI_FREE(data); + return stbi__errpuc("outofmem", "Out of memory"); + } + + for (j = 0; j < (int)y; ++j) { + unsigned char* src = data + j * x * img_n; + unsigned char* dest = good + j * x * req_comp; + +#define STBI__COMBO(a,b) ((a)*8+(b)) +#define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1, 2) { dest[0] = src[0]; dest[1] = 255; } break; + STBI__CASE(1, 3) { dest[0] = dest[1] = dest[2] = src[0]; } break; + STBI__CASE(1, 4) { dest[0] = dest[1] = dest[2] = src[0]; dest[3] = 255; } break; + STBI__CASE(2, 1) { dest[0] = src[0]; } break; + STBI__CASE(2, 3) { dest[0] = dest[1] = dest[2] = src[0]; } break; + STBI__CASE(2, 4) { dest[0] = dest[1] = dest[2] = src[0]; dest[3] = src[1]; } break; + STBI__CASE(3, 4) { dest[0] = src[0]; dest[1] = src[1]; dest[2] = src[2]; dest[3] = 255; } break; + STBI__CASE(3, 1) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); } break; + STBI__CASE(3, 2) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); dest[1] = 255; } break; + STBI__CASE(4, 1) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); } break; + STBI__CASE(4, 2) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); dest[1] = src[3]; } break; + STBI__CASE(4, 3) { dest[0] = src[0]; dest[1] = src[1]; dest[2] = src[2]; } break; + default: STBI_ASSERT(0); STBI_FREE(data); STBI_FREE(good); return stbi__errpuc("unsupported", "Unsupported format conversion"); + } +#undef STBI__CASE + } + + STBI_FREE(data); + return good; } +#endif -static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) +#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) +// nothing +#else +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) { - int i,j; - unsigned char *good; + return (stbi__uint16)(((r * 77) + (g * 150) + (29 * b)) >> 8); +} +#endif - if (req_comp == img_n) return data; - STBI_ASSERT(req_comp >= 1 && req_comp <= 4); +#if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) +// nothing +#else +static stbi__uint16* stbi__convert_format16(stbi__uint16* data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i, j; + stbi__uint16* good; - good = (unsigned char *) stbi__malloc(req_comp * x * y); - if (good == NULL) { - STBI_FREE(data); - return stbi__errpuc("outofmem", "Out of memory"); - } + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); - for (j=0; j < (int) y; ++j) { - unsigned char *src = data + j * x * img_n ; - unsigned char *dest = good + j * x * req_comp; - - #define COMBO(a,b) ((a)*8+(b)) - #define CASE(a,b) case COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) - // convert source image with img_n components to one with req_comp components; - // avoid switch per pixel, so use switch per scanline and massive macros - switch (COMBO(img_n, req_comp)) { - CASE(1,2) dest[0]=src[0], dest[1]=255; break; - CASE(1,3) dest[0]=dest[1]=dest[2]=src[0]; break; - CASE(1,4) dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; break; - CASE(2,1) dest[0]=src[0]; break; - CASE(2,3) dest[0]=dest[1]=dest[2]=src[0]; break; - CASE(2,4) dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; break; - CASE(3,4) dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; break; - CASE(3,1) dest[0]=stbi__compute_y(src[0],src[1],src[2]); break; - CASE(3,2) dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; break; - CASE(4,1) dest[0]=stbi__compute_y(src[0],src[1],src[2]); break; - CASE(4,2) dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; break; - CASE(4,3) dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; break; - default: STBI_ASSERT(0); - } - #undef CASE - } + good = (stbi__uint16*)stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16*)stbi__errpuc("outofmem", "Out of memory"); + } - STBI_FREE(data); - return good; + for (j = 0; j < (int)y; ++j) { + stbi__uint16* src = data + j * x * img_n; + stbi__uint16* dest = good + j * x * req_comp; + +#define STBI__COMBO(a,b) ((a)*8+(b)) +#define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1, 2) { dest[0] = src[0]; dest[1] = 0xffff; } break; + STBI__CASE(1, 3) { dest[0] = dest[1] = dest[2] = src[0]; } break; + STBI__CASE(1, 4) { dest[0] = dest[1] = dest[2] = src[0]; dest[3] = 0xffff; } break; + STBI__CASE(2, 1) { dest[0] = src[0]; } break; + STBI__CASE(2, 3) { dest[0] = dest[1] = dest[2] = src[0]; } break; + STBI__CASE(2, 4) { dest[0] = dest[1] = dest[2] = src[0]; dest[3] = src[1]; } break; + STBI__CASE(3, 4) { dest[0] = src[0]; dest[1] = src[1]; dest[2] = src[2]; dest[3] = 0xffff; } break; + STBI__CASE(3, 1) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); } break; + STBI__CASE(3, 2) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); dest[1] = 0xffff; } break; + STBI__CASE(4, 1) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); } break; + STBI__CASE(4, 2) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); dest[1] = src[3]; } break; + STBI__CASE(4, 3) { dest[0] = src[0]; dest[1] = src[1]; dest[2] = src[2]; } break; + default: STBI_ASSERT(0); STBI_FREE(data); STBI_FREE(good); return (stbi__uint16*)stbi__errpuc("unsupported", "Unsupported format conversion"); + } +#undef STBI__CASE + } + + STBI_FREE(data); + return good; } +#endif #ifndef STBI_NO_LINEAR -static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) -{ - int i,k,n; - float *output = (float *) stbi__malloc(x * y * comp * sizeof(float)); - if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } - // compute number of non-alpha components - if (comp & 1) n = comp; else n = comp-1; - for (i=0; i < x*y; ++i) { - for (k=0; k < n; ++k) { - output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); - } - if (k < comp) output[i*comp + k] = data[i*comp+k]/255.0f; - } - STBI_FREE(data); - return output; +static float* stbi__ldr_to_hdr(stbi_uc* data, int x, int y, int comp) +{ + int i, k, n; + float* output; + if (!data) return NULL; + output = (float*)stbi__malloc_mad4(x, y, comp, sizeof(float), 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp - 1; + for (i = 0; i < x * y; ++i) { + for (k = 0; k < n; ++k) { + output[i * comp + k] = (float)(pow(data[i * comp + k] / 255.0f, stbi__l2h_gamma) * stbi__l2h_scale); + } + } + if (n < comp) { + for (i = 0; i < x * y; ++i) { + output[i * comp + n] = data[i * comp + n] / 255.0f; + } + } + STBI_FREE(data); + return output; } #endif #ifndef STBI_NO_HDR #define stbi__float2int(x) ((int) (x)) -static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) -{ - int i,k,n; - stbi_uc *output = (stbi_uc *) stbi__malloc(x * y * comp); - if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } - // compute number of non-alpha components - if (comp & 1) n = comp; else n = comp-1; - for (i=0; i < x*y; ++i) { - for (k=0; k < n; ++k) { - float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; - if (z < 0) z = 0; - if (z > 255) z = 255; - output[i*comp + k] = (stbi_uc) stbi__float2int(z); - } - if (k < comp) { - float z = data[i*comp+k] * 255 + 0.5f; - if (z < 0) z = 0; - if (z > 255) z = 255; - output[i*comp + k] = (stbi_uc) stbi__float2int(z); - } - } - STBI_FREE(data); - return output; +static stbi_uc* stbi__hdr_to_ldr(float* data, int x, int y, int comp) +{ + int i, k, n; + stbi_uc* output; + if (!data) return NULL; + output = (stbi_uc*)stbi__malloc_mad3(x, y, comp, 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp - 1; + for (i = 0; i < x * y; ++i) { + for (k = 0; k < n; ++k) { + float z = (float)pow(data[i * comp + k] * stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i * comp + k] = (stbi_uc)stbi__float2int(z); + } + if (k < comp) { + float z = data[i * comp + k] * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i * comp + k] = (stbi_uc)stbi__float2int(z); + } + } + STBI_FREE(data); + return output; } #endif @@ -1436,249 +1937,261 @@ static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) typedef struct { - stbi_uc fast[1 << FAST_BITS]; - // weirdly, repacking this into AoS is a 10% speed loss, instead of a win - stbi__uint16 code[256]; - stbi_uc values[256]; - stbi_uc size[257]; - unsigned int maxcode[18]; - int delta[17]; // old 'firstsymbol' - old 'firstcode' + stbi_uc fast[1 << FAST_BITS]; + // weirdly, repacking this into AoS is a 10% speed loss, instead of a win + stbi__uint16 code[256]; + stbi_uc values[256]; + stbi_uc size[257]; + unsigned int maxcode[18]; + int delta[17]; // old 'firstsymbol' - old 'firstcode' } stbi__huffman; typedef struct { - stbi__context *s; - stbi__huffman huff_dc[4]; - stbi__huffman huff_ac[4]; - stbi_uc dequant[4][64]; - stbi__int16 fast_ac[4][1 << FAST_BITS]; - -// sizes for components, interleaved MCUs - int img_h_max, img_v_max; - int img_mcu_x, img_mcu_y; - int img_mcu_w, img_mcu_h; - -// definition of jpeg image component - struct - { - int id; - int h,v; - int tq; - int hd,ha; - int dc_pred; - - int x,y,w2,h2; - stbi_uc *data; - void *raw_data, *raw_coeff; - stbi_uc *linebuf; - short *coeff; // progressive only - int coeff_w, coeff_h; // number of 8x8 coefficient blocks - } img_comp[4]; - - stbi__uint32 code_buffer; // jpeg entropy-coded buffer - int code_bits; // number of valid bits - unsigned char marker; // marker seen while filling entropy buffer - int nomore; // flag if we saw a marker so must stop - - int progressive; - int spec_start; - int spec_end; - int succ_high; - int succ_low; - int eob_run; - - int scan_n, order[4]; - int restart_interval, todo; - -// kernels - void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]); - void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step); - stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs); + stbi__context* s; + stbi__huffman huff_dc[4]; + stbi__huffman huff_ac[4]; + stbi__uint16 dequant[4][64]; + stbi__int16 fast_ac[4][1 << FAST_BITS]; + + // sizes for components, interleaved MCUs + int img_h_max, img_v_max; + int img_mcu_x, img_mcu_y; + int img_mcu_w, img_mcu_h; + + // definition of jpeg image component + struct + { + int id; + int h, v; + int tq; + int hd, ha; + int dc_pred; + + int x, y, w2, h2; + stbi_uc* data; + void* raw_data, * raw_coeff; + stbi_uc* linebuf; + short* coeff; // progressive only + int coeff_w, coeff_h; // number of 8x8 coefficient blocks + } img_comp[4]; + + stbi__uint32 code_buffer; // jpeg entropy-coded buffer + int code_bits; // number of valid bits + unsigned char marker; // marker seen while filling entropy buffer + int nomore; // flag if we saw a marker so must stop + + int progressive; + int spec_start; + int spec_end; + int succ_high; + int succ_low; + int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; + + int scan_n, order[4]; + int restart_interval, todo; + + // kernels + void (*idct_block_kernel)(stbi_uc* out, int out_stride, short data[64]); + void (*YCbCr_to_RGB_kernel)(stbi_uc* out, const stbi_uc* y, const stbi_uc* pcb, const stbi_uc* pcr, int count, int step); + stbi_uc* (*resample_row_hv_2_kernel)(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs); } stbi__jpeg; -static int stbi__build_huffman(stbi__huffman *h, int *count) -{ - int i,j,k=0,code; - // build size list for each symbol (from JPEG spec) - for (i=0; i < 16; ++i) - for (j=0; j < count[i]; ++j) - h->size[k++] = (stbi_uc) (i+1); - h->size[k] = 0; - - // compute actual symbols (from jpeg spec) - code = 0; - k = 0; - for(j=1; j <= 16; ++j) { - // compute delta to add to code to compute symbol id - h->delta[j] = k - code; - if (h->size[k] == j) { - while (h->size[k] == j) - h->code[k++] = (stbi__uint16) (code++); - if (code-1 >= (1 << j)) return stbi__err("bad code lengths","Corrupt JPEG"); - } - // compute largest code + 1 for this size, preshifted as needed later - h->maxcode[j] = code << (16-j); - code <<= 1; - } - h->maxcode[j] = 0xffffffff; - - // build non-spec acceleration table; 255 is flag for not-accelerated - memset(h->fast, 255, 1 << FAST_BITS); - for (i=0; i < k; ++i) { - int s = h->size[i]; - if (s <= FAST_BITS) { - int c = h->code[i] << (FAST_BITS-s); - int m = 1 << (FAST_BITS-s); - for (j=0; j < m; ++j) { - h->fast[c+j] = (stbi_uc) i; - } - } - } - return 1; +static int stbi__build_huffman(stbi__huffman* h, int* count) +{ + int i, j, k = 0; + unsigned int code; + // build size list for each symbol (from JPEG spec) + for (i = 0; i < 16; ++i) { + for (j = 0; j < count[i]; ++j) { + h->size[k++] = (stbi_uc)(i + 1); + if (k >= 257) return stbi__err("bad size list", "Corrupt JPEG"); + } + } + h->size[k] = 0; + + // compute actual symbols (from jpeg spec) + code = 0; + k = 0; + for (j = 1; j <= 16; ++j) { + // compute delta to add to code to compute symbol id + h->delta[j] = k - code; + if (h->size[k] == j) { + while (h->size[k] == j) + h->code[k++] = (stbi__uint16)(code++); + if (code - 1 >= (1u << j)) return stbi__err("bad code lengths", "Corrupt JPEG"); + } + // compute largest code + 1 for this size, preshifted as needed later + h->maxcode[j] = code << (16 - j); + code <<= 1; + } + h->maxcode[j] = 0xffffffff; + + // build non-spec acceleration table; 255 is flag for not-accelerated + memset(h->fast, 255, 1 << FAST_BITS); + for (i = 0; i < k; ++i) { + int s = h->size[i]; + if (s <= FAST_BITS) { + int c = h->code[i] << (FAST_BITS - s); + int m = 1 << (FAST_BITS - s); + for (j = 0; j < m; ++j) { + h->fast[c + j] = (stbi_uc)i; + } + } + } + return 1; } // build a table that decodes both magnitude and value of small ACs in // one go. -static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) -{ - int i; - for (i=0; i < (1 << FAST_BITS); ++i) { - stbi_uc fast = h->fast[i]; - fast_ac[i] = 0; - if (fast < 255) { - int rs = h->values[fast]; - int run = (rs >> 4) & 15; - int magbits = rs & 15; - int len = h->size[fast]; - - if (magbits && len + magbits <= FAST_BITS) { - // magnitude code followed by receive_extend code - int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); - int m = 1 << (magbits - 1); - if (k < m) k += (-1 << magbits) + 1; - // if the result is small enough, we can fit it in fast_ac table - if (k >= -128 && k <= 127) - fast_ac[i] = (stbi__int16) ((k << 8) + (run << 4) + (len + magbits)); - } - } - } +static void stbi__build_fast_ac(stbi__int16* fast_ac, stbi__huffman* h) +{ + int i; + for (i = 0; i < (1 << FAST_BITS); ++i) { + stbi_uc fast = h->fast[i]; + fast_ac[i] = 0; + if (fast < 255) { + int rs = h->values[fast]; + int run = (rs >> 4) & 15; + int magbits = rs & 15; + int len = h->size[fast]; + + if (magbits && len + magbits <= FAST_BITS) { + // magnitude code followed by receive_extend code + int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); + int m = 1 << (magbits - 1); + if (k < m) k += (~0U << magbits) + 1; + // if the result is small enough, we can fit it in fast_ac table + if (k >= -128 && k <= 127) + fast_ac[i] = (stbi__int16)((k * 256) + (run * 16) + (len + magbits)); + } + } + } } -static void stbi__grow_buffer_unsafe(stbi__jpeg *j) +static void stbi__grow_buffer_unsafe(stbi__jpeg* j) { - do { - int b = j->nomore ? 0 : stbi__get8(j->s); - if (b == 0xff) { - int c = stbi__get8(j->s); - if (c != 0) { - j->marker = (unsigned char) c; - j->nomore = 1; - return; - } - } - j->code_buffer |= b << (24 - j->code_bits); - j->code_bits += 8; - } while (j->code_bits <= 24); + do { + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); + if (b == 0xff) { + int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes + if (c != 0) { + j->marker = (unsigned char)c; + j->nomore = 1; + return; + } + } + j->code_buffer |= b << (24 - j->code_bits); + j->code_bits += 8; + } while (j->code_bits <= 24); } // (1 << n) - 1 -static stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; +static const stbi__uint32 stbi__bmask[17] = { 0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535 }; // decode a jpeg huffman value from the bitstream -stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) -{ - unsigned int temp; - int c,k; - - if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - - // look at the top FAST_BITS and determine what symbol ID it is, - // if the code is <= FAST_BITS - c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); - k = h->fast[c]; - if (k < 255) { - int s = h->size[k]; - if (s > j->code_bits) - return -1; - j->code_buffer <<= s; - j->code_bits -= s; - return h->values[k]; - } +stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg* j, stbi__huffman* h) +{ + unsigned int temp; + int c, k; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + // look at the top FAST_BITS and determine what symbol ID it is, + // if the code is <= FAST_BITS + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1); + k = h->fast[c]; + if (k < 255) { + int s = h->size[k]; + if (s > j->code_bits) + return -1; + j->code_buffer <<= s; + j->code_bits -= s; + return h->values[k]; + } - // naive test is to shift the code_buffer down so k bits are - // valid, then test against maxcode. To speed this up, we've - // preshifted maxcode left so that it has (16-k) 0s at the - // end; in other words, regardless of the number of bits, it - // wants to be compared against something shifted to have 16; - // that way we don't need to shift inside the loop. - temp = j->code_buffer >> 16; - for (k=FAST_BITS+1 ; ; ++k) - if (temp < h->maxcode[k]) - break; - if (k == 17) { - // error! code not found - j->code_bits -= 16; - return -1; - } + // naive test is to shift the code_buffer down so k bits are + // valid, then test against maxcode. To speed this up, we've + // preshifted maxcode left so that it has (16-k) 0s at the + // end; in other words, regardless of the number of bits, it + // wants to be compared against something shifted to have 16; + // that way we don't need to shift inside the loop. + temp = j->code_buffer >> 16; + for (k = FAST_BITS + 1; ; ++k) + if (temp < h->maxcode[k]) + break; + if (k == 17) { + // error! code not found + j->code_bits -= 16; + return -1; + } - if (k > j->code_bits) - return -1; + if (k > j->code_bits) + return -1; - // convert the huffman code to the symbol id - c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; - STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); + // convert the huffman code to the symbol id + c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + if (c < 0 || c >= 256) // symbol id out of bounds! + return -1; + STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); - // convert the id to a symbol - j->code_bits -= k; - j->code_buffer <<= k; - return h->values[c]; + // convert the id to a symbol + j->code_bits -= k; + j->code_buffer <<= k; + return h->values[c]; } // bias[n] = (-1<code_bits < n) stbi__grow_buffer_unsafe(j); + unsigned int k; + int sgn; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + if (j->code_bits < n) return 0; // ran out of bits from stream, return 0s intead of continuing - sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB - k = stbi_lrot(j->code_buffer, n); - STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask))); - j->code_buffer = k & ~stbi__bmask[n]; - k &= stbi__bmask[n]; - j->code_bits -= n; - return k + (stbi__jbias[n] & ~sgn); + sgn = j->code_buffer >> 31; // sign bit always in MSB; 0 if MSB clear (positive), 1 if MSB set (negative) + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k + (stbi__jbias[n] & (sgn - 1)); } // get some unsigned bits -stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) +stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg* j, int n) { - unsigned int k; - if (j->code_bits < n) stbi__grow_buffer_unsafe(j); - k = stbi_lrot(j->code_buffer, n); - j->code_buffer = k & ~stbi__bmask[n]; - k &= stbi__bmask[n]; - j->code_bits -= n; - return k; + unsigned int k; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + if (j->code_bits < n) return 0; // ran out of bits from stream, return 0s intead of continuing + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k; } -stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) +stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg* j) { - unsigned int k; - if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); - k = j->code_buffer; - j->code_buffer <<= 1; - --j->code_bits; - return k & 0x80000000; + unsigned int k; + if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + if (j->code_bits < 1) return 0; // ran out of bits from stream, return 0s intead of continuing + k = j->code_buffer; + j->code_buffer <<= 1; + --j->code_bits; + return k & 0x80000000; } // given a value that's at position X in the zigzag stream, // where does it appear in the 8x8 matrix coded as row-major? -static stbi_uc stbi__jpeg_dezigzag[64+15] = +static const stbi_uc stbi__jpeg_dezigzag[64 + 15] = { 0, 1, 8, 16, 9, 2, 3, 10, 17, 24, 32, 25, 18, 11, 4, 5, @@ -1694,217 +2207,234 @@ static stbi_uc stbi__jpeg_dezigzag[64+15] = }; // decode one 64-entry block-- -static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi_uc *dequant) -{ - int diff,dc,k; - int t; - - if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - t = stbi__jpeg_huff_decode(j, hdc); - if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG"); - - // 0 all the ac values now so we can do it 32-bits at a time - memset(data,0,64*sizeof(data[0])); - - diff = t ? stbi__extend_receive(j, t) : 0; - dc = j->img_comp[b].dc_pred + diff; - j->img_comp[b].dc_pred = dc; - data[0] = (short) (dc * dequant[0]); - - // decode AC components, see JPEG spec - k = 1; - do { - unsigned int zig; - int c,r,s; - if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); - r = fac[c]; - if (r) { // fast-AC path - k += (r >> 4) & 15; // run - s = r & 15; // combined length - j->code_buffer <<= s; - j->code_bits -= s; - // decode into unzigzag'd location - zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short) ((r >> 8) * dequant[zig]); - } else { - int rs = stbi__jpeg_huff_decode(j, hac); - if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); - s = rs & 15; - r = rs >> 4; - if (s == 0) { - if (rs != 0xf0) break; // end block - k += 16; - } else { - k += r; +static int stbi__jpeg_decode_block(stbi__jpeg* j, short data[64], stbi__huffman* hdc, stbi__huffman* hac, stbi__int16* fac, int b, stbi__uint16* dequant) +{ + int diff, dc, k; + int t; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0 || t > 15) return stbi__err("bad huffman code", "Corrupt JPEG"); + + // 0 all the ac values now so we can do it 32-bits at a time + memset(data, 0, 64 * sizeof(data[0])); + + diff = t ? stbi__extend_receive(j, t) : 0; + if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) return stbi__err("bad delta", "Corrupt JPEG"); + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + if (!stbi__mul2shorts_valid(dc, dequant[0])) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + data[0] = (short)(dc * dequant[0]); + + // decode AC components, see JPEG spec + k = 1; + do { + unsigned int zig; + int c, r, s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + if (s > j->code_bits) return stbi__err("bad huffman code", "Combined length longer than code bits available"); + j->code_buffer <<= s; + j->code_bits -= s; // decode into unzigzag'd location zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]); - } - } - } while (k < 64); - return 1; + data[zig] = (short)((r >> 8) * dequant[zig]); + } + else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code", "Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (rs != 0xf0) break; // end block + k += 16; + } + else { + k += r; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short)(stbi__extend_receive(j, s) * dequant[zig]); + } + } + } while (k < 64); + return 1; } -static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b) +static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg* j, short data[64], stbi__huffman* hdc, int b) { - int diff,dc; - int t; - if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + int diff, dc; + int t; + if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - if (j->succ_high == 0) { - // first scan for DC coefficient, must be first - memset(data,0,64*sizeof(data[0])); // 0 all the ac values now - t = stbi__jpeg_huff_decode(j, hdc); - diff = t ? stbi__extend_receive(j, t) : 0; + if (j->succ_high == 0) { + // first scan for DC coefficient, must be first + memset(data, 0, 64 * sizeof(data[0])); // 0 all the ac values now + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0 || t > 15) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + diff = t ? stbi__extend_receive(j, t) : 0; - dc = j->img_comp[b].dc_pred + diff; - j->img_comp[b].dc_pred = dc; - data[0] = (short) (dc << j->succ_low); - } else { - // refinement scan for DC coefficient - if (stbi__jpeg_get_bit(j)) - data[0] += (short) (1 << j->succ_low); - } - return 1; + if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) return stbi__err("bad delta", "Corrupt JPEG"); + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + if (!stbi__mul2shorts_valid(dc, 1 << j->succ_low)) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + data[0] = (short)(dc * (1 << j->succ_low)); + } + else { + // refinement scan for DC coefficient + if (stbi__jpeg_get_bit(j)) + data[0] += (short)(1 << j->succ_low); + } + return 1; } // @OPTIMIZE: store non-zigzagged during the decode passes, // and only de-zigzag when dequantizing -static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac) +static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg* j, short data[64], stbi__huffman* hac, stbi__int16* fac) { - int k; - if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + int k; + if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - if (j->succ_high == 0) { - int shift = j->succ_low; + if (j->succ_high == 0) { + int shift = j->succ_low; - if (j->eob_run) { - --j->eob_run; - return 1; - } - - k = j->spec_start; - do { - unsigned int zig; - int c,r,s; - if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); - r = fac[c]; - if (r) { // fast-AC path - k += (r >> 4) & 15; // run - s = r & 15; // combined length - j->code_buffer <<= s; - j->code_bits -= s; - zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short) ((r >> 8) << shift); - } else { - int rs = stbi__jpeg_huff_decode(j, hac); - if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); - s = rs & 15; - r = rs >> 4; - if (s == 0) { - if (r < 15) { - j->eob_run = (1 << r); - if (r) - j->eob_run += stbi__jpeg_get_bits(j, r); - --j->eob_run; - break; - } - k += 16; - } else { - k += r; - zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short) (stbi__extend_receive(j,s) << shift); + if (j->eob_run) { + --j->eob_run; + return 1; + } + + k = j->spec_start; + do { + unsigned int zig; + int c, r, s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + if (s > j->code_bits) return stbi__err("bad huffman code", "Combined length longer than code bits available"); + j->code_buffer <<= s; + j->code_bits -= s; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short)((r >> 8) * (1 << shift)); } - } - } while (k <= j->spec_end); - } else { - // refinement scan for these AC coefficients - - short bit = (short) (1 << j->succ_low); - - if (j->eob_run) { - --j->eob_run; - for (k = j->spec_start; k <= j->spec_end; ++k) { - short *p = &data[stbi__jpeg_dezigzag[k]]; - if (*p != 0) - if (stbi__jpeg_get_bit(j)) - if ((*p & bit)==0) { - if (*p > 0) - *p += bit; - else - *p -= bit; - } - } - } else { - k = j->spec_start; - do { - int r,s; - int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh - if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); - s = rs & 15; - r = rs >> 4; - if (s == 0) { - if (r < 15) { - j->eob_run = (1 << r) - 1; - if (r) - j->eob_run += stbi__jpeg_get_bits(j, r); - r = 64; // force end of block - } else { - // r=15 s=0 should write 16 0s, so we just do - // a run of 15 0s and then write s (which is 0), - // so we don't have to do anything special here - } - } else { - if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); - // sign bit - if (stbi__jpeg_get_bit(j)) - s = bit; - else - s = -bit; + else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code", "Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r); + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + --j->eob_run; + break; + } + k += 16; + } + else { + k += r; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short)(stbi__extend_receive(j, s) * (1 << shift)); + } } - - // advance by r - while (k <= j->spec_end) { - short *p = &data[stbi__jpeg_dezigzag[k++]]; - if (*p != 0) { - if (stbi__jpeg_get_bit(j)) - if ((*p & bit)==0) { - if (*p > 0) - *p += bit; - else - *p -= bit; - } - } else { - if (r == 0) { - *p = (short) s; - break; - } - --r; - } + } while (k <= j->spec_end); + } + else { + // refinement scan for these AC coefficients + + short bit = (short)(1 << j->succ_low); + + if (j->eob_run) { + --j->eob_run; + for (k = j->spec_start; k <= j->spec_end; ++k) { + short* p = &data[stbi__jpeg_dezigzag[k]]; + if (*p != 0) + if (stbi__jpeg_get_bit(j)) + if ((*p & bit) == 0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } } - } while (k <= j->spec_end); - } - } - return 1; + } + else { + k = j->spec_start; + do { + int r, s; + int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh + if (rs < 0) return stbi__err("bad huffman code", "Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r) - 1; + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + r = 64; // force end of block + } + else { + // r=15 s=0 should write 16 0s, so we just do + // a run of 15 0s and then write s (which is 0), + // so we don't have to do anything special here + } + } + else { + if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); + // sign bit + if (stbi__jpeg_get_bit(j)) + s = bit; + else + s = -bit; + } + + // advance by r + while (k <= j->spec_end) { + short* p = &data[stbi__jpeg_dezigzag[k++]]; + if (*p != 0) { + if (stbi__jpeg_get_bit(j)) + if ((*p & bit) == 0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } + else { + if (r == 0) { + *p = (short)s; + break; + } + --r; + } + } + } while (k <= j->spec_end); + } + } + return 1; } // take a -128..127 value and stbi__clamp it and convert to 0..255 stbi_inline static stbi_uc stbi__clamp(int x) { - // trick to use a single test to catch both cases - if ((unsigned int) x > 255) { - if (x < 0) return 0; - if (x > 255) return 255; - } - return (stbi_uc) x; + // trick to use a single test to catch both cases + if ((unsigned int)x > 255) { + if (x < 0) return 0; + if (x > 255) return 255; + } + return (stbi_uc)x; } #define stbi__f2f(x) ((int) (((x) * 4096 + 0.5))) -#define stbi__fsh(x) ((x) << 12) +#define stbi__fsh(x) ((x) * 4096) // derived from jidctint -- DCT_ISLOW #define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \ @@ -1944,81 +2474,82 @@ stbi_inline static stbi_uc stbi__clamp(int x) t1 += p2+p4; \ t0 += p1+p3; -static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64]) -{ - int i,val[64],*v=val; - stbi_uc *o; - short *d = data; - - // columns - for (i=0; i < 8; ++i,++d, ++v) { - // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing - if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0 - && d[40]==0 && d[48]==0 && d[56]==0) { - // no shortcut 0 seconds - // (1|2|3|4|5|6|7)==0 0 seconds - // all separate -0.047 seconds - // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds - int dcterm = d[0] << 2; - v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; - } else { - STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56]) - // constants scaled things up by 1<<12; let's bring them back - // down, but keep 2 extra bits of precision - x0 += 512; x1 += 512; x2 += 512; x3 += 512; - v[ 0] = (x0+t3) >> 10; - v[56] = (x0-t3) >> 10; - v[ 8] = (x1+t2) >> 10; - v[48] = (x1-t2) >> 10; - v[16] = (x2+t1) >> 10; - v[40] = (x2-t1) >> 10; - v[24] = (x3+t0) >> 10; - v[32] = (x3-t0) >> 10; - } - } +static void stbi__idct_block(stbi_uc* out, int out_stride, short data[64]) +{ + int i, val[64], * v = val; + stbi_uc* o; + short* d = data; + + // columns + for (i = 0; i < 8; ++i, ++d, ++v) { + // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing + if (d[8] == 0 && d[16] == 0 && d[24] == 0 && d[32] == 0 + && d[40] == 0 && d[48] == 0 && d[56] == 0) { + // no shortcut 0 seconds + // (1|2|3|4|5|6|7)==0 0 seconds + // all separate -0.047 seconds + // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds + int dcterm = d[0] * 4; + v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; + } + else { + STBI__IDCT_1D(d[0], d[8], d[16], d[24], d[32], d[40], d[48], d[56]) + // constants scaled things up by 1<<12; let's bring them back + // down, but keep 2 extra bits of precision + x0 += 512; x1 += 512; x2 += 512; x3 += 512; + v[0] = (x0 + t3) >> 10; + v[56] = (x0 - t3) >> 10; + v[8] = (x1 + t2) >> 10; + v[48] = (x1 - t2) >> 10; + v[16] = (x2 + t1) >> 10; + v[40] = (x2 - t1) >> 10; + v[24] = (x3 + t0) >> 10; + v[32] = (x3 - t0) >> 10; + } + } - for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) { - // no fast case since the first 1D IDCT spread components out - STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7]) - // constants scaled things up by 1<<12, plus we had 1<<2 from first - // loop, plus horizontal and vertical each scale by sqrt(8) so together - // we've got an extra 1<<3, so 1<<17 total we need to remove. - // so we want to round that, which means adding 0.5 * 1<<17, - // aka 65536. Also, we'll end up with -128 to 127 that we want - // to encode as 0..255 by adding 128, so we'll add that before the shift - x0 += 65536 + (128<<17); - x1 += 65536 + (128<<17); - x2 += 65536 + (128<<17); - x3 += 65536 + (128<<17); - // tried computing the shifts into temps, or'ing the temps to see - // if any were out of range, but that was slower - o[0] = stbi__clamp((x0+t3) >> 17); - o[7] = stbi__clamp((x0-t3) >> 17); - o[1] = stbi__clamp((x1+t2) >> 17); - o[6] = stbi__clamp((x1-t2) >> 17); - o[2] = stbi__clamp((x2+t1) >> 17); - o[5] = stbi__clamp((x2-t1) >> 17); - o[3] = stbi__clamp((x3+t0) >> 17); - o[4] = stbi__clamp((x3-t0) >> 17); - } + for (i = 0, v = val, o = out; i < 8; ++i, v += 8, o += out_stride) { + // no fast case since the first 1D IDCT spread components out + STBI__IDCT_1D(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]) + // constants scaled things up by 1<<12, plus we had 1<<2 from first + // loop, plus horizontal and vertical each scale by sqrt(8) so together + // we've got an extra 1<<3, so 1<<17 total we need to remove. + // so we want to round that, which means adding 0.5 * 1<<17, + // aka 65536. Also, we'll end up with -128 to 127 that we want + // to encode as 0..255 by adding 128, so we'll add that before the shift + x0 += 65536 + (128 << 17); + x1 += 65536 + (128 << 17); + x2 += 65536 + (128 << 17); + x3 += 65536 + (128 << 17); + // tried computing the shifts into temps, or'ing the temps to see + // if any were out of range, but that was slower + o[0] = stbi__clamp((x0 + t3) >> 17); + o[7] = stbi__clamp((x0 - t3) >> 17); + o[1] = stbi__clamp((x1 + t2) >> 17); + o[6] = stbi__clamp((x1 - t2) >> 17); + o[2] = stbi__clamp((x2 + t1) >> 17); + o[5] = stbi__clamp((x2 - t1) >> 17); + o[3] = stbi__clamp((x3 + t0) >> 17); + o[4] = stbi__clamp((x3 - t0) >> 17); + } } #ifdef STBI_SSE2 // sse2 integer IDCT. not the fastest possible implementation but it // produces bit-identical results to the generic C version so it's // fully "transparent". -static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +static void stbi__idct_simd(stbi_uc* out, int out_stride, short data[64]) { - // This is constructed to match our regular (generic) integer IDCT exactly. - __m128i row0, row1, row2, row3, row4, row5, row6, row7; - __m128i tmp; + // This is constructed to match our regular (generic) integer IDCT exactly. + __m128i row0, row1, row2, row3, row4, row5, row6, row7; + __m128i tmp; - // dot product constant: even elems=x, odd elems=y - #define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) + // dot product constant: even elems=x, odd elems=y +#define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) - // out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) - // out(1) = c1[even]*x + c1[odd]*y - #define dct_rot(out0,out1, x,y,c0,c1) \ +// out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) +// out(1) = c1[even]*x + c1[odd]*y +#define dct_rot(out0,out1, x,y,c0,c1) \ __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \ __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \ __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ @@ -2027,22 +2558,22 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) // out = in << 12 (in 16-bit, out 32-bit) - #define dct_widen(out, in) \ +#define dct_widen(out, in) \ __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) // wide add - #define dct_wadd(out, a, b) \ +#define dct_wadd(out, a, b) \ __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ __m128i out##_h = _mm_add_epi32(a##_h, b##_h) // wide sub - #define dct_wsub(out, a, b) \ +#define dct_wsub(out, a, b) \ __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) // butterfly a/b, add bias, then shift by "s" and pack - #define dct_bfly32o(out0, out1, a,b,bias,s) \ +#define dct_bfly32o(out0, out1, a,b,bias,s) \ { \ __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ @@ -2053,18 +2584,18 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) } // 8-bit interleave step (for transposes) - #define dct_interleave8(a, b) \ +#define dct_interleave8(a, b) \ tmp = a; \ a = _mm_unpacklo_epi8(a, b); \ b = _mm_unpackhi_epi8(tmp, b) // 16-bit interleave step (for transposes) - #define dct_interleave16(a, b) \ +#define dct_interleave16(a, b) \ tmp = a; \ a = _mm_unpacklo_epi16(a, b); \ b = _mm_unpackhi_epi16(tmp, b) - #define dct_pass(bias,shift) \ +#define dct_pass(bias,shift) \ { \ /* even part */ \ dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \ @@ -2092,84 +2623,84 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) dct_bfly32o(row3,row4, x3,x4,bias,shift); \ } - __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); - __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f)); - __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); - __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); - __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f)); - __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f)); - __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f)); - __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f)); - - // rounding biases in column/row passes, see stbi__idct_block for explanation. - __m128i bias_0 = _mm_set1_epi32(512); - __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17)); - - // load - row0 = _mm_load_si128((const __m128i *) (data + 0*8)); - row1 = _mm_load_si128((const __m128i *) (data + 1*8)); - row2 = _mm_load_si128((const __m128i *) (data + 2*8)); - row3 = _mm_load_si128((const __m128i *) (data + 3*8)); - row4 = _mm_load_si128((const __m128i *) (data + 4*8)); - row5 = _mm_load_si128((const __m128i *) (data + 5*8)); - row6 = _mm_load_si128((const __m128i *) (data + 6*8)); - row7 = _mm_load_si128((const __m128i *) (data + 7*8)); - - // column pass - dct_pass(bias_0, 10); - - { - // 16bit 8x8 transpose pass 1 - dct_interleave16(row0, row4); - dct_interleave16(row1, row5); - dct_interleave16(row2, row6); - dct_interleave16(row3, row7); - - // transpose pass 2 - dct_interleave16(row0, row2); - dct_interleave16(row1, row3); - dct_interleave16(row4, row6); - dct_interleave16(row5, row7); - - // transpose pass 3 - dct_interleave16(row0, row1); - dct_interleave16(row2, row3); - dct_interleave16(row4, row5); - dct_interleave16(row6, row7); - } + __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); + __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f(0.765366865f), stbi__f2f(0.5411961f)); + __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); + __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); + __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f(0.298631336f), stbi__f2f(-1.961570560f)); + __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f(3.072711026f)); + __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f(2.053119869f), stbi__f2f(-0.390180644f)); + __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f(1.501321110f)); + + // rounding biases in column/row passes, see stbi__idct_block for explanation. + __m128i bias_0 = _mm_set1_epi32(512); + __m128i bias_1 = _mm_set1_epi32(65536 + (128 << 17)); + + // load + row0 = _mm_load_si128((const __m128i*) (data + 0 * 8)); + row1 = _mm_load_si128((const __m128i*) (data + 1 * 8)); + row2 = _mm_load_si128((const __m128i*) (data + 2 * 8)); + row3 = _mm_load_si128((const __m128i*) (data + 3 * 8)); + row4 = _mm_load_si128((const __m128i*) (data + 4 * 8)); + row5 = _mm_load_si128((const __m128i*) (data + 5 * 8)); + row6 = _mm_load_si128((const __m128i*) (data + 6 * 8)); + row7 = _mm_load_si128((const __m128i*) (data + 7 * 8)); + + // column pass + dct_pass(bias_0, 10); + + { + // 16bit 8x8 transpose pass 1 + dct_interleave16(row0, row4); + dct_interleave16(row1, row5); + dct_interleave16(row2, row6); + dct_interleave16(row3, row7); + + // transpose pass 2 + dct_interleave16(row0, row2); + dct_interleave16(row1, row3); + dct_interleave16(row4, row6); + dct_interleave16(row5, row7); + + // transpose pass 3 + dct_interleave16(row0, row1); + dct_interleave16(row2, row3); + dct_interleave16(row4, row5); + dct_interleave16(row6, row7); + } - // row pass - dct_pass(bias_1, 17); - - { - // pack - __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 - __m128i p1 = _mm_packus_epi16(row2, row3); - __m128i p2 = _mm_packus_epi16(row4, row5); - __m128i p3 = _mm_packus_epi16(row6, row7); - - // 8bit 8x8 transpose pass 1 - dct_interleave8(p0, p2); // a0e0a1e1... - dct_interleave8(p1, p3); // c0g0c1g1... - - // transpose pass 2 - dct_interleave8(p0, p1); // a0c0e0g0... - dct_interleave8(p2, p3); // b0d0f0h0... - - // transpose pass 3 - dct_interleave8(p0, p2); // a0b0c0d0... - dct_interleave8(p1, p3); // a4b4c4d4... - - // store - _mm_storel_epi64((__m128i *) out, p0); out += out_stride; - _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; - _mm_storel_epi64((__m128i *) out, p2); out += out_stride; - _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; - _mm_storel_epi64((__m128i *) out, p1); out += out_stride; - _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; - _mm_storel_epi64((__m128i *) out, p3); out += out_stride; - _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e)); - } + // row pass + dct_pass(bias_1, 17); + + { + // pack + __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 + __m128i p1 = _mm_packus_epi16(row2, row3); + __m128i p2 = _mm_packus_epi16(row4, row5); + __m128i p3 = _mm_packus_epi16(row6, row7); + + // 8bit 8x8 transpose pass 1 + dct_interleave8(p0, p2); // a0e0a1e1... + dct_interleave8(p1, p3); // c0g0c1g1... + + // transpose pass 2 + dct_interleave8(p0, p1); // a0c0e0g0... + dct_interleave8(p2, p3); // b0d0f0h0... + + // transpose pass 3 + dct_interleave8(p0, p2); // a0b0c0d0... + dct_interleave8(p1, p3); // a4b4c4d4... + + // store + _mm_storel_epi64((__m128i*) out, p0); out += out_stride; + _mm_storel_epi64((__m128i*) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i*) out, p2); out += out_stride; + _mm_storel_epi64((__m128i*) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i*) out, p1); out += out_stride; + _mm_storel_epi64((__m128i*) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i*) out, p3); out += out_stride; + _mm_storel_epi64((__m128i*) out, _mm_shuffle_epi32(p3, 0x4e)); + } #undef dct_const #undef dct_rot @@ -2188,22 +2719,22 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) // NEON integer IDCT. should produce bit-identical // results to the generic C version. -static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) -{ - int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; - - int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); - int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); - int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f)); - int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f)); - int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); - int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); - int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); - int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); - int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f)); - int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f)); - int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f)); - int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f)); +static void stbi__idct_simd(stbi_uc* out, int out_stride, short data[64]) +{ + int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; + + int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); + int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); + int16x4_t rot0_2 = vdup_n_s16(stbi__f2f(0.765366865f)); + int16x4_t rot1_0 = vdup_n_s16(stbi__f2f(1.175875602f)); + int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); + int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); + int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); + int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); + int16x4_t rot3_0 = vdup_n_s16(stbi__f2f(0.298631336f)); + int16x4_t rot3_1 = vdup_n_s16(stbi__f2f(2.053119869f)); + int16x4_t rot3_2 = vdup_n_s16(stbi__f2f(3.072711026f)); + int16x4_t rot3_3 = vdup_n_s16(stbi__f2f(1.501321110f)); #define dct_long_mul(out, inq, coeff) \ int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ @@ -2217,7 +2748,7 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) -// wide add + // wide add #define dct_wadd(out, a, b) \ int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ int32x4_t out##_h = vaddq_s32(a##_h, b##_h) @@ -2277,70 +2808,70 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) } // load - row0 = vld1q_s16(data + 0*8); - row1 = vld1q_s16(data + 1*8); - row2 = vld1q_s16(data + 2*8); - row3 = vld1q_s16(data + 3*8); - row4 = vld1q_s16(data + 4*8); - row5 = vld1q_s16(data + 5*8); - row6 = vld1q_s16(data + 6*8); - row7 = vld1q_s16(data + 7*8); - - // add DC bias - row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); - - // column pass - dct_pass(vrshrn_n_s32, 10); - - // 16bit 8x8 transpose - { -// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. -// whether compilers actually get this is another story, sadly. + row0 = vld1q_s16(data + 0 * 8); + row1 = vld1q_s16(data + 1 * 8); + row2 = vld1q_s16(data + 2 * 8); + row3 = vld1q_s16(data + 3 * 8); + row4 = vld1q_s16(data + 4 * 8); + row5 = vld1q_s16(data + 5 * 8); + row6 = vld1q_s16(data + 6 * 8); + row7 = vld1q_s16(data + 7 * 8); + + // add DC bias + row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); + + // column pass + dct_pass(vrshrn_n_s32, 10); + + // 16bit 8x8 transpose + { + // these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. + // whether compilers actually get this is another story, sadly. #define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; } #define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); } #define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); } // pass 1 - dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 - dct_trn16(row2, row3); - dct_trn16(row4, row5); - dct_trn16(row6, row7); - - // pass 2 - dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 - dct_trn32(row1, row3); - dct_trn32(row4, row6); - dct_trn32(row5, row7); - - // pass 3 - dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 - dct_trn64(row1, row5); - dct_trn64(row2, row6); - dct_trn64(row3, row7); + dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 + dct_trn16(row2, row3); + dct_trn16(row4, row5); + dct_trn16(row6, row7); + + // pass 2 + dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 + dct_trn32(row1, row3); + dct_trn32(row4, row6); + dct_trn32(row5, row7); + + // pass 3 + dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 + dct_trn64(row1, row5); + dct_trn64(row2, row6); + dct_trn64(row3, row7); #undef dct_trn16 #undef dct_trn32 #undef dct_trn64 - } + } - // row pass - // vrshrn_n_s32 only supports shifts up to 16, we need - // 17. so do a non-rounding shift of 16 first then follow - // up with a rounding shift by 1. - dct_pass(vshrn_n_s32, 16); - - { - // pack and round - uint8x8_t p0 = vqrshrun_n_s16(row0, 1); - uint8x8_t p1 = vqrshrun_n_s16(row1, 1); - uint8x8_t p2 = vqrshrun_n_s16(row2, 1); - uint8x8_t p3 = vqrshrun_n_s16(row3, 1); - uint8x8_t p4 = vqrshrun_n_s16(row4, 1); - uint8x8_t p5 = vqrshrun_n_s16(row5, 1); - uint8x8_t p6 = vqrshrun_n_s16(row6, 1); - uint8x8_t p7 = vqrshrun_n_s16(row7, 1); - - // again, these can translate into one instruction, but often don't. + // row pass + // vrshrn_n_s32 only supports shifts up to 16, we need + // 17. so do a non-rounding shift of 16 first then follow + // up with a rounding shift by 1. + dct_pass(vshrn_n_s32, 16); + + { + // pack and round + uint8x8_t p0 = vqrshrun_n_s16(row0, 1); + uint8x8_t p1 = vqrshrun_n_s16(row1, 1); + uint8x8_t p2 = vqrshrun_n_s16(row2, 1); + uint8x8_t p3 = vqrshrun_n_s16(row3, 1); + uint8x8_t p4 = vqrshrun_n_s16(row4, 1); + uint8x8_t p5 = vqrshrun_n_s16(row5, 1); + uint8x8_t p6 = vqrshrun_n_s16(row6, 1); + uint8x8_t p7 = vqrshrun_n_s16(row7, 1); + + // again, these can translate into one instruction, but often don't. #define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; } #define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); } #define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); } @@ -2349,37 +2880,37 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) // 8 bytes to each scan line! // 8x8 8-bit transpose pass 1 - dct_trn8_8(p0, p1); - dct_trn8_8(p2, p3); - dct_trn8_8(p4, p5); - dct_trn8_8(p6, p7); - - // pass 2 - dct_trn8_16(p0, p2); - dct_trn8_16(p1, p3); - dct_trn8_16(p4, p6); - dct_trn8_16(p5, p7); - - // pass 3 - dct_trn8_32(p0, p4); - dct_trn8_32(p1, p5); - dct_trn8_32(p2, p6); - dct_trn8_32(p3, p7); - - // store - vst1_u8(out, p0); out += out_stride; - vst1_u8(out, p1); out += out_stride; - vst1_u8(out, p2); out += out_stride; - vst1_u8(out, p3); out += out_stride; - vst1_u8(out, p4); out += out_stride; - vst1_u8(out, p5); out += out_stride; - vst1_u8(out, p6); out += out_stride; - vst1_u8(out, p7); + dct_trn8_8(p0, p1); + dct_trn8_8(p2, p3); + dct_trn8_8(p4, p5); + dct_trn8_8(p6, p7); + + // pass 2 + dct_trn8_16(p0, p2); + dct_trn8_16(p1, p3); + dct_trn8_16(p4, p6); + dct_trn8_16(p5, p7); + + // pass 3 + dct_trn8_32(p0, p4); + dct_trn8_32(p1, p5); + dct_trn8_32(p2, p6); + dct_trn8_32(p3, p7); + + // store + vst1_u8(out, p0); out += out_stride; + vst1_u8(out, p1); out += out_stride; + vst1_u8(out, p2); out += out_stride; + vst1_u8(out, p3); out += out_stride; + vst1_u8(out, p4); out += out_stride; + vst1_u8(out, p5); out += out_stride; + vst1_u8(out, p6); out += out_stride; + vst1_u8(out, p7); #undef dct_trn8_8 #undef dct_trn8_16 #undef dct_trn8_32 - } + } #undef dct_long_mul #undef dct_long_mac @@ -2396,15 +2927,15 @@ static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) // if there's a pending marker from the entropy stream, return that // otherwise, fetch from the stream and get a marker. if there's no // marker, return 0xff, which is never a valid marker value -static stbi_uc stbi__get_marker(stbi__jpeg *j) +static stbi_uc stbi__get_marker(stbi__jpeg* j) { - stbi_uc x; - if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } - x = stbi__get8(j->s); - if (x != 0xff) return STBI__MARKER_none; - while (x == 0xff) - x = stbi__get8(j->s); - return x; + stbi_uc x; + if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } + x = stbi__get8(j->s); + if (x != 0xff) return STBI__MARKER_none; + while (x == 0xff) + x = stbi__get8(j->s); // consume repeated 0xff fill bytes + return x; } // in each scan, we'll have scan_n components, and the order @@ -2413,352 +2944,431 @@ static stbi_uc stbi__get_marker(stbi__jpeg *j) // after a restart interval, stbi__jpeg_reset the entropy decoder and // the dc prediction -static void stbi__jpeg_reset(stbi__jpeg *j) -{ - j->code_bits = 0; - j->code_buffer = 0; - j->nomore = 0; - j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = 0; - j->marker = STBI__MARKER_none; - j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; - j->eob_run = 0; - // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, - // since we don't even allow 1<<30 pixels -} - -static int stbi__parse_entropy_coded_data(stbi__jpeg *z) -{ - stbi__jpeg_reset(z); - if (!z->progressive) { - if (z->scan_n == 1) { - int i,j; - STBI_SIMD_ALIGN(short, data[64]); - int n = z->order[0]; - // non-interleaved data, we just need to process one block at a time, - // in trivial scanline order - // number of blocks to do just depends on how many actual "pixels" this - // component has, independent of interleaved MCU blocking and such - int w = (z->img_comp[n].x+7) >> 3; - int h = (z->img_comp[n].y+7) >> 3; - for (j=0; j < h; ++j) { - for (i=0; i < w; ++i) { - int ha = z->img_comp[n].ha; - if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; - z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); - // every data block is an MCU, so countdown the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); - // if it's NOT a restart, then just bail, so we get corrupt data - // rather than no data - if (!STBI__RESTART(z->marker)) return 1; - stbi__jpeg_reset(z); - } +static void stbi__jpeg_reset(stbi__jpeg* j) +{ + j->code_bits = 0; + j->code_buffer = 0; + j->nomore = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; + j->marker = STBI__MARKER_none; + j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; + j->eob_run = 0; + // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, + // since we don't even allow 1<<30 pixels +} + +static int stbi__parse_entropy_coded_data(stbi__jpeg* z) +{ + stbi__jpeg_reset(z); + if (!z->progressive) { + if (z->scan_n == 1) { + int i, j; + STBI_SIMD_ALIGN(short, data[64]); + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x + 7) >> 3; + int h = (z->img_comp[n].y + 7) >> 3; + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc + z->img_comp[n].hd, z->huff_ac + ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * j * 8 + i * 8, z->img_comp[n].w2, data); + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + // if it's NOT a restart, then just bail, so we get corrupt data + // rather than no data + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } } - } - return 1; - } else { // interleaved - int i,j,k,x,y; - STBI_SIMD_ALIGN(short, data[64]); - for (j=0; j < z->img_mcu_y; ++j) { - for (i=0; i < z->img_mcu_x; ++i) { - // scan an interleaved mcu... process scan_n components in order - for (k=0; k < z->scan_n; ++k) { - int n = z->order[k]; - // scan out an mcu's worth of this component; that's just determined - // by the basic H and V specified for the component - for (y=0; y < z->img_comp[n].v; ++y) { - for (x=0; x < z->img_comp[n].h; ++x) { - int x2 = (i*z->img_comp[n].h + x)*8; - int y2 = (j*z->img_comp[n].v + y)*8; - int ha = z->img_comp[n].ha; - if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; - z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data); - } - } - } - // after all interleaved components, that's an interleaved MCU, - // so now count down the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); - if (!STBI__RESTART(z->marker)) return 1; - stbi__jpeg_reset(z); - } - } - } - return 1; - } - } else { - if (z->scan_n == 1) { - int i,j; - int n = z->order[0]; - // non-interleaved data, we just need to process one block at a time, - // in trivial scanline order - // number of blocks to do just depends on how many actual "pixels" this - // component has, independent of interleaved MCU blocking and such - int w = (z->img_comp[n].x+7) >> 3; - int h = (z->img_comp[n].y+7) >> 3; - for (j=0; j < h; ++j) { - for (i=0; i < w; ++i) { - short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); - if (z->spec_start == 0) { - if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) - return 0; - } else { - int ha = z->img_comp[n].ha; - if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) - return 0; - } - // every data block is an MCU, so countdown the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); - if (!STBI__RESTART(z->marker)) return 1; - stbi__jpeg_reset(z); - } + return 1; + } + else { // interleaved + int i, j, k, x, y; + STBI_SIMD_ALIGN(short, data[64]); + for (j = 0; j < z->img_mcu_y; ++j) { + for (i = 0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k = 0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y = 0; y < z->img_comp[n].v; ++y) { + for (x = 0; x < z->img_comp[n].h; ++x) { + int x2 = (i * z->img_comp[n].h + x) * 8; + int y2 = (j * z->img_comp[n].v + y) * 8; + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc + z->img_comp[n].hd, z->huff_ac + ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * y2 + x2, z->img_comp[n].w2, data); + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } } - } - return 1; - } else { // interleaved - int i,j,k,x,y; - for (j=0; j < z->img_mcu_y; ++j) { - for (i=0; i < z->img_mcu_x; ++i) { - // scan an interleaved mcu... process scan_n components in order - for (k=0; k < z->scan_n; ++k) { - int n = z->order[k]; - // scan out an mcu's worth of this component; that's just determined - // by the basic H and V specified for the component - for (y=0; y < z->img_comp[n].v; ++y) { - for (x=0; x < z->img_comp[n].h; ++x) { - int x2 = (i*z->img_comp[n].h + x); - int y2 = (j*z->img_comp[n].v + y); - short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + return 1; + } + } + else { + if (z->scan_n == 1) { + int i, j; + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x + 7) >> 3; + int h = (z->img_comp[n].y + 7) >> 3; + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + short* data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + if (z->spec_start == 0) { if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) - return 0; - } - } - } - // after all interleaved components, that's an interleaved MCU, - // so now count down the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); - if (!STBI__RESTART(z->marker)) return 1; - stbi__jpeg_reset(z); - } + return 0; + } + else { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) + return 0; + } + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } } - } - return 1; - } - } + return 1; + } + else { // interleaved + int i, j, k, x, y; + for (j = 0; j < z->img_mcu_y; ++j) { + for (i = 0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k = 0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y = 0; y < z->img_comp[n].v; ++y) { + for (x = 0; x < z->img_comp[n].h; ++x) { + int x2 = (i * z->img_comp[n].h + x); + int y2 = (j * z->img_comp[n].v + y); + short* data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } } -static void stbi__jpeg_dequantize(short *data, stbi_uc *dequant) +static void stbi__jpeg_dequantize(short* data, stbi__uint16* dequant) { - int i; - for (i=0; i < 64; ++i) - data[i] *= dequant[i]; + int i; + for (i = 0; i < 64; ++i) + data[i] *= dequant[i]; } -static void stbi__jpeg_finish(stbi__jpeg *z) +static void stbi__jpeg_finish(stbi__jpeg* z) { - if (z->progressive) { - // dequantize and idct the data - int i,j,n; - for (n=0; n < z->s->img_n; ++n) { - int w = (z->img_comp[n].x+7) >> 3; - int h = (z->img_comp[n].y+7) >> 3; - for (j=0; j < h; ++j) { - for (i=0; i < w; ++i) { - short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); - stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); - z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + if (z->progressive) { + // dequantize and idct the data + int i, j, n; + for (n = 0; n < z->s->img_n; ++n) { + int w = (z->img_comp[n].x + 7) >> 3; + int h = (z->img_comp[n].y + 7) >> 3; + for (j = 0; j < h; ++j) { + for (i = 0; i < w; ++i) { + short* data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); + z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * j * 8 + i * 8, z->img_comp[n].w2, data); + } } - } - } - } + } + } } -static int stbi__process_marker(stbi__jpeg *z, int m) +static int stbi__process_marker(stbi__jpeg* z, int m) { - int L; - switch (m) { - case STBI__MARKER_none: // no marker found - return stbi__err("expected marker","Corrupt JPEG"); + int L; + switch (m) { + case STBI__MARKER_none: // no marker found + return stbi__err("expected marker", "Corrupt JPEG"); - case 0xDD: // DRI - specify restart interval - if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG"); - z->restart_interval = stbi__get16be(z->s); - return 1; + case 0xDD: // DRI - specify restart interval + if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len", "Corrupt JPEG"); + z->restart_interval = stbi__get16be(z->s); + return 1; - case 0xDB: // DQT - define quantization table - L = stbi__get16be(z->s)-2; - while (L > 0) { + case 0xDB: // DQT - define quantization table + L = stbi__get16be(z->s) - 2; + while (L > 0) { int q = stbi__get8(z->s); - int p = q >> 4; - int t = q & 15,i; - if (p != 0) return stbi__err("bad DQT type","Corrupt JPEG"); - if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); - for (i=0; i < 64; ++i) - z->dequant[t][stbi__jpeg_dezigzag[i]] = stbi__get8(z->s); - L -= 65; - } - return L==0; - - case 0xC4: // DHT - define huffman table - L = stbi__get16be(z->s)-2; - while (L > 0) { - stbi_uc *v; - int sizes[16],i,n=0; + int p = q >> 4, sixteen = (p != 0); + int t = q & 15, i; + if (p != 0 && p != 1) return stbi__err("bad DQT type", "Corrupt JPEG"); + if (t > 3) return stbi__err("bad DQT table", "Corrupt JPEG"); + + for (i = 0; i < 64; ++i) + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); + } + return L == 0; + + case 0xC4: // DHT - define huffman table + L = stbi__get16be(z->s) - 2; + while (L > 0) { + stbi_uc* v; + int sizes[16], i, n = 0; int q = stbi__get8(z->s); int tc = q >> 4; int th = q & 15; - if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG"); - for (i=0; i < 16; ++i) { - sizes[i] = stbi__get8(z->s); - n += sizes[i]; + if (tc > 1 || th > 3) return stbi__err("bad DHT header", "Corrupt JPEG"); + for (i = 0; i < 16; ++i) { + sizes[i] = stbi__get8(z->s); + n += sizes[i]; } + if (n > 256) return stbi__err("bad DHT header", "Corrupt JPEG"); // Loop over i < n would write past end of values! L -= 17; if (tc == 0) { - if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; - v = z->huff_dc[th].values; - } else { - if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0; - v = z->huff_ac[th].values; + if (!stbi__build_huffman(z->huff_dc + th, sizes)) return 0; + v = z->huff_dc[th].values; + } + else { + if (!stbi__build_huffman(z->huff_ac + th, sizes)) return 0; + v = z->huff_ac[th].values; } - for (i=0; i < n; ++i) - v[i] = stbi__get8(z->s); + for (i = 0; i < n; ++i) + v[i] = stbi__get8(z->s); if (tc != 0) - stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); + stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); L -= n; - } - return L==0; - } - // check for comment block or APP blocks - if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { - stbi__skip(z->s, stbi__get16be(z->s)-2); - return 1; - } - return 0; + } + return L == 0; + } + + // check for comment block or APP blocks + if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len", "Corrupt JPEG"); + else + return stbi__err("bad APP len", "Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = { 'J','F','I','F','\0' }; + int ok = 1; + int i; + for (i = 0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } + else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = { 'A','d','o','b','e','\0' }; + int ok = 1; + int i; + for (i = 0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); + return 1; + } + + return stbi__err("unknown marker", "Corrupt JPEG"); } // after we see SOS -static int stbi__process_scan_header(stbi__jpeg *z) -{ - int i; - int Ls = stbi__get16be(z->s); - z->scan_n = stbi__get8(z->s); - if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG"); - if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG"); - for (i=0; i < z->scan_n; ++i) { - int id = stbi__get8(z->s), which; - int q = stbi__get8(z->s); - for (which = 0; which < z->s->img_n; ++which) - if (z->img_comp[which].id == id) - break; - if (which == z->s->img_n) return 0; // no match - z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG"); - z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG"); - z->order[i] = which; - } +static int stbi__process_scan_header(stbi__jpeg* z) +{ + int i; + int Ls = stbi__get16be(z->s); + z->scan_n = stbi__get8(z->s); + if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int)z->s->img_n) return stbi__err("bad SOS component count", "Corrupt JPEG"); + if (Ls != 6 + 2 * z->scan_n) return stbi__err("bad SOS len", "Corrupt JPEG"); + for (i = 0; i < z->scan_n; ++i) { + int id = stbi__get8(z->s), which; + int q = stbi__get8(z->s); + for (which = 0; which < z->s->img_n; ++which) + if (z->img_comp[which].id == id) + break; + if (which == z->s->img_n) return 0; // no match + z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff", "Corrupt JPEG"); + z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff", "Corrupt JPEG"); + z->order[i] = which; + } - { - int aa; - z->spec_start = stbi__get8(z->s); - z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 - aa = stbi__get8(z->s); - z->succ_high = (aa >> 4); - z->succ_low = (aa & 15); - if (z->progressive) { - if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) - return stbi__err("bad SOS", "Corrupt JPEG"); - } else { - if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG"); - if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG"); - z->spec_end = 63; - } - } + { + int aa; + z->spec_start = stbi__get8(z->s); + z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 + aa = stbi__get8(z->s); + z->succ_high = (aa >> 4); + z->succ_low = (aa & 15); + if (z->progressive) { + if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) + return stbi__err("bad SOS", "Corrupt JPEG"); + } + else { + if (z->spec_start != 0) return stbi__err("bad SOS", "Corrupt JPEG"); + if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS", "Corrupt JPEG"); + z->spec_end = 63; + } + } - return 1; + return 1; } -static int stbi__process_frame_header(stbi__jpeg *z, int scan) +static int stbi__free_jpeg_components(stbi__jpeg* z, int ncomp, int why) { - stbi__context *s = z->s; - int Lf,p,i,q, h_max=1,v_max=1,c; - Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG - p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline - s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG - s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires - c = stbi__get8(s); - if (c != 3 && c != 1) return stbi__err("bad component count","Corrupt JPEG"); // JFIF requires - s->img_n = c; - for (i=0; i < c; ++i) { - z->img_comp[i].data = NULL; - z->img_comp[i].linebuf = NULL; - } + int i; + for (i = 0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + +static int stbi__process_frame_header(stbi__jpeg* z, int scan) +{ + stbi__context* s = z->s; + int Lf, p, i, q, h_max = 1, v_max = 1, c; + Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len", "Corrupt JPEG"); // JPEG + p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit", "JPEG format not supported: 8-bit only"); // JPEG baseline + s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG + s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width", "Corrupt JPEG"); // JPEG requires + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__err("too large", "Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__err("too large", "Very large image (corrupt?)"); + c = stbi__get8(s); + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count", "Corrupt JPEG"); + s->img_n = c; + for (i = 0; i < c; ++i) { + z->img_comp[i].data = NULL; + z->img_comp[i].linebuf = NULL; + } - if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); - - for (i=0; i < s->img_n; ++i) { - z->img_comp[i].id = stbi__get8(s); - if (z->img_comp[i].id != i+1) // JFIF requires - if (z->img_comp[i].id != i) // some version of jpegtran outputs non-JFIF-compliant files! - return stbi__err("bad component ID","Corrupt JPEG"); - q = stbi__get8(s); - z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); - z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); - z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG"); - } + if (Lf != 8 + 3 * s->img_n) return stbi__err("bad SOF len", "Corrupt JPEG"); + + z->rgb = 0; + for (i = 0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; + z->img_comp[i].id = stbi__get8(s); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; + q = stbi__get8(s); + z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H", "Corrupt JPEG"); + z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V", "Corrupt JPEG"); + z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ", "Corrupt JPEG"); + } - if (scan != STBI__SCAN_load) return 1; + if (scan != STBI__SCAN_load) return 1; - if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); - for (i=0; i < s->img_n; ++i) { - if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; - if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; - } + for (i = 0; i < s->img_n; ++i) { + if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; + if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; + } - // compute interleaved mcu info - z->img_h_max = h_max; - z->img_v_max = v_max; - z->img_mcu_w = h_max * 8; - z->img_mcu_h = v_max * 8; - z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; - z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; - - for (i=0; i < s->img_n; ++i) { - // number of effective pixels (e.g. for non-interleaved MCU) - z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max; - z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max; - // to simplify generation, we'll allocate enough memory to decode - // the bogus oversized data from using interleaved MCUs and their - // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't - // discard the extra data until colorspace conversion - z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; - z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; - z->img_comp[i].raw_data = stbi__malloc(z->img_comp[i].w2 * z->img_comp[i].h2+15); - - if (z->img_comp[i].raw_data == NULL) { - for(--i; i >= 0; --i) { - STBI_FREE(z->img_comp[i].raw_data); - z->img_comp[i].data = NULL; - } - return stbi__err("outofmem", "Out of memory"); - } - // align blocks for idct using mmx/sse - z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); - z->img_comp[i].linebuf = NULL; - if (z->progressive) { - z->img_comp[i].coeff_w = (z->img_comp[i].w2 + 7) >> 3; - z->img_comp[i].coeff_h = (z->img_comp[i].h2 + 7) >> 3; - z->img_comp[i].raw_coeff = STBI_MALLOC(z->img_comp[i].coeff_w * z->img_comp[i].coeff_h * 64 * sizeof(short) + 15); - z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); - } else { - z->img_comp[i].coeff = 0; - z->img_comp[i].raw_coeff = 0; - } - } + // check that plane subsampling factors are integer ratios; our resamplers can't deal with fractional ratios + // and I've never seen a non-corrupted JPEG file actually use them + for (i = 0; i < s->img_n; ++i) { + if (h_max % z->img_comp[i].h != 0) return stbi__err("bad H", "Corrupt JPEG"); + if (v_max % z->img_comp[i].v != 0) return stbi__err("bad V", "Corrupt JPEG"); + } + + // compute interleaved mcu info + z->img_h_max = h_max; + z->img_v_max = v_max; + z->img_mcu_w = h_max * 8; + z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits + z->img_mcu_x = (s->img_x + z->img_mcu_w - 1) / z->img_mcu_w; + z->img_mcu_y = (s->img_y + z->img_mcu_h - 1) / z->img_mcu_h; + + for (i = 0; i < s->img_n; ++i) { + // number of effective pixels (e.g. for non-interleaved MCU) + z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max - 1) / h_max; + z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max - 1) / v_max; + // to simplify generation, we'll allocate enough memory to decode + // the bogus oversized data from using interleaved MCUs and their + // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't + // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) + z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; + z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i + 1, stbi__err("outofmem", "Out of memory")); + // align blocks for idct using mmx/sse + z->img_comp[i].data = (stbi_uc*)(((size_t)z->img_comp[i].raw_data + 15) & ~15); + if (z->progressive) { + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i + 1, stbi__err("outofmem", "Out of memory")); + z->img_comp[i].coeff = (short*)(((size_t)z->img_comp[i].raw_coeff + 15) & ~15); + } + } - return 1; + return 1; } // use comparisons since in some cases we handle more than one case (e.g. SOF) @@ -2770,658 +3380,730 @@ static int stbi__process_frame_header(stbi__jpeg *z, int scan) #define stbi__SOF_progressive(x) ((x) == 0xc2) -static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) -{ - int m; - z->marker = STBI__MARKER_none; // initialize cached marker to empty - m = stbi__get_marker(z); - if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); - if (scan == STBI__SCAN_type) return 1; - m = stbi__get_marker(z); - while (!stbi__SOF(m)) { - if (!stbi__process_marker(z,m)) return 0; - m = stbi__get_marker(z); - while (m == STBI__MARKER_none) { - // some files have extra padding after their blocks, so ok, we'll scan - if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); - m = stbi__get_marker(z); - } - } - z->progressive = stbi__SOF_progressive(m); - if (!stbi__process_frame_header(z, scan)) return 0; - return 1; +static int stbi__decode_jpeg_header(stbi__jpeg* z, int scan) +{ + int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 + z->marker = STBI__MARKER_none; // initialize cached marker to empty + m = stbi__get_marker(z); + if (!stbi__SOI(m)) return stbi__err("no SOI", "Corrupt JPEG"); + if (scan == STBI__SCAN_type) return 1; + m = stbi__get_marker(z); + while (!stbi__SOF(m)) { + if (!stbi__process_marker(z, m)) return 0; + m = stbi__get_marker(z); + while (m == STBI__MARKER_none) { + // some files have extra padding after their blocks, so ok, we'll scan + if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); + m = stbi__get_marker(z); + } + } + z->progressive = stbi__SOF_progressive(m); + if (!stbi__process_frame_header(z, scan)) return 0; + return 1; +} + +static int stbi__skip_jpeg_junk_at_end(stbi__jpeg* j) +{ + // some JPEGs have junk at end, skip over it but if we find what looks + // like a valid marker, resume there + while (!stbi__at_eof(j->s)) { + int x = stbi__get8(j->s); + while (x == 255) { // might be a marker + if (stbi__at_eof(j->s)) return STBI__MARKER_none; + x = stbi__get8(j->s); + if (x != 0x00 && x != 0xff) { + // not a stuffed zero or lead-in to another marker, looks + // like an actual marker, return it + return x; + } + // stuffed zero has x=0 now which ends the loop, meaning we go + // back to regular scan loop. + // repeated 0xff keeps trying to read the next byte of the marker. + } + } + return STBI__MARKER_none; } // decode image to YCbCr format -static int stbi__decode_jpeg_image(stbi__jpeg *j) +static int stbi__decode_jpeg_image(stbi__jpeg* j) { - int m; - for (m = 0; m < 4; m++) { - j->img_comp[m].raw_data = NULL; - j->img_comp[m].raw_coeff = NULL; - } - j->restart_interval = 0; - if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; - m = stbi__get_marker(j); - while (!stbi__EOI(m)) { - if (stbi__SOS(m)) { - if (!stbi__process_scan_header(j)) return 0; - if (!stbi__parse_entropy_coded_data(j)) return 0; - if (j->marker == STBI__MARKER_none ) { - // handle 0s at the end of image data from IP Kamera 9060 - while (!stbi__at_eof(j->s)) { - int x = stbi__get8(j->s); - if (x == 255) { - j->marker = stbi__get8(j->s); - break; - } else if (x != 0) { - return stbi__err("junk before marker", "Corrupt JPEG"); - } + int m; + for (m = 0; m < 4; m++) { + j->img_comp[m].raw_data = NULL; + j->img_comp[m].raw_coeff = NULL; + } + j->restart_interval = 0; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; + m = stbi__get_marker(j); + while (!stbi__EOI(m)) { + if (stbi__SOS(m)) { + if (!stbi__process_scan_header(j)) return 0; + if (!stbi__parse_entropy_coded_data(j)) return 0; + if (j->marker == STBI__MARKER_none) { + j->marker = stbi__skip_jpeg_junk_at_end(j); + // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 } - // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 - } - } else { - if (!stbi__process_marker(j, m)) return 0; - } - m = stbi__get_marker(j); - } - if (j->progressive) - stbi__jpeg_finish(j); - return 1; + m = stbi__get_marker(j); + if (STBI__RESTART(m)) + m = stbi__get_marker(j); + } + else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + m = stbi__get_marker(j); + } + else { + if (!stbi__process_marker(j, m)) return 1; + m = stbi__get_marker(j); + } + } + if (j->progressive) + stbi__jpeg_finish(j); + return 1; } // static jfif-centered resampling (across block boundaries) -typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1, - int w, int hs); +typedef stbi_uc* (*resample_row_func)(stbi_uc* out, stbi_uc* in0, stbi_uc* in1, + int w, int hs); #define stbi__div4(x) ((stbi_uc) ((x) >> 2)) -static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +static stbi_uc* resample_row_1(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs) { - STBI_NOTUSED(out); - STBI_NOTUSED(in_far); - STBI_NOTUSED(w); - STBI_NOTUSED(hs); - return in_near; + STBI_NOTUSED(out); + STBI_NOTUSED(in_far); + STBI_NOTUSED(w); + STBI_NOTUSED(hs); + return in_near; } -static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +static stbi_uc* stbi__resample_row_v_2(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs) { - // need to generate two samples vertically for every one in input - int i; - STBI_NOTUSED(hs); - for (i=0; i < w; ++i) - out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2); - return out; + // need to generate two samples vertically for every one in input + int i; + STBI_NOTUSED(hs); + for (i = 0; i < w; ++i) + out[i] = stbi__div4(3 * in_near[i] + in_far[i] + 2); + return out; } -static stbi_uc* stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +static stbi_uc* stbi__resample_row_h_2(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs) { - // need to generate two samples horizontally for every one in input - int i; - stbi_uc *input = in_near; + // need to generate two samples horizontally for every one in input + int i; + stbi_uc* input = in_near; - if (w == 1) { - // if only one sample, can't do any interpolation - out[0] = out[1] = input[0]; - return out; - } + if (w == 1) { + // if only one sample, can't do any interpolation + out[0] = out[1] = input[0]; + return out; + } - out[0] = input[0]; - out[1] = stbi__div4(input[0]*3 + input[1] + 2); - for (i=1; i < w-1; ++i) { - int n = 3*input[i]+2; - out[i*2+0] = stbi__div4(n+input[i-1]); - out[i*2+1] = stbi__div4(n+input[i+1]); - } - out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2); - out[i*2+1] = input[w-1]; + out[0] = input[0]; + out[1] = stbi__div4(input[0] * 3 + input[1] + 2); + for (i = 1; i < w - 1; ++i) { + int n = 3 * input[i] + 2; + out[i * 2 + 0] = stbi__div4(n + input[i - 1]); + out[i * 2 + 1] = stbi__div4(n + input[i + 1]); + } + out[i * 2 + 0] = stbi__div4(input[w - 2] * 3 + input[w - 1] + 2); + out[i * 2 + 1] = input[w - 1]; - STBI_NOTUSED(in_far); - STBI_NOTUSED(hs); + STBI_NOTUSED(in_far); + STBI_NOTUSED(hs); - return out; + return out; } #define stbi__div16(x) ((stbi_uc) ((x) >> 4)) -static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +static stbi_uc* stbi__resample_row_hv_2(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs) { - // need to generate 2x2 samples for every one in input - int i,t0,t1; - if (w == 1) { - out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); - return out; - } + // need to generate 2x2 samples for every one in input + int i, t0, t1; + if (w == 1) { + out[0] = out[1] = stbi__div4(3 * in_near[0] + in_far[0] + 2); + return out; + } - t1 = 3*in_near[0] + in_far[0]; - out[0] = stbi__div4(t1+2); - for (i=1; i < w; ++i) { - t0 = t1; - t1 = 3*in_near[i]+in_far[i]; - out[i*2-1] = stbi__div16(3*t0 + t1 + 8); - out[i*2 ] = stbi__div16(3*t1 + t0 + 8); - } - out[w*2-1] = stbi__div4(t1+2); + t1 = 3 * in_near[0] + in_far[0]; + out[0] = stbi__div4(t1 + 2); + for (i = 1; i < w; ++i) { + t0 = t1; + t1 = 3 * in_near[i] + in_far[i]; + out[i * 2 - 1] = stbi__div16(3 * t0 + t1 + 8); + out[i * 2] = stbi__div16(3 * t1 + t0 + 8); + } + out[w * 2 - 1] = stbi__div4(t1 + 2); - STBI_NOTUSED(hs); + STBI_NOTUSED(hs); - return out; + return out; } #if defined(STBI_SSE2) || defined(STBI_NEON) -static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +static stbi_uc* stbi__resample_row_hv_2_simd(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs) { - // need to generate 2x2 samples for every one in input - int i=0,t0,t1; + // need to generate 2x2 samples for every one in input + int i = 0, t0, t1; - if (w == 1) { - out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); - return out; - } + if (w == 1) { + out[0] = out[1] = stbi__div4(3 * in_near[0] + in_far[0] + 2); + return out; + } - t1 = 3*in_near[0] + in_far[0]; - // process groups of 8 pixels for as long as we can. - // note we can't handle the last pixel in a row in this loop - // because we need to handle the filter boundary conditions. - for (; i < ((w-1) & ~7); i += 8) { + t1 = 3 * in_near[0] + in_far[0]; + // process groups of 8 pixels for as long as we can. + // note we can't handle the last pixel in a row in this loop + // because we need to handle the filter boundary conditions. + for (; i < ((w - 1) & ~7); i += 8) { #if defined(STBI_SSE2) - // load and perform the vertical filtering pass - // this uses 3*x + y = 4*x + (y - x) - __m128i zero = _mm_setzero_si128(); - __m128i farb = _mm_loadl_epi64((__m128i *) (in_far + i)); - __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i)); - __m128i farw = _mm_unpacklo_epi8(farb, zero); - __m128i nearw = _mm_unpacklo_epi8(nearb, zero); - __m128i diff = _mm_sub_epi16(farw, nearw); - __m128i nears = _mm_slli_epi16(nearw, 2); - __m128i curr = _mm_add_epi16(nears, diff); // current row - - // horizontal filter works the same based on shifted vers of current - // row. "prev" is current row shifted right by 1 pixel; we need to - // insert the previous pixel value (from t1). - // "next" is current row shifted left by 1 pixel, with first pixel - // of next block of 8 pixels added in. - __m128i prv0 = _mm_slli_si128(curr, 2); - __m128i nxt0 = _mm_srli_si128(curr, 2); - __m128i prev = _mm_insert_epi16(prv0, t1, 0); - __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7); - - // horizontal filter, polyphase implementation since it's convenient: - // even pixels = 3*cur + prev = cur*4 + (prev - cur) - // odd pixels = 3*cur + next = cur*4 + (next - cur) - // note the shared term. - __m128i bias = _mm_set1_epi16(8); - __m128i curs = _mm_slli_epi16(curr, 2); - __m128i prvd = _mm_sub_epi16(prev, curr); - __m128i nxtd = _mm_sub_epi16(next, curr); - __m128i curb = _mm_add_epi16(curs, bias); - __m128i even = _mm_add_epi16(prvd, curb); - __m128i odd = _mm_add_epi16(nxtd, curb); - - // interleave even and odd pixels, then undo scaling. - __m128i int0 = _mm_unpacklo_epi16(even, odd); - __m128i int1 = _mm_unpackhi_epi16(even, odd); - __m128i de0 = _mm_srli_epi16(int0, 4); - __m128i de1 = _mm_srli_epi16(int1, 4); - - // pack and write output - __m128i outv = _mm_packus_epi16(de0, de1); - _mm_storeu_si128((__m128i *) (out + i*2), outv); + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + __m128i zero = _mm_setzero_si128(); + __m128i farb = _mm_loadl_epi64((__m128i*) (in_far + i)); + __m128i nearb = _mm_loadl_epi64((__m128i*) (in_near + i)); + __m128i farw = _mm_unpacklo_epi8(farb, zero); + __m128i nearw = _mm_unpacklo_epi8(nearb, zero); + __m128i diff = _mm_sub_epi16(farw, nearw); + __m128i nears = _mm_slli_epi16(nearw, 2); + __m128i curr = _mm_add_epi16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + __m128i prv0 = _mm_slli_si128(curr, 2); + __m128i nxt0 = _mm_srli_si128(curr, 2); + __m128i prev = _mm_insert_epi16(prv0, t1, 0); + __m128i next = _mm_insert_epi16(nxt0, 3 * in_near[i + 8] + in_far[i + 8], 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + __m128i bias = _mm_set1_epi16(8); + __m128i curs = _mm_slli_epi16(curr, 2); + __m128i prvd = _mm_sub_epi16(prev, curr); + __m128i nxtd = _mm_sub_epi16(next, curr); + __m128i curb = _mm_add_epi16(curs, bias); + __m128i even = _mm_add_epi16(prvd, curb); + __m128i odd = _mm_add_epi16(nxtd, curb); + + // interleave even and odd pixels, then undo scaling. + __m128i int0 = _mm_unpacklo_epi16(even, odd); + __m128i int1 = _mm_unpackhi_epi16(even, odd); + __m128i de0 = _mm_srli_epi16(int0, 4); + __m128i de1 = _mm_srli_epi16(int1, 4); + + // pack and write output + __m128i outv = _mm_packus_epi16(de0, de1); + _mm_storeu_si128((__m128i*) (out + i * 2), outv); #elif defined(STBI_NEON) - // load and perform the vertical filtering pass - // this uses 3*x + y = 4*x + (y - x) - uint8x8_t farb = vld1_u8(in_far + i); - uint8x8_t nearb = vld1_u8(in_near + i); - int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); - int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); - int16x8_t curr = vaddq_s16(nears, diff); // current row - - // horizontal filter works the same based on shifted vers of current - // row. "prev" is current row shifted right by 1 pixel; we need to - // insert the previous pixel value (from t1). - // "next" is current row shifted left by 1 pixel, with first pixel - // of next block of 8 pixels added in. - int16x8_t prv0 = vextq_s16(curr, curr, 7); - int16x8_t nxt0 = vextq_s16(curr, curr, 1); - int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); - int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7); - - // horizontal filter, polyphase implementation since it's convenient: - // even pixels = 3*cur + prev = cur*4 + (prev - cur) - // odd pixels = 3*cur + next = cur*4 + (next - cur) - // note the shared term. - int16x8_t curs = vshlq_n_s16(curr, 2); - int16x8_t prvd = vsubq_s16(prev, curr); - int16x8_t nxtd = vsubq_s16(next, curr); - int16x8_t even = vaddq_s16(curs, prvd); - int16x8_t odd = vaddq_s16(curs, nxtd); - - // undo scaling and round, then store with even/odd phases interleaved - uint8x8x2_t o; - o.val[0] = vqrshrun_n_s16(even, 4); - o.val[1] = vqrshrun_n_s16(odd, 4); - vst2_u8(out + i*2, o); -#endif - - // "previous" value for next iter - t1 = 3*in_near[i+7] + in_far[i+7]; - } + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + uint8x8_t farb = vld1_u8(in_far + i); + uint8x8_t nearb = vld1_u8(in_near + i); + int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); + int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); + int16x8_t curr = vaddq_s16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + int16x8_t prv0 = vextq_s16(curr, curr, 7); + int16x8_t nxt0 = vextq_s16(curr, curr, 1); + int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); + int16x8_t next = vsetq_lane_s16(3 * in_near[i + 8] + in_far[i + 8], nxt0, 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + int16x8_t curs = vshlq_n_s16(curr, 2); + int16x8_t prvd = vsubq_s16(prev, curr); + int16x8_t nxtd = vsubq_s16(next, curr); + int16x8_t even = vaddq_s16(curs, prvd); + int16x8_t odd = vaddq_s16(curs, nxtd); + + // undo scaling and round, then store with even/odd phases interleaved + uint8x8x2_t o; + o.val[0] = vqrshrun_n_s16(even, 4); + o.val[1] = vqrshrun_n_s16(odd, 4); + vst2_u8(out + i * 2, o); +#endif - t0 = t1; - t1 = 3*in_near[i] + in_far[i]; - out[i*2] = stbi__div16(3*t1 + t0 + 8); + // "previous" value for next iter + t1 = 3 * in_near[i + 7] + in_far[i + 7]; + } - for (++i; i < w; ++i) { - t0 = t1; - t1 = 3*in_near[i]+in_far[i]; - out[i*2-1] = stbi__div16(3*t0 + t1 + 8); - out[i*2 ] = stbi__div16(3*t1 + t0 + 8); - } - out[w*2-1] = stbi__div4(t1+2); - - STBI_NOTUSED(hs); - - return out; -} -#endif - -static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) -{ - // resample with nearest-neighbor - int i,j; - STBI_NOTUSED(in_far); - for (i=0; i < w; ++i) - for (j=0; j < hs; ++j) - out[i*hs+j] = in_near[i]; - return out; -} - -#ifdef STBI_JPEG_OLD -// this is the same YCbCr-to-RGB calculation that stb_image has used -// historically before the algorithm changes in 1.49 -#define float2fixed(x) ((int) ((x) * 65536 + 0.5)) -static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) -{ - int i; - for (i=0; i < count; ++i) { - int y_fixed = (y[i] << 16) + 32768; // rounding - int r,g,b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr*float2fixed(1.40200f); - g = y_fixed - cr*float2fixed(0.71414f) - cb*float2fixed(0.34414f); - b = y_fixed + cb*float2fixed(1.77200f); - r >>= 16; - g >>= 16; - b >>= 16; - if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } - if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } - if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } + t0 = t1; + t1 = 3 * in_near[i] + in_far[i]; + out[i * 2] = stbi__div16(3 * t1 + t0 + 8); + + for (++i; i < w; ++i) { + t0 = t1; + t1 = 3 * in_near[i] + in_far[i]; + out[i * 2 - 1] = stbi__div16(3 * t0 + t1 + 8); + out[i * 2] = stbi__div16(3 * t1 + t0 + 8); + } + out[w * 2 - 1] = stbi__div4(t1 + 2); + + STBI_NOTUSED(hs); + + return out; } -#else +#endif + +static stbi_uc* stbi__resample_row_generic(stbi_uc* out, stbi_uc* in_near, stbi_uc* in_far, int w, int hs) +{ + // resample with nearest-neighbor + int i, j; + STBI_NOTUSED(in_far); + for (i = 0; i < w; ++i) + for (j = 0; j < hs; ++j) + out[i * hs + j] = in_near[i]; + return out; +} + // this is a reduced-precision calculation of YCbCr-to-RGB introduced // to make sure the code produces the same results in both SIMD and scalar -#define float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) -static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) -{ - int i; - for (i=0; i < count; ++i) { - int y_fixed = (y[i] << 20) + (1<<19); // rounding - int r,g,b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr* float2fixed(1.40200f); - g = y_fixed + (cr*-float2fixed(0.71414f)) + ((cb*-float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb* float2fixed(1.77200f); - r >>= 20; - g >>= 20; - b >>= 20; - if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } - if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } - if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +static void stbi__YCbCr_to_RGB_row(stbi_uc* out, const stbi_uc* y, const stbi_uc* pcb, const stbi_uc* pcr, int count, int step) +{ + int i; + for (i = 0; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1 << 19); // rounding + int r, g, b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr * stbi__float2fixed(1.40200f); + g = y_fixed + (cr * -stbi__float2fixed(0.71414f)) + ((cb * -stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb * stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned)r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned)g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned)b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } } -#endif #if defined(STBI_SSE2) || defined(STBI_NEON) -static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) +static void stbi__YCbCr_to_RGB_simd(stbi_uc* out, stbi_uc const* y, stbi_uc const* pcb, stbi_uc const* pcr, int count, int step) { - int i = 0; + int i = 0; #ifdef STBI_SSE2 - // step == 3 is pretty ugly on the final interleave, and i'm not convinced - // it's useful in practice (you wouldn't use it for textures, for example). - // so just accelerate step == 4 case. - if (step == 4) { - // this is a fairly straightforward implementation and not super-optimized. - __m128i signflip = _mm_set1_epi8(-0x80); - __m128i cr_const0 = _mm_set1_epi16( (short) ( 1.40200f*4096.0f+0.5f)); - __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f)); - __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f)); - __m128i cb_const1 = _mm_set1_epi16( (short) ( 1.77200f*4096.0f+0.5f)); - __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128); - __m128i xw = _mm_set1_epi16(255); // alpha channel - - for (; i+7 < count; i += 8) { - // load - __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i)); - __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i)); - __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i)); - __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 - __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 - - // unpack to short (and left-shift cr, cb by 8) - __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); - __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); - __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); - - // color transform - __m128i yws = _mm_srli_epi16(yw, 4); - __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); - __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); - __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); - __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); - __m128i rws = _mm_add_epi16(cr0, yws); - __m128i gwt = _mm_add_epi16(cb0, yws); - __m128i bws = _mm_add_epi16(yws, cb1); - __m128i gws = _mm_add_epi16(gwt, cr1); - - // descale - __m128i rw = _mm_srai_epi16(rws, 4); - __m128i bw = _mm_srai_epi16(bws, 4); - __m128i gw = _mm_srai_epi16(gws, 4); - - // back to byte, set up for transpose - __m128i brb = _mm_packus_epi16(rw, bw); - __m128i gxb = _mm_packus_epi16(gw, xw); - - // transpose to interleave channels - __m128i t0 = _mm_unpacklo_epi8(brb, gxb); - __m128i t1 = _mm_unpackhi_epi8(brb, gxb); - __m128i o0 = _mm_unpacklo_epi16(t0, t1); - __m128i o1 = _mm_unpackhi_epi16(t0, t1); - - // store - _mm_storeu_si128((__m128i *) (out + 0), o0); - _mm_storeu_si128((__m128i *) (out + 16), o1); - out += 32; - } - } + // step == 3 is pretty ugly on the final interleave, and i'm not convinced + // it's useful in practice (you wouldn't use it for textures, for example). + // so just accelerate step == 4 case. + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + __m128i signflip = _mm_set1_epi8(-0x80); + __m128i cr_const0 = _mm_set1_epi16((short)(1.40200f * 4096.0f + 0.5f)); + __m128i cr_const1 = _mm_set1_epi16(-(short)(0.71414f * 4096.0f + 0.5f)); + __m128i cb_const0 = _mm_set1_epi16(-(short)(0.34414f * 4096.0f + 0.5f)); + __m128i cb_const1 = _mm_set1_epi16((short)(1.77200f * 4096.0f + 0.5f)); + __m128i y_bias = _mm_set1_epi8((char)(unsigned char)128); + __m128i xw = _mm_set1_epi16(255); // alpha channel + + for (; i + 7 < count; i += 8) { + // load + __m128i y_bytes = _mm_loadl_epi64((__m128i*) (y + i)); + __m128i cr_bytes = _mm_loadl_epi64((__m128i*) (pcr + i)); + __m128i cb_bytes = _mm_loadl_epi64((__m128i*) (pcb + i)); + __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 + __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 + + // unpack to short (and left-shift cr, cb by 8) + __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); + __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); + __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); + + // color transform + __m128i yws = _mm_srli_epi16(yw, 4); + __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); + __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); + __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); + __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); + __m128i rws = _mm_add_epi16(cr0, yws); + __m128i gwt = _mm_add_epi16(cb0, yws); + __m128i bws = _mm_add_epi16(yws, cb1); + __m128i gws = _mm_add_epi16(gwt, cr1); + + // descale + __m128i rw = _mm_srai_epi16(rws, 4); + __m128i bw = _mm_srai_epi16(bws, 4); + __m128i gw = _mm_srai_epi16(gws, 4); + + // back to byte, set up for transpose + __m128i brb = _mm_packus_epi16(rw, bw); + __m128i gxb = _mm_packus_epi16(gw, xw); + + // transpose to interleave channels + __m128i t0 = _mm_unpacklo_epi8(brb, gxb); + __m128i t1 = _mm_unpackhi_epi8(brb, gxb); + __m128i o0 = _mm_unpacklo_epi16(t0, t1); + __m128i o1 = _mm_unpackhi_epi16(t0, t1); + + // store + _mm_storeu_si128((__m128i*) (out + 0), o0); + _mm_storeu_si128((__m128i*) (out + 16), o1); + out += 32; + } + } #endif #ifdef STBI_NEON - // in this version, step=3 support would be easy to add. but is there demand? - if (step == 4) { - // this is a fairly straightforward implementation and not super-optimized. - uint8x8_t signflip = vdup_n_u8(0x80); - int16x8_t cr_const0 = vdupq_n_s16( (short) ( 1.40200f*4096.0f+0.5f)); - int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f)); - int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f)); - int16x8_t cb_const1 = vdupq_n_s16( (short) ( 1.77200f*4096.0f+0.5f)); - - for (; i+7 < count; i += 8) { - // load - uint8x8_t y_bytes = vld1_u8(y + i); - uint8x8_t cr_bytes = vld1_u8(pcr + i); - uint8x8_t cb_bytes = vld1_u8(pcb + i); - int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); - int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); - - // expand to s16 - int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); - int16x8_t crw = vshll_n_s8(cr_biased, 7); - int16x8_t cbw = vshll_n_s8(cb_biased, 7); - - // color transform - int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); - int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); - int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); - int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); - int16x8_t rws = vaddq_s16(yws, cr0); - int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); - int16x8_t bws = vaddq_s16(yws, cb1); - - // undo scaling, round, convert to byte - uint8x8x4_t o; - o.val[0] = vqrshrun_n_s16(rws, 4); - o.val[1] = vqrshrun_n_s16(gws, 4); - o.val[2] = vqrshrun_n_s16(bws, 4); - o.val[3] = vdup_n_u8(255); - - // store, interleaving r/g/b/a - vst4_u8(out, o); - out += 8*4; - } - } + // in this version, step=3 support would be easy to add. but is there demand? + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + uint8x8_t signflip = vdup_n_u8(0x80); + int16x8_t cr_const0 = vdupq_n_s16((short)(1.40200f * 4096.0f + 0.5f)); + int16x8_t cr_const1 = vdupq_n_s16(-(short)(0.71414f * 4096.0f + 0.5f)); + int16x8_t cb_const0 = vdupq_n_s16(-(short)(0.34414f * 4096.0f + 0.5f)); + int16x8_t cb_const1 = vdupq_n_s16((short)(1.77200f * 4096.0f + 0.5f)); + + for (; i + 7 < count; i += 8) { + // load + uint8x8_t y_bytes = vld1_u8(y + i); + uint8x8_t cr_bytes = vld1_u8(pcr + i); + uint8x8_t cb_bytes = vld1_u8(pcb + i); + int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); + int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); + + // expand to s16 + int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); + int16x8_t crw = vshll_n_s8(cr_biased, 7); + int16x8_t cbw = vshll_n_s8(cb_biased, 7); + + // color transform + int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); + int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); + int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); + int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); + int16x8_t rws = vaddq_s16(yws, cr0); + int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); + int16x8_t bws = vaddq_s16(yws, cb1); + + // undo scaling, round, convert to byte + uint8x8x4_t o; + o.val[0] = vqrshrun_n_s16(rws, 4); + o.val[1] = vqrshrun_n_s16(gws, 4); + o.val[2] = vqrshrun_n_s16(bws, 4); + o.val[3] = vdup_n_u8(255); + + // store, interleaving r/g/b/a + vst4_u8(out, o); + out += 8 * 4; + } + } #endif - for (; i < count; ++i) { - int y_fixed = (y[i] << 20) + (1<<19); // rounding - int r,g,b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr* float2fixed(1.40200f); - g = y_fixed + cr*-float2fixed(0.71414f) + ((cb*-float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb* float2fixed(1.77200f); - r >>= 20; - g >>= 20; - b >>= 20; - if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } - if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } - if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } + for (; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1 << 19); // rounding + int r, g, b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr * stbi__float2fixed(1.40200f); + g = y_fixed + cr * -stbi__float2fixed(0.71414f) + ((cb * -stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb * stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned)r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned)g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned)b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } } #endif // set up the kernels -static void stbi__setup_jpeg(stbi__jpeg *j) +static void stbi__setup_jpeg(stbi__jpeg* j) { - j->idct_block_kernel = stbi__idct_block; - j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; - j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; + j->idct_block_kernel = stbi__idct_block; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; #ifdef STBI_SSE2 - if (stbi__sse2_available()) { - j->idct_block_kernel = stbi__idct_simd; - #ifndef STBI_JPEG_OLD - j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - #endif - j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; - } + if (stbi__sse2_available()) { + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + } #endif #ifdef STBI_NEON - j->idct_block_kernel = stbi__idct_simd; - #ifndef STBI_JPEG_OLD - j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - #endif - j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; #endif } // clean up the temporary component buffers -static void stbi__cleanup_jpeg(stbi__jpeg *j) -{ - int i; - for (i=0; i < j->s->img_n; ++i) { - if (j->img_comp[i].raw_data) { - STBI_FREE(j->img_comp[i].raw_data); - j->img_comp[i].raw_data = NULL; - j->img_comp[i].data = NULL; - } - if (j->img_comp[i].raw_coeff) { - STBI_FREE(j->img_comp[i].raw_coeff); - j->img_comp[i].raw_coeff = 0; - j->img_comp[i].coeff = 0; - } - if (j->img_comp[i].linebuf) { - STBI_FREE(j->img_comp[i].linebuf); - j->img_comp[i].linebuf = NULL; - } - } +static void stbi__cleanup_jpeg(stbi__jpeg* j) +{ + stbi__free_jpeg_components(j, j->s->img_n, 0); } typedef struct { - resample_row_func resample; - stbi_uc *line0,*line1; - int hs,vs; // expansion factor in each axis - int w_lores; // horizontal pixels pre-expansion - int ystep; // how far through vertical expansion we are - int ypos; // which pre-expansion row we're on + resample_row_func resample; + stbi_uc* line0, * line1; + int hs, vs; // expansion factor in each axis + int w_lores; // horizontal pixels pre-expansion + int ystep; // how far through vertical expansion we are + int ypos; // which pre-expansion row we're on } stbi__resample; -static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) -{ - int n, decode_n; - z->s->img_n = 0; // make stbi__cleanup_jpeg safe - - // validate req_comp - if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); - - // load a jpeg image from whichever source, but leave in YCbCr format - if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } - - // determine actual number of components to generate - n = req_comp ? req_comp : z->s->img_n; - - if (z->s->img_n == 3 && n < 3) - decode_n = 1; - else - decode_n = z->s->img_n; - - // resample and color-convert - { - int k; - unsigned int i,j; - stbi_uc *output; - stbi_uc *coutput[4]; - - stbi__resample res_comp[4]; - - for (k=0; k < decode_n; ++k) { - stbi__resample *r = &res_comp[k]; - - // allocate line buffer big enough for upsampling off the edges - // with upsample factor of 4 - z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3); - if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } - - r->hs = z->img_h_max / z->img_comp[k].h; - r->vs = z->img_v_max / z->img_comp[k].v; - r->ystep = r->vs >> 1; - r->w_lores = (z->s->img_x + r->hs-1) / r->hs; - r->ypos = 0; - r->line0 = r->line1 = z->img_comp[k].data; - - if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; - else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; - else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; - else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; - else r->resample = stbi__resample_row_generic; - } - - // can't error after this so, this is safe - output = (stbi_uc *) stbi__malloc(n * z->s->img_x * z->s->img_y + 1); - if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } - - // now go ahead and resample - for (j=0; j < z->s->img_y; ++j) { - stbi_uc *out = output + n * z->s->img_x * j; - for (k=0; k < decode_n; ++k) { - stbi__resample *r = &res_comp[k]; - int y_bot = r->ystep >= (r->vs >> 1); - coutput[k] = r->resample(z->img_comp[k].linebuf, - y_bot ? r->line1 : r->line0, - y_bot ? r->line0 : r->line1, - r->w_lores, r->hs); - if (++r->ystep >= r->vs) { - r->ystep = 0; - r->line0 = r->line1; - if (++r->ypos < z->img_comp[k].y) - r->line1 += z->img_comp[k].w2; +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x * y + 128; + return (stbi_uc)((t + (t >> 8)) >> 8); +} + +static stbi_uc* load_jpeg_image(stbi__jpeg* z, int* out_x, int* out_y, int* comp, int req_comp) +{ + int n, decode_n, is_rgb; + z->s->img_n = 0; // make stbi__cleanup_jpeg safe + + // validate req_comp + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + + // load a jpeg image from whichever source, but leave in YCbCr format + if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } + + // determine actual number of components to generate + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); + + if (z->s->img_n == 3 && n < 3 && !is_rgb) + decode_n = 1; + else + decode_n = z->s->img_n; + + // nothing to do if no components requested; check this now to avoid + // accessing uninitialized coutput[0] later + if (decode_n <= 0) { stbi__cleanup_jpeg(z); return NULL; } + + // resample and color-convert + { + int k; + unsigned int i, j; + stbi_uc* output; + stbi_uc* coutput[4] = { NULL, NULL, NULL, NULL }; + + stbi__resample res_comp[4]; + + for (k = 0; k < decode_n; ++k) { + stbi__resample* r = &res_comp[k]; + + // allocate line buffer big enough for upsampling off the edges + // with upsample factor of 4 + z->img_comp[k].linebuf = (stbi_uc*)stbi__malloc(z->s->img_x + 3); + if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + r->hs = z->img_h_max / z->img_comp[k].h; + r->vs = z->img_v_max / z->img_comp[k].v; + r->ystep = r->vs >> 1; + r->w_lores = (z->s->img_x + r->hs - 1) / r->hs; + r->ypos = 0; + r->line0 = r->line1 = z->img_comp[k].data; + + if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; + else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; + else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; + else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; + else r->resample = stbi__resample_row_generic; + } + + // can't error after this so, this is safe + output = (stbi_uc*)stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); + if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + // now go ahead and resample + for (j = 0; j < z->s->img_y; ++j) { + stbi_uc* out = output + n * z->s->img_x * j; + for (k = 0; k < decode_n; ++k) { + stbi__resample* r = &res_comp[k]; + int y_bot = r->ystep >= (r->vs >> 1); + coutput[k] = r->resample(z->img_comp[k].linebuf, + y_bot ? r->line1 : r->line0, + y_bot ? r->line0 : r->line1, + r->w_lores, r->hs); + if (++r->ystep >= r->vs) { + r->ystep = 0; + r->line0 = r->line1; + if (++r->ypos < z->img_comp[k].y) + r->line1 += z->img_comp[k].w2; + } } - } - if (n >= 3) { - stbi_uc *y = coutput[0]; - if (z->s->img_n == 3) { - z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); - } else - for (i=0; i < z->s->img_x; ++i) { - out[0] = out[1] = out[2] = y[i]; - out[3] = 255; // not used if n==3 - out += n; - } - } else { - stbi_uc *y = coutput[0]; - if (n == 1) - for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; - else - for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; - } - } - stbi__cleanup_jpeg(z); - *out_x = z->s->img_x; - *out_y = z->s->img_y; - if (comp) *comp = z->s->img_n; // report original components, not output - return output; - } + if (n >= 3) { + stbi_uc* y = coutput[0]; + if (z->s->img_n == 3) { + if (is_rgb) { + for (i = 0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } + else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } + else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i = 0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } + else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i = 0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } + else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } + else + for (i = 0; i < z->s->img_x; ++i) { + out[0] = out[1] = out[2] = y[i]; + out[3] = 255; // not used if n==3 + out += n; + } + } + else { + if (is_rgb) { + if (n == 1) + for (i = 0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i = 0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } + else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i = 0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } + else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i = 0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } + else { + stbi_uc* y = coutput[0]; + if (n == 1) + for (i = 0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i = 0; i < z->s->img_x; ++i) { *out++ = y[i]; *out++ = 255; } + } + } + } + stbi__cleanup_jpeg(z); + *out_x = z->s->img_x; + *out_y = z->s->img_y; + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output + return output; + } } -static unsigned char *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void* stbi__jpeg_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) { - stbi__jpeg j; - j.s = s; - stbi__setup_jpeg(&j); - return load_jpeg_image(&j, x,y,comp,req_comp); + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + if (!j) return stbi__errpuc("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x, y, comp, req_comp); + STBI_FREE(j); + return result; } -static int stbi__jpeg_test(stbi__context *s) +static int stbi__jpeg_test(stbi__context* s) { - int r; - stbi__jpeg j; - j.s = s; - stbi__setup_jpeg(&j); - r = stbi__decode_jpeg_header(&j, STBI__SCAN_type); - stbi__rewind(s); - return r; + int r; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + if (!j) return stbi__err("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); + stbi__rewind(s); + STBI_FREE(j); + return r; } -static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) +static int stbi__jpeg_info_raw(stbi__jpeg* j, int* x, int* y, int* comp) { - if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { - stbi__rewind( j->s ); - return 0; - } - if (x) *x = j->s->img_x; - if (y) *y = j->s->img_y; - if (comp) *comp = j->s->img_n; - return 1; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { + stbi__rewind(j->s); + return 0; + } + if (x) *x = j->s->img_x; + if (y) *y = j->s->img_y; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; + return 1; } -static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__jpeg_info(stbi__context* s, int* x, int* y, int* comp) { - stbi__jpeg j; - j.s = s; - return stbi__jpeg_info_raw(&j, x, y, comp); + int result; + stbi__jpeg* j = (stbi__jpeg*)(stbi__malloc(sizeof(stbi__jpeg))); + if (!j) return stbi__err("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; } #endif @@ -3437,81 +4119,82 @@ static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) // fast-way is faster to check than jpeg huffman, but slow way is slower #define STBI__ZFAST_BITS 9 // accelerate all cases in default tables #define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) +#define STBI__ZNSYMS 288 // number of symbols in literal/length alphabet // zlib-style huffman encoding // (jpegs packs from left, zlib from right, so can't share code) typedef struct { - stbi__uint16 fast[1 << STBI__ZFAST_BITS]; - stbi__uint16 firstcode[16]; - int maxcode[17]; - stbi__uint16 firstsymbol[16]; - stbi_uc size[288]; - stbi__uint16 value[288]; + stbi__uint16 fast[1 << STBI__ZFAST_BITS]; + stbi__uint16 firstcode[16]; + int maxcode[17]; + stbi__uint16 firstsymbol[16]; + stbi_uc size[STBI__ZNSYMS]; + stbi__uint16 value[STBI__ZNSYMS]; } stbi__zhuffman; stbi_inline static int stbi__bitreverse16(int n) { - n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); - n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); - n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); - n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); - return n; + n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); + n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); + n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); + n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); + return n; } stbi_inline static int stbi__bit_reverse(int v, int bits) { - STBI_ASSERT(bits <= 16); - // to bit reverse n bits, reverse 16 and shift - // e.g. 11 bits, bit reverse and shift away 5 - return stbi__bitreverse16(v) >> (16-bits); -} - -static int stbi__zbuild_huffman(stbi__zhuffman *z, stbi_uc *sizelist, int num) -{ - int i,k=0; - int code, next_code[16], sizes[17]; - - // DEFLATE spec for generating codes - memset(sizes, 0, sizeof(sizes)); - memset(z->fast, 0, sizeof(z->fast)); - for (i=0; i < num; ++i) - ++sizes[sizelist[i]]; - sizes[0] = 0; - for (i=1; i < 16; ++i) - if (sizes[i] > (1 << i)) - return stbi__err("bad sizes", "Corrupt PNG"); - code = 0; - for (i=1; i < 16; ++i) { - next_code[i] = code; - z->firstcode[i] = (stbi__uint16) code; - z->firstsymbol[i] = (stbi__uint16) k; - code = (code + sizes[i]); - if (sizes[i]) - if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG"); - z->maxcode[i] = code << (16-i); // preshift for inner loop - code <<= 1; - k += sizes[i]; - } - z->maxcode[16] = 0x10000; // sentinel - for (i=0; i < num; ++i) { - int s = sizelist[i]; - if (s) { - int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; - stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i); - z->size [c] = (stbi_uc ) s; - z->value[c] = (stbi__uint16) i; - if (s <= STBI__ZFAST_BITS) { - int k = stbi__bit_reverse(next_code[s],s); - while (k < (1 << STBI__ZFAST_BITS)) { - z->fast[k] = fastv; - k += (1 << s); + STBI_ASSERT(bits <= 16); + // to bit reverse n bits, reverse 16 and shift + // e.g. 11 bits, bit reverse and shift away 5 + return stbi__bitreverse16(v) >> (16 - bits); +} + +static int stbi__zbuild_huffman(stbi__zhuffman* z, const stbi_uc* sizelist, int num) +{ + int i, k = 0; + int code, next_code[16], sizes[17]; + + // DEFLATE spec for generating codes + memset(sizes, 0, sizeof(sizes)); + memset(z->fast, 0, sizeof(z->fast)); + for (i = 0; i < num; ++i) + ++sizes[sizelist[i]]; + sizes[0] = 0; + for (i = 1; i < 16; ++i) + if (sizes[i] > (1 << i)) + return stbi__err("bad sizes", "Corrupt PNG"); + code = 0; + for (i = 1; i < 16; ++i) { + next_code[i] = code; + z->firstcode[i] = (stbi__uint16)code; + z->firstsymbol[i] = (stbi__uint16)k; + code = (code + sizes[i]); + if (sizes[i]) + if (code - 1 >= (1 << i)) return stbi__err("bad codelengths", "Corrupt PNG"); + z->maxcode[i] = code << (16 - i); // preshift for inner loop + code <<= 1; + k += sizes[i]; + } + z->maxcode[16] = 0x10000; // sentinel + for (i = 0; i < num; ++i) { + int s = sizelist[i]; + if (s) { + int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; + stbi__uint16 fastv = (stbi__uint16)((s << 9) | i); + z->size[c] = (stbi_uc)s; + z->value[c] = (stbi__uint16)i; + if (s <= STBI__ZFAST_BITS) { + int j = stbi__bit_reverse(next_code[s], s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); + } } - } - ++next_code[s]; - } - } - return 1; + ++next_code[s]; + } + } + return 1; } // zlib-from-memory implementation for PNG reading @@ -3522,240 +4205,283 @@ static int stbi__zbuild_huffman(stbi__zhuffman *z, stbi_uc *sizelist, int num) typedef struct { - stbi_uc *zbuffer, *zbuffer_end; - int num_bits; - stbi__uint32 code_buffer; + stbi_uc* zbuffer, * zbuffer_end; + int num_bits; + stbi__uint32 code_buffer; - char *zout; - char *zout_start; - char *zout_end; - int z_expandable; + char* zout; + char* zout_start; + char* zout_end; + int z_expandable; - stbi__zhuffman z_length, z_distance; + stbi__zhuffman z_length, z_distance; } stbi__zbuf; -stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) +stbi_inline static int stbi__zeof(stbi__zbuf* z) { - if (z->zbuffer >= z->zbuffer_end) return 0; - return *z->zbuffer++; + return (z->zbuffer >= z->zbuffer_end); } -static void stbi__fill_bits(stbi__zbuf *z) +stbi_inline static stbi_uc stbi__zget8(stbi__zbuf* z) { - do { - STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); - z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; - z->num_bits += 8; - } while (z->num_bits <= 24); + return stbi__zeof(z) ? 0 : *z->zbuffer++; } -stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n) +static void stbi__fill_bits(stbi__zbuf* z) { - unsigned int k; - if (z->num_bits < n) stbi__fill_bits(z); - k = z->code_buffer & ((1 << n) - 1); - z->code_buffer >>= n; - z->num_bits -= n; - return k; + do { + if (z->code_buffer >= (1U << z->num_bits)) { + z->zbuffer = z->zbuffer_end; /* treat this as EOF so we fail. */ + return; + } + z->code_buffer |= (unsigned int)stbi__zget8(z) << z->num_bits; + z->num_bits += 8; + } while (z->num_bits <= 24); } -static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) +stbi_inline static unsigned int stbi__zreceive(stbi__zbuf* z, int n) { - int b,s,k; - // not resolved by fast table, so compute it the slow way - // use jpeg approach, which requires MSbits at top - k = stbi__bit_reverse(a->code_buffer, 16); - for (s=STBI__ZFAST_BITS+1; ; ++s) - if (k < z->maxcode[s]) - break; - if (s == 16) return -1; // invalid code! - // code size is s, so: - b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; - STBI_ASSERT(z->size[b] == s); - a->code_buffer >>= s; - a->num_bits -= s; - return z->value[b]; + unsigned int k; + if (z->num_bits < n) stbi__fill_bits(z); + k = z->code_buffer & ((1 << n) - 1); + z->code_buffer >>= n; + z->num_bits -= n; + return k; } -stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) +static int stbi__zhuffman_decode_slowpath(stbi__zbuf* a, stbi__zhuffman* z) { - int b,s; - if (a->num_bits < 16) stbi__fill_bits(a); - b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; - if (b) { - s = b >> 9; - a->code_buffer >>= s; - a->num_bits -= s; - return b & 511; - } - return stbi__zhuffman_decode_slowpath(a, z); + int b, s, k; + // not resolved by fast table, so compute it the slow way + // use jpeg approach, which requires MSbits at top + k = stbi__bit_reverse(a->code_buffer, 16); + for (s = STBI__ZFAST_BITS + 1; ; ++s) + if (k < z->maxcode[s]) + break; + if (s >= 16) return -1; // invalid code! + // code size is s, so: + b = (k >> (16 - s)) - z->firstcode[s] + z->firstsymbol[s]; + if (b >= STBI__ZNSYMS) return -1; // some data was corrupt somewhere! + if (z->size[b] != s) return -1; // was originally an assert, but report failure instead. + a->code_buffer >>= s; + a->num_bits -= s; + return z->value[b]; +} + +stbi_inline static int stbi__zhuffman_decode(stbi__zbuf* a, stbi__zhuffman* z) +{ + int b, s; + if (a->num_bits < 16) { + if (stbi__zeof(a)) { + return -1; /* report error for unexpected end of data. */ + } + stbi__fill_bits(a); + } + b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; + if (b) { + s = b >> 9; + a->code_buffer >>= s; + a->num_bits -= s; + return b & 511; + } + return stbi__zhuffman_decode_slowpath(a, z); } -static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes +static int stbi__zexpand(stbi__zbuf* z, char* zout, int n) // need to make room for n bytes { - char *q; - int cur, limit; - z->zout = zout; - if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); - cur = (int) (z->zout - z->zout_start); - limit = (int) (z->zout_end - z->zout_start); - while (cur + n > limit) - limit *= 2; - q = (char *) STBI_REALLOC(z->zout_start, limit); - if (q == NULL) return stbi__err("outofmem", "Out of memory"); - z->zout_start = q; - z->zout = q + cur; - z->zout_end = q + limit; - return 1; + char* q; + unsigned int cur, limit, old_limit; + z->zout = zout; + if (!z->z_expandable) return stbi__err("output buffer limit", "Corrupt PNG"); + cur = (unsigned int)(z->zout - z->zout_start); + limit = old_limit = (unsigned)(z->zout_end - z->zout_start); + if (UINT_MAX - cur < (unsigned)n) return stbi__err("outofmem", "Out of memory"); + while (cur + n > limit) { + if (limit > UINT_MAX / 2) return stbi__err("outofmem", "Out of memory"); + limit *= 2; + } + q = (char*)STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); + if (q == NULL) return stbi__err("outofmem", "Out of memory"); + z->zout_start = q; + z->zout = q + cur; + z->zout_end = q + limit; + return 1; } -static int stbi__zlength_base[31] = { +static const int stbi__zlength_base[31] = { 3,4,5,6,7,8,9,10,11,13, 15,17,19,23,27,31,35,43,51,59, 67,83,99,115,131,163,195,227,258,0,0 }; -static int stbi__zlength_extra[31]= +static const int stbi__zlength_extra[31] = { 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; -static int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, -257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; - -static int stbi__zdist_extra[32] = -{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; - -static int stbi__parse_huffman_block(stbi__zbuf *a) -{ - char *zout = a->zout; - for(;;) { - int z = stbi__zhuffman_decode(a, &a->z_length); - if (z < 256) { - if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes - if (zout >= a->zout_end) { - if (!stbi__zexpand(a, zout, 1)) return 0; - zout = a->zout; - } - *zout++ = (char) z; - } else { - stbi_uc *p; - int len,dist; - if (z == 256) { - a->zout = zout; - return 1; - } - z -= 257; - len = stbi__zlength_base[z]; - if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); - z = stbi__zhuffman_decode(a, &a->z_distance); - if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); - dist = stbi__zdist_base[z]; - if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); - if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); - if (zout + len > a->zout_end) { - if (!stbi__zexpand(a, zout, len)) return 0; - zout = a->zout; - } - p = (stbi_uc *) (zout - dist); - if (dist == 1) { // run of one byte; common in images. - stbi_uc v = *p; - if (len) { do *zout++ = v; while (--len); } - } else { - if (len) { do *zout++ = *p++; while (--len); } - } - } - } +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0 }; + +static const int stbi__zdist_extra[32] = +{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; + +static int stbi__parse_huffman_block(stbi__zbuf* a) +{ + char* zout = a->zout; + for (;;) { + int z = stbi__zhuffman_decode(a, &a->z_length); + if (z < 256) { + if (z < 0) return stbi__err("bad huffman code", "Corrupt PNG"); // error in huffman codes + if (zout >= a->zout_end) { + if (!stbi__zexpand(a, zout, 1)) return 0; + zout = a->zout; + } + *zout++ = (char)z; + } + else { + stbi_uc* p; + int len, dist; + if (z == 256) { + a->zout = zout; + return 1; + } + if (z >= 286) return stbi__err("bad huffman code", "Corrupt PNG"); // per DEFLATE, length codes 286 and 287 must not appear in compressed data + z -= 257; + len = stbi__zlength_base[z]; + if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); + z = stbi__zhuffman_decode(a, &a->z_distance); + if (z < 0 || z >= 30) return stbi__err("bad huffman code", "Corrupt PNG"); // per DEFLATE, distance codes 30 and 31 must not appear in compressed data + dist = stbi__zdist_base[z]; + if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); + if (zout - a->zout_start < dist) return stbi__err("bad dist", "Corrupt PNG"); + if (zout + len > a->zout_end) { + if (!stbi__zexpand(a, zout, len)) return 0; + zout = a->zout; + } + p = (stbi_uc*)(zout - dist); + if (dist == 1) { // run of one byte; common in images. + stbi_uc v = *p; + if (len) { do *zout++ = v; while (--len); } + } + else { + if (len) { do *zout++ = *p++; while (--len); } + } + } + } } -static int stbi__compute_huffman_codes(stbi__zbuf *a) +static int stbi__compute_huffman_codes(stbi__zbuf* a) { - static stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; - stbi__zhuffman z_codelength; - stbi_uc lencodes[286+32+137];//padding for maximum single op - stbi_uc codelength_sizes[19]; - int i,n; + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + stbi__zhuffman z_codelength; + stbi_uc lencodes[286 + 32 + 137];//padding for maximum single op + stbi_uc codelength_sizes[19]; + int i, n; - int hlit = stbi__zreceive(a,5) + 257; - int hdist = stbi__zreceive(a,5) + 1; - int hclen = stbi__zreceive(a,4) + 4; + int hlit = stbi__zreceive(a, 5) + 257; + int hdist = stbi__zreceive(a, 5) + 1; + int hclen = stbi__zreceive(a, 4) + 4; + int ntot = hlit + hdist; - memset(codelength_sizes, 0, sizeof(codelength_sizes)); - for (i=0; i < hclen; ++i) { - int s = stbi__zreceive(a,3); - codelength_sizes[length_dezigzag[i]] = (stbi_uc) s; - } - if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; - - n = 0; - while (n < hlit + hdist) { - int c = stbi__zhuffman_decode(a, &z_codelength); - if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); - if (c < 16) - lencodes[n++] = (stbi_uc) c; - else if (c == 16) { - c = stbi__zreceive(a,2)+3; - memset(lencodes+n, lencodes[n-1], c); - n += c; - } else if (c == 17) { - c = stbi__zreceive(a,3)+3; - memset(lencodes+n, 0, c); - n += c; - } else { - STBI_ASSERT(c == 18); - c = stbi__zreceive(a,7)+11; - memset(lencodes+n, 0, c); - n += c; - } - } - if (n != hlit+hdist) return stbi__err("bad codelengths","Corrupt PNG"); - if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; - if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; - return 1; -} - -static int stbi__parse_uncomperssed_block(stbi__zbuf *a) -{ - stbi_uc header[4]; - int len,nlen,k; - if (a->num_bits & 7) - stbi__zreceive(a, a->num_bits & 7); // discard - // drain the bit-packed data into header - k = 0; - while (a->num_bits > 0) { - header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check - a->code_buffer >>= 8; - a->num_bits -= 8; - } - STBI_ASSERT(a->num_bits == 0); - // now fill header the normal way - while (k < 4) - header[k++] = stbi__zget8(a); - len = header[1] * 256 + header[0]; - nlen = header[3] * 256 + header[2]; - if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG"); - if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG"); - if (a->zout + len > a->zout_end) - if (!stbi__zexpand(a, a->zout, len)) return 0; - memcpy(a->zout, a->zbuffer, len); - a->zbuffer += len; - a->zout += len; - return 1; -} - -static int stbi__parse_zlib_header(stbi__zbuf *a) -{ - int cmf = stbi__zget8(a); - int cm = cmf & 15; - /* int cinfo = cmf >> 4; */ - int flg = stbi__zget8(a); - if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec - if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png - if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png - // window = 1 << (8 + cinfo)... but who cares, we fully buffer output - return 1; -} - -// @TODO: should statically initialize these for optimal thread safety -static stbi_uc stbi__zdefault_length[288], stbi__zdefault_distance[32]; -static void stbi__init_zdefaults(void) + memset(codelength_sizes, 0, sizeof(codelength_sizes)); + for (i = 0; i < hclen; ++i) { + int s = stbi__zreceive(a, 3); + codelength_sizes[length_dezigzag[i]] = (stbi_uc)s; + } + if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; + + n = 0; + while (n < ntot) { + int c = stbi__zhuffman_decode(a, &z_codelength); + if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); + if (c < 16) + lencodes[n++] = (stbi_uc)c; + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a, 2) + 3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n - 1]; + } + else if (c == 17) { + c = stbi__zreceive(a, 3) + 3; + } + else if (c == 18) { + c = stbi__zreceive(a, 7) + 11; + } + else { + return stbi__err("bad codelengths", "Corrupt PNG"); + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes + n, fill, c); + n += c; + } + } + if (n != ntot) return stbi__err("bad codelengths", "Corrupt PNG"); + if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, lencodes + hlit, hdist)) return 0; + return 1; +} + +static int stbi__parse_uncompressed_block(stbi__zbuf* a) +{ + stbi_uc header[4]; + int len, nlen, k; + if (a->num_bits & 7) + stbi__zreceive(a, a->num_bits & 7); // discard + // drain the bit-packed data into header + k = 0; + while (a->num_bits > 0) { + header[k++] = (stbi_uc)(a->code_buffer & 255); // suppress MSVC run-time check + a->code_buffer >>= 8; + a->num_bits -= 8; + } + if (a->num_bits < 0) return stbi__err("zlib corrupt", "Corrupt PNG"); + // now fill header the normal way + while (k < 4) + header[k++] = stbi__zget8(a); + len = header[1] * 256 + header[0]; + nlen = header[3] * 256 + header[2]; + if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt", "Corrupt PNG"); + if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer", "Corrupt PNG"); + if (a->zout + len > a->zout_end) + if (!stbi__zexpand(a, a->zout, len)) return 0; + memcpy(a->zout, a->zbuffer, len); + a->zbuffer += len; + a->zout += len; + return 1; +} + +static int stbi__parse_zlib_header(stbi__zbuf* a) +{ + int cmf = stbi__zget8(a); + int cm = cmf & 15; + /* int cinfo = cmf >> 4; */ + int flg = stbi__zget8(a); + if (stbi__zeof(a)) return stbi__err("bad zlib header", "Corrupt PNG"); // zlib spec + if ((cmf * 256 + flg) % 31 != 0) return stbi__err("bad zlib header", "Corrupt PNG"); // zlib spec + if (flg & 32) return stbi__err("no preset dict", "Corrupt PNG"); // preset dictionary not allowed in png + if (cm != 8) return stbi__err("bad compression", "Corrupt PNG"); // DEFLATE required for png + // window = 1 << (8 + cinfo)... but who cares, we fully buffer output + return 1; +} + +static const stbi_uc stbi__zdefault_length[STBI__ZNSYMS] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: { int i; // use <= to match clearly with spec for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; @@ -3765,119 +4491,125 @@ static void stbi__init_zdefaults(void) for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; } +*/ -static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) -{ - int final, type; - if (parse_header) - if (!stbi__parse_zlib_header(a)) return 0; - a->num_bits = 0; - a->code_buffer = 0; - do { - final = stbi__zreceive(a,1); - type = stbi__zreceive(a,2); - if (type == 0) { - if (!stbi__parse_uncomperssed_block(a)) return 0; - } else if (type == 3) { - return 0; - } else { - if (type == 1) { - // use fixed code lengths - if (!stbi__zdefault_distance[31]) stbi__init_zdefaults(); - if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; - if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; - } else { - if (!stbi__compute_huffman_codes(a)) return 0; - } - if (!stbi__parse_huffman_block(a)) return 0; - } - } while (!final); - return 1; +static int stbi__parse_zlib(stbi__zbuf* a, int parse_header) +{ + int final, type; + if (parse_header) + if (!stbi__parse_zlib_header(a)) return 0; + a->num_bits = 0; + a->code_buffer = 0; + do { + final = stbi__zreceive(a, 1); + type = stbi__zreceive(a, 2); + if (type == 0) { + if (!stbi__parse_uncompressed_block(a)) return 0; + } + else if (type == 3) { + return 0; + } + else { + if (type == 1) { + // use fixed code lengths + if (!stbi__zbuild_huffman(&a->z_length, stbi__zdefault_length, STBI__ZNSYMS)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; + } + else { + if (!stbi__compute_huffman_codes(a)) return 0; + } + if (!stbi__parse_huffman_block(a)) return 0; + } + } while (!final); + return 1; } -static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header) +static int stbi__do_zlib(stbi__zbuf* a, char* obuf, int olen, int exp, int parse_header) { - a->zout_start = obuf; - a->zout = obuf; - a->zout_end = obuf + olen; - a->z_expandable = exp; + a->zout_start = obuf; + a->zout = obuf; + a->zout_end = obuf + olen; + a->z_expandable = exp; - return stbi__parse_zlib(a, parse_header); + return stbi__parse_zlib(a, parse_header); } -STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen) +STBIDEF char* stbi_zlib_decode_malloc_guesssize(const char* buffer, int len, int initial_size, int* outlen) { - stbi__zbuf a; - char *p = (char *) stbi__malloc(initial_size); - if (p == NULL) return NULL; - a.zbuffer = (stbi_uc *) buffer; - a.zbuffer_end = (stbi_uc *) buffer + len; - if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { - if (outlen) *outlen = (int) (a.zout - a.zout_start); - return a.zout_start; - } else { - STBI_FREE(a.zout_start); - return NULL; - } + stbi__zbuf a; + char* p = (char*)stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc*)buffer; + a.zbuffer_end = (stbi_uc*)buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { + if (outlen) *outlen = (int)(a.zout - a.zout_start); + return a.zout_start; + } + else { + STBI_FREE(a.zout_start); + return NULL; + } } -STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen) +STBIDEF char* stbi_zlib_decode_malloc(char const* buffer, int len, int* outlen) { - return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); + return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); } -STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header) +STBIDEF char* stbi_zlib_decode_malloc_guesssize_headerflag(const char* buffer, int len, int initial_size, int* outlen, int parse_header) { - stbi__zbuf a; - char *p = (char *) stbi__malloc(initial_size); - if (p == NULL) return NULL; - a.zbuffer = (stbi_uc *) buffer; - a.zbuffer_end = (stbi_uc *) buffer + len; - if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { - if (outlen) *outlen = (int) (a.zout - a.zout_start); - return a.zout_start; - } else { - STBI_FREE(a.zout_start); - return NULL; - } + stbi__zbuf a; + char* p = (char*)stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc*)buffer; + a.zbuffer_end = (stbi_uc*)buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { + if (outlen) *outlen = (int)(a.zout - a.zout_start); + return a.zout_start; + } + else { + STBI_FREE(a.zout_start); + return NULL; + } } -STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen) +STBIDEF int stbi_zlib_decode_buffer(char* obuffer, int olen, char const* ibuffer, int ilen) { - stbi__zbuf a; - a.zbuffer = (stbi_uc *) ibuffer; - a.zbuffer_end = (stbi_uc *) ibuffer + ilen; - if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) - return (int) (a.zout - a.zout_start); - else - return -1; + stbi__zbuf a; + a.zbuffer = (stbi_uc*)ibuffer; + a.zbuffer_end = (stbi_uc*)ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) + return (int)(a.zout - a.zout_start); + else + return -1; } -STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen) +STBIDEF char* stbi_zlib_decode_noheader_malloc(char const* buffer, int len, int* outlen) { - stbi__zbuf a; - char *p = (char *) stbi__malloc(16384); - if (p == NULL) return NULL; - a.zbuffer = (stbi_uc *) buffer; - a.zbuffer_end = (stbi_uc *) buffer+len; - if (stbi__do_zlib(&a, p, 16384, 1, 0)) { - if (outlen) *outlen = (int) (a.zout - a.zout_start); - return a.zout_start; - } else { - STBI_FREE(a.zout_start); - return NULL; - } + stbi__zbuf a; + char* p = (char*)stbi__malloc(16384); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc*)buffer; + a.zbuffer_end = (stbi_uc*)buffer + len; + if (stbi__do_zlib(&a, p, 16384, 1, 0)) { + if (outlen) *outlen = (int)(a.zout - a.zout_start); + return a.zout_start; + } + else { + STBI_FREE(a.zout_start); + return NULL; + } } -STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen) +STBIDEF int stbi_zlib_decode_noheader_buffer(char* obuffer, int olen, const char* ibuffer, int ilen) { - stbi__zbuf a; - a.zbuffer = (stbi_uc *) ibuffer; - a.zbuffer_end = (stbi_uc *) ibuffer + ilen; - if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) - return (int) (a.zout - a.zout_start); - else - return -1; + stbi__zbuf a; + a.zbuffer = (stbi_uc*)ibuffer; + a.zbuffer_end = (stbi_uc*)ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) + return (int)(a.zout - a.zout_start); + else + return -1; } #endif @@ -3894,43 +4626,44 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char #ifndef STBI_NO_PNG typedef struct { - stbi__uint32 length; - stbi__uint32 type; + stbi__uint32 length; + stbi__uint32 type; } stbi__pngchunk; -static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) +static stbi__pngchunk stbi__get_chunk_header(stbi__context* s) { - stbi__pngchunk c; - c.length = stbi__get32be(s); - c.type = stbi__get32be(s); - return c; + stbi__pngchunk c; + c.length = stbi__get32be(s); + c.type = stbi__get32be(s); + return c; } -static int stbi__check_png_header(stbi__context *s) +static int stbi__check_png_header(stbi__context* s) { - static stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; - int i; - for (i=0; i < 8; ++i) - if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); - return 1; + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + int i; + for (i = 0; i < 8; ++i) + if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig", "Not a PNG"); + return 1; } typedef struct { - stbi__context *s; - stbi_uc *idata, *expanded, *out; + stbi__context* s; + stbi_uc* idata, * expanded, * out; + int depth; } stbi__png; enum { - STBI__F_none=0, - STBI__F_sub=1, - STBI__F_up=2, - STBI__F_avg=3, - STBI__F_paeth=4, - // synthetic filters used for first scanline to avoid needing a dummy row of 0s - STBI__F_avg_first, - STBI__F_paeth_first + STBI__F_none = 0, + STBI__F_sub = 1, + STBI__F_up = 2, + STBI__F_avg = 3, + STBI__F_paeth = 4, + // synthetic filters used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first, + STBI__F_paeth_first }; static stbi_uc first_row_filter[5] = @@ -3944,1098 +4677,1480 @@ static stbi_uc first_row_filter[5] = static int stbi__paeth(int a, int b, int c) { - int p = a + b - c; - int pa = abs(p-a); - int pb = abs(p-b); - int pc = abs(p-c); - if (pa <= pb && pa <= pc) return a; - if (pb <= pc) return b; - return c; + int p = a + b - c; + int pa = abs(p - a); + int pb = abs(p - b); + int pc = abs(p - c); + if (pa <= pb && pa <= pc) return a; + if (pb <= pc) return b; + return c; } -static stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; // create the png data from post-deflated data -static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) -{ - stbi__context *s = a->s; - stbi__uint32 i,j,stride = x*out_n; - stbi__uint32 img_len, img_width_bytes; - int k; - int img_n = s->img_n; // copy it into a local for later - - STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); - a->out = (stbi_uc *) stbi__malloc(x * y * out_n); // extra bytes to write off the end into - if (!a->out) return stbi__err("outofmem", "Out of memory"); - - img_width_bytes = (((img_n * x * depth) + 7) >> 3); - img_len = (img_width_bytes + 1) * y; - if (s->img_x == x && s->img_y == y) { - if (raw_len != img_len) return stbi__err("not enough pixels","Corrupt PNG"); - } else { // interlaced: - if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); - } - - for (j=0; j < y; ++j) { - stbi_uc *cur = a->out + stride*j; - stbi_uc *prior = cur - stride; - int filter = *raw++; - int filter_bytes = img_n; - int width = x; - if (filter > 4) - return stbi__err("invalid filter","Corrupt PNG"); - - if (depth < 8) { - STBI_ASSERT(img_width_bytes <= x); - cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place - filter_bytes = 1; - width = img_width_bytes; - } - - // if first row, use special filter that doesn't sample previous row - if (j == 0) filter = first_row_filter[filter]; - - // handle first byte explicitly - for (k=0; k < filter_bytes; ++k) { - switch (filter) { - case STBI__F_none : cur[k] = raw[k]; break; - case STBI__F_sub : cur[k] = raw[k]; break; - case STBI__F_up : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - case STBI__F_avg : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break; - case STBI__F_paeth : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break; - case STBI__F_avg_first : cur[k] = raw[k]; break; +static int stbi__create_png_image_raw(stbi__png* a, stbi_uc* raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) +{ + int bytes = (depth == 16 ? 2 : 1); + stbi__context* s = a->s; + stbi__uint32 i, j, stride = x * out_n * bytes; + stbi__uint32 img_len, img_width_bytes; + int k; + int img_n = s->img_n; // copy it into a local for later + + int output_bytes = out_n * bytes; + int filter_bytes = img_n * bytes; + int width = x; + + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n + 1); + a->out = (stbi_uc*)stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into + if (!a->out) return stbi__err("outofmem", "Out of memory"); + + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); + img_width_bytes = (((img_n * x * depth) + 7) >> 3); + img_len = (img_width_bytes + 1) * y; + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels", "Corrupt PNG"); + + for (j = 0; j < y; ++j) { + stbi_uc* cur = a->out + stride * j; + stbi_uc* prior; + int filter = *raw++; + + if (filter > 4) + return stbi__err("invalid filter", "Corrupt PNG"); + + if (depth < 8) { + if (img_width_bytes > x) return stbi__err("invalid width", "Corrupt PNG"); + cur += x * out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place + filter_bytes = 1; + width = img_width_bytes; + } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above + + // if first row, use special filter that doesn't sample previous row + if (j == 0) filter = first_row_filter[filter]; + + // handle first byte explicitly + for (k = 0; k < filter_bytes; ++k) { + switch (filter) { + case STBI__F_none: cur[k] = raw[k]; break; + case STBI__F_sub: cur[k] = raw[k]; break; + case STBI__F_up: cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; + case STBI__F_avg: cur[k] = STBI__BYTECAST(raw[k] + (prior[k] >> 1)); break; + case STBI__F_paeth: cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0, prior[k], 0)); break; + case STBI__F_avg_first: cur[k] = raw[k]; break; case STBI__F_paeth_first: cur[k] = raw[k]; break; - } - } - - if (depth == 8) { - if (img_n != out_n) - cur[img_n] = 255; // first pixel - raw += img_n; - cur += out_n; - prior += out_n; - } else { - raw += 1; - cur += 1; - prior += 1; - } - - // this is a little gross, so that we don't switch per-pixel or per-component - if (depth < 8 || img_n == out_n) { - int nk = (width - 1)*img_n; - #define CASE(f) \ + } + } + + if (depth == 8) { + if (img_n != out_n) + cur[img_n] = 255; // first pixel + raw += img_n; + cur += out_n; + prior += out_n; + } + else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes + 1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; + } + else { + raw += 1; + cur += 1; + prior += 1; + } + + // this is a little gross, so that we don't switch per-pixel or per-component + if (depth < 8 || img_n == out_n) { + int nk = (width - 1) * filter_bytes; +#define STBI__CASE(f) \ case f: \ for (k=0; k < nk; ++k) - switch (filter) { - // "none" filter turns into a memcpy here; make that explicit. + switch (filter) { + // "none" filter turns into a memcpy here; make that explicit. case STBI__F_none: memcpy(cur, raw, nk); break; - CASE(STBI__F_sub) cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); break; - CASE(STBI__F_up) cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - CASE(STBI__F_avg) cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); break; - CASE(STBI__F_paeth) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); break; - CASE(STBI__F_avg_first) cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); break; - CASE(STBI__F_paeth_first) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); break; - } - #undef CASE - raw += nk; - } else { - STBI_ASSERT(img_n+1 == out_n); - #define CASE(f) \ + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k - filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k - filter_bytes]) >> 1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - filter_bytes], prior[k], prior[k - filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k - filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - filter_bytes], 0, 0)); } break; + } +#undef STBI__CASE + raw += nk; + } + else { + STBI_ASSERT(img_n + 1 == out_n); +#define STBI__CASE(f) \ case f: \ - for (i=x-1; i >= 1; --i, cur[img_n]=255,raw+=img_n,cur+=out_n,prior+=out_n) \ - for (k=0; k < img_n; ++k) - switch (filter) { - CASE(STBI__F_none) cur[k] = raw[k]; break; - CASE(STBI__F_sub) cur[k] = STBI__BYTECAST(raw[k] + cur[k-out_n]); break; - CASE(STBI__F_up) cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; - CASE(STBI__F_avg) cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-out_n])>>1)); break; - CASE(STBI__F_paeth) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-out_n],prior[k],prior[k-out_n])); break; - CASE(STBI__F_avg_first) cur[k] = STBI__BYTECAST(raw[k] + (cur[k-out_n] >> 1)); break; - CASE(STBI__F_paeth_first) cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-out_n],0,0)); break; - } - #undef CASE - } - } + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) + switch (filter) { + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k - output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k - output_bytes]) >> 1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - output_bytes], prior[k], prior[k - output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k - output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - output_bytes], 0, 0)); } break; + } +#undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride * j; // start at the beginning of the row again + for (i = 0; i < x; ++i, cur += output_bytes) { + cur[filter_bytes + 1] = 255; + } + } + } + } - // we make a separate pass to expand bits to pixels; for performance, - // this could run two scanlines behind the above code, so it won't - // intefere with filtering but will still be in the cache. - if (depth < 8) { - for (j=0; j < y; ++j) { - stbi_uc *cur = a->out + stride*j; - stbi_uc *in = a->out + stride*j + x*out_n - img_width_bytes; - // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit - // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop - stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range - - // note that the final byte might overshoot and write more data than desired. - // we can allocate enough data that this never writes out of memory, but it - // could also overwrite the next scanline. can it overwrite non-empty data - // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. - // so we need to explicitly clamp the final ones - - if (depth == 4) { - for (k=x*img_n; k >= 2; k-=2, ++in) { - *cur++ = scale * ((*in >> 4) ); - *cur++ = scale * ((*in ) & 0x0f); + // we make a separate pass to expand bits to pixels; for performance, + // this could run two scanlines behind the above code, so it won't + // intefere with filtering but will still be in the cache. + if (depth < 8) { + for (j = 0; j < y; ++j) { + stbi_uc* cur = a->out + stride * j; + stbi_uc* in = a->out + stride * j + x * out_n - img_width_bytes; + // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit + // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop + stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + + // note that the final byte might overshoot and write more data than desired. + // we can allocate enough data that this never writes out of memory, but it + // could also overwrite the next scanline. can it overwrite non-empty data + // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. + // so we need to explicitly clamp the final ones + + if (depth == 4) { + for (k = x * img_n; k >= 2; k -= 2, ++in) { + *cur++ = scale * ((*in >> 4)); + *cur++ = scale * ((*in) & 0x0f); + } + if (k > 0) *cur++ = scale * ((*in >> 4)); } - if (k > 0) *cur++ = scale * ((*in >> 4) ); - } else if (depth == 2) { - for (k=x*img_n; k >= 4; k-=4, ++in) { - *cur++ = scale * ((*in >> 6) ); - *cur++ = scale * ((*in >> 4) & 0x03); - *cur++ = scale * ((*in >> 2) & 0x03); - *cur++ = scale * ((*in ) & 0x03); + else if (depth == 2) { + for (k = x * img_n; k >= 4; k -= 4, ++in) { + *cur++ = scale * ((*in >> 6)); + *cur++ = scale * ((*in >> 4) & 0x03); + *cur++ = scale * ((*in >> 2) & 0x03); + *cur++ = scale * ((*in) & 0x03); + } + if (k > 0) *cur++ = scale * ((*in >> 6)); + if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); + if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); } - if (k > 0) *cur++ = scale * ((*in >> 6) ); - if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); - if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); - } else if (depth == 1) { - for (k=x*img_n; k >= 8; k-=8, ++in) { - *cur++ = scale * ((*in >> 7) ); - *cur++ = scale * ((*in >> 6) & 0x01); - *cur++ = scale * ((*in >> 5) & 0x01); - *cur++ = scale * ((*in >> 4) & 0x01); - *cur++ = scale * ((*in >> 3) & 0x01); - *cur++ = scale * ((*in >> 2) & 0x01); - *cur++ = scale * ((*in >> 1) & 0x01); - *cur++ = scale * ((*in ) & 0x01); + else if (depth == 1) { + for (k = x * img_n; k >= 8; k -= 8, ++in) { + *cur++ = scale * ((*in >> 7)); + *cur++ = scale * ((*in >> 6) & 0x01); + *cur++ = scale * ((*in >> 5) & 0x01); + *cur++ = scale * ((*in >> 4) & 0x01); + *cur++ = scale * ((*in >> 3) & 0x01); + *cur++ = scale * ((*in >> 2) & 0x01); + *cur++ = scale * ((*in >> 1) & 0x01); + *cur++ = scale * ((*in) & 0x01); + } + if (k > 0) *cur++ = scale * ((*in >> 7)); + if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); + if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); + if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); + if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); + if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); + if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); } - if (k > 0) *cur++ = scale * ((*in >> 7) ); - if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); - if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); - if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); - if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); - if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); - if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); - } - if (img_n != out_n) { - // insert alpha = 255 - stbi_uc *cur = a->out + stride*j; - int i; - if (img_n == 1) { - for (i=x-1; i >= 0; --i) { - cur[i*2+1] = 255; - cur[i*2+0] = cur[i]; - } - } else { - STBI_ASSERT(img_n == 3); - for (i=x-1; i >= 0; --i) { - cur[i*4+3] = 255; - cur[i*4+2] = cur[i*3+2]; - cur[i*4+1] = cur[i*3+1]; - cur[i*4+0] = cur[i*3+0]; - } + if (img_n != out_n) { + int q; + // insert alpha = 255 + cur = a->out + stride * j; + if (img_n == 1) { + for (q = x - 1; q >= 0; --q) { + cur[q * 2 + 1] = 255; + cur[q * 2 + 0] = cur[q]; + } + } + else { + STBI_ASSERT(img_n == 3); + for (q = x - 1; q >= 0; --q) { + cur[q * 4 + 3] = 255; + cur[q * 4 + 2] = cur[q * 3 + 2]; + cur[q * 4 + 1] = cur[q * 3 + 1]; + cur[q * 4 + 0] = cur[q * 3 + 0]; + } + } } - } - } - } + } + } + else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc* cur = a->out; + stbi__uint16* cur16 = (stbi__uint16*)cur; + + for (i = 0; i < x * y * out_n; ++i, cur16++, cur += 2) { + *cur16 = (cur[0] << 8) | cur[1]; + } + } - return 1; -} - -static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) -{ - stbi_uc *final; - int p; - if (!interlaced) - return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); - - // de-interlacing - final = (stbi_uc *) stbi__malloc(a->s->img_x * a->s->img_y * out_n); - for (p=0; p < 7; ++p) { - int xorig[] = { 0,4,0,2,0,1,0 }; - int yorig[] = { 0,0,4,0,2,0,1 }; - int xspc[] = { 8,8,4,4,2,2,1 }; - int yspc[] = { 8,8,8,4,4,2,2 }; - int i,j,x,y; - // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 - x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p]; - y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p]; - if (x && y) { - stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; - if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { - STBI_FREE(final); - return 0; - } - for (j=0; j < y; ++j) { - for (i=0; i < x; ++i) { - int out_y = j*yspc[p]+yorig[p]; - int out_x = i*xspc[p]+xorig[p]; - memcpy(final + out_y*a->s->img_x*out_n + out_x*out_n, - a->out + (j*x+i)*out_n, out_n); + return 1; +} + +static int stbi__create_png_image(stbi__png* a, stbi_uc* image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) +{ + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; + stbi_uc* final; + int p; + if (!interlaced) + return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); + + // de-interlacing + final = (stbi_uc*)stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + if (!final) return stbi__err("outofmem", "Out of memory"); + for (p = 0; p < 7; ++p) { + int xorig[] = { 0,4,0,2,0,1,0 }; + int yorig[] = { 0,0,4,0,2,0,1 }; + int xspc[] = { 8,8,4,4,2,2,1 }; + int yspc[] = { 8,8,8,4,4,2,2 }; + int i, j, x, y; + // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 + x = (a->s->img_x - xorig[p] + xspc[p] - 1) / xspc[p]; + y = (a->s->img_y - yorig[p] + yspc[p] - 1) / yspc[p]; + if (x && y) { + stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; + if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { + STBI_FREE(final); + return 0; } - } - STBI_FREE(a->out); - image_data += img_len; - image_data_len -= img_len; - } - } - a->out = final; + for (j = 0; j < y; ++j) { + for (i = 0; i < x; ++i) { + int out_y = j * yspc[p] + yorig[p]; + int out_x = i * xspc[p] + xorig[p]; + memcpy(final + out_y * a->s->img_x * out_bytes + out_x * out_bytes, + a->out + (j * x + i) * out_bytes, out_bytes); + } + } + STBI_FREE(a->out); + image_data += img_len; + image_data_len -= img_len; + } + } + a->out = final; - return 1; + return 1; } -static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) +static int stbi__compute_transparency(stbi__png* z, stbi_uc tc[3], int out_n) { - stbi__context *s = z->s; - stbi__uint32 i, pixel_count = s->img_x * s->img_y; - stbi_uc *p = z->out; + stbi__context* s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc* p = z->out; - // compute color-based transparency, assuming we've - // already got 255 as the alpha value in the output - STBI_ASSERT(out_n == 2 || out_n == 4); + // compute color-based transparency, assuming we've + // already got 255 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); - if (out_n == 2) { - for (i=0; i < pixel_count; ++i) { - p[1] = (p[0] == tc[0] ? 0 : 255); - p += 2; - } - } else { - for (i=0; i < pixel_count; ++i) { - if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) - p[3] = 0; - p += 4; - } - } - return 1; + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 255); + p += 2; + } + } + else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__compute_transparency16(stbi__png* z, stbi__uint16 tc[3], int out_n) +{ + stbi__context* s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16* p = (stbi__uint16*)z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } + else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; } -static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) +static int stbi__expand_png_palette(stbi__png* a, stbi_uc* palette, int len, int pal_img_n) { - stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; - stbi_uc *p, *temp_out, *orig = a->out; + stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; + stbi_uc* p, * temp_out, * orig = a->out; - p = (stbi_uc *) stbi__malloc(pixel_count * pal_img_n); - if (p == NULL) return stbi__err("outofmem", "Out of memory"); + p = (stbi_uc*)stbi__malloc_mad2(pixel_count, pal_img_n, 0); + if (p == NULL) return stbi__err("outofmem", "Out of memory"); - // between here and free(out) below, exitting would leak - temp_out = p; + // between here and free(out) below, exitting would leak + temp_out = p; - if (pal_img_n == 3) { - for (i=0; i < pixel_count; ++i) { - int n = orig[i]*4; - p[0] = palette[n ]; - p[1] = palette[n+1]; - p[2] = palette[n+2]; - p += 3; - } - } else { - for (i=0; i < pixel_count; ++i) { - int n = orig[i]*4; - p[0] = palette[n ]; - p[1] = palette[n+1]; - p[2] = palette[n+2]; - p[3] = palette[n+3]; - p += 4; - } - } - STBI_FREE(a->out); - a->out = temp_out; + if (pal_img_n == 3) { + for (i = 0; i < pixel_count; ++i) { + int n = orig[i] * 4; + p[0] = palette[n]; + p[1] = palette[n + 1]; + p[2] = palette[n + 2]; + p += 3; + } + } + else { + for (i = 0; i < pixel_count; ++i) { + int n = orig[i] * 4; + p[0] = palette[n]; + p[1] = palette[n + 1]; + p[2] = palette[n + 2]; + p[3] = palette[n + 3]; + p += 4; + } + } + STBI_FREE(a->out); + a->out = temp_out; - STBI_NOTUSED(len); + STBI_NOTUSED(len); - return 1; + return 1; } -static int stbi__unpremultiply_on_load = 0; -static int stbi__de_iphone_flag = 0; +static int stbi__unpremultiply_on_load_global = 0; +static int stbi__de_iphone_flag_global = 0; STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) { - stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply; + stbi__unpremultiply_on_load_global = flag_true_if_should_unpremultiply; } STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) { - stbi__de_iphone_flag = flag_true_if_should_convert; + stbi__de_iphone_flag_global = flag_true_if_should_convert; } -static void stbi__de_iphone(stbi__png *z) +#ifndef STBI_THREAD_LOCAL +#define stbi__unpremultiply_on_load stbi__unpremultiply_on_load_global +#define stbi__de_iphone_flag stbi__de_iphone_flag_global +#else +static STBI_THREAD_LOCAL int stbi__unpremultiply_on_load_local, stbi__unpremultiply_on_load_set; +static STBI_THREAD_LOCAL int stbi__de_iphone_flag_local, stbi__de_iphone_flag_set; + +STBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply) { - stbi__context *s = z->s; - stbi__uint32 i, pixel_count = s->img_x * s->img_y; - stbi_uc *p = z->out; + stbi__unpremultiply_on_load_local = flag_true_if_should_unpremultiply; + stbi__unpremultiply_on_load_set = 1; +} - if (s->img_out_n == 3) { // convert bgr to rgb - for (i=0; i < pixel_count; ++i) { - stbi_uc t = p[0]; - p[0] = p[2]; - p[2] = t; - p += 3; - } - } else { - STBI_ASSERT(s->img_out_n == 4); - if (stbi__unpremultiply_on_load) { - // convert bgr to rgb and unpremultiply - for (i=0; i < pixel_count; ++i) { - stbi_uc a = p[3]; - stbi_uc t = p[0]; - if (a) { - p[0] = p[2] * 255 / a; - p[1] = p[1] * 255 / a; - p[2] = t * 255 / a; - } else { - p[0] = p[2]; - p[2] = t; - } - p += 4; - } - } else { - // convert bgr to rgb - for (i=0; i < pixel_count; ++i) { +STBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag_local = flag_true_if_should_convert; + stbi__de_iphone_flag_set = 1; +} + +#define stbi__unpremultiply_on_load (stbi__unpremultiply_on_load_set \ + ? stbi__unpremultiply_on_load_local \ + : stbi__unpremultiply_on_load_global) +#define stbi__de_iphone_flag (stbi__de_iphone_flag_set \ + ? stbi__de_iphone_flag_local \ + : stbi__de_iphone_flag_global) +#endif // STBI_THREAD_LOCAL + +static void stbi__de_iphone(stbi__png* z) +{ + stbi__context* s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc* p = z->out; + + if (s->img_out_n == 3) { // convert bgr to rgb + for (i = 0; i < pixel_count; ++i) { stbi_uc t = p[0]; p[0] = p[2]; p[2] = t; - p += 4; - } - } - } + p += 3; + } + } + else { + STBI_ASSERT(s->img_out_n == 4); + if (stbi__unpremultiply_on_load) { + // convert bgr to rgb and unpremultiply + for (i = 0; i < pixel_count; ++i) { + stbi_uc a = p[3]; + stbi_uc t = p[0]; + if (a) { + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = (t * 255 + half) / a; + } + else { + p[0] = p[2]; + p[2] = t; + } + p += 4; + } + } + else { + // convert bgr to rgb + for (i = 0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 4; + } + } + } } -#define STBI__PNG_TYPE(a,b,c,d) (((a) << 24) + ((b) << 16) + ((c) << 8) + (d)) +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) -static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) +static int stbi__parse_png_file(stbi__png* z, int scan, int req_comp) { - stbi_uc palette[1024], pal_img_n=0; - stbi_uc has_trans=0, tc[3]; - stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; - int first=1,k,interlace=0, color=0, depth=0, is_iphone=0; - stbi__context *s = z->s; + stbi_uc palette[1024], pal_img_n = 0; + stbi_uc has_trans = 0, tc[3] = { 0 }; + stbi__uint16 tc16[3]; + stbi__uint32 ioff = 0, idata_limit = 0, i, pal_len = 0; + int first = 1, k, interlace = 0, color = 0, is_iphone = 0; + stbi__context* s = z->s; - z->expanded = NULL; - z->idata = NULL; - z->out = NULL; + z->expanded = NULL; + z->idata = NULL; + z->out = NULL; - if (!stbi__check_png_header(s)) return 0; + if (!stbi__check_png_header(s)) return 0; - if (scan == STBI__SCAN_type) return 1; + if (scan == STBI__SCAN_type) return 1; - for (;;) { - stbi__pngchunk c = stbi__get_chunk_header(s); - switch (c.type) { - case STBI__PNG_TYPE('C','g','B','I'): + for (;;) { + stbi__pngchunk c = stbi__get_chunk_header(s); + switch (c.type) { + case STBI__PNG_TYPE('C', 'g', 'B', 'I'): is_iphone = 1; stbi__skip(s, c.length); break; - case STBI__PNG_TYPE('I','H','D','R'): { - int comp,filter; - if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); + case STBI__PNG_TYPE('I', 'H', 'D', 'R'): { + int comp, filter; + if (!first) return stbi__err("multiple IHDR", "Corrupt PNG"); first = 0; - if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); - s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); - s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); - depth = stbi__get8(s); if (depth != 1 && depth != 2 && depth != 4 && depth != 8) return stbi__err("1/2/4/8-bit only","PNG not supported: 1/2/4/8-bit only"); - color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); - if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); - comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); - filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); - interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG"); - if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG"); + if (c.length != 13) return stbi__err("bad IHDR len", "Corrupt PNG"); + s->img_x = stbi__get32be(s); + s->img_y = stbi__get32be(s); + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__err("too large", "Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__err("too large", "Very large image (corrupt?)"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only", "PNG not supported: 1/2/4/8/16-bit only"); + color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype", "Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype", "Corrupt PNG"); + if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype", "Corrupt PNG"); + comp = stbi__get8(s); if (comp) return stbi__err("bad comp method", "Corrupt PNG"); + filter = stbi__get8(s); if (filter) return stbi__err("bad filter method", "Corrupt PNG"); + interlace = stbi__get8(s); if (interlace > 1) return stbi__err("bad interlace method", "Corrupt PNG"); + if (!s->img_x || !s->img_y) return stbi__err("0-pixel image", "Corrupt PNG"); if (!pal_img_n) { - s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); - if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); - if (scan == STBI__SCAN_header) return 1; - } else { - // if paletted, then pal_n is our final components, and - // img_n is # components to decompress/filter. - s->img_n = 1; - if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); - // if SCAN_header, have to scan to see if we have a tRNS + s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); + if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + } + else { + // if paletted, then pal_n is our final components, and + // img_n is # components to decompress/filter. + s->img_n = 1; + if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large", "Corrupt PNG"); } + // even with SCAN_header, have to scan to see if we have a tRNS break; - } + } - case STBI__PNG_TYPE('P','L','T','E'): { + case STBI__PNG_TYPE('P', 'L', 'T', 'E'): { if (first) return stbi__err("first not IHDR", "Corrupt PNG"); - if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG"); + if (c.length > 256 * 3) return stbi__err("invalid PLTE", "Corrupt PNG"); pal_len = c.length / 3; - if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG"); - for (i=0; i < pal_len; ++i) { - palette[i*4+0] = stbi__get8(s); - palette[i*4+1] = stbi__get8(s); - palette[i*4+2] = stbi__get8(s); - palette[i*4+3] = 255; + if (pal_len * 3 != c.length) return stbi__err("invalid PLTE", "Corrupt PNG"); + for (i = 0; i < pal_len; ++i) { + palette[i * 4 + 0] = stbi__get8(s); + palette[i * 4 + 1] = stbi__get8(s); + palette[i * 4 + 2] = stbi__get8(s); + palette[i * 4 + 3] = 255; } break; - } + } - case STBI__PNG_TYPE('t','R','N','S'): { + case STBI__PNG_TYPE('t', 'R', 'N', 'S'): { if (first) return stbi__err("first not IHDR", "Corrupt PNG"); - if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG"); + if (z->idata) return stbi__err("tRNS after IDAT", "Corrupt PNG"); if (pal_img_n) { - if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } - if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG"); - if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG"); - pal_img_n = 4; - for (i=0; i < c.length; ++i) - palette[i*4+3] = stbi__get8(s); - } else { - if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); - if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); - has_trans = 1; - for (k=0; k < s->img_n; ++k) - tc[k] = (stbi_uc) (stbi__get16be(s) & 255) * stbi__depth_scale_table[depth]; // non 8-bit images will be larger + if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } + if (pal_len == 0) return stbi__err("tRNS before PLTE", "Corrupt PNG"); + if (c.length > pal_len) return stbi__err("bad tRNS len", "Corrupt PNG"); + pal_img_n = 4; + for (i = 0; i < c.length; ++i) + palette[i * 4 + 3] = stbi__get8(s); + } + else { + if (!(s->img_n & 1)) return stbi__err("tRNS with alpha", "Corrupt PNG"); + if (c.length != (stbi__uint32)s->img_n * 2) return stbi__err("bad tRNS len", "Corrupt PNG"); + has_trans = 1; + // non-paletted with tRNS = constant alpha. if header-scanning, we can stop now. + if (scan == STBI__SCAN_header) { ++s->img_n; return 1; } + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } + else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } } break; - } + } - case STBI__PNG_TYPE('I','D','A','T'): { + case STBI__PNG_TYPE('I', 'D', 'A', 'T'): { if (first) return stbi__err("first not IHDR", "Corrupt PNG"); - if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); - if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } + if (pal_img_n && !pal_len) return stbi__err("no PLTE", "Corrupt PNG"); + if (scan == STBI__SCAN_header) { + // header scan definitely stops at first IDAT + if (pal_img_n) + s->img_n = pal_img_n; + return 1; + } + if (c.length > (1u << 30)) return stbi__err("IDAT size limit", "IDAT section larger than 2^30 bytes"); if ((int)(ioff + c.length) < (int)ioff) return 0; if (ioff + c.length > idata_limit) { - stbi_uc *p; - if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; - while (ioff + c.length > idata_limit) - idata_limit *= 2; - p = (stbi_uc *) STBI_REALLOC(z->idata, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); - z->idata = p; + stbi__uint32 idata_limit_old = idata_limit; + stbi_uc* p; + if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; + while (ioff + c.length > idata_limit) + idata_limit *= 2; + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc*)STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + z->idata = p; } - if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); + if (!stbi__getn(s, z->idata + ioff, c.length)) return stbi__err("outofdata", "Corrupt PNG"); ioff += c.length; break; - } + } - case STBI__PNG_TYPE('I','E','N','D'): { + case STBI__PNG_TYPE('I', 'E', 'N', 'D'): { stbi__uint32 raw_len, bpl; if (first) return stbi__err("first not IHDR", "Corrupt PNG"); if (scan != STBI__SCAN_load) return 1; - if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); + if (z->idata == NULL) return stbi__err("no IDAT", "Corrupt PNG"); // initial guess for decoded data size to avoid unnecessary reallocs - bpl = (s->img_x * depth + 7) / 8; // bytes per line, per component + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; - z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); + z->expanded = (stbi_uc*)stbi_zlib_decode_malloc_guesssize_headerflag((char*)z->idata, ioff, raw_len, (int*)&raw_len, !is_iphone); if (z->expanded == NULL) return 0; // zlib should set error STBI_FREE(z->idata); z->idata = NULL; - if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans) - s->img_out_n = s->img_n+1; + if ((req_comp == s->img_n + 1 && req_comp != 3 && !pal_img_n) || has_trans) + s->img_out_n = s->img_n + 1; else - s->img_out_n = s->img_n; - if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, depth, color, interlace)) return 0; - if (has_trans) - if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + s->img_out_n = s->img_n; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } + else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) - stbi__de_iphone(z); + stbi__de_iphone(z); if (pal_img_n) { - // pal_img_n == 3 or 4 - s->img_n = pal_img_n; // record the actual colors we had - s->img_out_n = pal_img_n; - if (req_comp >= 3) s->img_out_n = req_comp; - if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) - return 0; + // pal_img_n == 3 or 4 + s->img_n = pal_img_n; // record the actual colors we had + s->img_out_n = pal_img_n; + if (req_comp >= 3) s->img_out_n = req_comp; + if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) + return 0; + } + else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; } STBI_FREE(z->expanded); z->expanded = NULL; + // end of PNG chunk, read and skip CRC + stbi__get32be(s); return 1; - } + } - default: + default: // if critical, fail if (first) return stbi__err("first not IHDR", "Corrupt PNG"); if ((c.type & (1 << 29)) == 0) { - #ifndef STBI_NO_FAILURE_STRINGS - // not threadsafe - static char invalid_chunk[] = "XXXX PNG chunk not known"; - invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); - invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); - invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); - invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); - #endif - return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); +#ifndef STBI_NO_FAILURE_STRINGS + // not threadsafe + static char invalid_chunk[] = "XXXX PNG chunk not known"; + invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); + invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); + invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); + invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); +#endif + return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); } stbi__skip(s, c.length); break; - } - // end of PNG chunk, read and skip CRC - stbi__get32be(s); - } + } + // end of PNG chunk, read and skip CRC + stbi__get32be(s); + } } -static unsigned char *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp) -{ - unsigned char *result=NULL; - if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); - if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { - result = p->out; - p->out = NULL; - if (req_comp && req_comp != p->s->img_out_n) { - result = stbi__convert_format(result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); - p->s->img_out_n = req_comp; - if (result == NULL) return result; - } - *x = p->s->img_x; - *y = p->s->img_y; - if (n) *n = p->s->img_out_n; - } - STBI_FREE(p->out); p->out = NULL; - STBI_FREE(p->expanded); p->expanded = NULL; - STBI_FREE(p->idata); p->idata = NULL; +static void* stbi__do_png(stbi__png* p, int* x, int* y, int* n, int req_comp, stbi__result_info* ri) +{ + void* result = NULL; + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth <= 8) + ri->bits_per_channel = 8; + else if (p->depth == 16) + ri->bits_per_channel = 16; + else + return stbi__errpuc("bad bits_per_channel", "PNG not supported: unsupported color depth"); + result = p->out; + p->out = NULL; + if (req_comp && req_comp != p->s->img_out_n) { + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char*)result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16*)result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + p->s->img_out_n = req_comp; + if (result == NULL) return result; + } + *x = p->s->img_x; + *y = p->s->img_y; + if (n) *n = p->s->img_n; + } + STBI_FREE(p->out); p->out = NULL; + STBI_FREE(p->expanded); p->expanded = NULL; + STBI_FREE(p->idata); p->idata = NULL; - return result; + return result; } -static unsigned char *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void* stbi__png_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) { - stbi__png p; - p.s = s; - return stbi__do_png(&p, x,y,comp,req_comp); + stbi__png p; + p.s = s; + return stbi__do_png(&p, x, y, comp, req_comp, ri); } -static int stbi__png_test(stbi__context *s) +static int stbi__png_test(stbi__context* s) { - int r; - r = stbi__check_png_header(s); - stbi__rewind(s); - return r; + int r; + r = stbi__check_png_header(s); + stbi__rewind(s); + return r; } -static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp) +static int stbi__png_info_raw(stbi__png* p, int* x, int* y, int* comp) { - if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { - stbi__rewind( p->s ); - return 0; - } - if (x) *x = p->s->img_x; - if (y) *y = p->s->img_y; - if (comp) *comp = p->s->img_n; - return 1; + if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { + stbi__rewind(p->s); + return 0; + } + if (x) *x = p->s->img_x; + if (y) *y = p->s->img_y; + if (comp) *comp = p->s->img_n; + return 1; +} + +static int stbi__png_info(stbi__context* s, int* x, int* y, int* comp) +{ + stbi__png p; + p.s = s; + return stbi__png_info_raw(&p, x, y, comp); } -static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__png_is16(stbi__context* s) { - stbi__png p; - p.s = s; - return stbi__png_info_raw(&p, x, y, comp); + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; } #endif // Microsoft/Windows BMP image #ifndef STBI_NO_BMP -static int stbi__bmp_test_raw(stbi__context *s) +static int stbi__bmp_test_raw(stbi__context* s) { - int r; - int sz; - if (stbi__get8(s) != 'B') return 0; - if (stbi__get8(s) != 'M') return 0; - stbi__get32le(s); // discard filesize - stbi__get16le(s); // discard reserved - stbi__get16le(s); // discard reserved - stbi__get32le(s); // discard data offset - sz = stbi__get32le(s); - r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); - return r; + int r; + int sz; + if (stbi__get8(s) != 'B') return 0; + if (stbi__get8(s) != 'M') return 0; + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + stbi__get32le(s); // discard data offset + sz = stbi__get32le(s); + r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); + return r; } -static int stbi__bmp_test(stbi__context *s) +static int stbi__bmp_test(stbi__context* s) { - int r = stbi__bmp_test_raw(s); - stbi__rewind(s); - return r; + int r = stbi__bmp_test_raw(s); + stbi__rewind(s); + return r; } // returns 0..31 for the highest set bit static int stbi__high_bit(unsigned int z) { - int n=0; - if (z == 0) return -1; - if (z >= 0x10000) n += 16, z >>= 16; - if (z >= 0x00100) n += 8, z >>= 8; - if (z >= 0x00010) n += 4, z >>= 4; - if (z >= 0x00004) n += 2, z >>= 2; - if (z >= 0x00002) n += 1, z >>= 1; - return n; + int n = 0; + if (z == 0) return -1; + if (z >= 0x10000) { n += 16; z >>= 16; } + if (z >= 0x00100) { n += 8; z >>= 8; } + if (z >= 0x00010) { n += 4; z >>= 4; } + if (z >= 0x00004) { n += 2; z >>= 2; } + if (z >= 0x00002) { n += 1;/* >>= 1;*/ } + return n; } static int stbi__bitcount(unsigned int a) { - a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 - a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 - a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits - a = (a + (a >> 8)); // max 16 per 8 bits - a = (a + (a >> 16)); // max 32 per 8 bits - return a & 0xff; + a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 + a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 + a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits + a = (a + (a >> 8)); // max 16 per 8 bits + a = (a + (a >> 16)); // max 32 per 8 bits + return a & 0xff; +} + +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. +static int stbi__shiftsigned(unsigned int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v < 256); + v >>= (8 - bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int)((unsigned)v * mul_table[bits]) >> shift_table[bits]; } -static int stbi__shiftsigned(int v, int shift, int bits) +typedef struct { - int result; - int z=0; + int bpp, offset, hsz; + unsigned int mr, mg, mb, ma, all_a; + int extra_read; +} stbi__bmp_data; + +static int stbi__bmp_set_mask_defaults(stbi__bmp_data* info, int compress) +{ + // BI_BITFIELDS specifies masks explicitly, don't override + if (compress == 3) + return 1; + + if (compress == 0) { + if (info->bpp == 16) { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } + else if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } + else { + // otherwise, use defaults, which is all-0 + info->mr = info->mg = info->mb = info->ma = 0; + } + return 1; + } + return 0; // error +} - if (shift < 0) v <<= -shift; - else v >>= shift; - result = v; +static void* stbi__bmp_parse_header(stbi__context* s, stbi__bmp_data* info) +{ + int hsz; + if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + info->extra_read = 14; - z = bits; - while (z < 8) { - result += v >> z; - z += bits; - } - return result; -} - -static stbi_uc *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) -{ - stbi_uc *out; - unsigned int mr=0,mg=0,mb=0,ma=0, fake_a=0; - stbi_uc pal[256][4]; - int psize=0,i,j,compress=0,width; - int bpp, flip_vertically, pad, target, offset, hsz; - if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); - stbi__get32le(s); // discard filesize - stbi__get16le(s); // discard reserved - stbi__get16le(s); // discard reserved - offset = stbi__get32le(s); - hsz = stbi__get32le(s); - if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); - if (hsz == 12) { - s->img_x = stbi__get16le(s); - s->img_y = stbi__get16le(s); - } else { - s->img_x = stbi__get32le(s); - s->img_y = stbi__get32le(s); - } - if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); - bpp = stbi__get16le(s); - if (bpp == 1) return stbi__errpuc("monochrome", "BMP type not supported: 1-bit"); - flip_vertically = ((int) s->img_y) > 0; - s->img_y = abs((int) s->img_y); - if (hsz == 12) { - if (bpp < 24) - psize = (offset - 14 - 24) / 3; - } else { - compress = stbi__get32le(s); - if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); - stbi__get32le(s); // discard sizeof - stbi__get32le(s); // discard hres - stbi__get32le(s); // discard vres - stbi__get32le(s); // discard colorsused - stbi__get32le(s); // discard max important - if (hsz == 40 || hsz == 56) { - if (hsz == 56) { - stbi__get32le(s); - stbi__get32le(s); - stbi__get32le(s); - stbi__get32le(s); - } - if (bpp == 16 || bpp == 32) { - mr = mg = mb = 0; - if (compress == 0) { - if (bpp == 32) { - mr = 0xffu << 16; - mg = 0xffu << 8; - mb = 0xffu << 0; - ma = 0xffu << 24; - fake_a = 1; // @TODO: check for cases like alpha value is all 0 and switch it to 255 - STBI_NOTUSED(fake_a); - } else { - mr = 31u << 10; - mg = 31u << 5; - mb = 31u << 0; - } - } else if (compress == 3) { - mr = stbi__get32le(s); - mg = stbi__get32le(s); - mb = stbi__get32le(s); - // not documented, but generated by photoshop and handled by mspaint - if (mr == mg && mg == mb) { - // ?!?!? - return stbi__errpuc("bad BMP", "bad BMP"); - } - } else - return stbi__errpuc("bad BMP", "bad BMP"); - } - } else { - STBI_ASSERT(hsz == 108 || hsz == 124); - mr = stbi__get32le(s); - mg = stbi__get32le(s); - mb = stbi__get32le(s); - ma = stbi__get32le(s); - stbi__get32le(s); // discard color space - for (i=0; i < 12; ++i) - stbi__get32le(s); // discard color space parameters - if (hsz == 124) { - stbi__get32le(s); // discard rendering intent - stbi__get32le(s); // discard offset of profile data - stbi__get32le(s); // discard size of profile data - stbi__get32le(s); // discard reserved - } - } - if (bpp < 16) - psize = (offset - 14 - hsz) >> 2; - } - s->img_n = ma ? 4 : 3; - if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 - target = req_comp; - else - target = s->img_n; // if they want monochrome, we'll post-convert - out = (stbi_uc *) stbi__malloc(target * s->img_x * s->img_y); - if (!out) return stbi__errpuc("outofmem", "Out of memory"); - if (bpp < 16) { - int z=0; - if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } - for (i=0; i < psize; ++i) { - pal[i][2] = stbi__get8(s); - pal[i][1] = stbi__get8(s); - pal[i][0] = stbi__get8(s); - if (hsz != 12) stbi__get8(s); - pal[i][3] = 255; - } - stbi__skip(s, offset - 14 - hsz - psize * (hsz == 12 ? 3 : 4)); - if (bpp == 4) width = (s->img_x + 1) >> 1; - else if (bpp == 8) width = s->img_x; - else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } - pad = (-width)&3; - for (j=0; j < (int) s->img_y; ++j) { - for (i=0; i < (int) s->img_x; i += 2) { - int v=stbi__get8(s),v2=0; - if (bpp == 4) { - v2 = v & 15; - v >>= 4; + if (info->offset < 0) return stbi__errpuc("bad BMP", "bad BMP"); + + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); + if (hsz == 12) { + s->img_x = stbi__get16le(s); + s->img_y = stbi__get16le(s); + } + else { + s->img_x = stbi__get32le(s); + s->img_y = stbi__get32le(s); + } + if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); + if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + if (compress >= 4) return stbi__errpuc("BMP JPEG/PNG", "BMP type not supported: unsupported compression"); // this includes PNG/JPEG modes + if (compress == 3 && info->bpp != 16 && info->bpp != 32) return stbi__errpuc("bad BMP", "bad BMP"); // bitfields requires 16 or 32 bits/pixel + stbi__get32le(s); // discard sizeof + stbi__get32le(s); // discard hres + stbi__get32le(s); // discard vres + stbi__get32le(s); // discard colorsused + stbi__get32le(s); // discard max important + if (hsz == 40 || hsz == 56) { + if (hsz == 56) { + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); } - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; - if (i+1 == (int) s->img_x) break; - v = (bpp == 8) ? stbi__get8(s) : v2; - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) out[z++] = 255; - } - stbi__skip(s, pad); - } - } else { - int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; - int z = 0; - int easy=0; - stbi__skip(s, offset - 14 - hsz); - if (bpp == 24) width = 3 * s->img_x; - else if (bpp == 16) width = 2*s->img_x; - else /* bpp = 32 and pad = 0 */ width=0; - pad = (-width) & 3; - if (bpp == 24) { - easy = 1; - } else if (bpp == 32) { - if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) - easy = 2; - } - if (!easy) { - if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } - // right shift amt to put high bit in position #7 - rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr); - gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); - bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); - ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); - } - for (j=0; j < (int) s->img_y; ++j) { - if (easy) { - for (i=0; i < (int) s->img_x; ++i) { - unsigned char a; - out[z+2] = stbi__get8(s); - out[z+1] = stbi__get8(s); - out[z+0] = stbi__get8(s); - z += 3; - a = (easy == 2 ? stbi__get8(s) : 255); - if (target == 4) out[z++] = a; + if (info->bpp == 16 || info->bpp == 32) { + if (compress == 0) { + stbi__bmp_set_mask_defaults(info, compress); + } + else if (compress == 3) { + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->extra_read += 12; + // not documented, but generated by photoshop and handled by mspaint + if (info->mr == info->mg && info->mg == info->mb) { + // ?!?!? + return stbi__errpuc("bad BMP", "bad BMP"); + } + } + else + return stbi__errpuc("bad BMP", "bad BMP"); } - } else { - for (i=0; i < (int) s->img_x; ++i) { - stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); - int a; - out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); - out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); - out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); - a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); - if (target == 4) out[z++] = STBI__BYTECAST(a); + } + else { + // V4/V5 header + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); + if (compress != 3) // override mr/mg/mb unless in BI_BITFIELDS mode, as per docs + stbi__bmp_set_mask_defaults(info, compress); + stbi__get32le(s); // discard color space + for (i = 0; i < 12; ++i) + stbi__get32le(s); // discard color space parameters + if (hsz == 124) { + stbi__get32le(s); // discard rendering intent + stbi__get32le(s); // discard offset of profile data + stbi__get32le(s); // discard size of profile data + stbi__get32le(s); // discard reserved } - } - stbi__skip(s, pad); - } - } - if (flip_vertically) { - stbi_uc t; - for (j=0; j < (int) s->img_y>>1; ++j) { - stbi_uc *p1 = out + j *s->img_x*target; - stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; - for (i=0; i < (int) s->img_x*target; ++i) { - t = p1[i], p1[i] = p2[i], p2[i] = t; - } - } - } + } + } + return (void*)1; +} - if (req_comp && req_comp != target) { - out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); - if (out == NULL) return out; // stbi__convert_format frees input on failure - } - *x = s->img_x; - *y = s->img_y; - if (comp) *comp = s->img_n; - return out; +static void* stbi__bmp_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) +{ + stbi_uc* out; + unsigned int mr = 0, mg = 0, mb = 0, ma = 0, all_a; + stbi_uc pal[256][4]; + int psize = 0, i, j, width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int)s->img_y) > 0; + s->img_y = abs((int)s->img_y); + + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - info.extra_read - 24) / 3; + } + else { + if (info.bpp < 16) + psize = (info.offset - info.extra_read - info.hsz) >> 2; + } + if (psize == 0) { + // accept some number of extra bytes after the header, but if the offset points either to before + // the header ends or implies a large amount of extra data, reject the file as malformed + int bytes_read_so_far = s->callback_already_read + (int)(s->img_buffer - s->img_buffer_original); + int header_limit = 1024; // max we actually read is below 256 bytes currently. + int extra_data_limit = 256 * 4; // what ordinarily goes here is a palette; 256 entries*4 bytes is its max size. + if (bytes_read_so_far <= 0 || bytes_read_so_far > header_limit) { + return stbi__errpuc("bad header", "Corrupt BMP"); + } + // we established that bytes_read_so_far is positive and sensible. + // the first half of this test rejects offsets that are either too small positives, or + // negative, and guarantees that info.offset >= bytes_read_so_far > 0. this in turn + // ensures the number computed in the second half of the test can't overflow. + if (info.offset < bytes_read_so_far || info.offset - bytes_read_so_far > extra_data_limit) { + return stbi__errpuc("bad offset", "Corrupt BMP"); + } + else { + stbi__skip(s, info.offset - bytes_read_so_far); + } + } + + if (info.bpp == 24 && ma == 0xff000000) + s->img_n = 3; + else + s->img_n = ma ? 4 : 3; + if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 + target = req_comp; + else + target = s->img_n; // if they want monochrome, we'll post-convert + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc*)stbi__malloc_mad3(target, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (info.bpp < 16) { + int z = 0; + if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } + for (i = 0; i < psize; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); + pal[i][3] = 255; + } + stbi__skip(s, info.offset - info.extra_read - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; + else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } + pad = (-width) & 3; + if (info.bpp == 1) { + for (j = 0; j < (int)s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i = 0; i < (int)s->img_x; ++i) { + int color = (v >> bit_offset) & 0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if (target == 4) out[z++] = 255; + if (i + 1 == (int)s->img_x) break; + if ((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } + } + stbi__skip(s, pad); + } + } + else { + for (j = 0; j < (int)s->img_y; ++j) { + for (i = 0; i < (int)s->img_x; i += 2) { + int v = stbi__get8(s), v2 = 0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i + 1 == (int)s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); + } + } + } + else { + int rshift = 0, gshift = 0, bshift = 0, ashift = 0, rcount = 0, gcount = 0, bcount = 0, acount = 0; + int z = 0; + int easy = 0; + stbi__skip(s, info.offset - info.extra_read - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2 * s->img_x; + else /* bpp = 32 and pad = 0 */ width = 0; + pad = (-width) & 3; + if (info.bpp == 24) { + easy = 1; + } + else if (info.bpp == 32) { + if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) + easy = 2; + } + if (!easy) { + if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + // right shift amt to put high bit in position #7 + rshift = stbi__high_bit(mr) - 7; rcount = stbi__bitcount(mr); + gshift = stbi__high_bit(mg) - 7; gcount = stbi__bitcount(mg); + bshift = stbi__high_bit(mb) - 7; bcount = stbi__bitcount(mb); + ashift = stbi__high_bit(ma) - 7; acount = stbi__bitcount(ma); + if (rcount > 8 || gcount > 8 || bcount > 8 || acount > 8) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + } + for (j = 0; j < (int)s->img_y; ++j) { + if (easy) { + for (i = 0; i < (int)s->img_x; ++i) { + unsigned char a; + out[z + 2] = stbi__get8(s); + out[z + 1] = stbi__get8(s); + out[z + 0] = stbi__get8(s); + z += 3; + a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; + if (target == 4) out[z++] = a; + } + } + else { + int bpp = info.bpp; + for (i = 0; i < (int)s->img_x; ++i) { + stbi__uint32 v = (bpp == 16 ? (stbi__uint32)stbi__get16le(s) : stbi__get32le(s)); + unsigned int a; + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); + a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; + if (target == 4) out[z++] = STBI__BYTECAST(a); + } + } + stbi__skip(s, pad); + } + } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i = 4 * s->img_x * s->img_y - 1; i >= 0; i -= 4) + out[i] = 255; + + if (flip_vertically) { + stbi_uc t; + for (j = 0; j < (int)s->img_y >> 1; ++j) { + stbi_uc* p1 = out + j * s->img_x * target; + stbi_uc* p2 = out + (s->img_y - 1 - j) * s->img_x * target; + for (i = 0; i < (int)s->img_x * target; ++i) { + t = p1[i]; p1[i] = p2[i]; p2[i] = t; + } + } + } + + if (req_comp && req_comp != target) { + out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + return out; } #endif // Targa Truevision - TGA // by Jonathan Dummer #ifndef STBI_NO_TGA -static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch (bits_per_pixel) { + case 8: return STBI_grey; + case 16: if (is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if (is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel / 8; + default: return 0; + } +} + +static int stbi__tga_info(stbi__context* s, int* x, int* y, int* comp) { - int tga_w, tga_h, tga_comp; - int sz; + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; stbi__get8(s); // discard Offset - sz = stbi__get8(s); // color type - if( sz > 1 ) { + tga_colormap_type = stbi__get8(s); // colormap type + if (tga_colormap_type > 1) { stbi__rewind(s); return 0; // only RGB or indexed allowed } - sz = stbi__get8(s); // image type - // only RGB or grey allowed, +/- RLE - if ((sz != 1) && (sz != 2) && (sz != 3) && (sz != 9) && (sz != 10) && (sz != 11)) return 0; - stbi__skip(s,9); + tga_image_type = stbi__get8(s); // image type + if (tga_colormap_type == 1) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s, 4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) { + stbi__rewind(s); + return 0; + } + stbi__skip(s, 4); // skip image x and y origin + tga_colormap_bpp = sz; + } + else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ((tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11)) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s, 9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } tga_w = stbi__get16le(s); - if( tga_w < 1 ) { + if (tga_w < 1) { stbi__rewind(s); return 0; // test width } tga_h = stbi__get16le(s); - if( tga_h < 1 ) { + if (tga_h < 1) { stbi__rewind(s); return 0; // test height } - sz = stbi__get8(s); // bits per pixel - // only RGB or RGBA or grey allowed - if ((sz != 8) && (sz != 16) && (sz != 24) && (sz != 32)) { + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if ((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } + else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if (!tga_comp) { stbi__rewind(s); return 0; } - tga_comp = sz; if (x) *x = tga_w; if (y) *y = tga_h; - if (comp) *comp = tga_comp / 8; + if (comp) *comp = tga_comp; return 1; // seems to have passed everything } -static int stbi__tga_test(stbi__context *s) -{ - int res; - int sz; - stbi__get8(s); // discard Offset - sz = stbi__get8(s); // color type - if ( sz > 1 ) return 0; // only RGB or indexed allowed - sz = stbi__get8(s); // image type - if ( (sz != 1) && (sz != 2) && (sz != 3) && (sz != 9) && (sz != 10) && (sz != 11) ) return 0; // only RGB or grey allowed, +/- RLE - stbi__get16be(s); // discard palette start - stbi__get16be(s); // discard palette length - stbi__get8(s); // discard bits per palette color entry - stbi__get16be(s); // discard x origin - stbi__get16be(s); // discard y origin - if ( stbi__get16be(s) < 1 ) return 0; // test width - if ( stbi__get16be(s) < 1 ) return 0; // test height - sz = stbi__get8(s); // bits per pixel - if ( (sz != 8) && (sz != 16) && (sz != 24) && (sz != 32) ) - res = 0; - else - res = 1; - stbi__rewind(s); - return res; -} - -static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) -{ - // read in the TGA header stuff - int tga_offset = stbi__get8(s); - int tga_indexed = stbi__get8(s); - int tga_image_type = stbi__get8(s); - int tga_is_RLE = 0; - int tga_palette_start = stbi__get16le(s); - int tga_palette_len = stbi__get16le(s); - int tga_palette_bits = stbi__get8(s); - int tga_x_origin = stbi__get16le(s); - int tga_y_origin = stbi__get16le(s); - int tga_width = stbi__get16le(s); - int tga_height = stbi__get16le(s); - int tga_bits_per_pixel = stbi__get8(s); - int tga_comp = tga_bits_per_pixel / 8; - int tga_inverted = stbi__get8(s); - // image data - unsigned char *tga_data; - unsigned char *tga_palette = NULL; - int i, j; - unsigned char raw_data[4]; - int RLE_count = 0; - int RLE_repeating = 0; - int read_next_pixel = 1; - - // do a tiny bit of precessing - if ( tga_image_type >= 8 ) - { - tga_image_type -= 8; - tga_is_RLE = 1; - } - /* int tga_alpha_bits = tga_inverted & 15; */ - tga_inverted = 1 - ((tga_inverted >> 5) & 1); - - // error check - if ( //(tga_indexed) || - (tga_width < 1) || (tga_height < 1) || - (tga_image_type < 1) || (tga_image_type > 3) || - ((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16) && - (tga_bits_per_pixel != 24) && (tga_bits_per_pixel != 32)) - ) - { - return NULL; // we don't report this as a bad TGA because we don't even know if it's TGA - } +static int stbi__tga_test(stbi__context* s) +{ + int res = 0; + int sz, tga_color_type; + stbi__get8(s); // discard Offset + tga_color_type = stbi__get8(s); // color type + if (tga_color_type > 1) goto errorEnd; // only RGB or indexed allowed + sz = stbi__get8(s); // image type + if (tga_color_type == 1) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s, 4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) goto errorEnd; + stbi__skip(s, 4); // skip image x and y origin + } + else { // "normal" image w/o colormap + if ((sz != 2) && (sz != 3) && (sz != 10) && (sz != 11)) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s, 9); // skip colormap specification and image x/y origin + } + if (stbi__get16le(s) < 1) goto errorEnd; // test width + if (stbi__get16le(s) < 1) goto errorEnd; // test height + sz = stbi__get8(s); // bits per pixel + if ((tga_color_type == 1) && (sz != 8) && (sz != 16)) goto errorEnd; // for colormapped images, bpp is size of an index + if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: + stbi__rewind(s); + return res; +} + +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context* s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255) / 31); + out[1] = (stbi_uc)((g * 255) / 31); + out[2] = (stbi_uc)((b * 255) / 31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void* stbi__tga_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) +{ + // read in the TGA header stuff + int tga_offset = stbi__get8(s); + int tga_indexed = stbi__get8(s); + int tga_image_type = stbi__get8(s); + int tga_is_RLE = 0; + int tga_palette_start = stbi__get16le(s); + int tga_palette_len = stbi__get16le(s); + int tga_palette_bits = stbi__get8(s); + int tga_x_origin = stbi__get16le(s); + int tga_y_origin = stbi__get16le(s); + int tga_width = stbi__get16le(s); + int tga_height = stbi__get16le(s); + int tga_bits_per_pixel = stbi__get8(s); + int tga_comp, tga_rgb16 = 0; + int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) + // image data + unsigned char* tga_data; + unsigned char* tga_palette = NULL; + int i, j; + unsigned char raw_data[4] = { 0 }; + int RLE_count = 0; + int RLE_repeating = 0; + int read_next_pixel = 1; + STBI_NOTUSED(ri); + STBI_NOTUSED(tga_x_origin); // @TODO + STBI_NOTUSED(tga_y_origin); // @TODO + + if (tga_height > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (tga_width > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + + // do a tiny bit of precessing + if (tga_image_type >= 8) + { + tga_image_type -= 8; + tga_is_RLE = 1; + } + tga_inverted = 1 - ((tga_inverted >> 5) & 1); - // If I'm paletted, then I'll use the number of bits from the palette - if ( tga_indexed ) - { - tga_comp = tga_palette_bits / 8; - } + // If I'm paletted, then I'll use the number of bits from the palette + if (tga_indexed) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); - // tga info - *x = tga_width; - *y = tga_height; - if (comp) *comp = tga_comp; + if (!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); - tga_data = (unsigned char*)stbi__malloc( (size_t)tga_width * tga_height * tga_comp ); - if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); + // tga info + *x = tga_width; + *y = tga_height; + if (comp) *comp = tga_comp; - // skip to the data's starting position (offset usually = 0) - stbi__skip(s, tga_offset ); + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); - if ( !tga_indexed && !tga_is_RLE) { - for (i=0; i < tga_height; ++i) { - int y = tga_inverted ? tga_height -i - 1 : i; - stbi_uc *tga_row = tga_data + y*tga_width*tga_comp; - stbi__getn(s, tga_row, tga_width * tga_comp); - } - } else { - // do I need to load a palette? - if ( tga_indexed) - { - // any data to skip? (offset usually = 0) - stbi__skip(s, tga_palette_start ); - // load the palette - tga_palette = (unsigned char*)stbi__malloc( tga_palette_len * tga_palette_bits / 8 ); - if (!tga_palette) { - STBI_FREE(tga_data); - return stbi__errpuc("outofmem", "Out of memory"); - } - if (!stbi__getn(s, tga_palette, tga_palette_len * tga_palette_bits / 8 )) { - STBI_FREE(tga_data); - STBI_FREE(tga_palette); - return stbi__errpuc("bad palette", "Corrupt TGA"); - } - } - // load the data - for (i=0; i < tga_width * tga_height; ++i) - { - // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? - if ( tga_is_RLE ) - { - if ( RLE_count == 0 ) - { - // yep, get the next byte as a RLE command - int RLE_cmd = stbi__get8(s); - RLE_count = 1 + (RLE_cmd & 127); - RLE_repeating = RLE_cmd >> 7; - read_next_pixel = 1; - } else if ( !RLE_repeating ) - { - read_next_pixel = 1; + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); + if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); + + // skip to the data's starting position (offset usually = 0) + stbi__skip(s, tga_offset); + + if (!tga_indexed && !tga_is_RLE && !tga_rgb16) { + for (i = 0; i < tga_height; ++i) { + int row = tga_inverted ? tga_height - i - 1 : i; + stbi_uc* tga_row = tga_data + row * tga_width * tga_comp; + stbi__getn(s, tga_row, tga_width * tga_comp); + } + } + else { + // do I need to load a palette? + if (tga_indexed) + { + if (tga_palette_len == 0) { /* you have to have at least one entry! */ + STBI_FREE(tga_data); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + + // any data to skip? (offset usually = 0) + stbi__skip(s, tga_palette_start); + // load the palette + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); + if (!tga_palette) { + STBI_FREE(tga_data); + return stbi__errpuc("outofmem", "Out of memory"); + } + if (tga_rgb16) { + stbi_uc* pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i = 0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } } - } else - { - read_next_pixel = 1; - } - // OK, if I need to read a pixel, do it now - if ( read_next_pixel ) - { - // load however much data we did have - if ( tga_indexed ) + else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + } + // load the data + for (i = 0; i < tga_width * tga_height; ++i) + { + // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? + if (tga_is_RLE) { - // read in 1 byte, then perform the lookup - int pal_idx = stbi__get8(s); - if ( pal_idx >= tga_palette_len ) - { - // invalid index - pal_idx = 0; - } - pal_idx *= tga_bits_per_pixel / 8; - for (j = 0; j*8 < tga_bits_per_pixel; ++j) - { - raw_data[j] = tga_palette[pal_idx+j]; - } - } else + if (RLE_count == 0) + { + // yep, get the next byte as a RLE command + int RLE_cmd = stbi__get8(s); + RLE_count = 1 + (RLE_cmd & 127); + RLE_repeating = RLE_cmd >> 7; + read_next_pixel = 1; + } + else if (!RLE_repeating) + { + read_next_pixel = 1; + } + } + else { - // read in the data raw - for (j = 0; j*8 < tga_bits_per_pixel; ++j) - { - raw_data[j] = stbi__get8(s); - } + read_next_pixel = 1; } - // clear the reading flag for the next pixel - read_next_pixel = 0; - } // end of reading a pixel - - // copy data - for (j = 0; j < tga_comp; ++j) - tga_data[i*tga_comp+j] = raw_data[j]; - - // in case we're in RLE mode, keep counting down - --RLE_count; - } - // do I need to invert the image? - if ( tga_inverted ) - { - for (j = 0; j*2 < tga_height; ++j) - { - int index1 = j * tga_width * tga_comp; - int index2 = (tga_height - 1 - j) * tga_width * tga_comp; - for (i = tga_width * tga_comp; i > 0; --i) + // OK, if I need to read a pixel, do it now + if (read_next_pixel) { - unsigned char temp = tga_data[index1]; - tga_data[index1] = tga_data[index2]; - tga_data[index2] = temp; - ++index1; - ++index2; + // load however much data we did have + if (tga_indexed) + { + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if (pal_idx >= tga_palette_len) { + // invalid index + pal_idx = 0; + } + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = tga_palette[pal_idx + j]; + } + } + else if (tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } + else { + // read in the data raw + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = stbi__get8(s); + } + } + // clear the reading flag for the next pixel + read_next_pixel = 0; + } // end of reading a pixel + + // copy data + for (j = 0; j < tga_comp; ++j) + tga_data[i * tga_comp + j] = raw_data[j]; + + // in case we're in RLE mode, keep counting down + --RLE_count; + } + // do I need to invert the image? + if (tga_inverted) + { + for (j = 0; j * 2 < tga_height; ++j) + { + int index1 = j * tga_width * tga_comp; + int index2 = (tga_height - 1 - j) * tga_width * tga_comp; + for (i = tga_width * tga_comp; i > 0; --i) + { + unsigned char temp = tga_data[index1]; + tga_data[index1] = tga_data[index2]; + tga_data[index2] = temp; + ++index1; + ++index2; + } } - } - } - // clear my palette, if I had one - if ( tga_palette != NULL ) - { - STBI_FREE( tga_palette ); - } - } + } + // clear my palette, if I had one + if (tga_palette != NULL) + { + STBI_FREE(tga_palette); + } + } - // swap RGB - if (tga_comp >= 3) - { - unsigned char* tga_pixel = tga_data; - for (i=0; i < tga_width * tga_height; ++i) - { - unsigned char temp = tga_pixel[0]; - tga_pixel[0] = tga_pixel[2]; - tga_pixel[2] = temp; - tga_pixel += tga_comp; - } - } + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) + { + unsigned char* tga_pixel = tga_data; + for (i = 0; i < tga_width * tga_height; ++i) + { + unsigned char temp = tga_pixel[0]; + tga_pixel[0] = tga_pixel[2]; + tga_pixel[2] = temp; + tga_pixel += tga_comp; + } + } - // convert to target component count - if (req_comp && req_comp != tga_comp) - tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); + // convert to target component count + if (req_comp && req_comp != tga_comp) + tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); - // the things I do to get rid of an error message, and yet keep - // Microsoft's C compilers happy... [8^( - tga_palette_start = tga_palette_len = tga_palette_bits = - tga_x_origin = tga_y_origin = 0; - // OK, done - return tga_data; + // the things I do to get rid of an error message, and yet keep + // Microsoft's C compilers happy... [8^( + tga_palette_start = tga_palette_len = tga_palette_bits = + tga_x_origin = tga_y_origin = 0; + STBI_NOTUSED(tga_palette_start); + // OK, done + return tga_data; } #endif @@ -5043,171 +6158,260 @@ static stbi_uc *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int // Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB #ifndef STBI_NO_PSD -static int stbi__psd_test(stbi__context *s) -{ - int r = (stbi__get32be(s) == 0x38425053); - stbi__rewind(s); - return r; -} - -static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) -{ - int pixelCount; - int channelCount, compression; - int channel, i, count, len; - int w,h; - stbi_uc *out; - - // Check identifier - if (stbi__get32be(s) != 0x38425053) // "8BPS" - return stbi__errpuc("not PSD", "Corrupt PSD image"); - - // Check file type version. - if (stbi__get16be(s) != 1) - return stbi__errpuc("wrong version", "Unsupported version of PSD image"); - - // Skip 6 reserved bytes. - stbi__skip(s, 6 ); - - // Read the number of channels (R, G, B, A, etc). - channelCount = stbi__get16be(s); - if (channelCount < 0 || channelCount > 16) - return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); - - // Read the rows and columns of the image. - h = stbi__get32be(s); - w = stbi__get32be(s); - - // Make sure the depth is 8 bits. - if (stbi__get16be(s) != 8) - return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 bit"); - - // Make sure the color mode is RGB. - // Valid options are: - // 0: Bitmap - // 1: Grayscale - // 2: Indexed color - // 3: RGB color - // 4: CMYK color - // 7: Multichannel - // 8: Duotone - // 9: Lab color - if (stbi__get16be(s) != 3) - return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); - - // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) - stbi__skip(s,stbi__get32be(s) ); - - // Skip the image resources. (resolution, pen tool paths, etc) - stbi__skip(s, stbi__get32be(s) ); - - // Skip the reserved data. - stbi__skip(s, stbi__get32be(s) ); - - // Find out if the data is compressed. - // Known values: - // 0: no compression - // 1: RLE compressed - compression = stbi__get16be(s); - if (compression > 1) - return stbi__errpuc("bad compression", "PSD has an unknown compression format"); - - // Create the destination image. - out = (stbi_uc *) stbi__malloc(4 * w*h); - if (!out) return stbi__errpuc("outofmem", "Out of memory"); - pixelCount = w*h; - - // Initialize the data to zero. - //memset( out, 0, pixelCount * 4 ); - - // Finally, the image data. - if (compression) { - // RLE as used by .PSD and .TIFF - // Loop until you get the number of unpacked bytes you are expecting: - // Read the next source byte into n. - // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. - // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. - // Else if n is 128, noop. - // Endloop - - // The RLE-compressed data is preceeded by a 2-byte data count for each row in the data, - // which we're going to just skip. - stbi__skip(s, h * channelCount * 2 ); - - // Read the RLE data by channel. - for (channel = 0; channel < 4; channel++) { - stbi_uc *p; - - p = out+channel; - if (channel >= channelCount) { - // Fill this channel with default data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = (channel == 3 ? 255 : 0); - } else { - // Read the RLE data. - count = 0; - while (count < pixelCount) { - len = stbi__get8(s); - if (len == 128) { - // No-op. - } else if (len < 128) { - // Copy next len+1 bytes literally. - len++; - count += len; - while (len) { - *p = stbi__get8(s); - p += 4; - len--; - } - } else if (len > 128) { - stbi_uc val; - // Next -len+1 bytes in the dest are replicated from next source byte. - // (Interpret len as a negative 8-bit int.) - len ^= 0x0FF; - len += 2; - val = stbi__get8(s); - count += len; - while (len) { - *p = val; - p += 4; - len--; - } - } +static int stbi__psd_test(stbi__context* s) +{ + int r = (stbi__get32be(s) == 0x38425053); + stbi__rewind(s); + return r; +} + +static int stbi__psd_decode_rle(stbi__context* s, stbi_uc* p, int pixelCount) +{ + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } + else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; } - } - } + } + else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } - } else { - // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) - // where each channel consists of an 8-bit value for each pixel in the image. - - // Read the data by channel. - for (channel = 0; channel < 4; channel++) { - stbi_uc *p; - - p = out + channel; - if (channel > channelCount) { - // Fill this channel with default data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = channel == 3 ? 255 : 0; - } else { - // Read the data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = stbi__get8(s); - } - } - } + return 1; +} + +static void* stbi__psd_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri, int bpc) +{ + int pixelCount; + int channelCount, compression; + int channel, i; + int bitdepth; + int w, h; + stbi_uc* out; + STBI_NOTUSED(ri); + + // Check identifier + if (stbi__get32be(s) != 0x38425053) // "8BPS" + return stbi__errpuc("not PSD", "Corrupt PSD image"); + + // Check file type version. + if (stbi__get16be(s) != 1) + return stbi__errpuc("wrong version", "Unsupported version of PSD image"); + + // Skip 6 reserved bytes. + stbi__skip(s, 6); + + // Read the number of channels (R, G, B, A, etc). + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) + return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); + + // Read the rows and columns of the image. + h = stbi__get32be(s); + w = stbi__get32be(s); + + if (h > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (w > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + + // Make sure the depth is 8 bits. + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); + + // Make sure the color mode is RGB. + // Valid options are: + // 0: Bitmap + // 1: Grayscale + // 2: Indexed color + // 3: RGB color + // 4: CMYK color + // 7: Multichannel + // 8: Duotone + // 9: Lab color + if (stbi__get16be(s) != 3) + return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); + + // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) + stbi__skip(s, stbi__get32be(s)); + + // Skip the image resources. (resolution, pen tool paths, etc) + stbi__skip(s, stbi__get32be(s)); + + // Skip the reserved data. + stbi__skip(s, stbi__get32be(s)); + + // Find out if the data is compressed. + // Known values: + // 0: no compression + // 1: RLE compressed + compression = stbi__get16be(s); + if (compression > 1) + return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + + // Create the destination image. + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc*)stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } + else + out = (stbi_uc*)stbi__malloc(4 * w * h); + + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + pixelCount = w * h; + + // Initialize the data to zero. + //memset( out, 0, pixelCount * 4 ); + + // Finally, the image data. + if (compression) { + // RLE as used by .PSD and .TIFF + // Loop until you get the number of unpacked bytes you are expecting: + // Read the next source byte into n. + // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. + // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. + // Else if n is 128, noop. + // Endloop + + // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, + // which we're going to just skip. + stbi__skip(s, h * channelCount * 2); + + // Read the RLE data by channel. + for (channel = 0; channel < 4; channel++) { + stbi_uc* p; + + p = out + channel; + if (channel >= channelCount) { + // Fill this channel with default data. + for (i = 0; i < pixelCount; i++, p += 4) + *p = (channel == 3 ? 255 : 0); + } + else { + // Read the RLE data. + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); + } + } + } - if (req_comp && req_comp != 4) { - out = stbi__convert_format(out, 4, req_comp, w, h); - if (out == NULL) return out; // stbi__convert_format frees input on failure - } + } + else { + // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. + + // Read the data by channel. + for (channel = 0; channel < 4; channel++) { + if (channel >= channelCount) { + // Fill this channel with default data. + if (bitdepth == 16 && bpc == 16) { + stbi__uint16* q = ((stbi__uint16*)out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } + else { + stbi_uc* p = out + channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } + } + else { + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16* q = ((stbi__uint16*)out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16)stbi__get16be(s); + } + else { + stbi_uc* p = out + channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc)(stbi__get16be(s) >> 8); + } + else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i = 0; i < w * h; ++i) { + stbi__uint16* pixel = (stbi__uint16*)out + 4 * i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16)(pixel[0] * ra + inv_a); + pixel[1] = (stbi__uint16)(pixel[1] * ra + inv_a); + pixel[2] = (stbi__uint16)(pixel[2] * ra + inv_a); + } + } + } + else { + for (i = 0; i < w * h; ++i) { + unsigned char* pixel = out + 4 * i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char)(pixel[0] * ra + inv_a); + pixel[1] = (unsigned char)(pixel[1] * ra + inv_a); + pixel[2] = (unsigned char)(pixel[2] * ra + inv_a); + } + } + } + } + + // convert to desired output format + if (req_comp && req_comp != 4) { + if (ri->bits_per_channel == 16) + out = (stbi_uc*)stbi__convert_format16((stbi__uint16*)out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } - if (comp) *comp = 4; - *y = h; - *x = w; + if (comp) *comp = 4; + *y = h; + *x = w; - return out; + return out; } #endif @@ -5219,209 +6423,217 @@ static stbi_uc *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int // See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/ #ifndef STBI_NO_PIC -static int stbi__pic_is4(stbi__context *s,const char *str) +static int stbi__pic_is4(stbi__context* s, const char* str) { - int i; - for (i=0; i<4; ++i) - if (stbi__get8(s) != (stbi_uc)str[i]) - return 0; + int i; + for (i = 0; i < 4; ++i) + if (stbi__get8(s) != (stbi_uc)str[i]) + return 0; - return 1; + return 1; } -static int stbi__pic_test_core(stbi__context *s) +static int stbi__pic_test_core(stbi__context* s) { - int i; + int i; - if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) - return 0; + if (!stbi__pic_is4(s, "\x53\x80\xF6\x34")) + return 0; - for(i=0;i<84;++i) - stbi__get8(s); + for (i = 0; i < 84; ++i) + stbi__get8(s); - if (!stbi__pic_is4(s,"PICT")) - return 0; + if (!stbi__pic_is4(s, "PICT")) + return 0; - return 1; + return 1; } typedef struct { - stbi_uc size,type,channel; + stbi_uc size, type, channel; } stbi__pic_packet; -static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest) +static stbi_uc* stbi__readval(stbi__context* s, int channel, stbi_uc* dest) { - int mask=0x80, i; + int mask = 0x80, i; - for (i=0; i<4; ++i, mask>>=1) { - if (channel & mask) { - if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short"); - dest[i]=stbi__get8(s); - } - } + for (i = 0; i < 4; ++i, mask >>= 1) { + if (channel & mask) { + if (stbi__at_eof(s)) return stbi__errpuc("bad file", "PIC file too short"); + dest[i] = stbi__get8(s); + } + } - return dest; + return dest; } -static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src) +static void stbi__copyval(int channel, stbi_uc* dest, const stbi_uc* src) { - int mask=0x80,i; + int mask = 0x80, i; - for (i=0;i<4; ++i, mask>>=1) - if (channel&mask) - dest[i]=src[i]; + for (i = 0; i < 4; ++i, mask >>= 1) + if (channel & mask) + dest[i] = src[i]; } -static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result) +static stbi_uc* stbi__pic_load_core(stbi__context* s, int width, int height, int* comp, stbi_uc* result) { - int act_comp=0,num_packets=0,y,chained; - stbi__pic_packet packets[10]; + int act_comp = 0, num_packets = 0, y, chained; + stbi__pic_packet packets[10]; - // this will (should...) cater for even some bizarre stuff like having data - // for the same channel in multiple packets. - do { - stbi__pic_packet *packet; + // this will (should...) cater for even some bizarre stuff like having data + // for the same channel in multiple packets. + do { + stbi__pic_packet* packet; - if (num_packets==sizeof(packets)/sizeof(packets[0])) - return stbi__errpuc("bad format","too many packets"); + if (num_packets == sizeof(packets) / sizeof(packets[0])) + return stbi__errpuc("bad format", "too many packets"); - packet = &packets[num_packets++]; + packet = &packets[num_packets++]; - chained = stbi__get8(s); - packet->size = stbi__get8(s); - packet->type = stbi__get8(s); - packet->channel = stbi__get8(s); + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); - act_comp |= packet->channel; + act_comp |= packet->channel; - if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (reading packets)"); - if (packet->size != 8) return stbi__errpuc("bad format","packet isn't 8bpp"); - } while (chained); + if (stbi__at_eof(s)) return stbi__errpuc("bad file", "file too short (reading packets)"); + if (packet->size != 8) return stbi__errpuc("bad format", "packet isn't 8bpp"); + } while (chained); - *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? + *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? - for(y=0; ytype) { + switch (packet->type) { default: - return stbi__errpuc("bad format","packet has bad compression type"); + return stbi__errpuc("bad format", "packet has bad compression type"); case 0: {//uncompressed - int x; + int x; - for(x=0;xchannel,dest)) - return 0; - break; + for (x = 0; x < width; ++x, dest += 4) + if (!stbi__readval(s, packet->channel, dest)) + return 0; + break; } case 1://Pure RLE - { - int left=width, i; + { + int left = width, i; - while (left>0) { - stbi_uc count,value[4]; + while (left > 0) { + stbi_uc count, value[4]; - count=stbi__get8(s); - if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pure read count)"); + count = stbi__get8(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file", "file too short (pure read count)"); - if (count > left) - count = (stbi_uc) left; + if (count > left) + count = (stbi_uc)left; - if (!stbi__readval(s,packet->channel,value)) return 0; + if (!stbi__readval(s, packet->channel, value)) return 0; - for(i=0; ichannel,dest,value); - left -= count; - } - } - break; + for (i = 0; i < count; ++i, dest += 4) + stbi__copyval(packet->channel, dest, value); + left -= count; + } + } + break; case 2: {//Mixed RLE - int left=width; - while (left>0) { - int count = stbi__get8(s), i; - if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (mixed read count)"); - - if (count >= 128) { // Repeated - stbi_uc value[4]; - int i; - - if (count==128) - count = stbi__get16be(s); - else - count -= 127; - if (count > left) - return stbi__errpuc("bad file","scanline overrun"); - - if (!stbi__readval(s,packet->channel,value)) - return 0; + int left = width; + while (left > 0) { + int count = stbi__get8(s), i; + if (stbi__at_eof(s)) return stbi__errpuc("bad file", "file too short (mixed read count)"); + + if (count >= 128) { // Repeated + stbi_uc value[4]; - for(i=0;ichannel,dest,value); - } else { // Raw - ++count; - if (count>left) return stbi__errpuc("bad file","scanline overrun"); - - for(i=0;ichannel,dest)) - return 0; - } - left-=count; - } - break; + if (count == 128) + count = stbi__get16be(s); + else + count -= 127; + if (count > left) + return stbi__errpuc("bad file", "scanline overrun"); + + if (!stbi__readval(s, packet->channel, value)) + return 0; + + for (i = 0; i < count; ++i, dest += 4) + stbi__copyval(packet->channel, dest, value); + } + else { // Raw + ++count; + if (count > left) return stbi__errpuc("bad file", "scanline overrun"); + + for (i = 0; i < count; ++i, dest += 4) + if (!stbi__readval(s, packet->channel, dest)) + return 0; + } + left -= count; + } + break; } - } - } - } + } + } + } - return result; + return result; } -static stbi_uc *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp) +static void* stbi__pic_load(stbi__context* s, int* px, int* py, int* comp, int req_comp, stbi__result_info* ri) { - stbi_uc *result; - int i, x,y; + stbi_uc* result; + int i, x, y, internal_comp; + STBI_NOTUSED(ri); - for (i=0; i<92; ++i) - stbi__get8(s); + if (!comp) comp = &internal_comp; - x = stbi__get16be(s); - y = stbi__get16be(s); - if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); - if ((1 << 28) / x < y) return stbi__errpuc("too large", "Image too large to decode"); + for (i = 0; i < 92; ++i) + stbi__get8(s); - stbi__get32be(s); //skip `ratio' - stbi__get16be(s); //skip `fields' - stbi__get16be(s); //skip `pad' + x = stbi__get16be(s); + y = stbi__get16be(s); - // intermediate buffer is RGBA - result = (stbi_uc *) stbi__malloc(x*y*4); - memset(result, 0xff, x*y*4); + if (y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (!stbi__pic_load_core(s,x,y,comp, result)) { - STBI_FREE(result); - result=0; - } - *px = x; - *py = y; - if (req_comp == 0) req_comp = *comp; - result=stbi__convert_format(result,4,req_comp,x,y); + if (stbi__at_eof(s)) return stbi__errpuc("bad file", "file too short (pic header)"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); + + stbi__get32be(s); //skip `ratio' + stbi__get16be(s); //skip `fields' + stbi__get16be(s); //skip `pad' - return result; + // intermediate buffer is RGBA + result = (stbi_uc*)stbi__malloc_mad3(x, y, 4, 0); + if (!result) return stbi__errpuc("outofmem", "Out of memory"); + memset(result, 0xff, x * y * 4); + + if (!stbi__pic_load_core(s, x, y, comp, result)) { + STBI_FREE(result); + result = 0; + } + *px = x; + *py = y; + if (req_comp == 0) req_comp = *comp; + result = stbi__convert_format(result, 4, req_comp, x, y); + + return result; } -static int stbi__pic_test(stbi__context *s) +static int stbi__pic_test(stbi__context* s) { - int r = stbi__pic_test_core(s); - stbi__rewind(s); - return r; + int r = stbi__pic_test_core(s); + stbi__rewind(s); + return r; } #endif @@ -5431,344 +6643,547 @@ static int stbi__pic_test(stbi__context *s) #ifndef STBI_NO_GIF typedef struct { - stbi__int16 prefix; - stbi_uc first; - stbi_uc suffix; + stbi__int16 prefix; + stbi_uc first; + stbi_uc suffix; } stbi__gif_lzw; typedef struct { - int w,h; - stbi_uc *out; // output buffer (always 4 components) - int flags, bgindex, ratio, transparent, eflags; - stbi_uc pal[256][4]; - stbi_uc lpal[256][4]; - stbi__gif_lzw codes[4096]; - stbi_uc *color_table; - int parse, step; - int lflags; - int start_x, start_y; - int max_x, max_y; - int cur_x, cur_y; - int line_size; + int w, h; + stbi_uc* out; // output buffer (always 4 components) + stbi_uc* background; // The current "background" as far as a gif is concerned + stbi_uc* history; + int flags, bgindex, ratio, transparent, eflags; + stbi_uc pal[256][4]; + stbi_uc lpal[256][4]; + stbi__gif_lzw codes[8192]; + stbi_uc* color_table; + int parse, step; + int lflags; + int start_x, start_y; + int max_x, max_y; + int cur_x, cur_y; + int line_size; + int delay; } stbi__gif; -static int stbi__gif_test_raw(stbi__context *s) +static int stbi__gif_test_raw(stbi__context* s) { - int sz; - if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; - sz = stbi__get8(s); - if (sz != '9' && sz != '7') return 0; - if (stbi__get8(s) != 'a') return 0; - return 1; + int sz; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; + sz = stbi__get8(s); + if (sz != '9' && sz != '7') return 0; + if (stbi__get8(s) != 'a') return 0; + return 1; } -static int stbi__gif_test(stbi__context *s) +static int stbi__gif_test(stbi__context* s) { - int r = stbi__gif_test_raw(s); - stbi__rewind(s); - return r; + int r = stbi__gif_test_raw(s); + stbi__rewind(s); + return r; } -static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp) +static void stbi__gif_parse_colortable(stbi__context* s, stbi_uc pal[256][4], int num_entries, int transp) { - int i; - for (i=0; i < num_entries; ++i) { - pal[i][2] = stbi__get8(s); - pal[i][1] = stbi__get8(s); - pal[i][0] = stbi__get8(s); - pal[i][3] = transp == i ? 0 : 255; - } + int i; + for (i = 0; i < num_entries; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + pal[i][3] = transp == i ? 0 : 255; + } } -static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info) +static int stbi__gif_header(stbi__context* s, stbi__gif* g, int* comp, int is_info) { - stbi_uc version; - if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') - return stbi__err("not GIF", "Corrupt GIF"); + stbi_uc version; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') + return stbi__err("not GIF", "Corrupt GIF"); + + version = stbi__get8(s); + if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); + if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); - version = stbi__get8(s); - if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); - if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); + stbi__g_failure_reason = ""; + g->w = stbi__get16le(s); + g->h = stbi__get16le(s); + g->flags = stbi__get8(s); + g->bgindex = stbi__get8(s); + g->ratio = stbi__get8(s); + g->transparent = -1; - stbi__g_failure_reason = ""; - g->w = stbi__get16le(s); - g->h = stbi__get16le(s); - g->flags = stbi__get8(s); - g->bgindex = stbi__get8(s); - g->ratio = stbi__get8(s); - g->transparent = -1; + if (g->w > STBI_MAX_DIMENSIONS) return stbi__err("too large", "Very large image (corrupt?)"); + if (g->h > STBI_MAX_DIMENSIONS) return stbi__err("too large", "Very large image (corrupt?)"); - if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments - if (is_info) return 1; + if (is_info) return 1; - if (g->flags & 0x80) - stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1); + if (g->flags & 0x80) + stbi__gif_parse_colortable(s, g->pal, 2 << (g->flags & 7), -1); - return 1; + return 1; } -static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) +static int stbi__gif_info_raw(stbi__context* s, int* x, int* y, int* comp) { - stbi__gif g; - if (!stbi__gif_header(s, &g, comp, 1)) { - stbi__rewind( s ); - return 0; - } - if (x) *x = g.w; - if (y) *y = g.h; - return 1; + stbi__gif* g = (stbi__gif*)stbi__malloc(sizeof(stbi__gif)); + if (!g) return stbi__err("outofmem", "Out of memory"); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); + stbi__rewind(s); + return 0; + } + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); + return 1; } -static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) +static void stbi__out_gif_code(stbi__gif* g, stbi__uint16 code) { - stbi_uc *p, *c; + stbi_uc* p, * c; + int idx; - // recurse to decode the prefixes, since the linked-list is backwards, - // and working backwards through an interleaved image would be nasty - if (g->codes[code].prefix >= 0) - stbi__out_gif_code(g, g->codes[code].prefix); + // recurse to decode the prefixes, since the linked-list is backwards, + // and working backwards through an interleaved image would be nasty + if (g->codes[code].prefix >= 0) + stbi__out_gif_code(g, g->codes[code].prefix); - if (g->cur_y >= g->max_y) return; + if (g->cur_y >= g->max_y) return; - p = &g->out[g->cur_x + g->cur_y]; - c = &g->color_table[g->codes[code].suffix * 4]; + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; - if (c[3] >= 128) { - p[0] = c[2]; - p[1] = c[1]; - p[2] = c[0]; - p[3] = c[3]; - } - g->cur_x += 4; + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; + p[0] = c[2]; + p[1] = c[1]; + p[2] = c[0]; + p[3] = c[3]; + } + g->cur_x += 4; - if (g->cur_x >= g->max_x) { - g->cur_x = g->start_x; - g->cur_y += g->step; + if (g->cur_x >= g->max_x) { + g->cur_x = g->start_x; + g->cur_y += g->step; - while (g->cur_y >= g->max_y && g->parse > 0) { - g->step = (1 << g->parse) * g->line_size; - g->cur_y = g->start_y + (g->step >> 1); - --g->parse; - } - } + while (g->cur_y >= g->max_y && g->parse > 0) { + g->step = (1 << g->parse) * g->line_size; + g->cur_y = g->start_y + (g->step >> 1); + --g->parse; + } + } } -static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) -{ - stbi_uc lzw_cs; - stbi__int32 len, code; - stbi__uint32 first; - stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; - stbi__gif_lzw *p; - - lzw_cs = stbi__get8(s); - if (lzw_cs > 12) return NULL; - clear = 1 << lzw_cs; - first = 1; - codesize = lzw_cs + 1; - codemask = (1 << codesize) - 1; - bits = 0; - valid_bits = 0; - for (code = 0; code < clear; code++) { - g->codes[code].prefix = -1; - g->codes[code].first = (stbi_uc) code; - g->codes[code].suffix = (stbi_uc) code; - } +static stbi_uc* stbi__process_gif_raster(stbi__context* s, stbi__gif* g) +{ + stbi_uc lzw_cs; + stbi__int32 len, init_code; + stbi__uint32 first; + stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; + stbi__gif_lzw* p; + + lzw_cs = stbi__get8(s); + if (lzw_cs > 12) return NULL; + clear = 1 << lzw_cs; + first = 1; + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + bits = 0; + valid_bits = 0; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc)init_code; + g->codes[init_code].suffix = (stbi_uc)init_code; + } - // support no starting clear code - avail = clear+2; - oldcode = -1; - - len = 0; - for(;;) { - if (valid_bits < codesize) { - if (len == 0) { - len = stbi__get8(s); // start new block - if (len == 0) - return g->out; - } - --len; - bits |= (stbi__int32) stbi__get8(s) << valid_bits; - valid_bits += 8; - } else { - stbi__int32 code = bits & codemask; - bits >>= codesize; - valid_bits -= codesize; - // @OPTIMIZE: is there some way we can accelerate the non-clear path? - if (code == clear) { // clear code - codesize = lzw_cs + 1; - codemask = (1 << codesize) - 1; - avail = clear + 2; - oldcode = -1; - first = 0; - } else if (code == clear + 1) { // end of stream code - stbi__skip(s, len); - while ((len = stbi__get8(s)) > 0) - stbi__skip(s,len); - return g->out; - } else if (code <= avail) { - if (first) return stbi__errpuc("no clear code", "Corrupt GIF"); - - if (oldcode >= 0) { - p = &g->codes[avail++]; - if (avail > 4096) return stbi__errpuc("too many codes", "Corrupt GIF"); - p->prefix = (stbi__int16) oldcode; - p->first = g->codes[oldcode].first; - p->suffix = (code == avail) ? p->first : g->codes[code].first; - } else if (code == avail) - return stbi__errpuc("illegal code in raster", "Corrupt GIF"); - - stbi__out_gif_code(g, (stbi__uint16) code); - - if ((avail & codemask) == 0 && avail <= 0x0FFF) { - codesize++; - codemask = (1 << codesize) - 1; + // support no starting clear code + avail = clear + 2; + oldcode = -1; + + len = 0; + for (;;) { + if (valid_bits < codesize) { + if (len == 0) { + len = stbi__get8(s); // start new block + if (len == 0) + return g->out; } - - oldcode = code; - } else { - return stbi__errpuc("illegal code in raster", "Corrupt GIF"); - } - } - } -} - -static void stbi__fill_gif_background(stbi__gif *g) -{ - int i; - stbi_uc *c = g->pal[g->bgindex]; - // @OPTIMIZE: write a dword at a time - for (i = 0; i < g->w * g->h * 4; i += 4) { - stbi_uc *p = &g->out[i]; - p[0] = c[2]; - p[1] = c[1]; - p[2] = c[0]; - p[3] = c[3]; - } + --len; + bits |= (stbi__int32)stbi__get8(s) << valid_bits; + valid_bits += 8; + } + else { + stbi__int32 code = bits & codemask; + bits >>= codesize; + valid_bits -= codesize; + // @OPTIMIZE: is there some way we can accelerate the non-clear path? + if (code == clear) { // clear code + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + avail = clear + 2; + oldcode = -1; + first = 0; + } + else if (code == clear + 1) { // end of stream code + stbi__skip(s, len); + while ((len = stbi__get8(s)) > 0) + stbi__skip(s, len); + return g->out; + } + else if (code <= avail) { + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } + + if (oldcode >= 0) { + p = &g->codes[avail++]; + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + + p->prefix = (stbi__int16)oldcode; + p->first = g->codes[oldcode].first; + p->suffix = (code == avail) ? p->first : g->codes[code].first; + } + else if (code == avail) + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + + stbi__out_gif_code(g, (stbi__uint16)code); + + if ((avail & codemask) == 0 && avail <= 0x0FFF) { + codesize++; + codemask = (1 << codesize) - 1; + } + + oldcode = code; + } + else { + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + } + } + } } // this function is designed to support animated gifs, although stb_image doesn't support it -static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp) -{ - int i; - stbi_uc *old_out = 0; - - if (g->out == 0) { - if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header - g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); - if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); - stbi__fill_gif_background(g); - } else { - // animated-gif-only path - if (((g->eflags & 0x1C) >> 2) == 3) { - old_out = g->out; - g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); - if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); - memcpy(g->out, old_out, g->w*g->h*4); - } - } +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc* stbi__gif_load_next(stbi__context* s, stbi__gif* g, int* comp, int req_comp, stbi_uc* two_back) +{ + int dispose; + int first_frame; + int pi; + int pcount; + STBI_NOTUSED(req_comp); + + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp, 0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) + return stbi__errpuc("too large", "GIF image is too large"); + pcount = g->w * g->h; + g->out = (stbi_uc*)stbi__malloc(4 * pcount); + g->background = (stbi_uc*)stbi__malloc(4 * pcount); + g->history = (stbi_uc*)stbi__malloc(pcount); + if (!g->out || !g->background || !g->history) + return stbi__errpuc("outofmem", "Out of memory"); + + // image is treated as "transparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to the color that was there the previous frame. + memset(g->out, 0x00, 4 * pcount); + memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) + memset(g->history, 0x00, pcount); // pixels that were affected previous frame + first_frame = 1; + } + else { + // second frame - how do we dispose of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy(&g->out[pi * 4], &two_back[pi * 4], 4); + } + } + } + else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy(&g->out[pi * 4], &g->background[pi * 4], 4); + } + } + } + else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy(g->background, g->out, 4 * g->w * g->h); + } - for (;;) { - switch (stbi__get8(s)) { - case 0x2C: /* Image Descriptor */ - { + // clear my history; + memset(g->history, 0x00, g->w * g->h); // pixels that were affected previous frame + + for (;;) { + int tag = stbi__get8(s); + switch (tag) { + case 0x2C: /* Image Descriptor */ + { stbi__int32 x, y, w, h; - stbi_uc *o; + stbi_uc* o; x = stbi__get16le(s); y = stbi__get16le(s); w = stbi__get16le(s); h = stbi__get16le(s); if (((x + w) > (g->w)) || ((y + h) > (g->h))) - return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); + return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); g->line_size = g->w * 4; g->start_x = x * 4; g->start_y = y * g->line_size; - g->max_x = g->start_x + w * 4; - g->max_y = g->start_y + h * g->line_size; - g->cur_x = g->start_x; - g->cur_y = g->start_y; + g->max_x = g->start_x + w * 4; + g->max_y = g->start_y + h * g->line_size; + g->cur_x = g->start_x; + g->cur_y = g->start_y; + + // if the width of the specified rectangle is 0, that means + // we may not see *any* pixels or the image is malformed; + // to make sure this is caught, move the current y down to + // max_y (which is what out_gif_code checks). + if (w == 0) + g->cur_y = g->max_y; g->lflags = stbi__get8(s); if (g->lflags & 0x40) { - g->step = 8 * g->line_size; // first interlaced spacing - g->parse = 3; - } else { - g->step = g->line_size; - g->parse = 0; + g->step = 8 * g->line_size; // first interlaced spacing + g->parse = 3; + } + else { + g->step = g->line_size; + g->parse = 0; } if (g->lflags & 0x80) { - stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); - g->color_table = (stbi_uc *) g->lpal; - } else if (g->flags & 0x80) { - for (i=0; i < 256; ++i) // @OPTIMIZE: stbi__jpeg_reset only the previous transparent - g->pal[i][3] = 255; - if (g->transparent >= 0 && (g->eflags & 0x01)) - g->pal[g->transparent][3] = 0; - g->color_table = (stbi_uc *) g->pal; - } else - return stbi__errpuc("missing color table", "Corrupt GIF"); + stbi__gif_parse_colortable(s, g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); + g->color_table = (stbi_uc*)g->lpal; + } + else if (g->flags & 0x80) { + g->color_table = (stbi_uc*)g->pal; + } + else + return stbi__errpuc("missing color table", "Corrupt GIF"); o = stbi__process_gif_raster(s, g); - if (o == NULL) return NULL; + if (!o) return NULL; + + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy(&g->out[pi * 4], &g->pal[g->bgindex], 4); + } + } + } - if (req_comp && req_comp != 4) - o = stbi__convert_format(o, 4, req_comp, g->w, g->h); return o; - } + } - case 0x21: // Comment Extension. - { + case 0x21: // Comment Extension. + { int len; - if (stbi__get8(s) == 0xF9) { // Graphic Control Extension. - len = stbi__get8(s); - if (len == 4) { - g->eflags = stbi__get8(s); - stbi__get16le(s); // delay - g->transparent = stbi__get8(s); - } else { - stbi__skip(s, len); - break; - } + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. + len = stbi__get8(s); + if (len == 4) { + g->eflags = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } + else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } + } + else { + stbi__skip(s, len); + break; + } + } + while ((len = stbi__get8(s)) != 0) { + stbi__skip(s, len); } - while ((len = stbi__get8(s)) != 0) - stbi__skip(s, len); break; - } + } - case 0x3B: // gif stream termination code - return (stbi_uc *) s; // using '1' causes warning on some compilers + case 0x3B: // gif stream termination code + return (stbi_uc*)s; // using '1' causes warning on some compilers - default: + default: return stbi__errpuc("unknown code", "Corrupt GIF"); - } - } + } + } +} + +static void* stbi__load_gif_main_outofmem(stbi__gif* g, stbi_uc* out, int** delays) +{ + STBI_FREE(g->out); + STBI_FREE(g->history); + STBI_FREE(g->background); + + if (out) STBI_FREE(out); + if (delays && *delays) STBI_FREE(*delays); + return stbi__errpuc("outofmem", "Out of memory"); +} + +static void* stbi__load_gif_main(stbi__context* s, int** delays, int* x, int* y, int* z, int* comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc* u = 0; + stbi_uc* out = 0; + stbi_uc* two_back = 0; + stbi__gif g; + int stride; + int out_size = 0; + int delays_size = 0; + + STBI_NOTUSED(out_size); + STBI_NOTUSED(delays_size); + + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc*)s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + void* tmp = (stbi_uc*)STBI_REALLOC_SIZED(out, out_size, layers * stride); + if (!tmp) + return stbi__load_gif_main_outofmem(&g, out, delays); + else { + out = (stbi_uc*)tmp; + out_size = layers * stride; + } + + if (delays) { + int* new_delays = (int*)STBI_REALLOC_SIZED(*delays, delays_size, sizeof(int) * layers); + if (!new_delays) + return stbi__load_gif_main_outofmem(&g, out, delays); + *delays = new_delays; + delays_size = layers * sizeof(int); + } + } + else { + out = (stbi_uc*)stbi__malloc(layers * stride); + if (!out) + return stbi__load_gif_main_outofmem(&g, out, delays); + out_size = layers * stride; + if (delays) { + *delays = (int*)stbi__malloc(layers * sizeof(int)); + if (!*delays) + return stbi__load_gif_main_outofmem(&g, out, delays); + delays_size = layers * sizeof(int); + } + } + memcpy(out + ((layers - 1) * stride), u, stride); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } + else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } } -static stbi_uc *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void* stbi__gif_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) { - stbi_uc *u = 0; - stbi__gif g; - memset(&g, 0, sizeof(g)); + stbi_uc* u = 0; + stbi__gif g; + memset(&g, 0, sizeof(g)); + STBI_NOTUSED(ri); - u = stbi__gif_load_next(s, &g, comp, req_comp); - if (u == (stbi_uc *) s) u = 0; // end of animated gif marker - if (u) { - *x = g.w; - *y = g.h; - } + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); + if (u == (stbi_uc*)s) u = 0; // end of animated gif marker + if (u) { + *x = g.w; + *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } + else if (g.out) { + // if there was an error and we allocated an image buffer, free it! + STBI_FREE(g.out); + } - return u; + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + + return u; } -static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__gif_info(stbi__context* s, int* x, int* y, int* comp) { - return stbi__gif_info_raw(s,x,y,comp); + return stbi__gif_info_raw(s, x, y, comp); } #endif @@ -5776,331 +7191,400 @@ static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) // Radiance RGBE HDR loader // originally by Nicolas Schulz #ifndef STBI_NO_HDR -static int stbi__hdr_test_core(stbi__context *s) +static int stbi__hdr_test_core(stbi__context* s, const char* signature) { - const char *signature = "#?RADIANCE\n"; - int i; - for (i=0; signature[i]; ++i) - if (stbi__get8(s) != signature[i]) - return 0; - return 1; + int i; + for (i = 0; signature[i]; ++i) + if (stbi__get8(s) != signature[i]) + return 0; + stbi__rewind(s); + return 1; } static int stbi__hdr_test(stbi__context* s) { - int r = stbi__hdr_test_core(s); - stbi__rewind(s); - return r; + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); + stbi__rewind(s); + if (!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } + return r; } #define STBI__HDR_BUFLEN 1024 -static char *stbi__hdr_gettoken(stbi__context *z, char *buffer) +static char* stbi__hdr_gettoken(stbi__context* z, char* buffer) { - int len=0; - char c = '\0'; + int len = 0; + char c = '\0'; - c = (char) stbi__get8(z); - - while (!stbi__at_eof(z) && c != '\n') { - buffer[len++] = c; - if (len == STBI__HDR_BUFLEN-1) { - // flush to end of line - while (!stbi__at_eof(z) && stbi__get8(z) != '\n') - ; - break; - } - c = (char) stbi__get8(z); - } + c = (char)stbi__get8(z); - buffer[len] = 0; - return buffer; -} + while (!stbi__at_eof(z) && c != '\n') { + buffer[len++] = c; + if (len == STBI__HDR_BUFLEN - 1) { + // flush to end of line + while (!stbi__at_eof(z) && stbi__get8(z) != '\n') + ; + break; + } + c = (char)stbi__get8(z); + } -static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) -{ - if ( input[3] != 0 ) { - float f1; - // Exponent - f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8)); - if (req_comp <= 2) - output[0] = (input[0] + input[1] + input[2]) * f1 / 3; - else { - output[0] = input[0] * f1; - output[1] = input[1] * f1; - output[2] = input[2] * f1; - } - if (req_comp == 2) output[1] = 1; - if (req_comp == 4) output[3] = 1; - } else { - switch (req_comp) { - case 4: output[3] = 1; /* fallthrough */ - case 3: output[0] = output[1] = output[2] = 0; - break; - case 2: output[1] = 1; /* fallthrough */ - case 1: output[0] = 0; - break; - } - } + buffer[len] = 0; + return buffer; +} + +static void stbi__hdr_convert(float* output, stbi_uc* input, int req_comp) +{ + if (input[3] != 0) { + float f1; + // Exponent + f1 = (float)ldexp(1.0f, input[3] - (int)(128 + 8)); + if (req_comp <= 2) + output[0] = (input[0] + input[1] + input[2]) * f1 / 3; + else { + output[0] = input[0] * f1; + output[1] = input[1] * f1; + output[2] = input[2] * f1; + } + if (req_comp == 2) output[1] = 1; + if (req_comp == 4) output[3] = 1; + } + else { + switch (req_comp) { + case 4: output[3] = 1; /* fallthrough */ + case 3: output[0] = output[1] = output[2] = 0; + break; + case 2: output[1] = 1; /* fallthrough */ + case 1: output[0] = 0; + break; + } + } } -static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) -{ - char buffer[STBI__HDR_BUFLEN]; - char *token; - int valid = 0; - int width, height; - stbi_uc *scanline; - float *hdr_data; - int len; - unsigned char count, value; - int i, j, k, c1,c2, z; - - - // Check identifier - if (strcmp(stbi__hdr_gettoken(s,buffer), "#?RADIANCE") != 0) - return stbi__errpf("not HDR", "Corrupt HDR image"); +static float* stbi__hdr_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) +{ + char buffer[STBI__HDR_BUFLEN]; + char* token; + int valid = 0; + int width, height; + stbi_uc* scanline; + float* hdr_data; + int len; + unsigned char count, value; + int i, j, k, c1, c2, z; + const char* headerToken; + STBI_NOTUSED(ri); + + // Check identifier + headerToken = stbi__hdr_gettoken(s, buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) + return stbi__errpf("not HDR", "Corrupt HDR image"); + + // Parse header + for (;;) { + token = stbi__hdr_gettoken(s, buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } - // Parse header - for(;;) { - token = stbi__hdr_gettoken(s,buffer); - if (token[0] == 0) break; - if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; - } + if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); + + // Parse width and height + // can't use sscanf() if we're not using stdio! + token = stbi__hdr_gettoken(s, buffer); + if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + height = (int)strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + width = (int)strtol(token, NULL, 10); + + if (height > STBI_MAX_DIMENSIONS) return stbi__errpf("too large", "Very large image (corrupt?)"); + if (width > STBI_MAX_DIMENSIONS) return stbi__errpf("too large", "Very large image (corrupt?)"); + + *x = width; + *y = height; + + if (comp) *comp = 3; + if (req_comp == 0) req_comp = 3; + + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + + // Read data + hdr_data = (float*)stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); + + // Load image data + // image data is stored as some number of sca + if (width < 8 || width >= 32768) { + // Read flat data + for (j = 0; j < height; ++j) { + for (i = 0; i < width; ++i) { + stbi_uc rgbe[4]; + main_decode_loop: + stbi__getn(s, rgbe, 4); + stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); + } + } + } + else { + // Read RLE-encoded data + scanline = NULL; + + for (j = 0; j < height; ++j) { + c1 = stbi__get8(s); + c2 = stbi__get8(s); + len = stbi__get8(s); + if (c1 != 2 || c2 != 2 || (len & 0x80)) { + // not run-length encoded, so we have to actually use THIS data as a decoded + // pixel (note this can't be a valid pixel--one of RGB must be >= 128) + stbi_uc rgbe[4]; + rgbe[0] = (stbi_uc)c1; + rgbe[1] = (stbi_uc)c2; + rgbe[2] = (stbi_uc)len; + rgbe[3] = (stbi_uc)stbi__get8(s); + stbi__hdr_convert(hdr_data, rgbe, req_comp); + i = 1; + j = 0; + STBI_FREE(scanline); + goto main_decode_loop; // yes, this makes no sense + } + len <<= 8; + len |= stbi__get8(s); + if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } + if (scanline == NULL) { + scanline = (stbi_uc*)stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } - if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); - - // Parse width and height - // can't use sscanf() if we're not using stdio! - token = stbi__hdr_gettoken(s,buffer); - if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); - token += 3; - height = (int) strtol(token, &token, 10); - while (*token == ' ') ++token; - if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); - token += 3; - width = (int) strtol(token, NULL, 10); - - *x = width; - *y = height; - - if (comp) *comp = 3; - if (req_comp == 0) req_comp = 3; - - // Read data - hdr_data = (float *) stbi__malloc(height * width * req_comp * sizeof(float)); - - // Load image data - // image data is stored as some number of sca - if ( width < 8 || width >= 32768) { - // Read flat data - for (j=0; j < height; ++j) { - for (i=0; i < width; ++i) { - stbi_uc rgbe[4]; - main_decode_loop: - stbi__getn(s, rgbe, 4); - stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); - } - } - } else { - // Read RLE-encoded data - scanline = NULL; - - for (j = 0; j < height; ++j) { - c1 = stbi__get8(s); - c2 = stbi__get8(s); - len = stbi__get8(s); - if (c1 != 2 || c2 != 2 || (len & 0x80)) { - // not run-length encoded, so we have to actually use THIS data as a decoded - // pixel (note this can't be a valid pixel--one of RGB must be >= 128) - stbi_uc rgbe[4]; - rgbe[0] = (stbi_uc) c1; - rgbe[1] = (stbi_uc) c2; - rgbe[2] = (stbi_uc) len; - rgbe[3] = (stbi_uc) stbi__get8(s); - stbi__hdr_convert(hdr_data, rgbe, req_comp); - i = 1; - j = 0; - STBI_FREE(scanline); - goto main_decode_loop; // yes, this makes no sense - } - len <<= 8; - len |= stbi__get8(s); - if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } - if (scanline == NULL) scanline = (stbi_uc *) stbi__malloc(width * 4); - - for (k = 0; k < 4; ++k) { - i = 0; - while (i < width) { - count = stbi__get8(s); - if (count > 128) { - // Run - value = stbi__get8(s); - count -= 128; - for (z = 0; z < count; ++z) - scanline[i++ * 4 + k] = value; - } else { - // Dump - for (z = 0; z < count; ++z) - scanline[i++ * 4 + k] = stbi__get8(s); - } + for (k = 0; k < 4; ++k) { + int nleft; + i = 0; + while ((nleft = width - i) > 0) { + count = stbi__get8(s); + if (count > 128) { + // Run + value = stbi__get8(s); + count -= 128; + if ((count == 0) || (count > nleft)) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = value; + } + else { + // Dump + if ((count == 0) || (count > nleft)) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = stbi__get8(s); + } + } } - } - for (i=0; i < width; ++i) - stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); - } - STBI_FREE(scanline); - } + for (i = 0; i < width; ++i) + stbi__hdr_convert(hdr_data + (j * width + i) * req_comp, scanline + i * 4, req_comp); + } + if (scanline) + STBI_FREE(scanline); + } - return hdr_data; + return hdr_data; } -static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__hdr_info(stbi__context* s, int* x, int* y, int* comp) { - char buffer[STBI__HDR_BUFLEN]; - char *token; - int valid = 0; + char buffer[STBI__HDR_BUFLEN]; + char* token; + int valid = 0; + int dummy; - if (strcmp(stbi__hdr_gettoken(s,buffer), "#?RADIANCE") != 0) { - stbi__rewind( s ); - return 0; - } + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - for(;;) { - token = stbi__hdr_gettoken(s,buffer); - if (token[0] == 0) break; - if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; - } + if (stbi__hdr_test(s) == 0) { + stbi__rewind(s); + return 0; + } - if (!valid) { - stbi__rewind( s ); - return 0; - } - token = stbi__hdr_gettoken(s,buffer); - if (strncmp(token, "-Y ", 3)) { - stbi__rewind( s ); - return 0; - } - token += 3; - *y = (int) strtol(token, &token, 10); - while (*token == ' ') ++token; - if (strncmp(token, "+X ", 3)) { - stbi__rewind( s ); - return 0; - } - token += 3; - *x = (int) strtol(token, NULL, 10); - *comp = 3; - return 1; + for (;;) { + token = stbi__hdr_gettoken(s, buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) { + stbi__rewind(s); + return 0; + } + token = stbi__hdr_gettoken(s, buffer); + if (strncmp(token, "-Y ", 3)) { + stbi__rewind(s); + return 0; + } + token += 3; + *y = (int)strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) { + stbi__rewind(s); + return 0; + } + token += 3; + *x = (int)strtol(token, NULL, 10); + *comp = 3; + return 1; } #endif // STBI_NO_HDR #ifndef STBI_NO_BMP -static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__bmp_info(stbi__context* s, int* x, int* y, int* comp) { - int hsz; - if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') { - stbi__rewind( s ); - return 0; - } - stbi__skip(s,12); - hsz = stbi__get32le(s); - if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) { - stbi__rewind( s ); - return 0; - } - if (hsz == 12) { - *x = stbi__get16le(s); - *y = stbi__get16le(s); - } else { - *x = stbi__get32le(s); - *y = stbi__get32le(s); - } - if (stbi__get16le(s) != 1) { - stbi__rewind( s ); - return 0; - } - *comp = stbi__get16le(s) / 8; - return 1; + void* p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + if (p == NULL) { + stbi__rewind(s); + return 0; + } + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) { + if (info.bpp == 24 && info.ma == 0xff000000) + *comp = 3; + else + *comp = info.ma ? 4 : 3; + } + return 1; } #endif #ifndef STBI_NO_PSD -static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__psd_info(stbi__context* s, int* x, int* y, int* comp) { - int channelCount; - if (stbi__get32be(s) != 0x38425053) { - stbi__rewind( s ); - return 0; - } - if (stbi__get16be(s) != 1) { - stbi__rewind( s ); - return 0; - } - stbi__skip(s, 6); - channelCount = stbi__get16be(s); - if (channelCount < 0 || channelCount > 16) { - stbi__rewind( s ); - return 0; - } - *y = stbi__get32be(s); - *x = stbi__get32be(s); - if (stbi__get16be(s) != 8) { - stbi__rewind( s ); - return 0; - } - if (stbi__get16be(s) != 3) { - stbi__rewind( s ); - return 0; - } - *comp = 4; - return 1; + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind(s); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind(s); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind(s); + return 0; + } + *y = stbi__get32be(s); + *x = stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { + stbi__rewind(s); + return 0; + } + if (stbi__get16be(s) != 3) { + stbi__rewind(s); + return 0; + } + *comp = 4; + return 1; +} + +static int stbi__psd_is16(stbi__context* s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind(s); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind(s); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind(s); + return 0; + } + STBI_NOTUSED(stbi__get32be(s)); + STBI_NOTUSED(stbi__get32be(s)); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind(s); + return 0; + } + return 1; } #endif #ifndef STBI_NO_PIC -static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__pic_info(stbi__context* s, int* x, int* y, int* comp) { - int act_comp=0,num_packets=0,chained; - stbi__pic_packet packets[10]; + int act_comp = 0, num_packets = 0, chained, dummy; + stbi__pic_packet packets[10]; - stbi__skip(s, 92); + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - *x = stbi__get16be(s); - *y = stbi__get16be(s); - if (stbi__at_eof(s)) return 0; - if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { - stbi__rewind( s ); - return 0; - } + if (!stbi__pic_is4(s, "\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); + + *x = stbi__get16be(s); + *y = stbi__get16be(s); + if (stbi__at_eof(s)) { + stbi__rewind(s); + return 0; + } + if ((*x) != 0 && (1 << 28) / (*x) < (*y)) { + stbi__rewind(s); + return 0; + } - stbi__skip(s, 8); + stbi__skip(s, 8); - do { - stbi__pic_packet *packet; + do { + stbi__pic_packet* packet; - if (num_packets==sizeof(packets)/sizeof(packets[0])) - return 0; + if (num_packets == sizeof(packets) / sizeof(packets[0])) + return 0; - packet = &packets[num_packets++]; - chained = stbi__get8(s); - packet->size = stbi__get8(s); - packet->type = stbi__get8(s); - packet->channel = stbi__get8(s); - act_comp |= packet->channel; + packet = &packets[num_packets++]; + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + act_comp |= packet->channel; - if (stbi__at_eof(s)) { - stbi__rewind( s ); - return 0; - } - if (packet->size != 8) { - stbi__rewind( s ); - return 0; - } - } while (chained); + if (stbi__at_eof(s)) { + stbi__rewind(s); + return 0; + } + if (packet->size != 8) { + stbi__rewind(s); + return 0; + } + } while (chained); - *comp = (act_comp & 0x10 ? 4 : 3); + *comp = (act_comp & 0x10 ? 4 : 3); - return 1; + return 1; } #endif @@ -6114,151 +7598,210 @@ static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) // Known limitations: // Does not support comments in the header section // Does not support ASCII image data (formats P2 and P3) -// Does not support 16-bit-per-channel #ifndef STBI_NO_PNM -static int stbi__pnm_test(stbi__context *s) +static int stbi__pnm_test(stbi__context* s) { - char p, t; - p = (char) stbi__get8(s); - t = (char) stbi__get8(s); - if (p != 'P' || (t != '5' && t != '6')) { - stbi__rewind( s ); - return 0; - } - return 1; + char p, t; + p = (char)stbi__get8(s); + t = (char)stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } + return 1; } -static stbi_uc *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp) +static void* stbi__pnm_load(stbi__context* s, int* x, int* y, int* comp, int req_comp, stbi__result_info* ri) { - stbi_uc *out; - if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) - return 0; - *x = s->img_x; - *y = s->img_y; - *comp = s->img_n; + stbi_uc* out; + STBI_NOTUSED(ri); + + ri->bits_per_channel = stbi__pnm_info(s, (int*)&s->img_x, (int*)&s->img_y, (int*)&s->img_n); + if (ri->bits_per_channel == 0) + return 0; - out = (stbi_uc *) stbi__malloc(s->img_n * s->img_x * s->img_y); - if (!out) return stbi__errpuc("outofmem", "Out of memory"); - stbi__getn(s, out, s->img_n * s->img_x * s->img_y); + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (req_comp && req_comp != s->img_n) { - out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); - if (out == NULL) return out; // stbi__convert_format frees input on failure - } - return out; + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + + if (!stbi__mad4sizes_valid(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0)) + return stbi__errpuc("too large", "PNM too large"); + + out = (stbi_uc*)stbi__malloc_mad4(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (!stbi__getn(s, out, s->img_n * s->img_x * s->img_y * (ri->bits_per_channel / 8))) { + STBI_FREE(out); + return stbi__errpuc("bad PNM", "PNM file truncated"); + } + + if (req_comp && req_comp != s->img_n) { + if (ri->bits_per_channel == 16) { + out = (stbi_uc*)stbi__convert_format16((stbi__uint16*)out, s->img_n, req_comp, s->img_x, s->img_y); + } + else { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + } + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + return out; } static int stbi__pnm_isspace(char c) { - return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; + return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; } -static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) +static void stbi__pnm_skip_whitespace(stbi__context* s, char* c) { - while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) - *c = (char) stbi__get8(s); + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char)stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r') + *c = (char)stbi__get8(s); + } } static int stbi__pnm_isdigit(char c) { - return c >= '0' && c <= '9'; + return c >= '0' && c <= '9'; } -static int stbi__pnm_getinteger(stbi__context *s, char *c) +static int stbi__pnm_getinteger(stbi__context* s, char* c) { - int value = 0; + int value = 0; - while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { - value = value*10 + (*c - '0'); - *c = (char) stbi__get8(s); - } + while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { + value = value * 10 + (*c - '0'); + *c = (char)stbi__get8(s); + if ((value > 214748364) || (value == 214748364 && *c > '7')) + return stbi__err("integer parse overflow", "Parsing an integer in the PPM header overflowed a 32-bit int"); + } - return value; + return value; } -static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) +static int stbi__pnm_info(stbi__context* s, int* x, int* y, int* comp) { - int maxv; - char c, p, t; + int maxv, dummy; + char c, p, t; - stbi__rewind( s ); + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - // Get identifier - p = (char) stbi__get8(s); - t = (char) stbi__get8(s); - if (p != 'P' || (t != '5' && t != '6')) { - stbi__rewind( s ); - return 0; - } + stbi__rewind(s); + + // Get identifier + p = (char)stbi__get8(s); + t = (char)stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } - *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm + *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm - c = (char) stbi__get8(s); - stbi__pnm_skip_whitespace(s, &c); + c = (char)stbi__get8(s); + stbi__pnm_skip_whitespace(s, &c); - *x = stbi__pnm_getinteger(s, &c); // read width - stbi__pnm_skip_whitespace(s, &c); + *x = stbi__pnm_getinteger(s, &c); // read width + if (*x == 0) + return stbi__err("invalid width", "PPM image header had zero or overflowing width"); + stbi__pnm_skip_whitespace(s, &c); - *y = stbi__pnm_getinteger(s, &c); // read height - stbi__pnm_skip_whitespace(s, &c); + *y = stbi__pnm_getinteger(s, &c); // read height + if (*y == 0) + return stbi__err("invalid width", "PPM image header had zero or overflowing width"); + stbi__pnm_skip_whitespace(s, &c); - maxv = stbi__pnm_getinteger(s, &c); // read max value + maxv = stbi__pnm_getinteger(s, &c); // read max value + if (maxv > 65535) + return stbi__err("max value > 65535", "PPM image supports only 8-bit and 16-bit images"); + else if (maxv > 255) + return 16; + else + return 8; +} - if (maxv > 255) - return stbi__err("max value > 255", "PPM image not 8-bit"); - else - return 1; +static int stbi__pnm_is16(stbi__context* s) +{ + if (stbi__pnm_info(s, NULL, NULL, NULL) == 16) + return 1; + return 0; } #endif -static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) +static int stbi__info_main(stbi__context* s, int* x, int* y, int* comp) { - #ifndef STBI_NO_JPEG - if (stbi__jpeg_info(s, x, y, comp)) return 1; - #endif +#ifndef STBI_NO_JPEG + if (stbi__jpeg_info(s, x, y, comp)) return 1; +#endif + +#ifndef STBI_NO_PNG + if (stbi__png_info(s, x, y, comp)) return 1; +#endif + +#ifndef STBI_NO_GIF + if (stbi__gif_info(s, x, y, comp)) return 1; +#endif - #ifndef STBI_NO_PNG - if (stbi__png_info(s, x, y, comp)) return 1; - #endif +#ifndef STBI_NO_BMP + if (stbi__bmp_info(s, x, y, comp)) return 1; +#endif + +#ifndef STBI_NO_PSD + if (stbi__psd_info(s, x, y, comp)) return 1; +#endif - #ifndef STBI_NO_GIF - if (stbi__gif_info(s, x, y, comp)) return 1; - #endif +#ifndef STBI_NO_PIC + if (stbi__pic_info(s, x, y, comp)) return 1; +#endif - #ifndef STBI_NO_BMP - if (stbi__bmp_info(s, x, y, comp)) return 1; - #endif +#ifndef STBI_NO_PNM + if (stbi__pnm_info(s, x, y, comp)) return 1; +#endif - #ifndef STBI_NO_PSD - if (stbi__psd_info(s, x, y, comp)) return 1; - #endif +#ifndef STBI_NO_HDR + if (stbi__hdr_info(s, x, y, comp)) return 1; +#endif - #ifndef STBI_NO_PIC - if (stbi__pic_info(s, x, y, comp)) return 1; - #endif + // test tga last because it's a crappy test! +#ifndef STBI_NO_TGA + if (stbi__tga_info(s, x, y, comp)) + return 1; +#endif + return stbi__err("unknown image type", "Image not of any known type, or corrupt"); +} - #ifndef STBI_NO_PNM - if (stbi__pnm_info(s, x, y, comp)) return 1; - #endif +static int stbi__is_16_main(stbi__context* s) +{ +#ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; +#endif - #ifndef STBI_NO_HDR - if (stbi__hdr_info(s, x, y, comp)) return 1; - #endif +#ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; +#endif - // test tga last because it's a crappy test! - #ifndef STBI_NO_TGA - if (stbi__tga_info(s, x, y, comp)) - return 1; - #endif - return stbi__err("unknown image type", "Image not of any known type, or corrupt"); +#ifndef STBI_NO_PNM + if (stbi__pnm_is16(s)) return 1; +#endif + return 0; } #ifndef STBI_NO_STDIO -STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) +STBIDEF int stbi_info(char const* filename, int* x, int* y, int* comp) { - FILE *f = stbi__fopen(filename, "rb"); + FILE* f = stbi__fopen(filename, "rb"); int result; if (!f) return stbi__err("can't fopen", "Unable to open file"); result = stbi_info_from_file(f, x, y, comp); @@ -6266,36 +7809,111 @@ STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) return result; } -STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) +STBIDEF int stbi_info_from_file(FILE* f, int* x, int* y, int* comp) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__info_main(&s, x, y, comp); + fseek(f, pos, SEEK_SET); + return r; +} + +STBIDEF int stbi_is_16_bit(char const* filename) +{ + FILE* f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE* f) { - int r; - stbi__context s; - long pos = ftell(f); - stbi__start_file(&s, f); - r = stbi__info_main(&s,x,y,comp); - fseek(f,pos,SEEK_SET); - return r; + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f, pos, SEEK_SET); + return r; } #endif // !STBI_NO_STDIO -STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) +STBIDEF int stbi_info_from_memory(stbi_uc const* buffer, int len, int* x, int* y, int* comp) +{ + stbi__context s; + stbi__start_mem(&s, buffer, len); + return stbi__info_main(&s, x, y, comp); +} + +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const* c, void* user, int* x, int* y, int* comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks*)c, user); + return stbi__info_main(&s, x, y, comp); +} + +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const* buffer, int len) { - stbi__context s; - stbi__start_mem(&s,buffer,len); - return stbi__info_main(&s,x,y,comp); + stbi__context s; + stbi__start_mem(&s, buffer, len); + return stbi__is_16_main(&s); } -STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp) +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const* c, void* user) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); - return stbi__info_main(&s,x,y,comp); + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks*)c, user); + return stbi__is_16_main(&s); } #endif // STB_IMAGE_IMPLEMENTATION /* revision history: + 2.20 (2019-02-07) support utf8 filenames in Windows; fix warnings and platform ifdefs + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit @@ -6436,3 +8054,46 @@ STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int 0.50 (2006-11-19) first released version */ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ \ No newline at end of file diff --git a/external/include/stb_image_write.h b/external/include/stb_image_write.h index 59052f9c..52536579 100644 --- a/external/include/stb_image_write.h +++ b/external/include/stb_image_write.h @@ -1,5 +1,5 @@ -/* stb_image_write - v0.98 - public domain - http://nothings.org/stb/stb_image_write.h - writes out PNG/BMP/TGA images to C stdio - Sean Barrett 2010 +/* stb_image_write - v1.16 - public domain - http://nothings.org/stb + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 no warranty implied; use at your own risk Before #including, @@ -12,29 +12,67 @@ ABOUT: - This header file is a library for writing images to C stdio. It could be - adapted to write to memory or a general streaming interface; let me know. + This header file is a library for writing images to C stdio or a callback. The PNG output is not optimal; it is 20-50% larger than the file - written by a decent optimizing implementation. This library is designed - for source code compactness and simplicitly, not optimal image file size - or run-time performance. + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. BUILDING: You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace malloc,realloc,free. - You can define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), + +UNICODE: + + If compiling for Windows and you wish to use Unicode filenames, compile + with + #define STBIW_WINDOWS_UTF8 + and pass utf8-encoded filenames. Call stbiw_convert_wchar_to_utf8 to convert + Windows wchar_t filenames to utf8. USAGE: - There are four functions, one for each image file format: + There are five functions, one for each image file format: int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); - int stbi_write_hdr(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. Each function returns 0 on failure and non-0 on success. @@ -58,69 +96,145 @@ writer, both because it is in BGR order and because it may have padding at the end of the line.) + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + HDR expects linear float data. Since the format is always 32-bit rgb(e) data, alpha (if provided) is discarded, and for monochrome data it is replicated across all three channels. + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + CREDITS: - PNG/BMP/TGA - Sean Barrett - HDR - Baldur Karlsson - TGA monochrome: - Jean-Sebastien Guay - misc enhancements: - Tim Kelsey + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + bugfixes: github:Chribba - + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + Andrew Kensler + LICENSE -This software is in the public domain. Where that dedication is not -recognized, you are granted a perpetual, irrevocable license to copy, -distribute, and modify this file as you see fit. + See end of file for license information. + */ #ifndef INCLUDE_STB_IMAGE_WRITE_H #define INCLUDE_STB_IMAGE_WRITE_H +#include + +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else #ifdef __cplusplus -extern "C" { +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif #endif -extern int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); -extern int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); -extern int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); -extern int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +STBIWDEF int stbi_write_tga_with_rle; +STBIWDEF int stbi_write_png_compression_level; +STBIWDEF int stbi_write_force_png_filter; +#endif -#ifdef __cplusplus -} +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const* filename, int w, int h, int comp, const void* data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const* filename, int w, int h, int comp, const void* data); +STBIWDEF int stbi_write_tga(char const* filename, int w, int h, int comp, const void* data); +STBIWDEF int stbi_write_hdr(char const* filename, int w, int h, int comp, const float* data); +STBIWDEF int stbi_write_jpg(char const* filename, int x, int y, int comp, const void* data, int quality); + +#ifdef STBIW_WINDOWS_UTF8 +STBIWDEF int stbiw_convert_wchar_to_utf8(char* buffer, size_t bufferlen, const wchar_t* input); +#endif #endif +typedef void stbi_write_func(void* context, void* data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func* func, void* context, int w, int h, int comp, const void* data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func* func, void* context, int w, int h, int comp, const void* data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func* func, void* context, int w, int h, int comp, const void* data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func* func, void* context, int w, int h, int comp, const float* data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func* func, void* context, int x, int y, int comp, const void* data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + #endif//INCLUDE_STB_IMAGE_WRITE_H #ifdef STB_IMAGE_WRITE_IMPLEMENTATION +#ifdef _WIN32 +#ifndef _CRT_SECURE_NO_WARNINGS +#define _CRT_SECURE_NO_WARNINGS +#endif +#ifndef _CRT_NONSTDC_NO_DEPRECATE +#define _CRT_NONSTDC_NO_DEPRECATE +#endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + #include #include -#include #include #include -#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && defined(STBIW_REALLOC) +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) // ok -#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) // ok #else -#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC." +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." #endif #ifndef STBIW_MALLOC -#define STBIW_MALLOC(sz) malloc(sz) -#define STBIW_REALLOC(p,sz) realloc(p,sz) -#define STBIW_FREE(p) free(p) +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) #endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) +#endif + + #ifndef STBIW_MEMMOVE #define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) #endif @@ -131,268 +245,587 @@ extern int stbi_write_hdr(char const *filename, int w, int h, int comp, const fl #define STBIW_ASSERT(x) assert(x) #endif +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +static int stbi__flip_vertically_on_write = 0; + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func* func; + void* context; + unsigned char buffer[64]; + int buf_used; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context* s, stbi_write_func* c, void* context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void* context, void* data, int size) +{ + fwrite(data, 1, size, (FILE*)context); +} + +#if defined(_WIN32) && defined(STBIW_WINDOWS_UTF8) +#ifdef __cplusplus +#define STBIW_EXTERN extern "C" +#else +#define STBIW_EXTERN extern +#endif +STBIW_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char* str, int cbmb, wchar_t* widestr, int cchwide); +STBIW_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t* widestr, int cchwide, char* str, int cbmb, const char* defchar, int* used_default); + +STBIWDEF int stbiw_convert_wchar_to_utf8(char* buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int)bufferlen, NULL, NULL); +} +#endif + +static FILE* stbiw__fopen(char const* filename, char const* mode) +{ + FILE* f; +#if defined(_WIN32) && defined(STBIW_WINDOWS_UTF8) + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename) / sizeof(*wFilename))) + return 0; + + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode) / sizeof(*wMode))) + return 0; + +#if defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; +#else + f = _wfopen(wFilename, wMode); +#endif + +#elif defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f = 0; +#else + f = fopen(filename, mode); +#endif + return f; +} + +static int stbi__start_write_file(stbi__write_context* s, const char* filename) +{ + FILE* f = stbiw__fopen(filename, "wb"); + stbi__start_write_callbacks(s, stbi__stdio_write, (void*)f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context* s) +{ + fclose((FILE*)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + typedef unsigned int stbiw_uint32; -typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; - -static void writefv(FILE *f, const char *fmt, va_list v) -{ - while (*fmt) { - switch (*fmt++) { - case ' ': break; - case '1': { unsigned char x = (unsigned char) va_arg(v, int); fputc(x,f); break; } - case '2': { int x = va_arg(v,int); unsigned char b[2]; - b[0] = (unsigned char) x; b[1] = (unsigned char) (x>>8); - fwrite(b,2,1,f); break; } - case '4': { stbiw_uint32 x = va_arg(v,int); unsigned char b[4]; - b[0]=(unsigned char)x; b[1]=(unsigned char)(x>>8); - b[2]=(unsigned char)(x>>16); b[3]=(unsigned char)(x>>24); - fwrite(b,4,1,f); break; } - default: +typedef int stb_image_write_test[sizeof(stbiw_uint32) == 4 ? 1 : -1]; + +static void stbiw__writefv(stbi__write_context* s, const char* fmt, va_list v) +{ + while (*fmt) { + switch (*fmt++) { + case ' ': break; + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context, &x, 1); + break; } + case '2': { int x = va_arg(v, int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x >> 8); + s->func(s->context, b, 2); + break; } + case '4': { stbiw_uint32 x = va_arg(v, int); + unsigned char b[4]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x >> 8); + b[2] = STBIW_UCHAR(x >> 16); + b[3] = STBIW_UCHAR(x >> 24); + s->func(s->context, b, 4); + break; } + default: STBIW_ASSERT(0); return; - } - } -} - -static void write3(FILE *f, unsigned char a, unsigned char b, unsigned char c) -{ - unsigned char arr[3]; - arr[0] = a, arr[1] = b, arr[2] = c; - fwrite(arr, 3, 1, f); -} - -static void write_pixels(FILE *f, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) -{ - unsigned char bg[3] = { 255, 0, 255}, px[3]; - stbiw_uint32 zero = 0; - int i,j,k, j_end; - - if (y <= 0) - return; - - if (vdir < 0) - j_end = -1, j = y-1; - else - j_end = y, j = 0; - - for (; j != j_end; j += vdir) { - for (i=0; i < x; ++i) { - unsigned char *d = (unsigned char *) data + (j*x+i)*comp; - if (write_alpha < 0) - fwrite(&d[comp-1], 1, 1, f); - switch (comp) { - case 1: fwrite(d, 1, 1, f); - break; - case 2: if (expand_mono) - write3(f, d[0],d[0],d[0]); // monochrome bmp - else - fwrite(d, 1, 1, f); // monochrome TGA - break; - case 4: - if (!write_alpha) { - // composite against pink background - for (k=0; k < 3; ++k) - px[k] = bg[k] + ((d[k] - bg[k]) * d[3])/255; - write3(f, px[1-rgb_dir],px[1],px[1+rgb_dir]); - break; - } - /* FALLTHROUGH */ - case 3: - write3(f, d[1-rgb_dir],d[1],d[1+rgb_dir]); - break; - } - if (write_alpha > 0) - fwrite(&d[comp-1], 1, 1, f); - } - fwrite(&zero,scanline_pad,1,f); - } -} - -static int outfile(char const *filename, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) -{ - FILE *f; - if (y < 0 || x < 0) return 0; - f = fopen(filename, "wb"); - if (f) { - va_list v; - va_start(v, fmt); - writefv(f, fmt, v); - va_end(v); - write_pixels(f,rgb_dir,vdir,x,y,comp,data,alpha,pad,expand_mono); - fclose(f); - } - return f != NULL; -} - -int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) -{ - int pad = (-x*3) & 3; - return outfile(filename,-1,-1,x,y,comp,1,(void *) data,0,pad, - "11 4 22 4" "4 44 22 444444", - 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header - 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header -} - -int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) -{ - int has_alpha = (comp == 2 || comp == 4); - int colorbytes = has_alpha ? comp-1 : comp; - int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 - return outfile(filename, -1,-1, x, y, comp, 0, (void *) data, has_alpha, 0, - "111 221 2222 11", 0,0,format, 0,0,0, 0,0,x,y, (colorbytes+has_alpha)*8, has_alpha*8); + } + } } -// ************************************************************************************************* -// Radiance RGBE HDR writer -// by Baldur Karlsson -#define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) +static void stbiw__writef(stbi__write_context* s, const char* fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} -void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) +static void stbiw__write_flush(stbi__write_context* s) { - int exponent; - float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); + if (s->buf_used) { + s->func(s->context, &s->buffer, s->buf_used); + s->buf_used = 0; + } +} - if (maxcomp < 1e-32) { - rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; - } else { - float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; +static void stbiw__putc(stbi__write_context* s, unsigned char c) +{ + s->func(s->context, &c, 1); +} - rgbe[0] = (unsigned char)(linear[0] * normalize); - rgbe[1] = (unsigned char)(linear[1] * normalize); - rgbe[2] = (unsigned char)(linear[2] * normalize); - rgbe[3] = (unsigned char)(exponent + 128); - } +static void stbiw__write1(stbi__write_context* s, unsigned char a) +{ + if ((size_t)s->buf_used + 1 > sizeof(s->buffer)) + stbiw__write_flush(s); + s->buffer[s->buf_used++] = a; } -void stbiw__write_run_data(FILE *f, int length, unsigned char databyte) +static void stbiw__write3(stbi__write_context* s, unsigned char a, unsigned char b, unsigned char c) { - unsigned char lengthbyte = (unsigned char) (length+128); - STBIW_ASSERT(length+128 <= 255); - fwrite(&lengthbyte, 1, 1, f); - fwrite(&databyte, 1, 1, f); + int n; + if ((size_t)s->buf_used + 3 > sizeof(s->buffer)) + stbiw__write_flush(s); + n = s->buf_used; + s->buf_used = n + 3; + s->buffer[n + 0] = a; + s->buffer[n + 1] = b; + s->buffer[n + 2] = c; } -void stbiw__write_dump_data(FILE *f, int length, unsigned char *data) +static void stbiw__write_pixel(stbi__write_context* s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char* d) { - unsigned char lengthbyte = (unsigned char )(length & 0xff); - STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code - fwrite(&lengthbyte, 1, 1, f); - fwrite(data, length, 1, f); + unsigned char bg[3] = { 255, 0, 255 }, px[3]; + int k; + + if (write_alpha < 0) + stbiw__write1(s, d[comp - 1]); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + stbiw__write1(s, d[0]); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + stbiw__write1(s, d[comp - 1]); } -void stbiw__write_hdr_scanline(FILE *f, int width, int comp, unsigned char *scratch, const float *scanline) +static void stbiw__write_pixels(stbi__write_context* s, int rgb_dir, int vdir, int x, int y, int comp, void* data, int write_alpha, int scanline_pad, int expand_mono) { - unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; - unsigned char rgbe[4]; - float linear[3]; - int x; + stbiw_uint32 zero = 0; + int i, j, j_end; + + if (y <= 0) + return; + + if (stbi__flip_vertically_on_write) + vdir *= -1; + + if (vdir < 0) { + j_end = -1; j = y - 1; + } + else { + j_end = y; j = 0; + } + + for (; j != j_end; j += vdir) { + for (i = 0; i < x; ++i) { + unsigned char* d = (unsigned char*)data + (j * x + i) * comp; + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); + } + stbiw__write_flush(s); + s->func(s->context, &zero, scanline_pad); + } +} - scanlineheader[2] = (width&0xff00)>>8; - scanlineheader[3] = (width&0x00ff); +static int stbiw__outfile(stbi__write_context* s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void* data, int alpha, int pad, const char* fmt, ...) +{ + if (y < 0 || x < 0) { + return 0; + } + else { + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); + stbiw__write_pixels(s, rgb_dir, vdir, x, y, comp, data, alpha, pad, expand_mono); + return 1; + } +} - /* skip RLE for images too small or large */ - if (width < 8 || width >= 32768) { - for (x=0; x < width; x++) { - switch (comp) { - case 4: /* fallthrough */ - case 3: linear[2] = scanline[x*comp + 2]; - linear[1] = scanline[x*comp + 1]; - linear[0] = scanline[x*comp + 0]; - break; - case 2: /* fallthrough */ - case 1: linear[0] = linear[1] = linear[2] = scanline[x*comp + 0]; - break; - } - stbiw__linear_to_rgbe(rgbe, linear); - fwrite(rgbe, 4, 1, f); - } - } else { - int c,r; - /* encode into scratch buffer */ - for (x=0; x < width; x++) { - switch(comp) { +static int stbi_write_bmp_core(stbi__write_context* s, int x, int y, int comp, const void* data) +{ + if (comp != 4) { + // write RGB bitmap + int pad = (-x * 3) & 3; + return stbiw__outfile(s, -1, -1, x, y, comp, 1, (void*)data, 0, pad, + "11 4 22 4" "4 44 22 444444", + 'B', 'M', 14 + 40 + (x * 3 + pad) * y, 0, 0, 14 + 40, // file header + 40, x, y, 1, 24, 0, 0, 0, 0, 0, 0); // bitmap header + } + else { + // RGBA bitmaps need a v4 header + // use BI_BITFIELDS mode with 32bpp and alpha mask + // (straight BI_RGB with alpha mask doesn't work in most readers) + return stbiw__outfile(s, -1, -1, x, y, comp, 1, (void*)data, 1, 0, + "11 4 22 4" "4 44 22 444444 4444 4 444 444 444 444", + 'B', 'M', 14 + 108 + x * y * 4, 0, 0, 14 + 108, // file header + 108, x, y, 1, 32, 3, 0, 0, 0, 0, 0, 0xff0000, 0xff00, 0xff, 0xff000000u, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0); // bitmap V4 header + } +} + +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func* func, void* context, int x, int y, int comp, const void* data) +{ + stbi__write_context s = { 0 }; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const* filename, int x, int y, int comp, const void* data) +{ + stbi__write_context s = { 0 }; + if (stbi__start_write_file(&s, filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } + else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context* s, int x, int y, int comp, void* data) +{ + int has_alpha = (comp == 2 || comp == 4); + int colorbytes = has_alpha ? comp - 1 : comp; + int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void*)data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } + else { + int i, j, k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0, 0, format + 8, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } + else { + j = y - 1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char* row = (unsigned char*)data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char* begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char* prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } + else { + --len; + break; + } + } + } + else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } + else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + stbiw__write1(s, header); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } + else { + unsigned char header = STBIW_UCHAR(len - 129); + stbiw__write1(s, header); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + stbiw__write_flush(s); + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func* func, void* context, int x, int y, int comp, const void* data) +{ + stbi__write_context s = { 0 }; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void*)data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const* filename, int x, int y, int comp, const void* data) +{ + stbi__write_context s = { 0 }; + if (stbi__start_write_file(&s, filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void*)data); + stbi__end_write_file(&s); + return r; + } + else + return 0; +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR writer +// by Baldur Karlsson + +#define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) + +#ifndef STBI_WRITE_NO_STDIO + +static void stbiw__linear_to_rgbe(unsigned char* rgbe, float* linear) +{ + int exponent; + float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); + + if (maxcomp < 1e-32f) { + rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; + } + else { + float normalize = (float)frexp(maxcomp, &exponent) * 256.0f / maxcomp; + + rgbe[0] = (unsigned char)(linear[0] * normalize); + rgbe[1] = (unsigned char)(linear[1] * normalize); + rgbe[2] = (unsigned char)(linear[2] * normalize); + rgbe[3] = (unsigned char)(exponent + 128); + } +} + +static void stbiw__write_run_data(stbi__write_context* s, int length, unsigned char databyte) +{ + unsigned char lengthbyte = STBIW_UCHAR(length + 128); + STBIW_ASSERT(length + 128 <= 255); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); +} + +static void stbiw__write_dump_data(stbi__write_context* s, int length, unsigned char* data) +{ + unsigned char lengthbyte = STBIW_UCHAR(length); + STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); +} + +static void stbiw__write_hdr_scanline(stbi__write_context* s, int width, int ncomp, unsigned char* scratch, float* scanline) +{ + unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; + unsigned char rgbe[4]; + float linear[3]; + int x; + + scanlineheader[2] = (width & 0xff00) >> 8; + scanlineheader[3] = (width & 0x00ff); + + /* skip RLE for images too small or large */ + if (width < 8 || width >= 32768) { + for (x = 0; x < width; x++) { + switch (ncomp) { case 4: /* fallthrough */ - case 3: linear[2] = scanline[x*comp + 2]; - linear[1] = scanline[x*comp + 1]; - linear[0] = scanline[x*comp + 0]; - break; - case 2: /* fallthrough */ - case 1: linear[0] = linear[1] = linear[2] = scanline[x*comp + 0]; - break; - } - stbiw__linear_to_rgbe(rgbe, linear); - scratch[x + width*0] = rgbe[0]; - scratch[x + width*1] = rgbe[1]; - scratch[x + width*2] = rgbe[2]; - scratch[x + width*3] = rgbe[3]; - } - - fwrite(scanlineheader, 4, 1, f); - - /* RLE each component separately */ - for (c=0; c < 4; c++) { - unsigned char *comp = &scratch[width*c]; - - x = 0; - while (x < width) { - // find first run - r = x; - while (r+2 < width) { - if (comp[r] == comp[r+1] && comp[r] == comp[r+2]) - break; - ++r; + case 3: linear[2] = scanline[x * ncomp + 2]; + linear[1] = scanline[x * ncomp + 1]; + linear[0] = scanline[x * ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x * ncomp + 0]; + break; } - if (r+2 >= width) - r = width; - // dump up to first run - while (x < r) { - int len = r-x; - if (len > 128) len = 128; - stbiw__write_dump_data(f, len, &comp[x]); - x += len; + stbiw__linear_to_rgbe(rgbe, linear); + s->func(s->context, rgbe, 4); + } + } + else { + int c, r; + /* encode into scratch buffer */ + for (x = 0; x < width; x++) { + switch (ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x * ncomp + 2]; + linear[1] = scanline[x * ncomp + 1]; + linear[0] = scanline[x * ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x * ncomp + 0]; + break; } - // if there's a run, output it - if (r+2 < width) { // same test as what we break out of in search loop, so only true if we break'd - // find next byte after run - while (r < width && comp[r] == comp[x]) - ++r; - // output run up to r - while (x < r) { - int len = r-x; - if (len > 127) len = 127; - stbiw__write_run_data(f, len, comp[x]); - x += len; - } + stbiw__linear_to_rgbe(rgbe, linear); + scratch[x + width * 0] = rgbe[0]; + scratch[x + width * 1] = rgbe[1]; + scratch[x + width * 2] = rgbe[2]; + scratch[x + width * 3] = rgbe[3]; + } + + s->func(s->context, scanlineheader, 4); + + /* RLE each component separately */ + for (c = 0; c < 4; c++) { + unsigned char* comp = &scratch[width * c]; + + x = 0; + while (x < width) { + // find first run + r = x; + while (r + 2 < width) { + if (comp[r] == comp[r + 1] && comp[r] == comp[r + 2]) + break; + ++r; + } + if (r + 2 >= width) + r = width; + // dump up to first run + while (x < r) { + int len = r - x; + if (len > 128) len = 128; + stbiw__write_dump_data(s, len, &comp[x]); + x += len; + } + // if there's a run, output it + if (r + 2 < width) { // same test as what we break out of in search loop, so only true if we break'd + // find next byte after run + while (r < width && comp[r] == comp[x]) + ++r; + // output run up to r + while (x < r) { + int len = r - x; + if (len > 127) len = 127; + stbiw__write_run_data(s, len, comp[x]); + x += len; + } + } } - } - } - } + } + } } -int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +static int stbi_write_hdr_core(stbi__write_context* s, int x, int y, int comp, float* data) { - int i; - FILE *f; - if (y <= 0 || x <= 0 || data == NULL) return 0; - f = fopen(filename, "wb"); - if (f) { - /* Each component is stored separately. Allocate scratch space for full output scanline. */ - unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); - fprintf(f, "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n" ); - fprintf(f, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n" , y, x); - for(i=0; i < y; i++) - stbiw__write_hdr_scanline(f, x, comp, scratch, data + comp*i*x); - STBIW_FREE(scratch); - fclose(f); - } - return f != NULL; + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. + unsigned char* scratch = (unsigned char*)STBIW_MALLOC(x * 4); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header) - 1); + +#ifdef __STDC_LIB_EXT1__ + len = sprintf_s(buffer, sizeof(buffer), "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + + for (i = 0; i < y; i++) + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp * x * (stbi__flip_vertically_on_write ? y - 1 - i : i)); + STBIW_FREE(scratch); + return 1; + } +} + +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func* func, void* context, int x, int y, int comp, const float* data) +{ + stbi__write_context s = { 0 }; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float*)data); } -///////////////////////////////////////////////////////// -// PNG +STBIWDEF int stbi_write_hdr(char const* filename, int x, int y, int comp, const float* data) +{ + stbi__write_context s = { 0 }; + if (stbi__start_write_file(&s, filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float*)data); + stbi__end_write_file(&s); + return r; + } + else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS // stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() -#define stbiw__sbraw(a) ((int *) (a) - 2) +#define stbiw__sbraw(a) ((int *) (void *) (a) - 2) #define stbiw__sbm(a) stbiw__sbraw(a)[0] #define stbiw__sbn(a) stbiw__sbraw(a)[1] @@ -404,57 +837,57 @@ int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *da #define stbiw__sbcount(a) ((a) ? stbiw__sbn(a) : 0) #define stbiw__sbfree(a) ((a) ? STBIW_FREE(stbiw__sbraw(a)),0 : 0) -static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) +static void* stbiw__sbgrowf(void** arr, int increment, int itemsize) { - int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; - void *p = STBIW_REALLOC(*arr ? stbiw__sbraw(*arr) : 0, itemsize * m + sizeof(int)*2); - STBIW_ASSERT(p); - if (p) { - if (!*arr) ((int *) p)[1] = 0; - *arr = (void *) ((int *) p + 2); - stbiw__sbm(*arr) = m; - } - return *arr; + int m = *arr ? 2 * stbiw__sbm(*arr) + increment : increment + 1; + void* p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr) * itemsize + sizeof(int) * 2) : 0, itemsize * m + sizeof(int) * 2); + STBIW_ASSERT(p); + if (p) { + if (!*arr) ((int*)p)[1] = 0; + *arr = (void*)((int*)p + 2); + stbiw__sbm(*arr) = m; + } + return *arr; } -static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) +static unsigned char* stbiw__zlib_flushf(unsigned char* data, unsigned int* bitbuffer, int* bitcount) { - while (*bitcount >= 8) { - stbiw__sbpush(data, (unsigned char) *bitbuffer); - *bitbuffer >>= 8; - *bitcount -= 8; - } - return data; + while (*bitcount >= 8) { + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); + *bitbuffer >>= 8; + *bitcount -= 8; + } + return data; } static int stbiw__zlib_bitrev(int code, int codebits) { - int res=0; - while (codebits--) { - res = (res << 1) | (code & 1); - code >>= 1; - } - return res; + int res = 0; + while (codebits--) { + res = (res << 1) | (code & 1); + code >>= 1; + } + return res; } -static unsigned int stbiw__zlib_countm(unsigned char *a, unsigned char *b, int limit) +static unsigned int stbiw__zlib_countm(unsigned char* a, unsigned char* b, int limit) { - int i; - for (i=0; i < limit && i < 258; ++i) - if (a[i] != b[i]) break; - return i; + int i; + for (i = 0; i < limit && i < 258; ++i) + if (a[i] != b[i]) break; + return i; } -static unsigned int stbiw__zhash(unsigned char *data) +static unsigned int stbiw__zhash(unsigned char* data) { - stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16); - hash ^= hash << 3; - hash += hash >> 5; - hash ^= hash << 4; - hash += hash >> 17; - hash ^= hash << 25; - hash += hash >> 6; - return hash; + stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16); + hash ^= hash << 3; + hash += hash >> 5; + hash ^= hash << 4; + hash += hash >> 17; + hash ^= hash << 25; + hash += hash >> 6; + return hash; } #define stbiw__zlib_flush() (out = stbiw__zlib_flushf(out, &bitbuf, &bitcount)) @@ -471,249 +904,782 @@ static unsigned int stbiw__zhash(unsigned char *data) #define stbiw__ZHASH 16384 -unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) -{ - static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; - static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; - static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; - static unsigned char disteb[] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; - unsigned int bitbuf=0; - int i,j, bitcount=0; - unsigned char *out = NULL; - unsigned char **hash_table[stbiw__ZHASH]; // 64KB on the stack! - if (quality < 5) quality = 5; - - stbiw__sbpush(out, 0x78); // DEFLATE 32K window - stbiw__sbpush(out, 0x5e); // FLEVEL = 1 - stbiw__zlib_add(1,1); // BFINAL = 1 - stbiw__zlib_add(1,2); // BTYPE = 1 -- fixed huffman - - for (i=0; i < stbiw__ZHASH; ++i) - hash_table[i] = NULL; - - i=0; - while (i < data_len-3) { - // hash next 3 bytes of data to be compressed - int h = stbiw__zhash(data+i)&(stbiw__ZHASH-1), best=3; - unsigned char *bestloc = 0; - unsigned char **hlist = hash_table[h]; - int n = stbiw__sbcount(hlist); - for (j=0; j < n; ++j) { - if (hlist[j]-data > i-32768) { // if entry lies within window - int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i); - if (d >= best) best=d,bestloc=hlist[j]; - } - } - // when hash table entry is too long, delete half the entries - if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2*quality) { - STBIW_MEMMOVE(hash_table[h], hash_table[h]+quality, sizeof(hash_table[h][0])*quality); - stbiw__sbn(hash_table[h]) = quality; - } - stbiw__sbpush(hash_table[h],data+i); - - if (bestloc) { - // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal - h = stbiw__zhash(data+i+1)&(stbiw__ZHASH-1); - hlist = hash_table[h]; - n = stbiw__sbcount(hlist); - for (j=0; j < n; ++j) { - if (hlist[j]-data > i-32767) { - int e = stbiw__zlib_countm(hlist[j], data+i+1, data_len-i-1); - if (e > best) { // if next match is better, bail on current match - bestloc = NULL; - break; - } +#endif // STBIW_ZLIB_COMPRESS + +STBIWDEF unsigned char* stbi_zlib_compress(unsigned char* data, int data_len, int* out_len, int quality) +{ +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin + static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; + static unsigned char lengtheb[] = { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; + static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; + static unsigned char disteb[] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; + unsigned int bitbuf = 0; + int i, j, bitcount = 0; + unsigned char* out = NULL; + unsigned char*** hash_table = (unsigned char***)STBIW_MALLOC(stbiw__ZHASH * sizeof(unsigned char**)); + if (hash_table == NULL) + return NULL; + if (quality < 5) quality = 5; + + stbiw__sbpush(out, 0x78); // DEFLATE 32K window + stbiw__sbpush(out, 0x5e); // FLEVEL = 1 + stbiw__zlib_add(1, 1); // BFINAL = 1 + stbiw__zlib_add(1, 2); // BTYPE = 1 -- fixed huffman + + for (i = 0; i < stbiw__ZHASH; ++i) + hash_table[i] = NULL; + + i = 0; + while (i < data_len - 3) { + // hash next 3 bytes of data to be compressed + int h = stbiw__zhash(data + i) & (stbiw__ZHASH - 1), best = 3; + unsigned char* bestloc = 0; + unsigned char** hlist = hash_table[h]; + int n = stbiw__sbcount(hlist); + for (j = 0; j < n; ++j) { + if (hlist[j] - data > i - 32768) { // if entry lies within window + int d = stbiw__zlib_countm(hlist[j], data + i, data_len - i); + if (d >= best) { best = d; bestloc = hlist[j]; } } - } - } - - if (bestloc) { - int d = (int) (data+i - bestloc); // distance back - STBIW_ASSERT(d <= 32767 && best <= 258); - for (j=0; best > lengthc[j+1]-1; ++j); - stbiw__zlib_huff(j+257); - if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]); - for (j=0; d > distc[j+1]-1; ++j); - stbiw__zlib_add(stbiw__zlib_bitrev(j,5),5); - if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]); - i += best; - } else { - stbiw__zlib_huffb(data[i]); - ++i; - } - } - // write out final bytes - for (;i < data_len; ++i) - stbiw__zlib_huffb(data[i]); - stbiw__zlib_huff(256); // end of block - // pad with 0 bits to byte boundary - while (bitcount) - stbiw__zlib_add(0,1); - - for (i=0; i < stbiw__ZHASH; ++i) - (void) stbiw__sbfree(hash_table[i]); - - { - // compute adler32 on input - unsigned int i=0, s1=1, s2=0, blocklen = data_len % 5552; - int j=0; - while (j < data_len) { - for (i=0; i < blocklen; ++i) s1 += data[j+i], s2 += s1; - s1 %= 65521, s2 %= 65521; - j += blocklen; - blocklen = 5552; - } - stbiw__sbpush(out, (unsigned char) (s2 >> 8)); - stbiw__sbpush(out, (unsigned char) s2); - stbiw__sbpush(out, (unsigned char) (s1 >> 8)); - stbiw__sbpush(out, (unsigned char) s1); - } - *out_len = stbiw__sbn(out); - // make returned pointer freeable - STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); - return (unsigned char *) stbiw__sbraw(out); -} - -unsigned int stbiw__crc32(unsigned char *buffer, int len) -{ - static unsigned int crc_table[256]; - unsigned int crc = ~0u; - int i,j; - if (crc_table[1] == 0) - for(i=0; i < 256; i++) - for (crc_table[i]=i, j=0; j < 8; ++j) - crc_table[i] = (crc_table[i] >> 1) ^ (crc_table[i] & 1 ? 0xedb88320 : 0); - for (i=0; i < len; ++i) - crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; - return ~crc; -} - -#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=(unsigned char)(a),(o)[1]=(unsigned char)(b),(o)[2]=(unsigned char)(c),(o)[3]=(unsigned char)(d),(o)+=4) + } + // when hash table entry is too long, delete half the entries + if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2 * quality) { + STBIW_MEMMOVE(hash_table[h], hash_table[h] + quality, sizeof(hash_table[h][0]) * quality); + stbiw__sbn(hash_table[h]) = quality; + } + stbiw__sbpush(hash_table[h], data + i); + + if (bestloc) { + // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal + h = stbiw__zhash(data + i + 1) & (stbiw__ZHASH - 1); + hlist = hash_table[h]; + n = stbiw__sbcount(hlist); + for (j = 0; j < n; ++j) { + if (hlist[j] - data > i - 32767) { + int e = stbiw__zlib_countm(hlist[j], data + i + 1, data_len - i - 1); + if (e > best) { // if next match is better, bail on current match + bestloc = NULL; + break; + } + } + } + } + + if (bestloc) { + int d = (int)(data + i - bestloc); // distance back + STBIW_ASSERT(d <= 32767 && best <= 258); + for (j = 0; best > lengthc[j + 1] - 1; ++j); + stbiw__zlib_huff(j + 257); + if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]); + for (j = 0; d > distc[j + 1] - 1; ++j); + stbiw__zlib_add(stbiw__zlib_bitrev(j, 5), 5); + if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]); + i += best; + } + else { + stbiw__zlib_huffb(data[i]); + ++i; + } + } + // write out final bytes + for (; i < data_len; ++i) + stbiw__zlib_huffb(data[i]); + stbiw__zlib_huff(256); // end of block + // pad with 0 bits to byte boundary + while (bitcount) + stbiw__zlib_add(0, 1); + + for (i = 0; i < stbiw__ZHASH; ++i) + (void)stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); + + // store uncompressed instead if compression was worse + if (stbiw__sbn(out) > data_len + 2 + ((data_len + 32766) / 32767) * 5) { + stbiw__sbn(out) = 2; // truncate to DEFLATE 32K window and FLEVEL = 1 + for (j = 0; j < data_len;) { + int blocklen = data_len - j; + if (blocklen > 32767) blocklen = 32767; + stbiw__sbpush(out, data_len - j == blocklen); // BFINAL = ?, BTYPE = 0 -- no compression + stbiw__sbpush(out, STBIW_UCHAR(blocklen)); // LEN + stbiw__sbpush(out, STBIW_UCHAR(blocklen >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(~blocklen)); // NLEN + stbiw__sbpush(out, STBIW_UCHAR(~blocklen >> 8)); + memcpy(out + stbiw__sbn(out), data + j, blocklen); + stbiw__sbn(out) += blocklen; + j += blocklen; + } + } + + { + // compute adler32 on input + unsigned int s1 = 1, s2 = 0; + int blocklen = (int)(data_len % 5552); + j = 0; + while (j < data_len) { + for (i = 0; i < blocklen; ++i) { s1 += data[j + i]; s2 += s1; } + s1 %= 65521; s2 %= 65521; + j += blocklen; + blocklen = 5552; + } + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); + } + *out_len = stbiw__sbn(out); + // make returned pointer freeable + STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); + return (unsigned char*)stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS +} + +static unsigned int stbiw__crc32(unsigned char* buffer, int len) +{ +#ifdef STBIW_CRC32 + return STBIW_CRC32(buffer, len); +#else + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + + unsigned int crc = ~0u; + int i; + for (i = 0; i < len; ++i) + crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; + return ~crc; +#endif +} + +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) #define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); #define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) -static void stbiw__wpcrc(unsigned char **data, int len) +static void stbiw__wpcrc(unsigned char** data, int len) { - unsigned int crc = stbiw__crc32(*data - len - 4, len+4); - stbiw__wp32(*data, crc); + unsigned int crc = stbiw__crc32(*data - len - 4, len + 4); + stbiw__wp32(*data, crc); } static unsigned char stbiw__paeth(int a, int b, int c) { - int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); - if (pa <= pb && pa <= pc) return (unsigned char) a; - if (pb <= pc) return (unsigned char) b; - return (unsigned char) c; -} - -unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) -{ - int ctype[5] = { -1, 0, 4, 2, 6 }; - unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; - unsigned char *out,*o, *filt, *zlib; - signed char *line_buffer; - int i,j,k,p,zlen; - - if (stride_bytes == 0) - stride_bytes = x * n; - - filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; - line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } - for (j=0; j < y; ++j) { - static int mapping[] = { 0,1,2,3,4 }; - static int firstmap[] = { 0,1,0,5,6 }; - int *mymap = j ? mapping : firstmap; - int best = 0, bestval = 0x7fffffff; - for (p=0; p < 2; ++p) { - for (k= p?best:0; k < 5; ++k) { - int type = mymap[k],est=0; - unsigned char *z = pixels + stride_bytes*j; - for (i=0; i < n; ++i) - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - (z[i-stride_bytes]>>1); break; - case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-stride_bytes],0)); break; - case 5: line_buffer[i] = z[i]; break; - case 6: line_buffer[i] = z[i]; break; - } - for (i=n; i < x*n; ++i) { - switch (type) { - case 0: line_buffer[i] = z[i]; break; - case 1: line_buffer[i] = z[i] - z[i-n]; break; - case 2: line_buffer[i] = z[i] - z[i-stride_bytes]; break; - case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-stride_bytes])>>1); break; - case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-stride_bytes], z[i-stride_bytes-n]); break; - case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; - case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; - } + int p = a + b - c, pa = abs(p - a), pb = abs(p - b), pc = abs(p - c); + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char* pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char* line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int* mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char* z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height - 1 - y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + + if (type == 0) { + memcpy(line_buffer, z, width * n); + return; + } + + // first loop isn't optimized since it's just one pixel + for (i = 0; i < n; ++i) { + switch (type) { + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i - signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i - signed_stride] >> 1); break; + case 4: line_buffer[i] = (signed char)(z[i] - stbiw__paeth(0, z[i - signed_stride], 0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + switch (type) { + case 1: for (i = n; i < width * n; ++i) line_buffer[i] = z[i] - z[i - n]; break; + case 2: for (i = n; i < width * n; ++i) line_buffer[i] = z[i] - z[i - signed_stride]; break; + case 3: for (i = n; i < width * n; ++i) line_buffer[i] = z[i] - ((z[i - n] + z[i - signed_stride]) >> 1); break; + case 4: for (i = n; i < width * n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i - n], z[i - signed_stride], z[i - signed_stride - n]); break; + case 5: for (i = n; i < width * n; ++i) line_buffer[i] = z[i] - (z[i - n] >> 1); break; + case 6: for (i = n; i < width * n; ++i) line_buffer[i] = z[i] - stbiw__paeth(z[i - n], 0, 0); break; + } +} + +STBIWDEF unsigned char* stbi_write_png_to_mem(const unsigned char* pixels, int stride_bytes, int x, int y, int n, int* out_len) +{ + int force_filter = stbi_write_force_png_filter; + int ctype[5] = { -1, 0, 4, 2, 6 }; + unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; + unsigned char* out, * o, * filt, * zlib; + signed char* line_buffer; + int j, zlen; + + if (stride_bytes == 0) + stride_bytes = x * n; + + if (force_filter >= 5) { + force_filter = -1; + } + + filt = (unsigned char*)STBIW_MALLOC((x * n + 1) * y); if (!filt) return 0; + line_buffer = (signed char*)STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } + for (j = 0; j < y; ++j) { + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, force_filter, line_buffer); + } + else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x * n; ++i) { + est += abs((signed char)line_buffer[i]); + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line((unsigned char*)(pixels), stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; + } + } + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j * (x * n + 1)] = (unsigned char)filter_type; + STBIW_MEMMOVE(filt + j * (x * n + 1) + 1, line_buffer, x * n); + } + STBIW_FREE(line_buffer); + zlib = stbi_zlib_compress(filt, y * (x * n + 1), &zlen, stbi_write_png_compression_level); + STBIW_FREE(filt); + if (!zlib) return 0; + + // each tag requires 12 bytes of overhead + out = (unsigned char*)STBIW_MALLOC(8 + 12 + 13 + 12 + zlen + 12); + if (!out) return 0; + *out_len = 8 + 12 + 13 + 12 + zlen + 12; + + o = out; + STBIW_MEMMOVE(o, sig, 8); o += 8; + stbiw__wp32(o, 13); // header length + stbiw__wptag(o, "IHDR"); + stbiw__wp32(o, x); + stbiw__wp32(o, y); + *o++ = 8; + *o++ = STBIW_UCHAR(ctype[n]); + *o++ = 0; + *o++ = 0; + *o++ = 0; + stbiw__wpcrc(&o, 13); + + stbiw__wp32(o, zlen); + stbiw__wptag(o, "IDAT"); + STBIW_MEMMOVE(o, zlib, zlen); + o += zlen; + STBIW_FREE(zlib); + stbiw__wpcrc(&o, zlen); + + stbiw__wp32(o, 0); + stbiw__wptag(o, "IEND"); + stbiw__wpcrc(&o, 0); + + STBIW_ASSERT(o == out + *out_len); + + return out; +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const* filename, int x, int y, int comp, const void* data, int stride_bytes) +{ + FILE* f; + int len; + unsigned char* png = stbi_write_png_to_mem((const unsigned char*)data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + + f = stbiw__fopen(filename, "wb"); + if (!f) { STBIW_FREE(png); return 0; } + fwrite(png, 1, len, f); + fclose(f); + STBIW_FREE(png); + return 1; +} +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func* func, void* context, int x, int y, int comp, const void* data, int stride_bytes) +{ + int len; + unsigned char* png = stbi_write_png_to_mem((const unsigned char*)data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context* s, int* bitBufP, int* bitCntP, const unsigned short* bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while (bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if (c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float* d0p, float* d1p, float* d2p, float* d3p, float* d4p, float* d5p, float* d6p, float* d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val - 1 : val; + bits[1] = 1; + while (tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1 << bits[1]) - 1); +} + +static int stbiw__jpg_processDU(stbi__write_context* s, int* bitBuf, int* bitCnt, float* CDU, int du_stride, float* fdtbl, int DC, const unsigned short HTDC[256][2], const unsigned short HTAC[256][2]) { + const unsigned short EOB[2] = { HTAC[0x00][0], HTAC[0x00][1] }; + const unsigned short M16zeroes[2] = { HTAC[0xF0][0], HTAC[0xF0][1] }; + int dataOff, i, j, n, diff, end0pos, x, y; + int DU[64]; + + // DCT rows + for (dataOff = 0, n = du_stride * 8; dataOff < n; dataOff += du_stride) { + stbiw__jpg_DCT(&CDU[dataOff], &CDU[dataOff + 1], &CDU[dataOff + 2], &CDU[dataOff + 3], &CDU[dataOff + 4], &CDU[dataOff + 5], &CDU[dataOff + 6], &CDU[dataOff + 7]); + } + // DCT columns + for (dataOff = 0; dataOff < 8; ++dataOff) { + stbiw__jpg_DCT(&CDU[dataOff], &CDU[dataOff + du_stride], &CDU[dataOff + du_stride * 2], &CDU[dataOff + du_stride * 3], &CDU[dataOff + du_stride * 4], + &CDU[dataOff + du_stride * 5], &CDU[dataOff + du_stride * 6], &CDU[dataOff + du_stride * 7]); + } + // Quantize/descale/zigzag the coefficients + for (y = 0, j = 0; y < 8; ++y) { + for (x = 0; x < 8; ++x, ++j) { + float v; + i = y * du_stride + x; + v = CDU[i] * fdtbl[j]; + // DU[stbiw__jpg_ZigZag[j]] = (int)(v < 0 ? ceilf(v - 0.5f) : floorf(v + 0.5f)); + // ceilf() and floorf() are C99, not C89, but I /think/ they're not needed here anyway? + DU[stbiw__jpg_ZigZag[j]] = (int)(v < 0 ? v - 0.5f : v + 0.5f); + } + } + + // Encode DC + diff = DU[0] - DC; + if (diff == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTDC[0]); + } + else { + unsigned short bits[2]; + stbiw__jpg_calcBits(diff, bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTDC[bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + // Encode ACs + end0pos = 63; + for (; (end0pos > 0) && (DU[end0pos] == 0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if (end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for (i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i] == 0 && i <= end0pos; ++i) { + } + nrzeroes = i - startpos; + if (nrzeroes >= 16) { + int lng = nrzeroes >> 4; + int nrmarker; + for (nrmarker = 1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes << 4) + bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if (end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context* s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 }; + static const unsigned char std_dc_luminance_values[] = { 0,1,2,3,4,5,6,7,8,9,10,11 }; + static const unsigned char std_ac_luminance_nrcodes[] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d }; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 }; + static const unsigned char std_dc_chrominance_values[] = { 0,1,2,3,4,5,6,7,8,9,10,11 }; + static const unsigned char std_ac_chrominance_nrcodes[] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 }; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9} }; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11} }; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = { 16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99 }; + static const int UVQT[] = { 17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 }; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k, subsample; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if (!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + subsample = quality <= 90 ? 1 : 0; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for (i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i] * quality + 50) / 100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char)(yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i] * quality + 50) / 100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char)(uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for (row = 0, k = 0; row < 8; ++row) { + for (col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height >> 8),STBIW_UCHAR(height),(unsigned char)(width >> 8),STBIW_UCHAR(width), + 3,1,(unsigned char)(subsample ? 0x22 : 0x11),0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes + 1), sizeof(std_dc_luminance_nrcodes) - 1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes + 1), sizeof(std_ac_luminance_nrcodes) - 1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes + 1), sizeof(std_dc_chrominance_nrcodes) - 1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes + 1), sizeof(std_ac_chrominance_nrcodes) - 1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = { 0x7F, 7 }; + int DCY = 0, DCU = 0, DCV = 0; + int bitBuf = 0, bitCnt = 0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + const unsigned char* dataR = (const unsigned char*)data; + const unsigned char* dataG = dataR + ofsG; + const unsigned char* dataB = dataR + ofsB; + int x, y, pos; + if (subsample) { + for (y = 0; y < height; y += 16) { + for (x = 0; x < width; x += 16) { + float Y[256], U[256], V[256]; + for (row = y, pos = 0; row < y + 16; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height - 1 - clamped_row) : clamped_row) * width * comp; + for (col = x; col < x + 16; ++col, ++pos) { + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width - 1)) * comp; + float r = dataR[p], g = dataG[p], b = dataB[p]; + Y[pos] = +0.29900f * r + 0.58700f * g + 0.11400f * b - 128; + U[pos] = -0.16874f * r - 0.33126f * g + 0.50000f * b; + V[pos] = +0.50000f * r - 0.41869f * g - 0.08131f * b; + } + } + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y + 0, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y + 8, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y + 128, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y + 136, 16, fdtbl_Y, DCY, YDC_HT, YAC_HT); + + // subsample U,V + { + float subU[64], subV[64]; + int yy, xx; + for (yy = 0, pos = 0; yy < 8; ++yy) { + for (xx = 0; xx < 8; ++xx, ++pos) { + int j = yy * 32 + xx * 2; + subU[pos] = (U[j + 0] + U[j + 1] + U[j + 16] + U[j + 17]) * 0.25f; + subV[pos] = (V[j + 0] + V[j + 1] + V[j + 16] + V[j + 17]) * 0.25f; + } + } + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, subU, 8, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, subV, 8, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + } + } + else { + for (y = 0; y < height; y += 8) { + for (x = 0; x < width; x += 8) { + float Y[64], U[64], V[64]; + for (row = y, pos = 0; row < y + 8; ++row) { + // row >= height => use last input row + int clamped_row = (row < height) ? row : height - 1; + int base_p = (stbi__flip_vertically_on_write ? (height - 1 - clamped_row) : clamped_row) * width * comp; + for (col = x; col < x + 8; ++col, ++pos) { + // if col >= width => use pixel from last input column + int p = base_p + ((col < width) ? col : (width - 1)) * comp; + float r = dataR[p], g = dataG[p], b = dataB[p]; + Y[pos] = +0.29900f * r + 0.58700f * g + 0.11400f * b - 128; + U[pos] = -0.16874f * r - 0.33126f * g + 0.50000f * b; + V[pos] = +0.50000f * r - 0.41869f * g - 0.08131f * b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, Y, 8, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, U, 8, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, V, 8, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } } - if (p) break; - for (i=0; i < x*n; ++i) - est += abs((signed char) line_buffer[i]); - if (est < bestval) { bestval = est; best = k; } - } - } - // when we get here, best contains the filter type, and line_buffer contains the data - filt[j*(x*n+1)] = (unsigned char) best; - STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); - } - STBIW_FREE(line_buffer); - zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, 8); // increase 8 to get smaller but use more memory - STBIW_FREE(filt); - if (!zlib) return 0; - - // each tag requires 12 bytes of overhead - out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12); - if (!out) return 0; - *out_len = 8 + 12+13 + 12+zlen + 12; - - o=out; - STBIW_MEMMOVE(o,sig,8); o+= 8; - stbiw__wp32(o, 13); // header length - stbiw__wptag(o, "IHDR"); - stbiw__wp32(o, x); - stbiw__wp32(o, y); - *o++ = 8; - *o++ = (unsigned char) ctype[n]; - *o++ = 0; - *o++ = 0; - *o++ = 0; - stbiw__wpcrc(&o,13); - - stbiw__wp32(o, zlen); - stbiw__wptag(o, "IDAT"); - STBIW_MEMMOVE(o, zlib, zlen); - o += zlen; - STBIW_FREE(zlib); - stbiw__wpcrc(&o, zlen); - - stbiw__wp32(o,0); - stbiw__wptag(o, "IEND"); - stbiw__wpcrc(&o,0); - - STBIW_ASSERT(o == out + *out_len); - - return out; -} - -int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) -{ - FILE *f; - int len; - unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); - if (!png) return 0; - f = fopen(filename, "wb"); - if (!f) { STBIW_FREE(png); return 0; } - fwrite(png, 1, len, f); - fclose(f); - STBIW_FREE(png); - return 1; + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func* func, void* context, int x, int y, int comp, const void* data, int quality) +{ + stbi__write_context s = { 0 }; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void*)data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const* filename, int x, int y, int comp, const void* data, int quality) +{ + stbi__write_context s = { 0 }; + if (stbi__start_write_file(&s, filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } + else + return 0; } +#endif + #endif // STB_IMAGE_WRITE_IMPLEMENTATION /* Revision history + 1.16 (2021-07-11) + make Deflate code emit uncompressed blocks when it would otherwise expand + support writing BMPs with alpha channel + 1.15 (2020-07-13) unknown + 1.14 (2020-02-02) updated JPEG writer to downsample chroma channels + 1.13 + 1.12 + 1.11 (2019-08-11) + + 1.10 (2019-02-07) + support utf8 filenames in Windows; fix warnings and platform ifdefs + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support 0.98 (2015-04-08) added STBIW_MALLOC, STBIW_ASSERT etc 0.97 (2015-01-18) @@ -722,7 +1688,7 @@ int stbi_write_png(char const *filename, int x, int y, int comp, const void *dat add HDR output fix monochrome BMP 0.95 (2014-08-17) - add monochrome TGA output + add monochrome TGA output 0.94 (2014-05-31) rename private functions to avoid conflicts with stb_image.h 0.93 (2014-05-27) @@ -733,3 +1699,45 @@ int stbi_write_png(char const *filename, int x, int y, int comp, const void *dat first public release 0.90 first internal release */ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ \ No newline at end of file diff --git a/img/BVH_render_time.png b/img/BVH_render_time.png new file mode 100644 index 00000000..6ae53409 Binary files /dev/null and b/img/BVH_render_time.png differ diff --git a/img/basic_cornell.5000samp.png b/img/basic_cornell.5000samp.png new file mode 100644 index 00000000..3e681cb1 Binary files /dev/null and b/img/basic_cornell.5000samp.png differ diff --git a/img/basic_demo.10000samp.png b/img/basic_demo.10000samp.png new file mode 100644 index 00000000..ee7d66cd Binary files /dev/null and b/img/basic_demo.10000samp.png differ diff --git a/img/basic_demo_closed.10000samp.png b/img/basic_demo_closed.10000samp.png new file mode 100644 index 00000000..460292ab Binary files /dev/null and b/img/basic_demo_closed.10000samp.png differ diff --git a/img/dof_1.5744samp.png b/img/dof_1.5744samp.png new file mode 100644 index 00000000..37fdeb6c Binary files /dev/null and b/img/dof_1.5744samp.png differ diff --git a/img/dof_2.5872samp.png b/img/dof_2.5872samp.png new file mode 100644 index 00000000..e4b5dfe9 Binary files /dev/null and b/img/dof_2.5872samp.png differ diff --git a/img/dof_3.5813samp.png b/img/dof_3.5813samp.png new file mode 100644 index 00000000..18b8b1ce Binary files /dev/null and b/img/dof_3.5813samp.png differ diff --git a/img/dof_4.5078samp.png b/img/dof_4.5078samp.png new file mode 100644 index 00000000..b43d9cbf Binary files /dev/null and b/img/dof_4.5078samp.png differ diff --git a/img/first_bounce_cache_on_rays.png b/img/first_bounce_cache_on_rays.png new file mode 100644 index 00000000..8e5a9947 Binary files /dev/null and b/img/first_bounce_cache_on_rays.png differ diff --git a/img/no_antialiasing.10000samp.png b/img/no_antialiasing.10000samp.png new file mode 100644 index 00000000..ff1e4257 Binary files /dev/null and b/img/no_antialiasing.10000samp.png differ diff --git a/img/seele_face_vertex.png b/img/seele_face_vertex.png new file mode 100644 index 00000000..8bf258c7 Binary files /dev/null and b/img/seele_face_vertex.png differ diff --git a/img/seele_portrait.10000samp.png b/img/seele_portrait.10000samp.png new file mode 100644 index 00000000..ec720dc5 Binary files /dev/null and b/img/seele_portrait.10000samp.png differ diff --git a/img/seele_scene.10000samp.png b/img/seele_scene.10000samp.png new file mode 100644 index 00000000..48464300 Binary files /dev/null and b/img/seele_scene.10000samp.png differ diff --git a/img/shading_cornell.10000samp.png b/img/shading_cornell.10000samp.png new file mode 100644 index 00000000..c9f53c70 Binary files /dev/null and b/img/shading_cornell.10000samp.png differ diff --git a/img/sort_by_material_on_scenes.png b/img/sort_by_material_on_scenes.png new file mode 100644 index 00000000..c8590dbe Binary files /dev/null and b/img/sort_by_material_on_scenes.png differ diff --git a/img/stream_compaction_depth.png b/img/stream_compaction_depth.png new file mode 100644 index 00000000..1eadd920 Binary files /dev/null and b/img/stream_compaction_depth.png differ diff --git a/imgui.ini b/imgui.ini new file mode 100644 index 00000000..5b69491a --- /dev/null +++ b/imgui.ini @@ -0,0 +1,10 @@ +[Window][Debug##Default] +Pos=60,60 +Size=400,400 +Collapsed=0 + +[Window][Path Tracer Analytics] +Pos=60,60 +Size=339,157 +Collapsed=0 + diff --git a/scenes/basic_demo.txt b/scenes/basic_demo.txt new file mode 100644 index 00000000..6f2f3dfe --- /dev/null +++ b/scenes/basic_demo.txt @@ -0,0 +1,182 @@ +// Emissive material (light) +MATERIAL 0 +RGB 1 1 1 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 10 + +// Diffuse white +MATERIAL 1 +RGB .98 .98 .98 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Diffuse red +MATERIAL 2 +RGB .85 .35 .35 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Diffuse green +MATERIAL 3 +RGB .35 .85 .35 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Glass +MATERIAL 4 +RGB .1 .1 .1 +SPECEX 0 +SPECRGB .9 .9 .98 +REFL 1 +REFR 1 +REFRIOR 1.55 +EMITTANCE 0 + +// Mirror +MATERIAL 5 +RGB .1 .1 .1 +SPECEX 0 +SPECRGB .98 .98 .98 +REFL 1 +REFR 0 +REFRIOR 1.55 +EMITTANCE 0 + +// Matte White +MATERIAL 6 +RGB .85 .81 .78 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Diffuse blue +MATERIAL 7 +RGB .35 .35 .85 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Camera +CAMERA +RES 800 800 +FOVY 45 +ITERATIONS 10000 +DEPTH 8 +FILE basic_demo +EYE 0 5 10.5 +LOOKAT 0 5 0 +UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 + + +// Ceiling light +OBJECT 0 +cube +material 0 +TRANS 0 10 1.5 +ROTAT 10 0 0 +SCALE 3 .3 3 + +// Floor +OBJECT 1 +cube +material 1 +TRANS 0 0 0 +ROTAT 0 0 0 +SCALE 10 .01 10 + +// Ceiling +OBJECT 2 +cube +material 1 +TRANS 0 10 0 +ROTAT 0 0 90 +SCALE .01 10 10 + +// Back wall +OBJECT 3 +cube +material 7 +TRANS 0 5 -5 +ROTAT 0 90 0 +SCALE .01 10 10 + +// Left wall +OBJECT 4 +cube +material 2 +TRANS -5 5 0 +ROTAT 0 0 0 +SCALE .01 10 10 + +// Right wall +OBJECT 5 +cube +material 3 +TRANS 5 5 0 +ROTAT 0 0 0 +SCALE .01 10 10 + +// Glass Cube +OBJECT 6 +cube +material 4 +TRANS -2.5 0 -2.3 +ROTAT 0 17.8 0 +SCALE 2.1 8.6 2.1 + +// Mirror Cube +OBJECT 7 +cube +material 5 +TRANS 3 1 -3 +ROTAT 0 -17.5 0 +SCALE 2 2 2 + +// Sphere +OBJECT 8 +sphere +material 6 +TRANS -2.8 0.7 -3.4 +ROTAT 0 0 0 +SCALE 1.5 1.5 1.5 + +// Sphere +OBJECT 9 +sphere +material 6 +TRANS -2.1 2.3 -2.3 +ROTAT 0 0 0 +SCALE 1.5 1.5 1.5 + +// Seele +OBJECT 10 +mesh +../scenes/gltf/seele_light.gltf +material 3 +TRANS 0 0 0 +ROTAT 0 0 0 +SCALE 5 5 5 \ No newline at end of file diff --git a/scenes/basic_demo_closed.txt b/scenes/basic_demo_closed.txt new file mode 100644 index 00000000..f5a9fe1f --- /dev/null +++ b/scenes/basic_demo_closed.txt @@ -0,0 +1,190 @@ +// Emissive material (light) +MATERIAL 0 +RGB 1 1 1 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 7 + +// Diffuse white +MATERIAL 1 +RGB .98 .98 .98 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Diffuse red +MATERIAL 2 +RGB .85 .35 .35 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Diffuse green +MATERIAL 3 +RGB .35 .85 .35 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Glass +MATERIAL 4 +RGB .1 .1 .1 +SPECEX 0 +SPECRGB .9 .9 .98 +REFL 1 +REFR 1 +REFRIOR 1.55 +EMITTANCE 0 + +// Mirror +MATERIAL 5 +RGB .1 .1 .1 +SPECEX 0 +SPECRGB .98 .98 .98 +REFL 1 +REFR 0 +REFRIOR 1.55 +EMITTANCE 0 + +// Matte White +MATERIAL 6 +RGB .85 .81 .78 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Diffuse blue +MATERIAL 7 +RGB .35 .35 .85 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Camera +CAMERA +RES 800 800 +FOVY 45 +ITERATIONS 10000 +DEPTH 8 +FILE basic_demo_closed +EYE 0 5 4.9 +LOOKAT 0 5 0 +UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 + + +// Ceiling light +OBJECT 0 +cube +material 0 +TRANS 0 10 0 +ROTAT 10 0 0 +SCALE 3 .3 3 + +// Floor +OBJECT 1 +cube +material 1 +TRANS 0 0 0 +ROTAT 0 0 0 +SCALE 10 .01 10 + +// Ceiling +OBJECT 2 +cube +material 1 +TRANS 0 10 0 +ROTAT 0 0 90 +SCALE .01 10 10 + +// Back wall +OBJECT 3 +cube +material 7 +TRANS 0 5 -5 +ROTAT 0 90 0 +SCALE .01 10 10 + +// Left wall +OBJECT 4 +cube +material 2 +TRANS -5 5 0 +ROTAT 0 0 0 +SCALE .01 10 10 + +// Right wall +OBJECT 5 +cube +material 3 +TRANS 5 5 0 +ROTAT 0 0 0 +SCALE .01 10 10 + +// Glass Cube +OBJECT 6 +cube +material 4 +TRANS -2.5 0 -2.3 +ROTAT 0 17.8 0 +SCALE 2.1 8.6 2.1 + +// Mirror Cube +OBJECT 7 +cube +material 5 +TRANS 3 1 -3 +ROTAT 0 -17.5 0 +SCALE 2 2 2 + +// Sphere +OBJECT 8 +sphere +material 6 +TRANS -2.8 0.7 -3.4 +ROTAT 0 0 0 +SCALE 1.5 1.5 1.5 + +// Sphere +OBJECT 9 +sphere +material 6 +TRANS -2.1 2.3 -2.3 +ROTAT 0 0 0 +SCALE 1.5 1.5 1.5 + +// Seele +OBJECT 10 +mesh +../scenes/gltf/seele_light.gltf +material 3 +TRANS 0 0 -1 +ROTAT 0 0 0 +SCALE 5 5 5 + +// Cage wall +OBJECT 11 +cube +material 7 +TRANS 0 5 5 +ROTAT 0 90 0 +SCALE .01 10 10 \ No newline at end of file diff --git a/scenes/cornell.txt b/scenes/cornell.txt index 83ff8202..3d424629 100644 --- a/scenes/cornell.txt +++ b/scenes/cornell.txt @@ -42,10 +42,30 @@ EMITTANCE 0 MATERIAL 4 RGB .98 .98 .98 SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Glass +MATERIAL 5 +RGB .1 .1 .1 +SPECEX 0 +SPECRGB .98 .98 .98 +REFL 1 +REFR 1 +REFRIOR 1.55 +EMITTANCE 0 + +// Mirror +MATERIAL 6 +RGB .1 .1 .1 +SPECEX 0 SPECRGB .98 .98 .98 REFL 1 REFR 0 -REFRIOR 0 +REFRIOR 1.55 EMITTANCE 0 // Camera @@ -58,6 +78,8 @@ FILE cornell EYE 0.0 5 10.5 LOOKAT 0 5 0 UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 // Ceiling light @@ -112,6 +134,22 @@ SCALE .01 10 10 OBJECT 6 sphere material 4 -TRANS -1 4 -1 +TRANS 0 4 -1 ROTAT 0 0 0 SCALE 3 3 3 + +// Glass Cube +OBJECT 7 +cube +material 5 +TRANS -2.5 0 -1 +ROTAT 0 17.8 0 +SCALE 2.1 8.6 2.1 + +// Mirror Cube +OBJECT 8 +cube +material 6 +TRANS 3 1 -1.5 +ROTAT 0 -17.5 0 +SCALE 2 2 2 \ No newline at end of file diff --git a/scenes/gltf/Box.gltf b/scenes/gltf/Box.gltf new file mode 100644 index 00000000..72131173 --- /dev/null +++ b/scenes/gltf/Box.gltf @@ -0,0 +1,181 @@ +{ + "asset": { + "generator": "COLLADA2GLTF", + "version": "2.0" + }, + "scene": 0, + "scenes": [ + { + "nodes": [ + 0 + ] + } + ], + "nodes": [ + { + "children": [ + 1 + ], + "matrix": [ + 1.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + -1.0, + 0.0, + 0.0, + 1.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.0 + ] + }, + { + "mesh": 0 + } + ], + "meshes": [ + { + "primitives": [ + { + "attributes": { + "NORMAL": 1, + "POSITION": 2, + "TEXCOORD_0": 3 + }, + "indices": 0, + "mode": 4, + "material": 0 + } + ], + "name": "Mesh" + } + ], + "accessors": [ + { + "bufferView": 0, + "byteOffset": 0, + 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" + } + ] +} diff --git a/scenes/gltf/seele_right.glb b/scenes/gltf/seele_right.glb new file mode 100644 index 00000000..d9399a80 Binary files /dev/null and b/scenes/gltf/seele_right.glb differ diff --git a/scenes/gltf/triple_seele.glb b/scenes/gltf/triple_seele.glb new file mode 100644 index 00000000..b6e19ebf Binary files /dev/null and b/scenes/gltf/triple_seele.glb differ diff --git a/scenes/seeles.txt b/scenes/seeles.txt new file mode 100644 index 00000000..e2ecf8b0 --- /dev/null +++ b/scenes/seeles.txt @@ -0,0 +1,85 @@ +// Emissive material (light) +MATERIAL 0 +RGB 1 1 1 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 10 + +// Diffuse white +MATERIAL 1 +RGB .98 .98 .98 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Camera +CAMERA +RES 1000 1000 +FOVY 45 +ITERATIONS 10000 +DEPTH 8 +FILE cornell +EYE 0 5 10.5 +LOOKAT 0 5 0 +UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 + + +// Ceiling light +OBJECT 0 +cube +material 0 +TRANS 0 15 1.5 +ROTAT 0 0 0 +SCALE 10 .3 10 + +// Stage +OBJECT 1 +mesh +scenes/gltf/scene_dome.glb +material -1 +TRANS 0 0 0 +ROTAT 0 0 0 +SCALE 2 2 2 + +// Seele Center +OBJECT 2 +mesh +scenes/gltf/seele_center.glb +material -1 +TRANS 0 0 -5 +ROTAT 0 0 0 +SCALE 5 5 5 + +// Seele Left +OBJECT 3 +mesh +scenes/gltf/seele_left.glb +material -1 +TRANS 0 0 -5 +ROTAT 0 0 0 +SCALE 5 5 5 + +// Seele Right +OBJECT 4 +mesh +scenes/gltf/seele_right.glb +material -1 +TRANS 0 0 -5 +ROTAT 0 0 0 +SCALE 5 5 5 + +// Floor +OBJECT 5 +cube +material 1 +TRANS 0 -5 0 +ROTAT 0 0 0 +SCALE 100 .1 100 \ No newline at end of file diff --git a/scenes/seeles_one_obj.txt b/scenes/seeles_one_obj.txt new file mode 100644 index 00000000..090ddd4c --- /dev/null +++ b/scenes/seeles_one_obj.txt @@ -0,0 +1,68 @@ +// Emissive material (light) +// 4000k 1 .82 .64 +// 4800k 1 .88 .77 +MATERIAL 0 +RGB 1 .88 .77 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 5 + +// Diffuse white +MATERIAL 1 +RGB .78 .78 .78 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Camera +CAMERA +RES 1000 1000 +FOVY 45 +ITERATIONS 10000 +DEPTH 8 +FILE seele_scene +EYE 0 1 10.5 +LOOKAT 0 4 0 +UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 + + +// Ceiling light +OBJECT 0 +cube +material 0 +TRANS -5 17 13.5 +ROTAT 30 45 0 +SCALE 10 .3 10 + +// Ceiling light +OBJECT 1 +cube +material 0 +TRANS 5 17 13.5 +ROTAT 30 -45 0 +SCALE 10 .3 10 + +// Stage +OBJECT 2 +mesh +../scenes/gltf/triple_seele.glb +material -1 +TRANS 0 0 -1 +ROTAT 0 0 0 +SCALE 5 5 5 + +// Floor +OBJECT 3 +cube +material 1 +TRANS 0 -1 0 +ROTAT 0 0 0 +SCALE 50 .1 50 \ No newline at end of file diff --git a/scenes/sphere.txt b/scenes/sphere.txt index a74b5458..6ea04ec9 100644 --- a/scenes/sphere.txt +++ b/scenes/sphere.txt @@ -18,6 +18,8 @@ FILE sphere EYE 0.0 5 10.5 LOOKAT 0 5 0 UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 // Sphere OBJECT 0 diff --git a/scenes/test.txt b/scenes/test.txt new file mode 100644 index 00000000..090ddd4c --- /dev/null +++ b/scenes/test.txt @@ -0,0 +1,68 @@ +// Emissive material (light) +// 4000k 1 .82 .64 +// 4800k 1 .88 .77 +MATERIAL 0 +RGB 1 .88 .77 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 5 + +// Diffuse white +MATERIAL 1 +RGB .78 .78 .78 +SPECEX 0 +SPECRGB 0 0 0 +REFL 0 +REFR 0 +REFRIOR 0 +EMITTANCE 0 + +// Camera +CAMERA +RES 1000 1000 +FOVY 45 +ITERATIONS 10000 +DEPTH 8 +FILE seele_scene +EYE 0 1 10.5 +LOOKAT 0 4 0 +UP 0 1 0 +FOCALDIST 5 +LENSRADIUS 0 + + +// Ceiling light +OBJECT 0 +cube +material 0 +TRANS -5 17 13.5 +ROTAT 30 45 0 +SCALE 10 .3 10 + +// Ceiling light +OBJECT 1 +cube +material 0 +TRANS 5 17 13.5 +ROTAT 30 -45 0 +SCALE 10 .3 10 + +// Stage +OBJECT 2 +mesh +../scenes/gltf/triple_seele.glb +material -1 +TRANS 0 0 -1 +ROTAT 0 0 0 +SCALE 5 5 5 + +// Floor +OBJECT 3 +cube +material 1 +TRANS 0 -1 0 +ROTAT 0 0 0 +SCALE 50 .1 50 \ No newline at end of file diff --git a/src/interactions.h b/src/interactions.h index f969e458..e8ba65a8 100644 --- a/src/interactions.h +++ b/src/interactions.h @@ -1,6 +1,26 @@ #pragma once #include "intersections.h" +#include + +__host__ __device__ +glm::vec2 ConcentricSampleDisk(const glm::vec2& u) { + // adapted from PBRT 13.6.2 + glm::vec2 uOffset = 2.f * u - 1.f; + if (uOffset == glm::vec2(0.f)) { + return uOffset; + } + float theta, r; + if (abs(uOffset.x) > abs(uOffset.y)) { + r = uOffset.x; + theta = QUARTER_PI * (uOffset.y / uOffset.x); + } + else { + r = uOffset.y; + theta = HALF_PI - QUARTER_PI * (uOffset.x / uOffset.y); + } + return r * glm::vec2(cos(theta), sin(theta)); +} // CHECKITOUT /** @@ -41,6 +61,84 @@ glm::vec3 calculateRandomDirectionInHemisphere( + sin(around) * over * perpendicularDirection2; } +__host__ __device__ +void Sample_f_diffuse(glm::vec3 color, thrust::default_random_engine& rng, + glm::vec3 intersect, glm::vec3 normal, PathSegment& pathSegment) { + glm::vec3 wi = calculateRandomDirectionInHemisphere(normal, rng); + float pdf = wi.z * INV_PI; + if (pdf == 0.f) { + // terminate on invalid sample + pathSegment.remainingBounces = 0; + pathSegment.color = glm::vec3(0.f); + } + else { + pathSegment.color *= color; // * INV_PI * glm::abs(glm::dot(wi, normal)) / (glm::abs(glm::dot(wi, normal)) * INV_PI) the other terms cancel out + pathSegment.ray.origin = intersect + 0.0001f * wi; + pathSegment.ray.direction = wi; + } +} + +__host__ __device__ +void Sample_f_specular_refl(glm::vec3 color, glm::vec3 intersect, glm::vec3 normal, + PathSegment& pathSegment) { + glm::vec3 wi = glm::reflect(pathSegment.ray.direction, normal); + pathSegment.color *= color; // pdf = 1, abscos(theta_wo) and absdot(wi, n) = cos(theta_j) cancels out + pathSegment.ray.origin = intersect + 0.0001f * wi; + pathSegment.ray.direction = wi; +} + +__host__ __device__ +void Sample_f_specular_trans(glm::vec3 color, glm::vec3 intersect, glm::vec3 normal, + float ior, PathSegment& pathSegment) { + glm::vec3 wi; + if (glm::dot(pathSegment.ray.direction, normal) < 0) { + wi = glm::normalize(glm::refract(glm::normalize(pathSegment.ray.direction), glm::normalize(normal), 1.f / ior)); + } else { + wi = glm::normalize(glm::refract(glm::normalize(pathSegment.ray.direction), glm::normalize(-normal), ior)); + } + pathSegment.color *= color; // pdf = 1, abscos(theta_wo) and absdot(wi, n) = cos(theta_j) cancels out + pathSegment.ray.origin = intersect + 0.0001f * wi; + pathSegment.ray.direction = wi; +} + +__host__ __device__ +glm::vec3 FresnelDielectricEval(float cosThetaI, float ior) { + cosThetaI = glm::clamp(cosThetaI, -1.f, 1.f); + float etaI = 1.f; + float etaT = ior; + + if (cosThetaI <= 0.f) { + etaI = ior; + etaT = 1.f; + cosThetaI = abs(cosThetaI); + } + + float sinThetaI = sqrt(FLOATMAX(0.f, 1 - cosThetaI * cosThetaI)); + float sinThetaT = etaI / etaT * sinThetaI; + if (sinThetaT >= 1.f) { + return glm::vec3(1.f); + } + float cosThetaT = sqrt(FLOATMAX(0.f, 1 - sinThetaT * sinThetaT)); + float Rparl = ((etaT * cosThetaI) - (etaI * cosThetaT)) / ((etaT * cosThetaI) + (etaI * cosThetaT)); + float Rperp = ((etaI * cosThetaI) - (etaT * cosThetaT)) / ((etaI * cosThetaI) + (etaT * cosThetaT)); + return glm::vec3((Rparl * Rparl + Rperp * Rperp) / 2); +} + +__host__ __device__ +void Sample_f_specular_fresnel(glm::vec3 color, thrust::default_random_engine& rng, + glm::vec3 intersect, glm::vec3 normal, float ior, PathSegment& pathSegment) { + thrust::uniform_real_distribution u01(0, 1); + // double contribute since only sample each half the times + if (u01(rng) < 0.5) { + Sample_f_specular_refl(color, intersect, normal, pathSegment); + pathSegment.color *= 2.f * FresnelDielectricEval(glm::dot(pathSegment.ray.direction, normal), ior); + } else { + Sample_f_specular_trans(color, intersect, normal, ior, pathSegment); + pathSegment.color *= 2.f * (glm::vec3(1.f) - FresnelDielectricEval(glm::dot(pathSegment.ray.direction, normal), ior)); + } +} + + /** * Scatter a ray with some probabilities according to the material properties. * For example, a diffuse surface scatters in a cosine-weighted hemisphere. @@ -66,14 +164,64 @@ glm::vec3 calculateRandomDirectionInHemisphere( * * You may need to change the parameter list for your purposes! */ -__host__ __device__ +__device__ void scatterRay( - PathSegment & pathSegment, + PathSegment &pathSegment, glm::vec3 intersect, - glm::vec3 normal, + ShadeableIntersection &isect, const Material &m, + const cudaTextureObject_t* const texObjs, thrust::default_random_engine &rng) { // TODO: implement this. // A basic implementation of pure-diffuse shading will just call the // calculateRandomDirectionInHemisphere defined above. + // sample f + // if pdf invalid, set numBounces = 0, set color black + // multiply color by throughput + // spawn new ray + thrust::uniform_real_distribution u01(0, 1); + + if (m.textureid >= 0) { + float4 texCol = tex2D(texObjs[m.textureid], isect.uv.x, isect.uv.y); + glm::vec4 col = glm::vec4(texCol.x, texCol.y, texCol.z, texCol.w); + if (u01(rng) < col.a) { + Sample_f_diffuse(glm::vec3(col), rng, intersect, isect.surfaceNormal, pathSegment); + pathSegment.color /= fmax(col.a, EPSILON); + } else { + pathSegment.ray.origin = intersect + 0.0001f * pathSegment.ray.direction; + pathSegment.color /= fmax(1.f - col.a, EPSILON); + } + /*Sample_f_diffuse(glm::vec3(isect.uv.x, isect.uv.y, 1.f), rng, intersect, isect.surfaceNormal, pathSegment);*/ + return; + } + if (m.color == glm::vec3(0.f) && m.specular.color == glm::vec3(0.f)) { + // black always gives black color in our sampling methods + return; + } + if (!(m.hasReflective || m.hasRefractive)) { + Sample_f_diffuse(m.color, rng, intersect, isect.surfaceNormal, pathSegment); + return; + } + + float diffuseLumin = LUMINANCE(m.color); + float specularLumin = LUMINANCE(m.specular.color); + float pSampleDiffuse = diffuseLumin / (diffuseLumin + specularLumin); + if (u01(rng) < pSampleDiffuse) { + Sample_f_diffuse(m.color, rng, intersect, isect.surfaceNormal, pathSegment); + pathSegment.color /= fmax(pSampleDiffuse, EPSILON); + } else { + if (!m.hasReflective) { + Sample_f_specular_trans(m.specular.color, intersect, isect.surfaceNormal, m.indexOfRefraction, pathSegment); + } else if (!m.hasRefractive) { + Sample_f_specular_refl(m.specular.color, intersect, isect.surfaceNormal, pathSegment); + } else { + Sample_f_specular_fresnel(m.specular.color, rng, intersect, isect.surfaceNormal, m.indexOfRefraction, pathSegment); + } + if (glm::dot(pathSegment.ray.direction, pathSegment.ray.direction) == 0) { + pathSegment.color = glm::vec3(0.f, 0.f, 0.f); + pathSegment.remainingBounces = 0; + } else { + pathSegment.color /= fmax((1 - pSampleDiffuse), EPSILON); + } + } } diff --git a/src/intersections.h b/src/intersections.h index b1504071..b91eafaa 100644 --- a/src/intersections.h +++ b/src/intersections.h @@ -22,10 +22,10 @@ __host__ __device__ inline unsigned int utilhash(unsigned int a) { // CHECKITOUT /** * Compute a point at parameter value `t` on ray `r`. - * Falls slightly short so that it doesn't intersect the object it's hitting. + * Warning: Point may interact with intersectiong object. */ -__host__ __device__ glm::vec3 getPointOnRay(Ray r, float t) { - return r.origin + (t - .0001f) * glm::normalize(r.direction); +__host__ __device__ glm::vec3 getPointOnRay(Ray& r, float t) { + return r.origin + t * glm::normalize(r.direction); } /** @@ -45,8 +45,8 @@ __host__ __device__ glm::vec3 multiplyMV(glm::mat4 m, glm::vec4 v) { * @param outside Output param for whether the ray came from outside. * @return Ray parameter `t` value. -1 if no intersection. */ -__host__ __device__ float boxIntersectionTest(Geom box, Ray r, - glm::vec3 &intersectionPoint, glm::vec3 &normal, bool &outside) { +__host__ __device__ float boxIntersectionTest(Geom& box, Ray& r, + glm::vec3 &normal, bool &outside) { Ray q; q.origin = multiplyMV(box.inverseTransform, glm::vec4(r.origin , 1.0f)); q.direction = glm::normalize(multiplyMV(box.inverseTransform, glm::vec4(r.direction, 0.0f))); @@ -82,7 +82,7 @@ __host__ __device__ float boxIntersectionTest(Geom box, Ray r, tmin_n = tmax_n; outside = false; } - intersectionPoint = multiplyMV(box.transform, glm::vec4(getPointOnRay(q, tmin), 1.0f)); + glm::vec3 intersectionPoint = multiplyMV(box.transform, glm::vec4(getPointOnRay(q, tmin), 1.0f)); normal = glm::normalize(multiplyMV(box.invTranspose, glm::vec4(tmin_n, 0.0f))); return glm::length(r.origin - intersectionPoint); } @@ -99,8 +99,8 @@ __host__ __device__ float boxIntersectionTest(Geom box, Ray r, * @param outside Output param for whether the ray came from outside. * @return Ray parameter `t` value. -1 if no intersection. */ -__host__ __device__ float sphereIntersectionTest(Geom sphere, Ray r, - glm::vec3 &intersectionPoint, glm::vec3 &normal, bool &outside) { +__host__ __device__ float sphereIntersectionTest(Geom& sphere, Ray& r, + glm::vec3 &normal, bool &outside) { float radius = .5; glm::vec3 ro = multiplyMV(sphere.inverseTransform, glm::vec4(r.origin, 1.0f)); @@ -134,11 +134,135 @@ __host__ __device__ float sphereIntersectionTest(Geom sphere, Ray r, glm::vec3 objspaceIntersection = getPointOnRay(rt, t); - intersectionPoint = multiplyMV(sphere.transform, glm::vec4(objspaceIntersection, 1.f)); + glm::vec3 intersectionPoint = multiplyMV(sphere.transform, glm::vec4(objspaceIntersection, 1.f)); normal = glm::normalize(multiplyMV(sphere.invTranspose, glm::vec4(objspaceIntersection, 0.f))); - if (!outside) { + /*if (!outside) { normal = -normal; + }*/ + + return glm::length(r.origin - intersectionPoint); +} + +__host__ __device__ float boundingBoxIntersectionTest(glm::vec3 ro, glm::vec3 rdR, AABoundBox& bounds) { + float tx1 = (bounds.minCoord.x - ro.x) * rdR.x; + float tx2 = (bounds.maxCoord.x - ro.x) * rdR.x; + float tmin = min(tx1, tx2); + float tmax = max(tx1, tx2); + float ty1 = (bounds.minCoord.y - ro.y) * rdR.y; + float ty2 = (bounds.maxCoord.y - ro.y) * rdR.y; + tmin = max(tmin, min(ty1, ty2)); + tmax = min(tmax, max(ty1, ty2)); + float tz1 = (bounds.minCoord.z - ro.z) * rdR.z; + float tz2 = (bounds.maxCoord.z - ro.z) * rdR.z; + tmin = max(tmin, min(tz1, tz2)); + tmax = min(tmax, max(tz1, tz2)); + if (tmax >= tmin && tmax > 0) { + return tmin; + } + return -1; +} + +__host__ __device__ glm::vec3 barycentric(glm::vec3 p, glm::vec3 a, glm::vec3 b, glm::vec3 c) +{ + const glm::vec3 v0 = b - a, v1 = c - a, v2 = p - a; + const float d00 = glm::dot(v0, v0); + const float d01 = glm::dot(v0, v1); + const float d11 = glm::dot(v1, v1); + const float d20 = glm::dot(v2, v0); + const float d21 = glm::dot(v2, v1); + const float invDenom = 1.f / (d00 * d11 - d01 * d01); + const float v = (d11 * d20 - d01 * d21) * invDenom; + const float w = (d00 * d21 - d01 * d20) * invDenom; + const float u = 1.0f - v - w; + return glm::vec3(u, v, w); +} + +//__host__ __device__ glm::vec3 barycentric(glm::vec3 p, glm::vec3 t1, glm::vec3 t2, glm::vec3 t3) { +// glm::vec3 edge1 = t2 - t1; +// glm::vec3 edge2 = t3 - t2; +// float S = 1 / glm::length(glm::cross(edge1, edge2)); +// +// edge1 = p - t2; +// edge2 = p - t3; +// float S1 = glm::length(glm::cross(edge1, edge2)); +// +// edge1 = p - t1; +// edge2 = p - t3; +// float S2 = glm::length(glm::cross(edge1, edge2)); +// +// edge1 = p - t1; +// edge2 = p - t2; +// float S3 = glm::length(glm::cross(edge1, edge2)); +// +// return glm::vec3(S1 * S, S2 * S, S3 * S); +//} + + +__host__ __device__ float meshIntersectionTest(Geom& mesh, BVHNode* BVHNodes, Triangle* Prims, + Ray& r, glm::vec3& normal, glm::vec2& uv, int& primIdx, float t_min) { + glm::vec3 ro = multiplyMV(mesh.inverseTransform, glm::vec4(r.origin, 1.f)); + glm::vec3 rd = glm::normalize(multiplyMV(mesh.inverseTransform, glm::vec4(r.direction, 0.f))); + glm::vec3 rdR = 1.f / rd; + + primIdx = -1; + +#if USE_BVH + int stack[32]; + int stackPtr = 0; + + stack[stackPtr++] = mesh.bvhRootIdx; + + while (stackPtr > 0) { + BVHNode& node = BVHNodes[stack[--stackPtr]]; + float t = boundingBoxIntersectionTest(ro, rdR, node.bounds); + if (t > 0 && t < t_min) { + if (node.isLeaf()) { + for (int testPrimIdx = node.leftInd; testPrimIdx < node.leftInd + node.primCnt; testPrimIdx++) { + Triangle& prim = Prims[testPrimIdx]; + glm::vec3 baryPoint; + if (glm::intersectRayTriangle(ro, rd, prim.v1.pos, prim.v2.pos, prim.v3.pos, baryPoint)) { + if (baryPoint.z < t_min) { + t_min = baryPoint.z; + primIdx = testPrimIdx; + } + } + + } + } + else { + stack[stackPtr++] = node.leftInd; + stack[stackPtr++] = node.leftInd + 1; + } + } } +#else + for (int testPrimIdx = mesh.primStartIdx; testPrimIdx < mesh.primStartIdx + mesh.primCnt; testPrimIdx++) { + Triangle& prim = Prims[testPrimIdx]; + glm::vec3 baryPoint; + if (glm::intersectRayTriangle(ro, rd, prim.v1.pos, prim.v2.pos, prim.v3.pos, baryPoint)) { + if (baryPoint.z < t_min) { + t_min = baryPoint.z; + primIdx = testPrimIdx; + } + } + } +#endif + if (primIdx == -1) { + return -1; + } + + Triangle& prim = Prims[primIdx]; + Vertex& v1 = prim.v1; + Vertex& v2 = prim.v2; + Vertex& v3 = prim.v3; + + glm::vec3 objSpaceIntersection = ro + rd * t_min; + glm::vec3 baryWeights = barycentric(objSpaceIntersection, v1.pos, v2.pos, v3.pos); + glm::vec3 objSpaceNormal = glm::normalize(baryWeights.x * v1.nor + baryWeights.y * v2.nor + baryWeights.z * v3.nor); + + glm::vec3 intersectionPoint = multiplyMV(mesh.transform, glm::vec4(objSpaceIntersection, 1.f)); + normal = glm::normalize(multiplyMV(mesh.invTranspose, glm::vec4(objSpaceNormal, 0.f))); + uv = baryWeights.x * v1.uv + baryWeights.y * v2.uv + baryWeights.z * v3.uv; return glm::length(r.origin - intersectionPoint); } diff --git a/src/main.cpp b/src/main.cpp index 96127b6d..1a4ee418 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -32,6 +32,7 @@ int height; //------------------------------- int main(int argc, char** argv) { + startTimeString = currentTimeString(); if (argc < 2) { @@ -70,6 +71,9 @@ int main(int argc, char** argv) { ogLookAt = cam.lookAt; zoom = glm::length(cam.position - ogLookAt); + guiData->focalDistance = cam.focalDistance; + guiData->lensRadius = cam.lensRadius; + // Initialize CUDA and GL components init(); @@ -78,7 +82,7 @@ int main(int argc, char** argv) { InitDataContainer(guiData); // GLFW main loop - mainLoop(); + mainLoop(camchanged); return 0; } @@ -124,7 +128,13 @@ void runCuda() { cam.position = cameraPosition; cameraPosition += cam.lookAt; cam.position = cameraPosition; + + cam.focalDistance = guiData->focalDistance; + cam.lensRadius = guiData->lensRadius; + camchanged = false; + + guiData->fbCached = false; } // Map OpenGL buffer object for writing from CUDA on a single GPU diff --git a/src/pathtrace.cu b/src/pathtrace.cu index fd2a4641..6bcb3007 100644 --- a/src/pathtrace.cu +++ b/src/pathtrace.cu @@ -4,11 +4,14 @@ #include #include #include +#include +#include #include "sceneStructs.h" #include "scene.h" #include "glm/glm.hpp" #include "glm/gtx/norm.hpp" +#include "glm/gtx/projection.hpp" #include "utilities.h" #include "pathtrace.h" #include "intersections.h" @@ -38,6 +41,22 @@ void checkCUDAErrorFn(const char* msg, const char* file, int line) { #endif } +struct is_traceable { + __host__ __device__ + bool operator()(const PathSegment p) + { + return p.remainingBounces > 0; + } +}; + +struct no_color { + __host__ __device__ + bool operator()(const PathSegment p) + { + return p.color == glm::vec3(0.f); + } +}; + __host__ __device__ thrust::default_random_engine makeSeededRandomEngine(int iter, int index, int depth) { int h = utilhash((1 << 31) | (depth << 22) | iter) ^ utilhash(index); @@ -76,6 +95,18 @@ static PathSegment* dev_paths = NULL; static ShadeableIntersection* dev_intersections = NULL; // TODO: static variables for device memory, any extra info you need, etc // ... +static thrust::device_ptr dev_thrust_intersections = NULL; +static thrust::device_ptr dev_thrust_paths = NULL; +static ShadeableIntersection* dev_fbc = NULL; +static thrust::device_ptr dev_thrust_fbc = NULL; + +static Triangle* dev_prims = NULL; +static BVHNode* dev_bvh = NULL; + +static int numTextures = 0; +static cudaTextureObject_t* host_textureObjs = NULL; +static cudaTextureObject_t* dev_textureObjs = NULL; +static cudaArray_t* host_textureDataPtrs = NULL; void InitDataContainer(GuiDataContainer* imGuiData) { @@ -103,18 +134,74 @@ void pathtraceInit(Scene* scene) { cudaMemset(dev_intersections, 0, pixelcount * sizeof(ShadeableIntersection)); // TODO: initialize any extra device memeory you need + dev_thrust_intersections = thrust::device_ptr(dev_intersections); + dev_thrust_paths = thrust::device_ptr(dev_paths); + + cudaMalloc(&dev_fbc, pixelcount * sizeof(ShadeableIntersection)); + cudaMemset(dev_fbc, 0, pixelcount * sizeof(ShadeableIntersection)); + dev_thrust_fbc = thrust::device_ptr(dev_fbc); + + cudaMalloc(&dev_prims, scene->prims.size() * sizeof(Triangle)); + cudaMemcpy(dev_prims, scene->prims.data(), scene->prims.size() * sizeof(Triangle), cudaMemcpyHostToDevice); + + cudaMalloc(&dev_bvh, scene->BVHNodes.size() * sizeof(BVHNode)); + cudaMemcpy(dev_bvh, scene->BVHNodes.data(), scene->BVHNodes.size() * sizeof(BVHNode), cudaMemcpyHostToDevice); + + // textures + numTextures = scene->textures.size(); + if (numTextures > 0) { + host_textureObjs = new cudaTextureObject_t[numTextures]; + host_textureDataPtrs = new cudaArray_t[numTextures]; + + for (int i = 0; i < numTextures; i++) { + const Texture& tex = scene->textures[i]; + cudaChannelFormatDesc channelDesc = cudaCreateChannelDesc(); + cudaMallocArray(&host_textureDataPtrs[i], &channelDesc, tex.width, tex.height); + cudaMemcpy2DToArray(host_textureDataPtrs[i], 0, 0, tex.host_buffer, tex.width * sizeof(float4), tex.width * sizeof(float4), tex.height, cudaMemcpyHostToDevice); + + cudaResourceDesc resDesc; + memset(&resDesc, 0, sizeof(resDesc)); + resDesc.resType = cudaResourceTypeArray; + resDesc.res.array.array = host_textureDataPtrs[i]; + + cudaTextureDesc texDesc; + memset(&texDesc, 0, sizeof(cudaTextureDesc)); + texDesc.addressMode[0] = cudaAddressModeWrap; + texDesc.addressMode[1] = cudaAddressModeWrap; + texDesc.filterMode = cudaFilterModeLinear; + texDesc.readMode = cudaReadModeElementType; + texDesc.normalizedCoords = 1; + + cudaCreateTextureObject(&host_textureObjs[i], &resDesc, &texDesc, NULL); + } + + cudaMalloc(&dev_textureObjs, numTextures * sizeof(cudaTextureObject_t)); + cudaMemcpy(dev_textureObjs, host_textureObjs, numTextures * sizeof(cudaTextureObject_t), cudaMemcpyHostToDevice); + } checkCUDAError("pathtraceInit"); } void pathtraceFree() { + checkCUDAError("before pathtraceFree"); cudaFree(dev_image); // no-op if dev_image is null cudaFree(dev_paths); cudaFree(dev_geoms); cudaFree(dev_materials); cudaFree(dev_intersections); // TODO: clean up any extra device memory you created - + cudaFree(dev_fbc); + cudaFree(dev_prims); + cudaFree(dev_bvh); + if (numTextures > 0) { + cudaFree(dev_textureObjs); + for (int i = 0; i < numTextures; i++) { + cudaDestroyTextureObject(host_textureObjs[i]); + cudaFreeArray(host_textureDataPtrs[i]); + } + delete[] host_textureDataPtrs; + delete[] host_textureObjs; + } checkCUDAError("pathtraceFree"); } @@ -135,15 +222,29 @@ __global__ void generateRayFromCamera(Camera cam, int iter, int traceDepth, Path int index = x + (y * cam.resolution.x); PathSegment& segment = pathSegments[index]; - segment.ray.origin = cam.position; + thrust::default_random_engine rng = makeSeededRandomEngine(iter, index, 0); + thrust::uniform_real_distribution u01(0, 1); + segment.color = glm::vec3(1.0f, 1.0f, 1.0f); // TODO: implement antialiasing by jittering the ray - segment.ray.direction = glm::normalize(cam.view - - cam.right * cam.pixelLength.x * ((float)x - (float)cam.resolution.x * 0.5f) - - cam.up * cam.pixelLength.y * ((float)y - (float)cam.resolution.y * 0.5f) + glm::vec3 baseDirection = glm::normalize(cam.view + - cam.right * cam.pixelLength.x * ((float)x - (float)cam.resolution.x * 0.5f + u01(rng)) + - cam.up * cam.pixelLength.y * ((float)y - (float)cam.resolution.y * 0.5f + u01(rng)) ); + if (cam.lensRadius > 0.f) { + float ft = cam.focalDistance / glm::length(glm::proj(baseDirection, cam.view)); + glm::vec3 pFocus = cam.position + (baseDirection * ft); + glm::vec2 pLens = cam.lensRadius * ConcentricSampleDisk(glm::vec2(u01(rng), u01(rng))); + segment.ray.origin = cam.position + (pLens.x * cam.right + pLens.y * cam.up); + segment.ray.direction = glm::normalize(pFocus - segment.ray.origin); + } + else { + segment.ray.origin = cam.position; + segment.ray.direction = baseDirection; + } + segment.pixelIndex = index; segment.remainingBounces = traceDepth; } @@ -154,12 +255,12 @@ __global__ void generateRayFromCamera(Camera cam, int iter, int traceDepth, Path // Generating new rays is handled in your shader(s). // Feel free to modify the code below. __global__ void computeIntersections( - int depth - , int num_paths - , PathSegment* pathSegments - , Geom* geoms - , int geoms_size - , ShadeableIntersection* intersections + int depth, + int num_paths, PathSegment* pathSegments, + Geom* geoms, int geoms_size, + Triangle* prims, + BVHNode* bvh, + ShadeableIntersection* intersections ) { int path_index = blockIdx.x * blockDim.x + threadIdx.x; @@ -169,14 +270,14 @@ __global__ void computeIntersections( PathSegment pathSegment = pathSegments[path_index]; float t; - glm::vec3 intersect_point; glm::vec3 normal; float t_min = FLT_MAX; int hit_geom_index = -1; bool outside = true; - glm::vec3 tmp_intersect; glm::vec3 tmp_normal; + glm::vec2 tmp_uv; + int tmp_primIdx = -1; // naive parse through global geoms @@ -186,21 +287,23 @@ __global__ void computeIntersections( if (geom.type == CUBE) { - t = boxIntersectionTest(geom, pathSegment.ray, tmp_intersect, tmp_normal, outside); + t = boxIntersectionTest(geom, pathSegment.ray, tmp_normal, outside); } else if (geom.type == SPHERE) { - t = sphereIntersectionTest(geom, pathSegment.ray, tmp_intersect, tmp_normal, outside); + t = sphereIntersectionTest(geom, pathSegment.ray, tmp_normal, outside); } // TODO: add more intersection tests here... triangle? metaball? CSG? - + else if (geom.type == MESH) + { + t = meshIntersectionTest(geom, bvh, prims, pathSegment.ray, tmp_normal, tmp_uv, tmp_primIdx, t_min); + } // Compute the minimum t from the intersection tests to determine what // scene geometry object was hit first. if (t > 0.0f && t_min > t) { t_min = t; hit_geom_index = i; - intersect_point = tmp_intersect; normal = tmp_normal; } } @@ -213,8 +316,13 @@ __global__ void computeIntersections( { //The ray hits something intersections[path_index].t = t_min; - intersections[path_index].materialId = geoms[hit_geom_index].materialid; intersections[path_index].surfaceNormal = normal; + if (tmp_primIdx != -1) { + intersections[path_index].materialId = prims[tmp_primIdx].materialid; + intersections[path_index].uv = tmp_uv; + } else { + intersections[path_index].materialId = geoms[hit_geom_index].materialid; + } } } } @@ -273,6 +381,55 @@ __global__ void shadeFakeMaterial( } } +__global__ void shadeMaterialNaive( + int iter, + int depth, + int num_paths, + ShadeableIntersection* shadeableIntersections, + PathSegment* pathSegments, + Material* materials, + cudaTextureObject_t* texObjs +) +{ + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_paths) + { + ShadeableIntersection intersection = shadeableIntersections[idx]; + PathSegment segment = pathSegments[idx]; + if (intersection.t > 0.0f) { // if the intersection exists... + // Set up the RNG + thrust::default_random_engine rng = makeSeededRandomEngine(iter, idx, depth); + + Material& material = materials[intersection.materialId]; + + // If the material indicates that the object was a light, "light" the ray + if (material.emittance > 0.0f) { + segment.color *= (material.color * material.emittance); + segment.remainingBounces = 0; + } + // Otherwise, do some pseudo-lighting computation. This is actually more + // like what you would expect from shading in a rasterizer like OpenGL. + // TODO: replace this! you should be able to start with basically a one-liner + else { + scatterRay(segment, getPointOnRay(segment.ray, intersection.t), intersection, material, texObjs, rng); + if (--segment.remainingBounces < 1) { + segment.color = glm::vec3(0.f); + } + } + // If there was no intersection, color the ray black. + // Lots of renderers use 4 channel color, RGBA, where A = alpha, often + // used for opacity, in which case they can indicate "no opacity". + // This can be useful for post-processing and image compositing. + } + else { + segment.color = glm::vec3(0.0f); + segment.remainingBounces = 0; + } + pathSegments[idx] = segment; + } +} + + // Add the current iteration's output to the overall image __global__ void finalGather(int nPaths, glm::vec3* image, PathSegment* iterationPaths) { @@ -281,6 +438,14 @@ __global__ void finalGather(int nPaths, glm::vec3* image, PathSegment* iteration if (index < nPaths) { PathSegment iterationPath = iterationPaths[index]; + /*if (isnan(iterationPath.color.x) || isnan(iterationPath.color.y) || isnan(iterationPath.color.z)) + { + image[iterationPath.pixelIndex] = glm::vec3(1, 0, 1); + } + else + { + image[iterationPath.pixelIndex] += iterationPath.color; + }*/ image[iterationPath.pixelIndex] += iterationPath.color; } } @@ -338,49 +503,70 @@ void pathtrace(uchar4* pbo, int frame, int iter) { checkCUDAError("generate camera ray"); int depth = 0; - PathSegment* dev_path_end = dev_paths + pixelcount; - int num_paths = dev_path_end - dev_paths; + thrust::device_ptr dev_thrust_paths_end; + int num_paths = pixelcount; + dim3 numblocksPathSegmentTracing = (num_paths + blockSize1d - 1) / blockSize1d; // --- PathSegment Tracing Stage --- // Shoot ray into scene, bounce between objects, push shading chunks - bool iterationComplete = false; while (!iterationComplete) { - - // clean shading chunks - cudaMemset(dev_intersections, 0, pixelcount * sizeof(ShadeableIntersection)); - - // tracing - dim3 numblocksPathSegmentTracing = (num_paths + blockSize1d - 1) / blockSize1d; - computeIntersections << > > ( - depth - , num_paths - , dev_paths - , dev_geoms - , hst_scene->geoms.size() - , dev_intersections + if (guiData->cacheFirstBounce && guiData->fbCached && depth == 0) { + thrust::copy(dev_thrust_fbc, dev_thrust_fbc + pixelcount, dev_thrust_intersections); + checkCUDAError("load first bounce cache"); + } + else { + // clean shading chunks + cudaMemset(dev_intersections, 0, pixelcount * sizeof(ShadeableIntersection)); + + // tracing + computeIntersections << > > ( + depth, num_paths, dev_paths, + dev_geoms, hst_scene->geoms.size(), + dev_prims, dev_bvh, + dev_intersections ); - checkCUDAError("trace one bounce"); - cudaDeviceSynchronize(); + checkCUDAError("trace one bounce"); + cudaDeviceSynchronize(); + } depth++; - // TODO: + if (guiData->sortMaterial) { + thrust::sort_by_key(dev_thrust_intersections, dev_thrust_intersections + num_paths, dev_thrust_paths); + checkCUDAError("sort by material"); + } + + if (guiData->cacheFirstBounce && !(guiData->fbCached)) { + thrust::copy(dev_thrust_intersections, dev_thrust_intersections + pixelcount, dev_thrust_fbc); + checkCUDAError("compute first bounce cache"); + guiData->fbCached = true; + } + // --- Shading Stage --- // Shade path segments based on intersections and generate new rays by - // evaluating the BSDF. - // Start off with just a big kernel that handles all the different - // materials you have in the scenefile. - // TODO: compare between directly shading the path segments and shading - // path segments that have been reshuffled to be contiguous in memory. + // evaluating the BSDF. + // Start off with just a big kernel that handles all the different + // materials you have in the scenefile. + // TODO: compare between directly shading the path segments and shading + // path segments that have been reshuffled to be contiguous in memory. - shadeFakeMaterial << > > ( + shadeMaterialNaive << > > ( iter, + depth, num_paths, dev_intersections, dev_paths, - dev_materials + dev_materials, + dev_textureObjs ); - iterationComplete = true; // TODO: should be based off stream compaction results. + checkCUDAError("Naive Shading"); + // partition rays so that those terminated are not rerun next round + dev_thrust_paths_end = thrust::partition(dev_thrust_paths, dev_thrust_paths + num_paths, is_traceable()); + checkCUDAError("partition terminated rays to back"); + num_paths = dev_thrust_paths_end - dev_thrust_paths; + numblocksPathSegmentTracing = (num_paths + blockSize1d - 1) / blockSize1d; + + iterationComplete = num_paths == 0; // TODO: should be based off stream compaction results. if (guiData != NULL) { @@ -388,14 +574,23 @@ void pathtrace(uchar4* pbo, int frame, int iter) { } } + // remove paths that make no contribution + dev_thrust_paths_end = thrust::remove_if(dev_thrust_paths, dev_thrust_paths + pixelcount, no_color()); + checkCUDAError("remove no contribution paths"); + num_paths = dev_thrust_paths_end - dev_thrust_paths; + /*std::cout << num_paths << std::endl;*/ // Assemble this iteration and apply it to the image - dim3 numBlocksPixels = (pixelcount + blockSize1d - 1) / blockSize1d; - finalGather << > > (num_paths, dev_image, dev_paths); + if (num_paths > 0) { + dim3 numBlocksPixels = (num_paths + blockSize1d - 1) / blockSize1d; + finalGather << > > (num_paths, dev_image, dev_paths); + checkCUDAError("finalgather"); + } /////////////////////////////////////////////////////////////////////////// // Send results to OpenGL buffer for rendering sendImageToPBO << > > (pbo, cam.resolution, iter, dev_image); + checkCUDAError("sendPBO"); // Retrieve image from GPU cudaMemcpy(hst_scene->state.image.data(), dev_image, diff --git a/src/preview.cpp b/src/preview.cpp index bd52a686..35f1e650 100644 --- a/src/preview.cpp +++ b/src/preview.cpp @@ -109,7 +109,8 @@ void cleanupCuda() { } void initCuda() { - cudaGLSetGLDevice(0); + // cudaSetDevice(1); + cudaGLSetGLDevice(1); // Clean up on program exit atexit(cleanupCuda); @@ -189,7 +190,7 @@ void InitImguiData(GuiDataContainer* guiData) // LOOK: Un-Comment to check ImGui Usage -void RenderImGui() +void RenderImGui(bool& camchanged) { mouseOverImGuiWinow = io->WantCaptureMouse; @@ -219,6 +220,10 @@ void RenderImGui() //ImGui::Text("counter = %d", counter); ImGui::Text("Traced Depth %d", imguiData->TracedDepth); ImGui::Text("Application average %.3f ms/frame (%.1f FPS)", 1000.0f / ImGui::GetIO().Framerate, ImGui::GetIO().Framerate); + camchanged |= ImGui::SliderFloat("Focal Distance", &imguiData->focalDistance, 0.f, 25.f); + camchanged |= ImGui::SliderFloat("Lens Radius", &imguiData->lensRadius, 0.f, 2.f); + ImGui::Checkbox("Sort buffers by material type", &(imguiData->sortMaterial)); + ImGui::Checkbox("Cache First Bounce", &(imguiData->cacheFirstBounce)); ImGui::End(); @@ -232,7 +237,7 @@ bool MouseOverImGuiWindow() return mouseOverImGuiWinow; } -void mainLoop() { +void mainLoop(bool& camchanged) { while (!glfwWindowShouldClose(window)) { glfwPollEvents(); @@ -253,7 +258,7 @@ void mainLoop() { glDrawElements(GL_TRIANGLES, 6, GL_UNSIGNED_SHORT, 0); // Render ImGui Stuff - RenderImGui(); + RenderImGui(camchanged); glfwSwapBuffers(window); } diff --git a/src/preview.h b/src/preview.h index 800b149a..9cf52017 100644 --- a/src/preview.h +++ b/src/preview.h @@ -4,7 +4,7 @@ extern GLuint pbo; std::string currentTimeString(); bool init(); -void mainLoop(); +void mainLoop(bool& camchanged); bool MouseOverImGuiWindow(); void InitImguiData(GuiDataContainer* guiData); \ No newline at end of file diff --git a/src/scene.cpp b/src/scene.cpp index 3fb6239a..f3dbb0ba 100644 --- a/src/scene.cpp +++ b/src/scene.cpp @@ -4,7 +4,11 @@ #include #include +#define TINYGLTF_IMPLEMENTATION +#include "tinygltf/tiny_gltf.h" + Scene::Scene(string filename) { + cout << "Reading scene from " << filename << " ..." << endl; cout << " " << endl; char* fname = (char*)filename.c_str(); @@ -32,6 +36,158 @@ Scene::Scene(string filename) { } } +int Scene::loadMesh(const string& fp, int& primStartIdx, int& primCnt) { + tinygltf::Model model; + tinygltf::TinyGLTF loader; + string err; + string warn; + bool ret; + + // Read from file + if (utilityCore::matchFileExtension(fp, "gltf")) { + ret = loader.LoadASCIIFromFile(&model, &err, &warn, fp); + } + else if (utilityCore::matchFileExtension(fp, "glb")) { + ret = loader.LoadBinaryFromFile(&model, &err, &warn, fp); + } + else { + cout << "Reading " << fp << "failed! Unsupported file type." << endl; + exit(-1); + } + if (!warn.empty()) { + cout << "TinyGLTF Warnings: " << warn << endl; + } + if (!err.empty()) { + cout << "TinyGLTF Errors: " << err << endl; + } + if (!ret) { + exit(-1); + } + cout << "Successfully read file. Processing..." << endl; + + // Load materials to memory + int matStartIdx = materials.size(); + for (const tinygltf::Material& material : model.materials) { + const int textureIdx = material.pbrMetallicRoughness.baseColorTexture.index; + if (textureIdx < 0) { + continue; + } + const tinygltf::Image& img = model.images[model.textures[textureIdx].source]; + textures.emplace_back(); + Texture& tex = textures.back(); + tex.host_buffer = new float[img.width * img.height * 4]; + for (int i = 0; i < img.image.size(); i++) { + tex.host_buffer[i] = img.image[i] / 255.f; + } + tex.width = img.width; + tex.height = img.height; + tex.channels = 4; + materials.emplace_back(); + Material& mat = materials.back(); + mat.textureid = textures.size() - 1; + mat.color = glm::vec3(1.f, 0.f, 0.f); + mat.emittance = 0; + mat.hasReflective = 0; + mat.hasRefractive = 0; + mat.indexOfRefraction = 0; + mat.specular.color = glm::vec3(0.f); + mat.specular.exponent = 0; + } + + // Load primitives + primStartIdx = prims.size(); + primCnt = 0; + for (const tinygltf::Mesh& mesh : model.meshes) { + for (const tinygltf::Primitive& primitive : mesh.primitives) { + const int primMatId = primitive.material >= 0 ? matStartIdx + primitive.material : -1; + + const tinygltf::Accessor& posAccessor = model.accessors[primitive.attributes.at("POSITION")]; + const tinygltf::BufferView& posBufferView = model.bufferViews[posAccessor.bufferView]; + const tinygltf::Buffer& posBuffer = model.buffers[posBufferView.buffer]; + const float* posArray = reinterpret_cast(&posBuffer.data[posBufferView.byteOffset + posAccessor.byteOffset]); + + const float* norArray = nullptr; + if (primitive.attributes.find("NORMAL") != primitive.attributes.end()) { + const tinygltf::Accessor& norAccessor = model.accessors[primitive.attributes.at("NORMAL")]; + const tinygltf::BufferView& norBufferView = model.bufferViews[norAccessor.bufferView]; + const tinygltf::Buffer& norBuffer = model.buffers[norBufferView.buffer]; + norArray = reinterpret_cast(&norBuffer.data[norBufferView.byteOffset + norAccessor.byteOffset]); + } + + const float* uvArray = nullptr; + if (primitive.attributes.find("TEXCOORD_0") != primitive.attributes.end()) { + const tinygltf::Accessor& uvAccessor = model.accessors[primitive.attributes.at("TEXCOORD_0")]; + const tinygltf::BufferView& uvBufferView = model.bufferViews[uvAccessor.bufferView]; + const tinygltf::Buffer& uvBuffer = model.buffers[uvBufferView.buffer]; + uvArray = reinterpret_cast(&uvBuffer.data[uvBufferView.byteOffset + uvAccessor.byteOffset]); + } + + if (primitive.indices < 0) { + // vertices are not shared (not indexed) + for (size_t i = 0; i < posAccessor.count; i += 3) { + Triangle triangle; + triangle.v1.pos = glm::vec3(posArray[i * 3], posArray[i * 3 + 1], posArray[i * 3 + 2]); + triangle.v2.pos = glm::vec3(posArray[(i + 1) * 3], posArray[(i + 1) * 3 + 1], posArray[(i + 1) * 3 + 2]); + triangle.v3.pos = glm::vec3(posArray[(i + 2) * 3], posArray[(i + 2) * 3 + 1], posArray[(i + 2) * 3 + 2]); + + if (norArray) { + triangle.v1.nor = glm::vec3(norArray[i * 3], norArray[i * 3 + 1], norArray[i * 3 + 2]); + triangle.v2.nor = glm::vec3(norArray[(i + 1) * 3], norArray[(i + 1) * 3 + 1], norArray[(i + 1) * 3 + 2]); + triangle.v3.nor = glm::vec3(norArray[(i + 2) * 3], norArray[(i + 2) * 3 + 1], norArray[(i + 2) * 3 + 2]); + } + + if (uvArray) { + triangle.v1.uv = glm::vec2(uvArray[i * 2], uvArray[i * 2 + 1]); + triangle.v2.uv = glm::vec2(uvArray[(i + 1) * 2], uvArray[(i + 1) * 2 + 1]); + triangle.v3.uv = glm::vec2(uvArray[(i + 2) * 2], uvArray[(i + 2) * 2 + 1]); + } + + triangle.centroid = (triangle.v1.pos + triangle.v2.pos + triangle.v3.pos) * 0.33333333333f; + triangle.materialid = primMatId; + prims.push_back(triangle); + ++primCnt; + } + } else { + const tinygltf::Accessor& indAccessor = model.accessors[primitive.indices]; + const tinygltf::BufferView& indBufferView = model.bufferViews[indAccessor.bufferView]; + const tinygltf::Buffer& indBuffer = model.buffers[indBufferView.buffer]; + + const uint16_t* indArray = reinterpret_cast(&indBuffer.data[indBufferView.byteOffset + indAccessor.byteOffset]); + for (size_t i = 0; i < indAccessor.count; i += 3) { + Triangle triangle; + + const int v1 = indArray[i]; + const int v2 = indArray[i + 1]; + const int v3 = indArray[i + 2]; + + triangle.v1.pos = glm::vec3(posArray[v1 * 3], posArray[v1 * 3 + 1], posArray[v1 * 3 + 2]); + triangle.v2.pos = glm::vec3(posArray[v2 * 3], posArray[v2 * 3 + 1], posArray[v2 * 3 + 2]); + triangle.v3.pos = glm::vec3(posArray[v3 * 3], posArray[v3 * 3 + 1], posArray[v3 * 3 + 2]); + + if (norArray) { + triangle.v1.nor = glm::vec3(norArray[v1 * 3], norArray[v1 * 3 + 1], norArray[v1 * 3 + 2]); + triangle.v2.nor = glm::vec3(norArray[v2 * 3], norArray[v2 * 3 + 1], norArray[v2 * 3 + 2]); + triangle.v3.nor = glm::vec3(norArray[v3 * 3], norArray[v3 * 3 + 1], norArray[v3 * 3 + 2]); + } + + if (uvArray) { + triangle.v1.uv = glm::vec2(uvArray[v1 * 2], uvArray[v1 * 2 + 1]); + triangle.v2.uv = glm::vec2(uvArray[v2 * 2], uvArray[v2 * 2 + 1]); + triangle.v3.uv = glm::vec2(uvArray[v3 * 2], uvArray[v3 * 2 + 1]); + } + + triangle.centroid = (triangle.v1.pos + triangle.v2.pos + triangle.v3.pos) * 0.33333333333f; + triangle.materialid = primMatId; + prims.push_back(triangle); + ++primCnt; + } + } + } + } + + return buildBVH(primStartIdx, primCnt); +} + int Scene::loadGeom(string objectid) { int id = atoi(objectid.c_str()); if (id != geoms.size()) { @@ -52,6 +208,14 @@ int Scene::loadGeom(string objectid) { cout << "Creating new cube..." << endl; newGeom.type = CUBE; } + else if (strcmp(line.c_str(), "mesh") == 0) { + cout << "Reading Mesh..." << endl; + string meshFp; + utilityCore::safeGetline(fp_in, meshFp); + newGeom.type = MESH; + newGeom.bvhRootIdx = loadMesh(meshFp, newGeom.primStartIdx, newGeom.primCnt); + cout << "Loaded " << utilityCore::extractFilename(meshFp) << " with " << newGeom.primCnt << " triangles." << endl; + } } //link material @@ -124,6 +288,10 @@ int Scene::loadCamera() { camera.lookAt = glm::vec3(atof(tokens[1].c_str()), atof(tokens[2].c_str()), atof(tokens[3].c_str())); } else if (strcmp(tokens[0].c_str(), "UP") == 0) { camera.up = glm::vec3(atof(tokens[1].c_str()), atof(tokens[2].c_str()), atof(tokens[3].c_str())); + } else if (strcmp(tokens[0].c_str(), "FOCALDIST") == 0) { + camera.focalDistance = atof(tokens[1].c_str()); + } else if (strcmp(tokens[0].c_str(), "LENSRADIUS") == 0) { + camera.lensRadius = atof(tokens[1].c_str()); } utilityCore::safeGetline(fp_in, line); @@ -186,3 +354,113 @@ int Scene::loadMaterial(string materialid) { return 1; } } + +int Scene::buildBVH(int startPrim, int numPrim) { + // a BVH for a mesh is at most 2N - 1 nodes, where N is number of triangles in mesh + BVHNodes.reserve(BVHNodes.size() + 2 * numPrim); + + int rootIdx = BVHNodes.size(); + BVHNodes.emplace_back(startPrim, numPrim); + BVHNode& root = BVHNodes[rootIdx]; + bvhUpdateBounds(root); + bvhSubdivide(root); + return rootIdx; +} + +void Scene::bvhUpdateBounds(BVHNode& node) { + node.bounds = AABoundBox(); + for (int i = 0; i < node.primCnt; i++) { + const Triangle& t = prims[node.leftInd + i]; + node.bounds.grow(t); + } +} + +float Scene::bvhBestSplitPlane(BVHNode& node, int& axis, float& splitPos, AABoundBox& leftChild, AABoundBox& rightChild) { + float bestCost = FLT_MAX; + for (int testAxis = 0; testAxis < 3; testAxis++) { + float boundsMin = FLT_MAX; + float boundsMax = -FLT_MAX; + for (int i = 0; i < node.primCnt; i++) { + const Triangle& triangle = prims[node.leftInd + i]; + boundsMin = min(boundsMin, triangle.centroid[testAxis]); + boundsMax = max(boundsMax, triangle.centroid[testAxis]); + } + if (boundsMin == boundsMax) continue; + // populate the bins + BVHBin bin[NUM_BVHBINS]; + float scale = NUM_BVHBINS / (boundsMax - boundsMin); + for (int i = 0; i < node.primCnt; i++) { + const Triangle& triangle = prims[node.leftInd + i]; + int binIdx = min(NUM_BVHBINS - 1, (int)((triangle.centroid[testAxis] - boundsMin) * scale)); + bin[binIdx].primCnt++; + bin[binIdx].bounds.grow(triangle); + } + // gather data for the planes between the NUM_BVHBINS bins + AABoundBox leftBoxes[NUM_BVHBINS - 1], rightBoxes[NUM_BVHBINS - 1]; + int leftCount[NUM_BVHBINS - 1], rightCount[NUM_BVHBINS - 1]; + AABoundBox leftBox, rightBox; + int leftSum = 0, rightSum = 0; + for (int i = 0; i < NUM_BVHBINS - 1; i++) { + leftSum += bin[i].primCnt; + leftCount[i] = leftSum; + leftBox.grow(bin[i].bounds); + leftBoxes[i] = leftBox; + rightSum += bin[NUM_BVHBINS - 1 - i].primCnt; + rightCount[NUM_BVHBINS - 2 - i] = rightSum; + rightBox.grow(bin[NUM_BVHBINS - 1 - i].bounds); + rightBoxes[NUM_BVHBINS - 2 - i] = rightBox; + } + // calculate SAH cost for the NUM_BVHBINS planes + scale = (boundsMax - boundsMin) / NUM_BVHBINS; + for (int i = 0; i < NUM_BVHBINS - 1; i++) { + float planeCost = leftCount[i] * leftBoxes[i].surfaceArea() + rightCount[i] * rightBoxes[i].surfaceArea(); + if (planeCost < bestCost) { + axis = testAxis; + splitPos = boundsMin + scale * (i + 1); + leftChild = leftBoxes[i]; + rightChild = rightBoxes[i]; + bestCost = planeCost; + } + } + } + return bestCost; +} + +void Scene::bvhSubdivide(BVHNode& node) { + // determine split axis and position + int axis = 0; + float splitPos; + AABoundBox leftChildBox, rightChildBox; + float splitCost = bvhBestSplitPlane(node, axis, splitPos, leftChildBox, rightChildBox); + if (splitCost >= node.scanCost()) { + return; + } + // partition by split position + int i = node.leftInd; + int j = i + node.primCnt - 1; + while (i <= j) { + if (prims[i].centroid[axis] < splitPos) { + i++; + } + else { + std::swap(prims[i], prims[j--]); + } + } + + // abort split if one of the sides is empty + int leftCount = i - node.leftInd; + if (leftCount == 0 || leftCount == node.primCnt) return; + + // create child nodes + int leftChildIdx = BVHNodes.size(); // one greater than last node in vector + BVHNodes.emplace_back(leftChildBox, node.leftInd, leftCount); // construct leftChild directly in vector + int rightChildIdx = BVHNodes.size(); // one greater than last node (left child) in vector + BVHNodes.emplace_back(rightChildBox, i, node.primCnt - leftCount); + + node.leftInd = leftChildIdx; + node.primCnt = 0; + + // recursively build child nodes + bvhSubdivide(BVHNodes[leftChildIdx]); + bvhSubdivide(BVHNodes[rightChildIdx]); +} diff --git a/src/scene.h b/src/scene.h index f29a9171..fb295e1f 100644 --- a/src/scene.h +++ b/src/scene.h @@ -16,11 +16,20 @@ class Scene { int loadMaterial(string materialid); int loadGeom(string objectid); int loadCamera(); + int loadMesh(const string& fp, int& primStartIdx, int& primCnt); + + int buildBVH(int startPrim, int numPrim); // return BVHRoot Idx, take start Idx in Prim Buffer + Prims Cnt in Mesh + void bvhUpdateBounds(BVHNode& node); + float bvhBestSplitPlane(BVHNode& node, int& axis, float& splitPos, AABoundBox& leftChild, AABoundBox& rightChild); + void bvhSubdivide(BVHNode& node); public: Scene(string filename); ~Scene(); std::vector geoms; std::vector materials; + std::vector textures; + std::vector prims; + std::vector BVHNodes; RenderState state; }; diff --git a/src/sceneStructs.h b/src/sceneStructs.h index da4dbf30..70425d17 100644 --- a/src/sceneStructs.h +++ b/src/sceneStructs.h @@ -10,6 +10,7 @@ enum GeomType { SPHERE, CUBE, + MESH, }; struct Ray { @@ -20,6 +21,9 @@ struct Ray { struct Geom { enum GeomType type; int materialid; + int bvhRootIdx; + int primStartIdx; + int primCnt; glm::vec3 translation; glm::vec3 rotation; glm::vec3 scale; @@ -28,6 +32,64 @@ struct Geom { glm::mat4 invTranspose; }; +struct Vertex { + glm::vec3 pos; + glm::vec3 nor; + glm::vec2 uv; +}; + +struct Triangle { + Vertex v1; + Vertex v2; + Vertex v3; + glm::vec3 centroid; + int materialid; +}; + +struct AABoundBox { + glm::vec3 minCoord = glm::vec3(FLT_MAX); + glm::vec3 maxCoord = glm::vec3(-FLT_MAX); + __host__ __device__ void grow(const glm::vec3& p) { + minCoord = min(minCoord, p); + maxCoord = max(maxCoord, p); + } + __host__ __device__ void grow(const Triangle& t) { + grow(t.v1.pos); + grow(t.v2.pos); + grow(t.v3.pos); + } + __host__ __device__ void grow(const AABoundBox& o) { + grow(o.minCoord); + grow(o.maxCoord); + } + __host__ __device__ float surfaceArea() const { + glm::vec3 diag = maxCoord - minCoord; + return diag.x * diag.y + diag.y * diag.z + diag.z * diag.x; + } +}; + +struct BVHNode { + AABoundBox bounds; + int leftInd; // first index of triangle if leaf, else index of left child node + int primCnt; // only non-zero if it's a leaf (only leaf has primitives) + + BVHNode() : bounds(AABoundBox()), leftInd(0), primCnt(0) {} + BVHNode(int leftInd, int primCnt) : leftInd(leftInd), primCnt(primCnt) {} + BVHNode(AABoundBox bounds, int leftInd, int primCnt) : bounds(bounds), leftInd(leftInd), primCnt(primCnt) {} + + __host__ __device__ bool isLeaf() const { + return primCnt > 0; + } + __host__ __device__ float scanCost() const { + return primCnt * bounds.surfaceArea(); + } +}; + +struct BVHBin { + AABoundBox bounds; + int primCnt = 0; +}; + struct Material { glm::vec3 color; struct { @@ -38,6 +100,14 @@ struct Material { float hasRefractive; float indexOfRefraction; float emittance; + int textureid = -1; +}; + +struct Texture { + float* host_buffer; + int width; + int height; + int channels; }; struct Camera { @@ -49,6 +119,8 @@ struct Camera { glm::vec3 right; glm::vec2 fov; glm::vec2 pixelLength; + float focalDistance; + float lensRadius; }; struct RenderState { @@ -72,5 +144,11 @@ struct PathSegment { struct ShadeableIntersection { float t; glm::vec3 surfaceNormal; + glm::vec2 uv; int materialId; + + __host__ __device__ + bool operator<(const ShadeableIntersection& o) const { + return materialId < o.materialId; + } }; diff --git a/src/tinygltf/json.hpp b/src/tinygltf/json.hpp new file mode 100644 index 00000000..87475ab3 --- /dev/null +++ b/src/tinygltf/json.hpp @@ -0,0 +1,26753 @@ +/* + __ _____ _____ _____ + __| | __| | | | JSON for Modern C++ +| | |__ | | | | | | version 3.10.4 +|_____|_____|_____|_|___| https://github.com/nlohmann/json + +Licensed under the MIT License . +SPDX-License-Identifier: MIT +Copyright (c) 2013-2019 Niels Lohmann . + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +*/ + +#ifndef INCLUDE_NLOHMANN_JSON_HPP_ +#define INCLUDE_NLOHMANN_JSON_HPP_ + +#define NLOHMANN_JSON_VERSION_MAJOR 3 +#define NLOHMANN_JSON_VERSION_MINOR 10 +#define NLOHMANN_JSON_VERSION_PATCH 4 + +#include // all_of, find, for_each +#include // nullptr_t, ptrdiff_t, size_t +#include // hash, less +#include // initializer_list +#ifndef JSON_NO_IO + #include // istream, ostream +#endif // JSON_NO_IO +#include // random_access_iterator_tag +#include // unique_ptr +#include // accumulate +#include // string, stoi, to_string +#include // declval, forward, move, pair, swap +#include // vector + +// #include + + +#include +#include + +// #include + + +#include // transform +#include // array +#include // forward_list +#include // inserter, front_inserter, end +#include // map +#include // string +#include // tuple, make_tuple +#include // is_arithmetic, is_same, is_enum, underlying_type, is_convertible +#include // unordered_map +#include // pair, declval +#include // valarray + +// #include + + +#include // exception +#include // runtime_error +#include // to_string +#include // vector + +// #include + + +#include // array +#include // size_t +#include // uint8_t +#include // string + +namespace nlohmann +{ +namespace detail +{ +/////////////////////////// +// JSON type enumeration // +/////////////////////////// + +/*! +@brief the JSON type enumeration + +This enumeration collects the different JSON types. It is internally used to +distinguish the stored values, and the functions @ref basic_json::is_null(), +@ref basic_json::is_object(), @ref basic_json::is_array(), +@ref basic_json::is_string(), @ref basic_json::is_boolean(), +@ref basic_json::is_number() (with @ref basic_json::is_number_integer(), +@ref basic_json::is_number_unsigned(), and @ref basic_json::is_number_float()), +@ref basic_json::is_discarded(), @ref basic_json::is_primitive(), and +@ref basic_json::is_structured() rely on it. + +@note There are three enumeration entries (number_integer, number_unsigned, and +number_float), because the library distinguishes these three types for numbers: +@ref basic_json::number_unsigned_t is used for unsigned integers, +@ref basic_json::number_integer_t is used for signed integers, and +@ref basic_json::number_float_t is used for floating-point numbers or to +approximate integers which do not fit in the limits of their respective type. + +@sa see @ref basic_json::basic_json(const value_t value_type) -- create a JSON +value with the default value for a given type + +@since version 1.0.0 +*/ +enum class value_t : std::uint8_t +{ + null, ///< null value + object, ///< object (unordered set of name/value pairs) + array, ///< array (ordered collection of values) + string, ///< string value + boolean, ///< boolean value + number_integer, ///< number value (signed integer) + number_unsigned, ///< number value (unsigned integer) + number_float, ///< number value (floating-point) + binary, ///< binary array (ordered collection of bytes) + discarded ///< discarded by the parser callback function +}; + +/*! +@brief comparison operator for JSON types + +Returns an ordering that is similar to Python: +- order: null < boolean < number < object < array < string < binary +- furthermore, each type is not smaller than itself +- discarded values are not comparable +- binary is represented as a b"" string in python and directly comparable to a + string; however, making a binary array directly comparable with a string would + be surprising behavior in a JSON file. + +@since version 1.0.0 +*/ +inline bool operator<(const value_t lhs, const value_t rhs) noexcept +{ + static constexpr std::array order = {{ + 0 /* null */, 3 /* object */, 4 /* array */, 5 /* string */, + 1 /* boolean */, 2 /* integer */, 2 /* unsigned */, 2 /* float */, + 6 /* binary */ + } + }; + + const auto l_index = static_cast(lhs); + const auto r_index = static_cast(rhs); + return l_index < order.size() && r_index < order.size() && order[l_index] < order[r_index]; +} +} // namespace detail +} // namespace nlohmann + +// #include + + +#include +// #include + + +#include // declval, pair +// #include + + +/* Hedley - https://nemequ.github.io/hedley + * Created by Evan Nemerson + * + * To the extent possible under law, the author(s) have dedicated all + * copyright and related and neighboring rights to this software to + * the public domain worldwide. This software is distributed without + * any warranty. + * + * For details, see . + * SPDX-License-Identifier: CC0-1.0 + */ + +#if !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < 15) +#if defined(JSON_HEDLEY_VERSION) + #undef JSON_HEDLEY_VERSION +#endif +#define JSON_HEDLEY_VERSION 15 + +#if defined(JSON_HEDLEY_STRINGIFY_EX) + #undef JSON_HEDLEY_STRINGIFY_EX +#endif +#define JSON_HEDLEY_STRINGIFY_EX(x) #x + +#if defined(JSON_HEDLEY_STRINGIFY) + #undef JSON_HEDLEY_STRINGIFY +#endif +#define JSON_HEDLEY_STRINGIFY(x) JSON_HEDLEY_STRINGIFY_EX(x) + +#if defined(JSON_HEDLEY_CONCAT_EX) + #undef JSON_HEDLEY_CONCAT_EX +#endif +#define JSON_HEDLEY_CONCAT_EX(a,b) a##b + +#if defined(JSON_HEDLEY_CONCAT) + #undef JSON_HEDLEY_CONCAT +#endif +#define JSON_HEDLEY_CONCAT(a,b) JSON_HEDLEY_CONCAT_EX(a,b) + +#if defined(JSON_HEDLEY_CONCAT3_EX) + #undef JSON_HEDLEY_CONCAT3_EX +#endif +#define JSON_HEDLEY_CONCAT3_EX(a,b,c) a##b##c + +#if defined(JSON_HEDLEY_CONCAT3) + #undef JSON_HEDLEY_CONCAT3 +#endif +#define JSON_HEDLEY_CONCAT3(a,b,c) JSON_HEDLEY_CONCAT3_EX(a,b,c) + +#if defined(JSON_HEDLEY_VERSION_ENCODE) + #undef JSON_HEDLEY_VERSION_ENCODE +#endif +#define JSON_HEDLEY_VERSION_ENCODE(major,minor,revision) (((major) * 1000000) + ((minor) * 1000) + (revision)) + +#if defined(JSON_HEDLEY_VERSION_DECODE_MAJOR) + #undef JSON_HEDLEY_VERSION_DECODE_MAJOR +#endif +#define JSON_HEDLEY_VERSION_DECODE_MAJOR(version) ((version) / 1000000) + +#if defined(JSON_HEDLEY_VERSION_DECODE_MINOR) + #undef JSON_HEDLEY_VERSION_DECODE_MINOR +#endif +#define JSON_HEDLEY_VERSION_DECODE_MINOR(version) (((version) % 1000000) / 1000) + +#if defined(JSON_HEDLEY_VERSION_DECODE_REVISION) + #undef JSON_HEDLEY_VERSION_DECODE_REVISION +#endif +#define JSON_HEDLEY_VERSION_DECODE_REVISION(version) ((version) % 1000) + +#if defined(JSON_HEDLEY_GNUC_VERSION) + #undef JSON_HEDLEY_GNUC_VERSION +#endif +#if defined(__GNUC__) && defined(__GNUC_PATCHLEVEL__) + #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__) +#elif defined(__GNUC__) + #define JSON_HEDLEY_GNUC_VERSION JSON_HEDLEY_VERSION_ENCODE(__GNUC__, __GNUC_MINOR__, 0) +#endif + +#if defined(JSON_HEDLEY_GNUC_VERSION_CHECK) + #undef JSON_HEDLEY_GNUC_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_GNUC_VERSION) + #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GNUC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_MSVC_VERSION) + #undef JSON_HEDLEY_MSVC_VERSION +#endif +#if defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 140000000) && !defined(__ICL) + #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 10000000, (_MSC_FULL_VER % 10000000) / 100000, (_MSC_FULL_VER % 100000) / 100) +#elif defined(_MSC_FULL_VER) && !defined(__ICL) + #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_FULL_VER / 1000000, (_MSC_FULL_VER % 1000000) / 10000, (_MSC_FULL_VER % 10000) / 10) +#elif defined(_MSC_VER) && !defined(__ICL) + #define JSON_HEDLEY_MSVC_VERSION JSON_HEDLEY_VERSION_ENCODE(_MSC_VER / 100, _MSC_VER % 100, 0) +#endif + +#if defined(JSON_HEDLEY_MSVC_VERSION_CHECK) + #undef JSON_HEDLEY_MSVC_VERSION_CHECK +#endif +#if !defined(JSON_HEDLEY_MSVC_VERSION) + #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (0) +#elif defined(_MSC_VER) && (_MSC_VER >= 1400) + #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 10000000) + (minor * 100000) + (patch))) +#elif defined(_MSC_VER) && (_MSC_VER >= 1200) + #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_FULL_VER >= ((major * 1000000) + (minor * 10000) + (patch))) +#else + #define JSON_HEDLEY_MSVC_VERSION_CHECK(major,minor,patch) (_MSC_VER >= ((major * 100) + (minor))) +#endif + +#if defined(JSON_HEDLEY_INTEL_VERSION) + #undef JSON_HEDLEY_INTEL_VERSION +#endif +#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && !defined(__ICL) + #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, __INTEL_COMPILER_UPDATE) +#elif defined(__INTEL_COMPILER) && !defined(__ICL) + #define JSON_HEDLEY_INTEL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER / 100, __INTEL_COMPILER % 100, 0) +#endif + +#if defined(JSON_HEDLEY_INTEL_VERSION_CHECK) + #undef JSON_HEDLEY_INTEL_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_INTEL_VERSION) + #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_INTEL_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_INTEL_CL_VERSION) + #undef JSON_HEDLEY_INTEL_CL_VERSION +#endif +#if defined(__INTEL_COMPILER) && defined(__INTEL_COMPILER_UPDATE) && defined(__ICL) + #define JSON_HEDLEY_INTEL_CL_VERSION JSON_HEDLEY_VERSION_ENCODE(__INTEL_COMPILER, __INTEL_COMPILER_UPDATE, 0) +#endif + +#if defined(JSON_HEDLEY_INTEL_CL_VERSION_CHECK) + #undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_INTEL_CL_VERSION) + #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_INTEL_CL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_INTEL_CL_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_PGI_VERSION) + #undef JSON_HEDLEY_PGI_VERSION +#endif +#if defined(__PGI) && defined(__PGIC__) && defined(__PGIC_MINOR__) && defined(__PGIC_PATCHLEVEL__) + #define JSON_HEDLEY_PGI_VERSION JSON_HEDLEY_VERSION_ENCODE(__PGIC__, __PGIC_MINOR__, __PGIC_PATCHLEVEL__) +#endif + +#if defined(JSON_HEDLEY_PGI_VERSION_CHECK) + #undef JSON_HEDLEY_PGI_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_PGI_VERSION) + #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PGI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_PGI_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_SUNPRO_VERSION) + #undef JSON_HEDLEY_SUNPRO_VERSION +#endif +#if defined(__SUNPRO_C) && (__SUNPRO_C > 0x1000) + #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_C >> 16) & 0xf) * 10) + ((__SUNPRO_C >> 12) & 0xf), (((__SUNPRO_C >> 8) & 0xf) * 10) + ((__SUNPRO_C >> 4) & 0xf), (__SUNPRO_C & 0xf) * 10) +#elif defined(__SUNPRO_C) + #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_C >> 8) & 0xf, (__SUNPRO_C >> 4) & 0xf, (__SUNPRO_C) & 0xf) +#elif defined(__SUNPRO_CC) && (__SUNPRO_CC > 0x1000) + #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((((__SUNPRO_CC >> 16) & 0xf) * 10) + ((__SUNPRO_CC >> 12) & 0xf), (((__SUNPRO_CC >> 8) & 0xf) * 10) + ((__SUNPRO_CC >> 4) & 0xf), (__SUNPRO_CC & 0xf) * 10) +#elif defined(__SUNPRO_CC) + #define JSON_HEDLEY_SUNPRO_VERSION JSON_HEDLEY_VERSION_ENCODE((__SUNPRO_CC >> 8) & 0xf, (__SUNPRO_CC >> 4) & 0xf, (__SUNPRO_CC) & 0xf) +#endif + +#if defined(JSON_HEDLEY_SUNPRO_VERSION_CHECK) + #undef JSON_HEDLEY_SUNPRO_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_SUNPRO_VERSION) + #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_SUNPRO_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_SUNPRO_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION) + #undef JSON_HEDLEY_EMSCRIPTEN_VERSION +#endif +#if defined(__EMSCRIPTEN__) + #define JSON_HEDLEY_EMSCRIPTEN_VERSION JSON_HEDLEY_VERSION_ENCODE(__EMSCRIPTEN_major__, __EMSCRIPTEN_minor__, __EMSCRIPTEN_tiny__) +#endif + +#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK) + #undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_EMSCRIPTEN_VERSION) + #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_EMSCRIPTEN_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_ARM_VERSION) + #undef JSON_HEDLEY_ARM_VERSION +#endif +#if defined(__CC_ARM) && defined(__ARMCOMPILER_VERSION) + #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCOMPILER_VERSION / 1000000, (__ARMCOMPILER_VERSION % 1000000) / 10000, (__ARMCOMPILER_VERSION % 10000) / 100) +#elif defined(__CC_ARM) && defined(__ARMCC_VERSION) + #define JSON_HEDLEY_ARM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ARMCC_VERSION / 1000000, (__ARMCC_VERSION % 1000000) / 10000, (__ARMCC_VERSION % 10000) / 100) +#endif + +#if defined(JSON_HEDLEY_ARM_VERSION_CHECK) + #undef JSON_HEDLEY_ARM_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_ARM_VERSION) + #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_ARM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_ARM_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_IBM_VERSION) + #undef JSON_HEDLEY_IBM_VERSION +#endif +#if defined(__ibmxl__) + #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__ibmxl_version__, __ibmxl_release__, __ibmxl_modification__) +#elif defined(__xlC__) && defined(__xlC_ver__) + #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, (__xlC_ver__ >> 8) & 0xff) +#elif defined(__xlC__) + #define JSON_HEDLEY_IBM_VERSION JSON_HEDLEY_VERSION_ENCODE(__xlC__ >> 8, __xlC__ & 0xff, 0) +#endif + +#if defined(JSON_HEDLEY_IBM_VERSION_CHECK) + #undef JSON_HEDLEY_IBM_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_IBM_VERSION) + #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IBM_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_IBM_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_VERSION) + #undef JSON_HEDLEY_TI_VERSION +#endif +#if \ + defined(__TI_COMPILER_VERSION__) && \ + ( \ + defined(__TMS470__) || defined(__TI_ARM__) || \ + defined(__MSP430__) || \ + defined(__TMS320C2000__) \ + ) +#if (__TI_COMPILER_VERSION__ >= 16000000) + #define JSON_HEDLEY_TI_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif +#endif + +#if defined(JSON_HEDLEY_TI_VERSION_CHECK) + #undef JSON_HEDLEY_TI_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_VERSION) + #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_CL2000_VERSION) + #undef JSON_HEDLEY_TI_CL2000_VERSION +#endif +#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C2000__) + #define JSON_HEDLEY_TI_CL2000_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif + +#if defined(JSON_HEDLEY_TI_CL2000_VERSION_CHECK) + #undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_CL2000_VERSION) + #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL2000_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_CL2000_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_CL430_VERSION) + #undef JSON_HEDLEY_TI_CL430_VERSION +#endif +#if defined(__TI_COMPILER_VERSION__) && defined(__MSP430__) + #define JSON_HEDLEY_TI_CL430_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif + +#if defined(JSON_HEDLEY_TI_CL430_VERSION_CHECK) + #undef JSON_HEDLEY_TI_CL430_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_CL430_VERSION) + #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL430_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_CL430_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_ARMCL_VERSION) + #undef JSON_HEDLEY_TI_ARMCL_VERSION +#endif +#if defined(__TI_COMPILER_VERSION__) && (defined(__TMS470__) || defined(__TI_ARM__)) + #define JSON_HEDLEY_TI_ARMCL_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif + +#if defined(JSON_HEDLEY_TI_ARMCL_VERSION_CHECK) + #undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_ARMCL_VERSION) + #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_ARMCL_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_CL6X_VERSION) + #undef JSON_HEDLEY_TI_CL6X_VERSION +#endif +#if defined(__TI_COMPILER_VERSION__) && defined(__TMS320C6X__) + #define JSON_HEDLEY_TI_CL6X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif + +#if defined(JSON_HEDLEY_TI_CL6X_VERSION_CHECK) + #undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_CL6X_VERSION) + #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL6X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_CL6X_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_CL7X_VERSION) + #undef JSON_HEDLEY_TI_CL7X_VERSION +#endif +#if defined(__TI_COMPILER_VERSION__) && defined(__C7000__) + #define JSON_HEDLEY_TI_CL7X_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif + +#if defined(JSON_HEDLEY_TI_CL7X_VERSION_CHECK) + #undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_CL7X_VERSION) + #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CL7X_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_CL7X_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TI_CLPRU_VERSION) + #undef JSON_HEDLEY_TI_CLPRU_VERSION +#endif +#if defined(__TI_COMPILER_VERSION__) && defined(__PRU__) + #define JSON_HEDLEY_TI_CLPRU_VERSION JSON_HEDLEY_VERSION_ENCODE(__TI_COMPILER_VERSION__ / 1000000, (__TI_COMPILER_VERSION__ % 1000000) / 1000, (__TI_COMPILER_VERSION__ % 1000)) +#endif + +#if defined(JSON_HEDLEY_TI_CLPRU_VERSION_CHECK) + #undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TI_CLPRU_VERSION) + #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TI_CLPRU_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_CRAY_VERSION) + #undef JSON_HEDLEY_CRAY_VERSION +#endif +#if defined(_CRAYC) + #if defined(_RELEASE_PATCHLEVEL) + #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, _RELEASE_PATCHLEVEL) + #else + #define JSON_HEDLEY_CRAY_VERSION JSON_HEDLEY_VERSION_ENCODE(_RELEASE_MAJOR, _RELEASE_MINOR, 0) + #endif +#endif + +#if defined(JSON_HEDLEY_CRAY_VERSION_CHECK) + #undef JSON_HEDLEY_CRAY_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_CRAY_VERSION) + #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_CRAY_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_CRAY_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_IAR_VERSION) + #undef JSON_HEDLEY_IAR_VERSION +#endif +#if defined(__IAR_SYSTEMS_ICC__) + #if __VER__ > 1000 + #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE((__VER__ / 1000000), ((__VER__ / 1000) % 1000), (__VER__ % 1000)) + #else + #define JSON_HEDLEY_IAR_VERSION JSON_HEDLEY_VERSION_ENCODE(__VER__ / 100, __VER__ % 100, 0) + #endif +#endif + +#if defined(JSON_HEDLEY_IAR_VERSION_CHECK) + #undef JSON_HEDLEY_IAR_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_IAR_VERSION) + #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_IAR_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_IAR_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_TINYC_VERSION) + #undef JSON_HEDLEY_TINYC_VERSION +#endif +#if defined(__TINYC__) + #define JSON_HEDLEY_TINYC_VERSION JSON_HEDLEY_VERSION_ENCODE(__TINYC__ / 1000, (__TINYC__ / 100) % 10, __TINYC__ % 100) +#endif + +#if defined(JSON_HEDLEY_TINYC_VERSION_CHECK) + #undef JSON_HEDLEY_TINYC_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_TINYC_VERSION) + #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_TINYC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_TINYC_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_DMC_VERSION) + #undef JSON_HEDLEY_DMC_VERSION +#endif +#if defined(__DMC__) + #define JSON_HEDLEY_DMC_VERSION JSON_HEDLEY_VERSION_ENCODE(__DMC__ >> 8, (__DMC__ >> 4) & 0xf, __DMC__ & 0xf) +#endif + +#if defined(JSON_HEDLEY_DMC_VERSION_CHECK) + #undef JSON_HEDLEY_DMC_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_DMC_VERSION) + #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_DMC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_DMC_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_COMPCERT_VERSION) + #undef JSON_HEDLEY_COMPCERT_VERSION +#endif +#if defined(__COMPCERT_VERSION__) + #define JSON_HEDLEY_COMPCERT_VERSION JSON_HEDLEY_VERSION_ENCODE(__COMPCERT_VERSION__ / 10000, (__COMPCERT_VERSION__ / 100) % 100, __COMPCERT_VERSION__ % 100) +#endif + +#if defined(JSON_HEDLEY_COMPCERT_VERSION_CHECK) + #undef JSON_HEDLEY_COMPCERT_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_COMPCERT_VERSION) + #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_COMPCERT_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_COMPCERT_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_PELLES_VERSION) + #undef JSON_HEDLEY_PELLES_VERSION +#endif +#if defined(__POCC__) + #define JSON_HEDLEY_PELLES_VERSION JSON_HEDLEY_VERSION_ENCODE(__POCC__ / 100, __POCC__ % 100, 0) +#endif + +#if defined(JSON_HEDLEY_PELLES_VERSION_CHECK) + #undef JSON_HEDLEY_PELLES_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_PELLES_VERSION) + #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_PELLES_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_PELLES_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_MCST_LCC_VERSION) + #undef JSON_HEDLEY_MCST_LCC_VERSION +#endif +#if defined(__LCC__) && defined(__LCC_MINOR__) + #define JSON_HEDLEY_MCST_LCC_VERSION JSON_HEDLEY_VERSION_ENCODE(__LCC__ / 100, __LCC__ % 100, __LCC_MINOR__) +#endif + +#if defined(JSON_HEDLEY_MCST_LCC_VERSION_CHECK) + #undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_MCST_LCC_VERSION) + #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_MCST_LCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_MCST_LCC_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_GCC_VERSION) + #undef JSON_HEDLEY_GCC_VERSION +#endif +#if \ + defined(JSON_HEDLEY_GNUC_VERSION) && \ + !defined(__clang__) && \ + !defined(JSON_HEDLEY_INTEL_VERSION) && \ + !defined(JSON_HEDLEY_PGI_VERSION) && \ + !defined(JSON_HEDLEY_ARM_VERSION) && \ + !defined(JSON_HEDLEY_CRAY_VERSION) && \ + !defined(JSON_HEDLEY_TI_VERSION) && \ + !defined(JSON_HEDLEY_TI_ARMCL_VERSION) && \ + !defined(JSON_HEDLEY_TI_CL430_VERSION) && \ + !defined(JSON_HEDLEY_TI_CL2000_VERSION) && \ + !defined(JSON_HEDLEY_TI_CL6X_VERSION) && \ + !defined(JSON_HEDLEY_TI_CL7X_VERSION) && \ + !defined(JSON_HEDLEY_TI_CLPRU_VERSION) && \ + !defined(__COMPCERT__) && \ + !defined(JSON_HEDLEY_MCST_LCC_VERSION) + #define JSON_HEDLEY_GCC_VERSION JSON_HEDLEY_GNUC_VERSION +#endif + +#if defined(JSON_HEDLEY_GCC_VERSION_CHECK) + #undef JSON_HEDLEY_GCC_VERSION_CHECK +#endif +#if defined(JSON_HEDLEY_GCC_VERSION) + #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (JSON_HEDLEY_GCC_VERSION >= JSON_HEDLEY_VERSION_ENCODE(major, minor, patch)) +#else + #define JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) (0) +#endif + +#if defined(JSON_HEDLEY_HAS_ATTRIBUTE) + #undef JSON_HEDLEY_HAS_ATTRIBUTE +#endif +#if \ + defined(__has_attribute) && \ + ( \ + (!defined(JSON_HEDLEY_IAR_VERSION) || JSON_HEDLEY_IAR_VERSION_CHECK(8,5,9)) \ + ) +# define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) __has_attribute(attribute) +#else +# define JSON_HEDLEY_HAS_ATTRIBUTE(attribute) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_ATTRIBUTE) + #undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE +#endif +#if defined(__has_attribute) + #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute) +#else + #define JSON_HEDLEY_GNUC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_ATTRIBUTE) + #undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE +#endif +#if defined(__has_attribute) + #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_HAS_ATTRIBUTE(attribute) +#else + #define JSON_HEDLEY_GCC_HAS_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE) + #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE +#endif +#if \ + defined(__has_cpp_attribute) && \ + defined(__cplusplus) && \ + (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0)) + #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) __has_cpp_attribute(attribute) +#else + #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) (0) +#endif + +#if defined(JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS) + #undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS +#endif +#if !defined(__cplusplus) || !defined(__has_cpp_attribute) + #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0) +#elif \ + !defined(JSON_HEDLEY_PGI_VERSION) && \ + !defined(JSON_HEDLEY_IAR_VERSION) && \ + (!defined(JSON_HEDLEY_SUNPRO_VERSION) || JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0)) && \ + (!defined(JSON_HEDLEY_MSVC_VERSION) || JSON_HEDLEY_MSVC_VERSION_CHECK(19,20,0)) + #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(ns::attribute) +#else + #define JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(ns,attribute) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE) + #undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE +#endif +#if defined(__has_cpp_attribute) && defined(__cplusplus) + #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute) +#else + #define JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE) + #undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE +#endif +#if defined(__has_cpp_attribute) && defined(__cplusplus) + #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) __has_cpp_attribute(attribute) +#else + #define JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_HAS_BUILTIN) + #undef JSON_HEDLEY_HAS_BUILTIN +#endif +#if defined(__has_builtin) + #define JSON_HEDLEY_HAS_BUILTIN(builtin) __has_builtin(builtin) +#else + #define JSON_HEDLEY_HAS_BUILTIN(builtin) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_BUILTIN) + #undef JSON_HEDLEY_GNUC_HAS_BUILTIN +#endif +#if defined(__has_builtin) + #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin) +#else + #define JSON_HEDLEY_GNUC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_BUILTIN) + #undef JSON_HEDLEY_GCC_HAS_BUILTIN +#endif +#if defined(__has_builtin) + #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) __has_builtin(builtin) +#else + #define JSON_HEDLEY_GCC_HAS_BUILTIN(builtin,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_HAS_FEATURE) + #undef JSON_HEDLEY_HAS_FEATURE +#endif +#if defined(__has_feature) + #define JSON_HEDLEY_HAS_FEATURE(feature) __has_feature(feature) +#else + #define JSON_HEDLEY_HAS_FEATURE(feature) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_FEATURE) + #undef JSON_HEDLEY_GNUC_HAS_FEATURE +#endif +#if defined(__has_feature) + #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature) +#else + #define JSON_HEDLEY_GNUC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_FEATURE) + #undef JSON_HEDLEY_GCC_HAS_FEATURE +#endif +#if defined(__has_feature) + #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) __has_feature(feature) +#else + #define JSON_HEDLEY_GCC_HAS_FEATURE(feature,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_HAS_EXTENSION) + #undef JSON_HEDLEY_HAS_EXTENSION +#endif +#if defined(__has_extension) + #define JSON_HEDLEY_HAS_EXTENSION(extension) __has_extension(extension) +#else + #define JSON_HEDLEY_HAS_EXTENSION(extension) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_EXTENSION) + #undef JSON_HEDLEY_GNUC_HAS_EXTENSION +#endif +#if defined(__has_extension) + #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension) +#else + #define JSON_HEDLEY_GNUC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_EXTENSION) + #undef JSON_HEDLEY_GCC_HAS_EXTENSION +#endif +#if defined(__has_extension) + #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) __has_extension(extension) +#else + #define JSON_HEDLEY_GCC_HAS_EXTENSION(extension,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE) + #undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE +#endif +#if defined(__has_declspec_attribute) + #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) __has_declspec_attribute(attribute) +#else + #define JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE) + #undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE +#endif +#if defined(__has_declspec_attribute) + #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute) +#else + #define JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE) + #undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE +#endif +#if defined(__has_declspec_attribute) + #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) __has_declspec_attribute(attribute) +#else + #define JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE(attribute,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_HAS_WARNING) + #undef JSON_HEDLEY_HAS_WARNING +#endif +#if defined(__has_warning) + #define JSON_HEDLEY_HAS_WARNING(warning) __has_warning(warning) +#else + #define JSON_HEDLEY_HAS_WARNING(warning) (0) +#endif + +#if defined(JSON_HEDLEY_GNUC_HAS_WARNING) + #undef JSON_HEDLEY_GNUC_HAS_WARNING +#endif +#if defined(__has_warning) + #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning) +#else + #define JSON_HEDLEY_GNUC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GNUC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_GCC_HAS_WARNING) + #undef JSON_HEDLEY_GCC_HAS_WARNING +#endif +#if defined(__has_warning) + #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) __has_warning(warning) +#else + #define JSON_HEDLEY_GCC_HAS_WARNING(warning,major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if \ + (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \ + defined(__clang__) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,0,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0) || \ + JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,17) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(8,0,0) || \ + (JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) && defined(__C99_PRAGMA_OPERATOR)) + #define JSON_HEDLEY_PRAGMA(value) _Pragma(#value) +#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) + #define JSON_HEDLEY_PRAGMA(value) __pragma(value) +#else + #define JSON_HEDLEY_PRAGMA(value) +#endif + +#if defined(JSON_HEDLEY_DIAGNOSTIC_PUSH) + #undef JSON_HEDLEY_DIAGNOSTIC_PUSH +#endif +#if defined(JSON_HEDLEY_DIAGNOSTIC_POP) + #undef JSON_HEDLEY_DIAGNOSTIC_POP +#endif +#if defined(__clang__) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("clang diagnostic push") + #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("clang diagnostic pop") +#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("warning(push)") + #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("warning(pop)") +#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("GCC diagnostic push") + #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("GCC diagnostic pop") +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH __pragma(warning(push)) + #define JSON_HEDLEY_DIAGNOSTIC_POP __pragma(warning(pop)) +#elif JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("push") + #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("pop") +#elif \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,4,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("diag_push") + #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("diag_pop") +#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0) + #define JSON_HEDLEY_DIAGNOSTIC_PUSH _Pragma("warning(push)") + #define JSON_HEDLEY_DIAGNOSTIC_POP _Pragma("warning(pop)") +#else + #define JSON_HEDLEY_DIAGNOSTIC_PUSH + #define JSON_HEDLEY_DIAGNOSTIC_POP +#endif + +/* JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_ is for + HEDLEY INTERNAL USE ONLY. API subject to change without notice. */ +#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_) + #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_ +#endif +#if defined(__cplusplus) +# if JSON_HEDLEY_HAS_WARNING("-Wc++98-compat") +# if JSON_HEDLEY_HAS_WARNING("-Wc++17-extensions") +# if JSON_HEDLEY_HAS_WARNING("-Wc++1z-extensions") +# define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \ + _Pragma("clang diagnostic ignored \"-Wc++17-extensions\"") \ + _Pragma("clang diagnostic ignored \"-Wc++1z-extensions\"") \ + xpr \ + JSON_HEDLEY_DIAGNOSTIC_POP +# else +# define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \ + _Pragma("clang diagnostic ignored \"-Wc++17-extensions\"") \ + xpr \ + JSON_HEDLEY_DIAGNOSTIC_POP +# endif +# else +# define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(xpr) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("clang diagnostic ignored \"-Wc++98-compat\"") \ + xpr \ + JSON_HEDLEY_DIAGNOSTIC_POP +# endif +# endif +#endif +#if !defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(x) x +#endif + +#if defined(JSON_HEDLEY_CONST_CAST) + #undef JSON_HEDLEY_CONST_CAST +#endif +#if defined(__cplusplus) +# define JSON_HEDLEY_CONST_CAST(T, expr) (const_cast(expr)) +#elif \ + JSON_HEDLEY_HAS_WARNING("-Wcast-qual") || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) +# define JSON_HEDLEY_CONST_CAST(T, expr) (__extension__ ({ \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL \ + ((T) (expr)); \ + JSON_HEDLEY_DIAGNOSTIC_POP \ + })) +#else +# define JSON_HEDLEY_CONST_CAST(T, expr) ((T) (expr)) +#endif + +#if defined(JSON_HEDLEY_REINTERPRET_CAST) + #undef JSON_HEDLEY_REINTERPRET_CAST +#endif +#if defined(__cplusplus) + #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) (reinterpret_cast(expr)) +#else + #define JSON_HEDLEY_REINTERPRET_CAST(T, expr) ((T) (expr)) +#endif + +#if defined(JSON_HEDLEY_STATIC_CAST) + #undef JSON_HEDLEY_STATIC_CAST +#endif +#if defined(__cplusplus) + #define JSON_HEDLEY_STATIC_CAST(T, expr) (static_cast(expr)) +#else + #define JSON_HEDLEY_STATIC_CAST(T, expr) ((T) (expr)) +#endif + +#if defined(JSON_HEDLEY_CPP_CAST) + #undef JSON_HEDLEY_CPP_CAST +#endif +#if defined(__cplusplus) +# if JSON_HEDLEY_HAS_WARNING("-Wold-style-cast") +# define JSON_HEDLEY_CPP_CAST(T, expr) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("clang diagnostic ignored \"-Wold-style-cast\"") \ + ((T) (expr)) \ + JSON_HEDLEY_DIAGNOSTIC_POP +# elif JSON_HEDLEY_IAR_VERSION_CHECK(8,3,0) +# define JSON_HEDLEY_CPP_CAST(T, expr) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("diag_suppress=Pe137") \ + JSON_HEDLEY_DIAGNOSTIC_POP +# else +# define JSON_HEDLEY_CPP_CAST(T, expr) ((T) (expr)) +# endif +#else +# define JSON_HEDLEY_CPP_CAST(T, expr) (expr) +#endif + +#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED) + #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wdeprecated-declarations") + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("clang diagnostic ignored \"-Wdeprecated-declarations\"") +#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("warning(disable:1478 1786)") +#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:1478 1786)) +#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1216,1444,1445") +#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1444") +#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"") +#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED __pragma(warning(disable:4996)) +#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1215,1444") +#elif \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress 1291,1718") +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && !defined(__cplusplus) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("error_messages(off,E_DEPRECATED_ATT,E_DEPRECATED_ATT_MESS)") +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) && defined(__cplusplus) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("error_messages(off,symdeprecated,symdeprecated2)") +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("diag_suppress=Pe1444,Pe1215") +#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,90,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED _Pragma("warn(disable:2241)") +#else + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED +#endif + +#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS) + #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas") + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("clang diagnostic ignored \"-Wunknown-pragmas\"") +#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("warning(disable:161)") +#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:161)) +#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 1675") +#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("GCC diagnostic ignored \"-Wunknown-pragmas\"") +#elif JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS __pragma(warning(disable:4068)) +#elif \ + JSON_HEDLEY_TI_VERSION_CHECK(16,9,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 163") +#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 163") +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress=Pe161") +#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS _Pragma("diag_suppress 161") +#else + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS +#endif + +#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES) + #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wunknown-attributes") + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("clang diagnostic ignored \"-Wunknown-attributes\"") +#elif JSON_HEDLEY_GCC_VERSION_CHECK(4,6,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("GCC diagnostic ignored \"-Wdeprecated-declarations\"") +#elif JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("warning(disable:1292)") +#elif JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:1292)) +#elif JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES __pragma(warning(disable:5030)) +#elif JSON_HEDLEY_PGI_VERSION_CHECK(20,7,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097,1098") +#elif JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097") +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("error_messages(off,attrskipunsup)") +#elif \ + JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1173") +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress=Pe1097") +#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES _Pragma("diag_suppress 1097") +#else + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES +#endif + +#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL) + #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wcast-qual") + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("clang diagnostic ignored \"-Wcast-qual\"") +#elif JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("warning(disable:2203 2331)") +#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL _Pragma("GCC diagnostic ignored \"-Wcast-qual\"") +#else + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL +#endif + +#if defined(JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION) + #undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wunused-function") + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("clang diagnostic ignored \"-Wunused-function\"") +#elif JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("GCC diagnostic ignored \"-Wunused-function\"") +#elif JSON_HEDLEY_MSVC_VERSION_CHECK(1,0,0) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION __pragma(warning(disable:4505)) +#elif JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION _Pragma("diag_suppress 3142") +#else + #define JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION +#endif + +#if defined(JSON_HEDLEY_DEPRECATED) + #undef JSON_HEDLEY_DEPRECATED +#endif +#if defined(JSON_HEDLEY_DEPRECATED_FOR) + #undef JSON_HEDLEY_DEPRECATED_FOR +#endif +#if \ + JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated("Since " # since)) + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated("Since " #since "; use " #replacement)) +#elif \ + (JSON_HEDLEY_HAS_EXTENSION(attribute_deprecated_with_message) && !defined(JSON_HEDLEY_IAR_VERSION)) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,13,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(18,1,0) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(18,1,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,3,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,3,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__("Since " #since))) + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__("Since " #since "; use " #replacement))) +#elif defined(__cplusplus) && (__cplusplus >= 201402L) + #define JSON_HEDLEY_DEPRECATED(since) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated("Since " #since)]]) + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[deprecated("Since " #since "; use " #replacement)]]) +#elif \ + JSON_HEDLEY_HAS_ATTRIBUTE(deprecated) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \ + JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0) + #define JSON_HEDLEY_DEPRECATED(since) __attribute__((__deprecated__)) + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __attribute__((__deprecated__)) +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \ + JSON_HEDLEY_PELLES_VERSION_CHECK(6,50,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_DEPRECATED(since) __declspec(deprecated) + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) __declspec(deprecated) +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_DEPRECATED(since) _Pragma("deprecated") + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) _Pragma("deprecated") +#else + #define JSON_HEDLEY_DEPRECATED(since) + #define JSON_HEDLEY_DEPRECATED_FOR(since, replacement) +#endif + +#if defined(JSON_HEDLEY_UNAVAILABLE) + #undef JSON_HEDLEY_UNAVAILABLE +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(warning) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,3,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_UNAVAILABLE(available_since) __attribute__((__warning__("Not available until " #available_since))) +#else + #define JSON_HEDLEY_UNAVAILABLE(available_since) +#endif + +#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT) + #undef JSON_HEDLEY_WARN_UNUSED_RESULT +#endif +#if defined(JSON_HEDLEY_WARN_UNUSED_RESULT_MSG) + #undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(warn_unused_result) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_WARN_UNUSED_RESULT __attribute__((__warn_unused_result__)) + #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) __attribute__((__warn_unused_result__)) +#elif (JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard) >= 201907L) + #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]]) + #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard(msg)]]) +#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(nodiscard) + #define JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]]) + #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[nodiscard]]) +#elif defined(_Check_return_) /* SAL */ + #define JSON_HEDLEY_WARN_UNUSED_RESULT _Check_return_ + #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) _Check_return_ +#else + #define JSON_HEDLEY_WARN_UNUSED_RESULT + #define JSON_HEDLEY_WARN_UNUSED_RESULT_MSG(msg) +#endif + +#if defined(JSON_HEDLEY_SENTINEL) + #undef JSON_HEDLEY_SENTINEL +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(sentinel) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_SENTINEL(position) __attribute__((__sentinel__(position))) +#else + #define JSON_HEDLEY_SENTINEL(position) +#endif + +#if defined(JSON_HEDLEY_NO_RETURN) + #undef JSON_HEDLEY_NO_RETURN +#endif +#if JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_NO_RETURN __noreturn +#elif \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__)) +#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L + #define JSON_HEDLEY_NO_RETURN _Noreturn +#elif defined(__cplusplus) && (__cplusplus >= 201103L) + #define JSON_HEDLEY_NO_RETURN JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[noreturn]]) +#elif \ + JSON_HEDLEY_HAS_ATTRIBUTE(noreturn) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,2,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0) + #define JSON_HEDLEY_NO_RETURN __attribute__((__noreturn__)) +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) + #define JSON_HEDLEY_NO_RETURN _Pragma("does_not_return") +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_NO_RETURN __declspec(noreturn) +#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus) + #define JSON_HEDLEY_NO_RETURN _Pragma("FUNC_NEVER_RETURNS;") +#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0) + #define JSON_HEDLEY_NO_RETURN __attribute((noreturn)) +#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0) + #define JSON_HEDLEY_NO_RETURN __declspec(noreturn) +#else + #define JSON_HEDLEY_NO_RETURN +#endif + +#if defined(JSON_HEDLEY_NO_ESCAPE) + #undef JSON_HEDLEY_NO_ESCAPE +#endif +#if JSON_HEDLEY_HAS_ATTRIBUTE(noescape) + #define JSON_HEDLEY_NO_ESCAPE __attribute__((__noescape__)) +#else + #define JSON_HEDLEY_NO_ESCAPE +#endif + +#if defined(JSON_HEDLEY_UNREACHABLE) + #undef JSON_HEDLEY_UNREACHABLE +#endif +#if defined(JSON_HEDLEY_UNREACHABLE_RETURN) + #undef JSON_HEDLEY_UNREACHABLE_RETURN +#endif +#if defined(JSON_HEDLEY_ASSUME) + #undef JSON_HEDLEY_ASSUME +#endif +#if \ + JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_ASSUME(expr) __assume(expr) +#elif JSON_HEDLEY_HAS_BUILTIN(__builtin_assume) + #define JSON_HEDLEY_ASSUME(expr) __builtin_assume(expr) +#elif \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0) + #if defined(__cplusplus) + #define JSON_HEDLEY_ASSUME(expr) std::_nassert(expr) + #else + #define JSON_HEDLEY_ASSUME(expr) _nassert(expr) + #endif +#endif +#if \ + (JSON_HEDLEY_HAS_BUILTIN(__builtin_unreachable) && (!defined(JSON_HEDLEY_ARM_VERSION))) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,5,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(18,10,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(13,1,5) || \ + JSON_HEDLEY_CRAY_VERSION_CHECK(10,0,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_UNREACHABLE() __builtin_unreachable() +#elif defined(JSON_HEDLEY_ASSUME) + #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0) +#endif +#if !defined(JSON_HEDLEY_ASSUME) + #if defined(JSON_HEDLEY_UNREACHABLE) + #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, ((expr) ? 1 : (JSON_HEDLEY_UNREACHABLE(), 1))) + #else + #define JSON_HEDLEY_ASSUME(expr) JSON_HEDLEY_STATIC_CAST(void, expr) + #endif +#endif +#if defined(JSON_HEDLEY_UNREACHABLE) + #if \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0) + #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (JSON_HEDLEY_STATIC_CAST(void, JSON_HEDLEY_ASSUME(0)), (value)) + #else + #define JSON_HEDLEY_UNREACHABLE_RETURN(value) JSON_HEDLEY_UNREACHABLE() + #endif +#else + #define JSON_HEDLEY_UNREACHABLE_RETURN(value) return (value) +#endif +#if !defined(JSON_HEDLEY_UNREACHABLE) + #define JSON_HEDLEY_UNREACHABLE() JSON_HEDLEY_ASSUME(0) +#endif + +JSON_HEDLEY_DIAGNOSTIC_PUSH +#if JSON_HEDLEY_HAS_WARNING("-Wpedantic") + #pragma clang diagnostic ignored "-Wpedantic" +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wc++98-compat-pedantic") && defined(__cplusplus) + #pragma clang diagnostic ignored "-Wc++98-compat-pedantic" +#endif +#if JSON_HEDLEY_GCC_HAS_WARNING("-Wvariadic-macros",4,0,0) + #if defined(__clang__) + #pragma clang diagnostic ignored "-Wvariadic-macros" + #elif defined(JSON_HEDLEY_GCC_VERSION) + #pragma GCC diagnostic ignored "-Wvariadic-macros" + #endif +#endif +#if defined(JSON_HEDLEY_NON_NULL) + #undef JSON_HEDLEY_NON_NULL +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(nonnull) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) + #define JSON_HEDLEY_NON_NULL(...) __attribute__((__nonnull__(__VA_ARGS__))) +#else + #define JSON_HEDLEY_NON_NULL(...) +#endif +JSON_HEDLEY_DIAGNOSTIC_POP + +#if defined(JSON_HEDLEY_PRINTF_FORMAT) + #undef JSON_HEDLEY_PRINTF_FORMAT +#endif +#if defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && !defined(__USE_MINGW_ANSI_STDIO) + #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(ms_printf, string_idx, first_to_check))) +#elif defined(__MINGW32__) && JSON_HEDLEY_GCC_HAS_ATTRIBUTE(format,4,4,0) && defined(__USE_MINGW_ANSI_STDIO) + #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(gnu_printf, string_idx, first_to_check))) +#elif \ + JSON_HEDLEY_HAS_ATTRIBUTE(format) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(5,6,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __attribute__((__format__(__printf__, string_idx, first_to_check))) +#elif JSON_HEDLEY_PELLES_VERSION_CHECK(6,0,0) + #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) __declspec(vaformat(printf,string_idx,first_to_check)) +#else + #define JSON_HEDLEY_PRINTF_FORMAT(string_idx,first_to_check) +#endif + +#if defined(JSON_HEDLEY_CONSTEXPR) + #undef JSON_HEDLEY_CONSTEXPR +#endif +#if defined(__cplusplus) + #if __cplusplus >= 201103L + #define JSON_HEDLEY_CONSTEXPR JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(constexpr) + #endif +#endif +#if !defined(JSON_HEDLEY_CONSTEXPR) + #define JSON_HEDLEY_CONSTEXPR +#endif + +#if defined(JSON_HEDLEY_PREDICT) + #undef JSON_HEDLEY_PREDICT +#endif +#if defined(JSON_HEDLEY_LIKELY) + #undef JSON_HEDLEY_LIKELY +#endif +#if defined(JSON_HEDLEY_UNLIKELY) + #undef JSON_HEDLEY_UNLIKELY +#endif +#if defined(JSON_HEDLEY_UNPREDICTABLE) + #undef JSON_HEDLEY_UNPREDICTABLE +#endif +#if JSON_HEDLEY_HAS_BUILTIN(__builtin_unpredictable) + #define JSON_HEDLEY_UNPREDICTABLE(expr) __builtin_unpredictable((expr)) +#endif +#if \ + (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect_with_probability) && !defined(JSON_HEDLEY_PGI_VERSION)) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(9,0,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) +# define JSON_HEDLEY_PREDICT(expr, value, probability) __builtin_expect_with_probability( (expr), (value), (probability)) +# define JSON_HEDLEY_PREDICT_TRUE(expr, probability) __builtin_expect_with_probability(!!(expr), 1 , (probability)) +# define JSON_HEDLEY_PREDICT_FALSE(expr, probability) __builtin_expect_with_probability(!!(expr), 0 , (probability)) +# define JSON_HEDLEY_LIKELY(expr) __builtin_expect (!!(expr), 1 ) +# define JSON_HEDLEY_UNLIKELY(expr) __builtin_expect (!!(expr), 0 ) +#elif \ + (JSON_HEDLEY_HAS_BUILTIN(__builtin_expect) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,15,0) && defined(__cplusplus)) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,7,0) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,1,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,27) || \ + JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) +# define JSON_HEDLEY_PREDICT(expr, expected, probability) \ + (((probability) >= 0.9) ? __builtin_expect((expr), (expected)) : (JSON_HEDLEY_STATIC_CAST(void, expected), (expr))) +# define JSON_HEDLEY_PREDICT_TRUE(expr, probability) \ + (__extension__ ({ \ + double hedley_probability_ = (probability); \ + ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 1) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 0) : !!(expr))); \ + })) +# define JSON_HEDLEY_PREDICT_FALSE(expr, probability) \ + (__extension__ ({ \ + double hedley_probability_ = (probability); \ + ((hedley_probability_ >= 0.9) ? __builtin_expect(!!(expr), 0) : ((hedley_probability_ <= 0.1) ? __builtin_expect(!!(expr), 1) : !!(expr))); \ + })) +# define JSON_HEDLEY_LIKELY(expr) __builtin_expect(!!(expr), 1) +# define JSON_HEDLEY_UNLIKELY(expr) __builtin_expect(!!(expr), 0) +#else +# define JSON_HEDLEY_PREDICT(expr, expected, probability) (JSON_HEDLEY_STATIC_CAST(void, expected), (expr)) +# define JSON_HEDLEY_PREDICT_TRUE(expr, probability) (!!(expr)) +# define JSON_HEDLEY_PREDICT_FALSE(expr, probability) (!!(expr)) +# define JSON_HEDLEY_LIKELY(expr) (!!(expr)) +# define JSON_HEDLEY_UNLIKELY(expr) (!!(expr)) +#endif +#if !defined(JSON_HEDLEY_UNPREDICTABLE) + #define JSON_HEDLEY_UNPREDICTABLE(expr) JSON_HEDLEY_PREDICT(expr, 1, 0.5) +#endif + +#if defined(JSON_HEDLEY_MALLOC) + #undef JSON_HEDLEY_MALLOC +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(malloc) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_MALLOC __attribute__((__malloc__)) +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) + #define JSON_HEDLEY_MALLOC _Pragma("returns_new_memory") +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_MALLOC __declspec(restrict) +#else + #define JSON_HEDLEY_MALLOC +#endif + +#if defined(JSON_HEDLEY_PURE) + #undef JSON_HEDLEY_PURE +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(pure) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(2,96,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) +# define JSON_HEDLEY_PURE __attribute__((__pure__)) +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) +# define JSON_HEDLEY_PURE _Pragma("does_not_write_global_data") +#elif defined(__cplusplus) && \ + ( \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(2,0,1) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(4,0,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) \ + ) +# define JSON_HEDLEY_PURE _Pragma("FUNC_IS_PURE;") +#else +# define JSON_HEDLEY_PURE +#endif + +#if defined(JSON_HEDLEY_CONST) + #undef JSON_HEDLEY_CONST +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(const) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(2,5,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_CONST __attribute__((__const__)) +#elif \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) + #define JSON_HEDLEY_CONST _Pragma("no_side_effect") +#else + #define JSON_HEDLEY_CONST JSON_HEDLEY_PURE +#endif + +#if defined(JSON_HEDLEY_RESTRICT) + #undef JSON_HEDLEY_RESTRICT +#endif +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && !defined(__cplusplus) + #define JSON_HEDLEY_RESTRICT restrict +#elif \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,1,0) || \ + JSON_HEDLEY_MSVC_VERSION_CHECK(14,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(17,10,0) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,4) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,1,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,14,0) && defined(__cplusplus)) || \ + JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) || \ + defined(__clang__) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_RESTRICT __restrict +#elif JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,3,0) && !defined(__cplusplus) + #define JSON_HEDLEY_RESTRICT _Restrict +#else + #define JSON_HEDLEY_RESTRICT +#endif + +#if defined(JSON_HEDLEY_INLINE) + #undef JSON_HEDLEY_INLINE +#endif +#if \ + (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) || \ + (defined(__cplusplus) && (__cplusplus >= 199711L)) + #define JSON_HEDLEY_INLINE inline +#elif \ + defined(JSON_HEDLEY_GCC_VERSION) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(6,2,0) + #define JSON_HEDLEY_INLINE __inline__ +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,1,0) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(3,1,0) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,2,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(8,0,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_INLINE __inline +#else + #define JSON_HEDLEY_INLINE +#endif + +#if defined(JSON_HEDLEY_ALWAYS_INLINE) + #undef JSON_HEDLEY_ALWAYS_INLINE +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(always_inline) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \ + JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0) +# define JSON_HEDLEY_ALWAYS_INLINE __attribute__((__always_inline__)) JSON_HEDLEY_INLINE +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(12,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) +# define JSON_HEDLEY_ALWAYS_INLINE __forceinline +#elif defined(__cplusplus) && \ + ( \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) \ + ) +# define JSON_HEDLEY_ALWAYS_INLINE _Pragma("FUNC_ALWAYS_INLINE;") +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) +# define JSON_HEDLEY_ALWAYS_INLINE _Pragma("inline=forced") +#else +# define JSON_HEDLEY_ALWAYS_INLINE JSON_HEDLEY_INLINE +#endif + +#if defined(JSON_HEDLEY_NEVER_INLINE) + #undef JSON_HEDLEY_NEVER_INLINE +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(noinline) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(10,1,0) || \ + JSON_HEDLEY_TI_VERSION_CHECK(15,12,0) || \ + (JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(4,8,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_ARMCL_VERSION_CHECK(5,2,0) || \ + (JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL2000_VERSION_CHECK(6,4,0) || \ + (JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,0,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(4,3,0) || \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) || \ + JSON_HEDLEY_TI_CL7X_VERSION_CHECK(1,2,0) || \ + JSON_HEDLEY_TI_CLPRU_VERSION_CHECK(2,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) || \ + JSON_HEDLEY_IAR_VERSION_CHECK(8,10,0) + #define JSON_HEDLEY_NEVER_INLINE __attribute__((__noinline__)) +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(13,10,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline) +#elif JSON_HEDLEY_PGI_VERSION_CHECK(10,2,0) + #define JSON_HEDLEY_NEVER_INLINE _Pragma("noinline") +#elif JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,0,0) && defined(__cplusplus) + #define JSON_HEDLEY_NEVER_INLINE _Pragma("FUNC_CANNOT_INLINE;") +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) + #define JSON_HEDLEY_NEVER_INLINE _Pragma("inline=never") +#elif JSON_HEDLEY_COMPCERT_VERSION_CHECK(3,2,0) + #define JSON_HEDLEY_NEVER_INLINE __attribute((noinline)) +#elif JSON_HEDLEY_PELLES_VERSION_CHECK(9,0,0) + #define JSON_HEDLEY_NEVER_INLINE __declspec(noinline) +#else + #define JSON_HEDLEY_NEVER_INLINE +#endif + +#if defined(JSON_HEDLEY_PRIVATE) + #undef JSON_HEDLEY_PRIVATE +#endif +#if defined(JSON_HEDLEY_PUBLIC) + #undef JSON_HEDLEY_PUBLIC +#endif +#if defined(JSON_HEDLEY_IMPORT) + #undef JSON_HEDLEY_IMPORT +#endif +#if defined(_WIN32) || defined(__CYGWIN__) +# define JSON_HEDLEY_PRIVATE +# define JSON_HEDLEY_PUBLIC __declspec(dllexport) +# define JSON_HEDLEY_IMPORT __declspec(dllimport) +#else +# if \ + JSON_HEDLEY_HAS_ATTRIBUTE(visibility) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \ + JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,11,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \ + ( \ + defined(__TI_EABI__) && \ + ( \ + (JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,2,0) && defined(__TI_GNU_ATTRIBUTE_SUPPORT__)) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(7,5,0) \ + ) \ + ) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) +# define JSON_HEDLEY_PRIVATE __attribute__((__visibility__("hidden"))) +# define JSON_HEDLEY_PUBLIC __attribute__((__visibility__("default"))) +# else +# define JSON_HEDLEY_PRIVATE +# define JSON_HEDLEY_PUBLIC +# endif +# define JSON_HEDLEY_IMPORT extern +#endif + +#if defined(JSON_HEDLEY_NO_THROW) + #undef JSON_HEDLEY_NO_THROW +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(nothrow) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,3,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_NO_THROW __attribute__((__nothrow__)) +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(13,1,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) + #define JSON_HEDLEY_NO_THROW __declspec(nothrow) +#else + #define JSON_HEDLEY_NO_THROW +#endif + +#if defined(JSON_HEDLEY_FALL_THROUGH) + #undef JSON_HEDLEY_FALL_THROUGH +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(fallthrough) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(7,0,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_FALL_THROUGH __attribute__((__fallthrough__)) +#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS(clang,fallthrough) + #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[clang::fallthrough]]) +#elif JSON_HEDLEY_HAS_CPP_ATTRIBUTE(fallthrough) + #define JSON_HEDLEY_FALL_THROUGH JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_([[fallthrough]]) +#elif defined(__fallthrough) /* SAL */ + #define JSON_HEDLEY_FALL_THROUGH __fallthrough +#else + #define JSON_HEDLEY_FALL_THROUGH +#endif + +#if defined(JSON_HEDLEY_RETURNS_NON_NULL) + #undef JSON_HEDLEY_RETURNS_NON_NULL +#endif +#if \ + JSON_HEDLEY_HAS_ATTRIBUTE(returns_nonnull) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_RETURNS_NON_NULL __attribute__((__returns_nonnull__)) +#elif defined(_Ret_notnull_) /* SAL */ + #define JSON_HEDLEY_RETURNS_NON_NULL _Ret_notnull_ +#else + #define JSON_HEDLEY_RETURNS_NON_NULL +#endif + +#if defined(JSON_HEDLEY_ARRAY_PARAM) + #undef JSON_HEDLEY_ARRAY_PARAM +#endif +#if \ + defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) && \ + !defined(__STDC_NO_VLA__) && \ + !defined(__cplusplus) && \ + !defined(JSON_HEDLEY_PGI_VERSION) && \ + !defined(JSON_HEDLEY_TINYC_VERSION) + #define JSON_HEDLEY_ARRAY_PARAM(name) (name) +#else + #define JSON_HEDLEY_ARRAY_PARAM(name) +#endif + +#if defined(JSON_HEDLEY_IS_CONSTANT) + #undef JSON_HEDLEY_IS_CONSTANT +#endif +#if defined(JSON_HEDLEY_REQUIRE_CONSTEXPR) + #undef JSON_HEDLEY_REQUIRE_CONSTEXPR +#endif +/* JSON_HEDLEY_IS_CONSTEXPR_ is for + HEDLEY INTERNAL USE ONLY. API subject to change without notice. */ +#if defined(JSON_HEDLEY_IS_CONSTEXPR_) + #undef JSON_HEDLEY_IS_CONSTEXPR_ +#endif +#if \ + JSON_HEDLEY_HAS_BUILTIN(__builtin_constant_p) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,19) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(4,1,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \ + JSON_HEDLEY_TI_CL6X_VERSION_CHECK(6,1,0) || \ + (JSON_HEDLEY_SUNPRO_VERSION_CHECK(5,10,0) && !defined(__cplusplus)) || \ + JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \ + JSON_HEDLEY_MCST_LCC_VERSION_CHECK(1,25,10) + #define JSON_HEDLEY_IS_CONSTANT(expr) __builtin_constant_p(expr) +#endif +#if !defined(__cplusplus) +# if \ + JSON_HEDLEY_HAS_BUILTIN(__builtin_types_compatible_p) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(3,4,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(13,1,0) || \ + JSON_HEDLEY_CRAY_VERSION_CHECK(8,1,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(5,4,0) || \ + JSON_HEDLEY_TINYC_VERSION_CHECK(0,9,24) +#if defined(__INTPTR_TYPE__) + #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0)), int*) +#else + #include + #define JSON_HEDLEY_IS_CONSTEXPR_(expr) __builtin_types_compatible_p(__typeof__((1 ? (void*) ((intptr_t) ((expr) * 0)) : (int*) 0)), int*) +#endif +# elif \ + ( \ + defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) && \ + !defined(JSON_HEDLEY_SUNPRO_VERSION) && \ + !defined(JSON_HEDLEY_PGI_VERSION) && \ + !defined(JSON_HEDLEY_IAR_VERSION)) || \ + (JSON_HEDLEY_HAS_EXTENSION(c_generic_selections) && !defined(JSON_HEDLEY_IAR_VERSION)) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(17,0,0) || \ + JSON_HEDLEY_IBM_VERSION_CHECK(12,1,0) || \ + JSON_HEDLEY_ARM_VERSION_CHECK(5,3,0) +#if defined(__INTPTR_TYPE__) + #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((__INTPTR_TYPE__) ((expr) * 0)) : (int*) 0), int*: 1, void*: 0) +#else + #include + #define JSON_HEDLEY_IS_CONSTEXPR_(expr) _Generic((1 ? (void*) ((intptr_t) * 0) : (int*) 0), int*: 1, void*: 0) +#endif +# elif \ + defined(JSON_HEDLEY_GCC_VERSION) || \ + defined(JSON_HEDLEY_INTEL_VERSION) || \ + defined(JSON_HEDLEY_TINYC_VERSION) || \ + defined(JSON_HEDLEY_TI_ARMCL_VERSION) || \ + JSON_HEDLEY_TI_CL430_VERSION_CHECK(18,12,0) || \ + defined(JSON_HEDLEY_TI_CL2000_VERSION) || \ + defined(JSON_HEDLEY_TI_CL6X_VERSION) || \ + defined(JSON_HEDLEY_TI_CL7X_VERSION) || \ + defined(JSON_HEDLEY_TI_CLPRU_VERSION) || \ + defined(__clang__) +# define JSON_HEDLEY_IS_CONSTEXPR_(expr) ( \ + sizeof(void) != \ + sizeof(*( \ + 1 ? \ + ((void*) ((expr) * 0L) ) : \ +((struct { char v[sizeof(void) * 2]; } *) 1) \ + ) \ + ) \ + ) +# endif +#endif +#if defined(JSON_HEDLEY_IS_CONSTEXPR_) + #if !defined(JSON_HEDLEY_IS_CONSTANT) + #define JSON_HEDLEY_IS_CONSTANT(expr) JSON_HEDLEY_IS_CONSTEXPR_(expr) + #endif + #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (JSON_HEDLEY_IS_CONSTEXPR_(expr) ? (expr) : (-1)) +#else + #if !defined(JSON_HEDLEY_IS_CONSTANT) + #define JSON_HEDLEY_IS_CONSTANT(expr) (0) + #endif + #define JSON_HEDLEY_REQUIRE_CONSTEXPR(expr) (expr) +#endif + +#if defined(JSON_HEDLEY_BEGIN_C_DECLS) + #undef JSON_HEDLEY_BEGIN_C_DECLS +#endif +#if defined(JSON_HEDLEY_END_C_DECLS) + #undef JSON_HEDLEY_END_C_DECLS +#endif +#if defined(JSON_HEDLEY_C_DECL) + #undef JSON_HEDLEY_C_DECL +#endif +#if defined(__cplusplus) + #define JSON_HEDLEY_BEGIN_C_DECLS extern "C" { + #define JSON_HEDLEY_END_C_DECLS } + #define JSON_HEDLEY_C_DECL extern "C" +#else + #define JSON_HEDLEY_BEGIN_C_DECLS + #define JSON_HEDLEY_END_C_DECLS + #define JSON_HEDLEY_C_DECL +#endif + +#if defined(JSON_HEDLEY_STATIC_ASSERT) + #undef JSON_HEDLEY_STATIC_ASSERT +#endif +#if \ + !defined(__cplusplus) && ( \ + (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) || \ + (JSON_HEDLEY_HAS_FEATURE(c_static_assert) && !defined(JSON_HEDLEY_INTEL_CL_VERSION)) || \ + JSON_HEDLEY_GCC_VERSION_CHECK(6,0,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) || \ + defined(_Static_assert) \ + ) +# define JSON_HEDLEY_STATIC_ASSERT(expr, message) _Static_assert(expr, message) +#elif \ + (defined(__cplusplus) && (__cplusplus >= 201103L)) || \ + JSON_HEDLEY_MSVC_VERSION_CHECK(16,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) +# define JSON_HEDLEY_STATIC_ASSERT(expr, message) JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(static_assert(expr, message)) +#else +# define JSON_HEDLEY_STATIC_ASSERT(expr, message) +#endif + +#if defined(JSON_HEDLEY_NULL) + #undef JSON_HEDLEY_NULL +#endif +#if defined(__cplusplus) + #if __cplusplus >= 201103L + #define JSON_HEDLEY_NULL JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_(nullptr) + #elif defined(NULL) + #define JSON_HEDLEY_NULL NULL + #else + #define JSON_HEDLEY_NULL JSON_HEDLEY_STATIC_CAST(void*, 0) + #endif +#elif defined(NULL) + #define JSON_HEDLEY_NULL NULL +#else + #define JSON_HEDLEY_NULL ((void*) 0) +#endif + +#if defined(JSON_HEDLEY_MESSAGE) + #undef JSON_HEDLEY_MESSAGE +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas") +# define JSON_HEDLEY_MESSAGE(msg) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \ + JSON_HEDLEY_PRAGMA(message msg) \ + JSON_HEDLEY_DIAGNOSTIC_POP +#elif \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,4,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) +# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message msg) +#elif JSON_HEDLEY_CRAY_VERSION_CHECK(5,0,0) +# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(_CRI message msg) +#elif JSON_HEDLEY_IAR_VERSION_CHECK(8,0,0) +# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg)) +#elif JSON_HEDLEY_PELLES_VERSION_CHECK(2,0,0) +# define JSON_HEDLEY_MESSAGE(msg) JSON_HEDLEY_PRAGMA(message(msg)) +#else +# define JSON_HEDLEY_MESSAGE(msg) +#endif + +#if defined(JSON_HEDLEY_WARNING) + #undef JSON_HEDLEY_WARNING +#endif +#if JSON_HEDLEY_HAS_WARNING("-Wunknown-pragmas") +# define JSON_HEDLEY_WARNING(msg) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS \ + JSON_HEDLEY_PRAGMA(clang warning msg) \ + JSON_HEDLEY_DIAGNOSTIC_POP +#elif \ + JSON_HEDLEY_GCC_VERSION_CHECK(4,8,0) || \ + JSON_HEDLEY_PGI_VERSION_CHECK(18,4,0) || \ + JSON_HEDLEY_INTEL_VERSION_CHECK(13,0,0) +# define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(GCC warning msg) +#elif \ + JSON_HEDLEY_MSVC_VERSION_CHECK(15,0,0) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) +# define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_PRAGMA(message(msg)) +#else +# define JSON_HEDLEY_WARNING(msg) JSON_HEDLEY_MESSAGE(msg) +#endif + +#if defined(JSON_HEDLEY_REQUIRE) + #undef JSON_HEDLEY_REQUIRE +#endif +#if defined(JSON_HEDLEY_REQUIRE_MSG) + #undef JSON_HEDLEY_REQUIRE_MSG +#endif +#if JSON_HEDLEY_HAS_ATTRIBUTE(diagnose_if) +# if JSON_HEDLEY_HAS_WARNING("-Wgcc-compat") +# define JSON_HEDLEY_REQUIRE(expr) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("clang diagnostic ignored \"-Wgcc-compat\"") \ + __attribute__((diagnose_if(!(expr), #expr, "error"))) \ + JSON_HEDLEY_DIAGNOSTIC_POP +# define JSON_HEDLEY_REQUIRE_MSG(expr,msg) \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("clang diagnostic ignored \"-Wgcc-compat\"") \ + __attribute__((diagnose_if(!(expr), msg, "error"))) \ + JSON_HEDLEY_DIAGNOSTIC_POP +# else +# define JSON_HEDLEY_REQUIRE(expr) __attribute__((diagnose_if(!(expr), #expr, "error"))) +# define JSON_HEDLEY_REQUIRE_MSG(expr,msg) __attribute__((diagnose_if(!(expr), msg, "error"))) +# endif +#else +# define JSON_HEDLEY_REQUIRE(expr) +# define JSON_HEDLEY_REQUIRE_MSG(expr,msg) +#endif + +#if defined(JSON_HEDLEY_FLAGS) + #undef JSON_HEDLEY_FLAGS +#endif +#if JSON_HEDLEY_HAS_ATTRIBUTE(flag_enum) && (!defined(__cplusplus) || JSON_HEDLEY_HAS_WARNING("-Wbitfield-enum-conversion")) + #define JSON_HEDLEY_FLAGS __attribute__((__flag_enum__)) +#else + #define JSON_HEDLEY_FLAGS +#endif + +#if defined(JSON_HEDLEY_FLAGS_CAST) + #undef JSON_HEDLEY_FLAGS_CAST +#endif +#if JSON_HEDLEY_INTEL_VERSION_CHECK(19,0,0) +# define JSON_HEDLEY_FLAGS_CAST(T, expr) (__extension__ ({ \ + JSON_HEDLEY_DIAGNOSTIC_PUSH \ + _Pragma("warning(disable:188)") \ + ((T) (expr)); \ + JSON_HEDLEY_DIAGNOSTIC_POP \ + })) +#else +# define JSON_HEDLEY_FLAGS_CAST(T, expr) JSON_HEDLEY_STATIC_CAST(T, expr) +#endif + +#if defined(JSON_HEDLEY_EMPTY_BASES) + #undef JSON_HEDLEY_EMPTY_BASES +#endif +#if \ + (JSON_HEDLEY_MSVC_VERSION_CHECK(19,0,23918) && !JSON_HEDLEY_MSVC_VERSION_CHECK(20,0,0)) || \ + JSON_HEDLEY_INTEL_CL_VERSION_CHECK(2021,1,0) + #define JSON_HEDLEY_EMPTY_BASES __declspec(empty_bases) +#else + #define JSON_HEDLEY_EMPTY_BASES +#endif + +/* Remaining macros are deprecated. */ + +#if defined(JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK) + #undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK +#endif +#if defined(__clang__) + #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) (0) +#else + #define JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK(major,minor,patch) JSON_HEDLEY_GCC_VERSION_CHECK(major,minor,patch) +#endif + +#if defined(JSON_HEDLEY_CLANG_HAS_ATTRIBUTE) + #undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE +#endif +#define JSON_HEDLEY_CLANG_HAS_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_ATTRIBUTE(attribute) + +#if defined(JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE) + #undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE +#endif +#define JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_CPP_ATTRIBUTE(attribute) + +#if defined(JSON_HEDLEY_CLANG_HAS_BUILTIN) + #undef JSON_HEDLEY_CLANG_HAS_BUILTIN +#endif +#define JSON_HEDLEY_CLANG_HAS_BUILTIN(builtin) JSON_HEDLEY_HAS_BUILTIN(builtin) + +#if defined(JSON_HEDLEY_CLANG_HAS_FEATURE) + #undef JSON_HEDLEY_CLANG_HAS_FEATURE +#endif +#define JSON_HEDLEY_CLANG_HAS_FEATURE(feature) JSON_HEDLEY_HAS_FEATURE(feature) + +#if defined(JSON_HEDLEY_CLANG_HAS_EXTENSION) + #undef JSON_HEDLEY_CLANG_HAS_EXTENSION +#endif +#define JSON_HEDLEY_CLANG_HAS_EXTENSION(extension) JSON_HEDLEY_HAS_EXTENSION(extension) + +#if defined(JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE) + #undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE +#endif +#define JSON_HEDLEY_CLANG_HAS_DECLSPEC_ATTRIBUTE(attribute) JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE(attribute) + +#if defined(JSON_HEDLEY_CLANG_HAS_WARNING) + #undef JSON_HEDLEY_CLANG_HAS_WARNING +#endif +#define JSON_HEDLEY_CLANG_HAS_WARNING(warning) JSON_HEDLEY_HAS_WARNING(warning) + +#endif /* !defined(JSON_HEDLEY_VERSION) || (JSON_HEDLEY_VERSION < X) */ + +// #include + + +#include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +template struct make_void +{ + using type = void; +}; +template using void_t = typename make_void::type; +} // namespace detail +} // namespace nlohmann + + +// https://en.cppreference.com/w/cpp/experimental/is_detected +namespace nlohmann +{ +namespace detail +{ +struct nonesuch +{ + nonesuch() = delete; + ~nonesuch() = delete; + nonesuch(nonesuch const&) = delete; + nonesuch(nonesuch const&&) = delete; + void operator=(nonesuch const&) = delete; + void operator=(nonesuch&&) = delete; +}; + +template class Op, + class... Args> +struct detector +{ + using value_t = std::false_type; + using type = Default; +}; + +template class Op, class... Args> +struct detector>, Op, Args...> +{ + using value_t = std::true_type; + using type = Op; +}; + +template class Op, class... Args> +using is_detected = typename detector::value_t; + +template class Op, class... Args> +struct is_detected_lazy : is_detected { }; + +template class Op, class... Args> +using detected_t = typename detector::type; + +template class Op, class... Args> +using detected_or = detector; + +template class Op, class... Args> +using detected_or_t = typename detected_or::type; + +template class Op, class... Args> +using is_detected_exact = std::is_same>; + +template class Op, class... Args> +using is_detected_convertible = + std::is_convertible, To>; +} // namespace detail +} // namespace nlohmann + + +// This file contains all internal macro definitions +// You MUST include macro_unscope.hpp at the end of json.hpp to undef all of them + +// exclude unsupported compilers +#if !defined(JSON_SKIP_UNSUPPORTED_COMPILER_CHECK) + #if defined(__clang__) + #if (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) < 30400 + #error "unsupported Clang version - see https://github.com/nlohmann/json#supported-compilers" + #endif + #elif defined(__GNUC__) && !(defined(__ICC) || defined(__INTEL_COMPILER)) + #if (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) < 40800 + #error "unsupported GCC version - see https://github.com/nlohmann/json#supported-compilers" + #endif + #endif +#endif + +// C++ language standard detection +// if the user manually specified the used c++ version this is skipped +#if !defined(JSON_HAS_CPP_20) && !defined(JSON_HAS_CPP_17) && !defined(JSON_HAS_CPP_14) && !defined(JSON_HAS_CPP_11) + #if (defined(__cplusplus) && __cplusplus >= 202002L) || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L) + #define JSON_HAS_CPP_20 + #define JSON_HAS_CPP_17 + #define JSON_HAS_CPP_14 + #elif (defined(__cplusplus) && __cplusplus >= 201703L) || (defined(_HAS_CXX17) && _HAS_CXX17 == 1) // fix for issue #464 + #define JSON_HAS_CPP_17 + #define JSON_HAS_CPP_14 + #elif (defined(__cplusplus) && __cplusplus >= 201402L) || (defined(_HAS_CXX14) && _HAS_CXX14 == 1) + #define JSON_HAS_CPP_14 + #endif + // the cpp 11 flag is always specified because it is the minimal required version + #define JSON_HAS_CPP_11 +#endif + +// disable documentation warnings on clang +#if defined(__clang__) + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wdocumentation" + #pragma clang diagnostic ignored "-Wdocumentation-unknown-command" +#endif + +// allow to disable exceptions +#if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || defined(_CPPUNWIND)) && !defined(JSON_NOEXCEPTION) + #define JSON_THROW(exception) throw exception + #define JSON_TRY try + #define JSON_CATCH(exception) catch(exception) + #define JSON_INTERNAL_CATCH(exception) catch(exception) +#else + #include + #define JSON_THROW(exception) std::abort() + #define JSON_TRY if(true) + #define JSON_CATCH(exception) if(false) + #define JSON_INTERNAL_CATCH(exception) if(false) +#endif + +// override exception macros +#if defined(JSON_THROW_USER) + #undef JSON_THROW + #define JSON_THROW JSON_THROW_USER +#endif +#if defined(JSON_TRY_USER) + #undef JSON_TRY + #define JSON_TRY JSON_TRY_USER +#endif +#if defined(JSON_CATCH_USER) + #undef JSON_CATCH + #define JSON_CATCH JSON_CATCH_USER + #undef JSON_INTERNAL_CATCH + #define JSON_INTERNAL_CATCH JSON_CATCH_USER +#endif +#if defined(JSON_INTERNAL_CATCH_USER) + #undef JSON_INTERNAL_CATCH + #define JSON_INTERNAL_CATCH JSON_INTERNAL_CATCH_USER +#endif + +// allow to override assert +#if !defined(JSON_ASSERT) + #include // assert + #define JSON_ASSERT(x) assert(x) +#endif + +// allow to access some private functions (needed by the test suite) +#if defined(JSON_TESTS_PRIVATE) + #define JSON_PRIVATE_UNLESS_TESTED public +#else + #define JSON_PRIVATE_UNLESS_TESTED private +#endif + +/*! +@brief macro to briefly define a mapping between an enum and JSON +@def NLOHMANN_JSON_SERIALIZE_ENUM +@since version 3.4.0 +*/ +#define NLOHMANN_JSON_SERIALIZE_ENUM(ENUM_TYPE, ...) \ + template \ + inline void to_json(BasicJsonType& j, const ENUM_TYPE& e) \ + { \ + static_assert(std::is_enum::value, #ENUM_TYPE " must be an enum!"); \ + static const std::pair m[] = __VA_ARGS__; \ + auto it = std::find_if(std::begin(m), std::end(m), \ + [e](const std::pair& ej_pair) -> bool \ + { \ + return ej_pair.first == e; \ + }); \ + j = ((it != std::end(m)) ? it : std::begin(m))->second; \ + } \ + template \ + inline void from_json(const BasicJsonType& j, ENUM_TYPE& e) \ + { \ + static_assert(std::is_enum::value, #ENUM_TYPE " must be an enum!"); \ + static const std::pair m[] = __VA_ARGS__; \ + auto it = std::find_if(std::begin(m), std::end(m), \ + [&j](const std::pair& ej_pair) -> bool \ + { \ + return ej_pair.second == j; \ + }); \ + e = ((it != std::end(m)) ? it : std::begin(m))->first; \ + } + +// Ugly macros to avoid uglier copy-paste when specializing basic_json. They +// may be removed in the future once the class is split. + +#define NLOHMANN_BASIC_JSON_TPL_DECLARATION \ + template class ObjectType, \ + template class ArrayType, \ + class StringType, class BooleanType, class NumberIntegerType, \ + class NumberUnsignedType, class NumberFloatType, \ + template class AllocatorType, \ + template class JSONSerializer, \ + class BinaryType> + +#define NLOHMANN_BASIC_JSON_TPL \ + basic_json + +// Macros to simplify conversion from/to types + +#define NLOHMANN_JSON_EXPAND( x ) x +#define NLOHMANN_JSON_GET_MACRO(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, _61, _62, _63, _64, NAME,...) NAME +#define NLOHMANN_JSON_PASTE(...) NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_GET_MACRO(__VA_ARGS__, \ + NLOHMANN_JSON_PASTE64, \ + NLOHMANN_JSON_PASTE63, \ + NLOHMANN_JSON_PASTE62, \ + NLOHMANN_JSON_PASTE61, \ + NLOHMANN_JSON_PASTE60, \ + NLOHMANN_JSON_PASTE59, \ + NLOHMANN_JSON_PASTE58, \ + NLOHMANN_JSON_PASTE57, \ + NLOHMANN_JSON_PASTE56, \ + NLOHMANN_JSON_PASTE55, \ + NLOHMANN_JSON_PASTE54, \ + NLOHMANN_JSON_PASTE53, \ + NLOHMANN_JSON_PASTE52, \ + NLOHMANN_JSON_PASTE51, \ + NLOHMANN_JSON_PASTE50, \ + NLOHMANN_JSON_PASTE49, \ + NLOHMANN_JSON_PASTE48, \ + NLOHMANN_JSON_PASTE47, \ + NLOHMANN_JSON_PASTE46, \ + NLOHMANN_JSON_PASTE45, \ + NLOHMANN_JSON_PASTE44, \ + NLOHMANN_JSON_PASTE43, \ + NLOHMANN_JSON_PASTE42, \ + NLOHMANN_JSON_PASTE41, \ + NLOHMANN_JSON_PASTE40, \ + NLOHMANN_JSON_PASTE39, \ + NLOHMANN_JSON_PASTE38, \ + NLOHMANN_JSON_PASTE37, \ + NLOHMANN_JSON_PASTE36, \ + NLOHMANN_JSON_PASTE35, \ + NLOHMANN_JSON_PASTE34, \ + NLOHMANN_JSON_PASTE33, \ + NLOHMANN_JSON_PASTE32, \ + NLOHMANN_JSON_PASTE31, \ + NLOHMANN_JSON_PASTE30, \ + NLOHMANN_JSON_PASTE29, \ + NLOHMANN_JSON_PASTE28, \ + NLOHMANN_JSON_PASTE27, \ + NLOHMANN_JSON_PASTE26, \ + NLOHMANN_JSON_PASTE25, \ + NLOHMANN_JSON_PASTE24, \ + NLOHMANN_JSON_PASTE23, \ + NLOHMANN_JSON_PASTE22, \ + NLOHMANN_JSON_PASTE21, \ + NLOHMANN_JSON_PASTE20, \ + NLOHMANN_JSON_PASTE19, \ + NLOHMANN_JSON_PASTE18, \ + NLOHMANN_JSON_PASTE17, \ + NLOHMANN_JSON_PASTE16, \ + NLOHMANN_JSON_PASTE15, \ + NLOHMANN_JSON_PASTE14, \ + NLOHMANN_JSON_PASTE13, \ + NLOHMANN_JSON_PASTE12, \ + NLOHMANN_JSON_PASTE11, \ + NLOHMANN_JSON_PASTE10, \ + NLOHMANN_JSON_PASTE9, \ + NLOHMANN_JSON_PASTE8, \ + NLOHMANN_JSON_PASTE7, \ + NLOHMANN_JSON_PASTE6, \ + NLOHMANN_JSON_PASTE5, \ + NLOHMANN_JSON_PASTE4, \ + NLOHMANN_JSON_PASTE3, \ + NLOHMANN_JSON_PASTE2, \ + NLOHMANN_JSON_PASTE1)(__VA_ARGS__)) +#define NLOHMANN_JSON_PASTE2(func, v1) func(v1) +#define NLOHMANN_JSON_PASTE3(func, v1, v2) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE2(func, v2) +#define NLOHMANN_JSON_PASTE4(func, v1, v2, v3) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE3(func, v2, v3) +#define NLOHMANN_JSON_PASTE5(func, v1, v2, v3, v4) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE4(func, v2, v3, v4) +#define NLOHMANN_JSON_PASTE6(func, v1, v2, v3, v4, v5) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE5(func, v2, v3, v4, v5) +#define NLOHMANN_JSON_PASTE7(func, v1, v2, v3, v4, v5, v6) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE6(func, v2, v3, v4, v5, v6) +#define NLOHMANN_JSON_PASTE8(func, v1, v2, v3, v4, v5, v6, v7) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE7(func, v2, v3, v4, v5, v6, v7) +#define NLOHMANN_JSON_PASTE9(func, v1, v2, v3, v4, v5, v6, v7, v8) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE8(func, v2, v3, v4, v5, v6, v7, v8) +#define NLOHMANN_JSON_PASTE10(func, v1, v2, v3, v4, v5, v6, v7, v8, v9) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE9(func, v2, v3, v4, v5, v6, v7, v8, v9) +#define NLOHMANN_JSON_PASTE11(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE10(func, v2, v3, v4, v5, v6, v7, v8, v9, v10) +#define NLOHMANN_JSON_PASTE12(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE11(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11) +#define NLOHMANN_JSON_PASTE13(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE12(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) +#define NLOHMANN_JSON_PASTE14(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE13(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) +#define NLOHMANN_JSON_PASTE15(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE14(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14) +#define NLOHMANN_JSON_PASTE16(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE15(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15) +#define NLOHMANN_JSON_PASTE17(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE16(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16) +#define NLOHMANN_JSON_PASTE18(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE17(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17) +#define NLOHMANN_JSON_PASTE19(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE18(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18) +#define NLOHMANN_JSON_PASTE20(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE19(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19) +#define NLOHMANN_JSON_PASTE21(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE20(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20) +#define NLOHMANN_JSON_PASTE22(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE21(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21) +#define NLOHMANN_JSON_PASTE23(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE22(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22) +#define NLOHMANN_JSON_PASTE24(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE23(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23) +#define NLOHMANN_JSON_PASTE25(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE24(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24) +#define NLOHMANN_JSON_PASTE26(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE25(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25) +#define NLOHMANN_JSON_PASTE27(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE26(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26) +#define NLOHMANN_JSON_PASTE28(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE27(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27) +#define NLOHMANN_JSON_PASTE29(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE28(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28) +#define NLOHMANN_JSON_PASTE30(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE29(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29) +#define NLOHMANN_JSON_PASTE31(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE30(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30) +#define NLOHMANN_JSON_PASTE32(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE31(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31) +#define NLOHMANN_JSON_PASTE33(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE32(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32) +#define NLOHMANN_JSON_PASTE34(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE33(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33) +#define NLOHMANN_JSON_PASTE35(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE34(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34) +#define NLOHMANN_JSON_PASTE36(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE35(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35) +#define NLOHMANN_JSON_PASTE37(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE36(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36) +#define NLOHMANN_JSON_PASTE38(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE37(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37) +#define NLOHMANN_JSON_PASTE39(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE38(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38) +#define NLOHMANN_JSON_PASTE40(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE39(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39) +#define NLOHMANN_JSON_PASTE41(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE40(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40) +#define NLOHMANN_JSON_PASTE42(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE41(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41) +#define NLOHMANN_JSON_PASTE43(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE42(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42) +#define NLOHMANN_JSON_PASTE44(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE43(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43) +#define NLOHMANN_JSON_PASTE45(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE44(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44) +#define NLOHMANN_JSON_PASTE46(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE45(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45) +#define NLOHMANN_JSON_PASTE47(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE46(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46) +#define NLOHMANN_JSON_PASTE48(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE47(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47) +#define NLOHMANN_JSON_PASTE49(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE48(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48) +#define NLOHMANN_JSON_PASTE50(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE49(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49) +#define NLOHMANN_JSON_PASTE51(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE50(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50) +#define NLOHMANN_JSON_PASTE52(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE51(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51) +#define NLOHMANN_JSON_PASTE53(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE52(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52) +#define NLOHMANN_JSON_PASTE54(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE53(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53) +#define NLOHMANN_JSON_PASTE55(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE54(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54) +#define NLOHMANN_JSON_PASTE56(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE55(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55) +#define NLOHMANN_JSON_PASTE57(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE56(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56) +#define NLOHMANN_JSON_PASTE58(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE57(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57) +#define NLOHMANN_JSON_PASTE59(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE58(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58) +#define NLOHMANN_JSON_PASTE60(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE59(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59) +#define NLOHMANN_JSON_PASTE61(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE60(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60) +#define NLOHMANN_JSON_PASTE62(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE61(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61) +#define NLOHMANN_JSON_PASTE63(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE62(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62) +#define NLOHMANN_JSON_PASTE64(func, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63) NLOHMANN_JSON_PASTE2(func, v1) NLOHMANN_JSON_PASTE63(func, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28, v29, v30, v31, v32, v33, v34, v35, v36, v37, v38, v39, v40, v41, v42, v43, v44, v45, v46, v47, v48, v49, v50, v51, v52, v53, v54, v55, v56, v57, v58, v59, v60, v61, v62, v63) + +#define NLOHMANN_JSON_TO(v1) nlohmann_json_j[#v1] = nlohmann_json_t.v1; +#define NLOHMANN_JSON_FROM(v1) nlohmann_json_j.at(#v1).get_to(nlohmann_json_t.v1); + +/*! +@brief macro +@def NLOHMANN_DEFINE_TYPE_INTRUSIVE +@since version 3.9.0 +*/ +#define NLOHMANN_DEFINE_TYPE_INTRUSIVE(Type, ...) \ + friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ + friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) } + +/*! +@brief macro +@def NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE +@since version 3.9.0 +*/ +#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE(Type, ...) \ + inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ + inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) } + + +// inspired from https://stackoverflow.com/a/26745591 +// allows to call any std function as if (e.g. with begin): +// using std::begin; begin(x); +// +// it allows using the detected idiom to retrieve the return type +// of such an expression +#define NLOHMANN_CAN_CALL_STD_FUNC_IMPL(std_name) \ + namespace detail { \ + using std::std_name; \ + \ + template \ + using result_of_##std_name = decltype(std_name(std::declval()...)); \ + } \ + \ + namespace detail2 { \ + struct std_name##_tag \ + { \ + }; \ + \ + template \ + std_name##_tag std_name(T&&...); \ + \ + template \ + using result_of_##std_name = decltype(std_name(std::declval()...)); \ + \ + template \ + struct would_call_std_##std_name \ + { \ + static constexpr auto const value = ::nlohmann::detail:: \ + is_detected_exact::value; \ + }; \ + } /* namespace detail2 */ \ + \ + template \ + struct would_call_std_##std_name : detail2::would_call_std_##std_name \ + { \ + } + +#ifndef JSON_USE_IMPLICIT_CONVERSIONS + #define JSON_USE_IMPLICIT_CONVERSIONS 1 +#endif + +#if JSON_USE_IMPLICIT_CONVERSIONS + #define JSON_EXPLICIT +#else + #define JSON_EXPLICIT explicit +#endif + +#ifndef JSON_DIAGNOSTICS + #define JSON_DIAGNOSTICS 0 +#endif + + +namespace nlohmann +{ +namespace detail +{ + +/*! +@brief replace all occurrences of a substring by another string + +@param[in,out] s the string to manipulate; changed so that all + occurrences of @a f are replaced with @a t +@param[in] f the substring to replace with @a t +@param[in] t the string to replace @a f + +@pre The search string @a f must not be empty. **This precondition is +enforced with an assertion.** + +@since version 2.0.0 +*/ +inline void replace_substring(std::string& s, const std::string& f, + const std::string& t) +{ + JSON_ASSERT(!f.empty()); + for (auto pos = s.find(f); // find first occurrence of f + pos != std::string::npos; // make sure f was found + s.replace(pos, f.size(), t), // replace with t, and + pos = s.find(f, pos + t.size())) // find next occurrence of f + {} +} + +/*! + * @brief string escaping as described in RFC 6901 (Sect. 4) + * @param[in] s string to escape + * @return escaped string + * + * Note the order of escaping "~" to "~0" and "/" to "~1" is important. + */ +inline std::string escape(std::string s) +{ + replace_substring(s, "~", "~0"); + replace_substring(s, "/", "~1"); + return s; +} + +/*! + * @brief string unescaping as described in RFC 6901 (Sect. 4) + * @param[in] s string to unescape + * @return unescaped string + * + * Note the order of escaping "~1" to "/" and "~0" to "~" is important. + */ +static void unescape(std::string& s) +{ + replace_substring(s, "~1", "/"); + replace_substring(s, "~0", "~"); +} + +} // namespace detail +} // namespace nlohmann + +// #include + + +#include // size_t + +namespace nlohmann +{ +namespace detail +{ +/// struct to capture the start position of the current token +struct position_t +{ + /// the total number of characters read + std::size_t chars_read_total = 0; + /// the number of characters read in the current line + std::size_t chars_read_current_line = 0; + /// the number of lines read + std::size_t lines_read = 0; + + /// conversion to size_t to preserve SAX interface + constexpr operator size_t() const + { + return chars_read_total; + } +}; + +} // namespace detail +} // namespace nlohmann + +// #include + + +namespace nlohmann +{ +namespace detail +{ +//////////////// +// exceptions // +//////////////// + +/*! +@brief general exception of the @ref basic_json class + +This class is an extension of `std::exception` objects with a member @a id for +exception ids. It is used as the base class for all exceptions thrown by the +@ref basic_json class. This class can hence be used as "wildcard" to catch +exceptions. + +Subclasses: +- @ref parse_error for exceptions indicating a parse error +- @ref invalid_iterator for exceptions indicating errors with iterators +- @ref type_error for exceptions indicating executing a member function with + a wrong type +- @ref out_of_range for exceptions indicating access out of the defined range +- @ref other_error for exceptions indicating other library errors + +@internal +@note To have nothrow-copy-constructible exceptions, we internally use + `std::runtime_error` which can cope with arbitrary-length error messages. + Intermediate strings are built with static functions and then passed to + the actual constructor. +@endinternal + +@liveexample{The following code shows how arbitrary library exceptions can be +caught.,exception} + +@since version 3.0.0 +*/ +class exception : public std::exception +{ + public: + /// returns the explanatory string + const char* what() const noexcept override + { + return m.what(); + } + + /// the id of the exception + const int id; // NOLINT(cppcoreguidelines-non-private-member-variables-in-classes) + + protected: + JSON_HEDLEY_NON_NULL(3) + exception(int id_, const char* what_arg) : id(id_), m(what_arg) {} + + static std::string name(const std::string& ename, int id_) + { + return "[json.exception." + ename + "." + std::to_string(id_) + "] "; + } + + template + static std::string diagnostics(const BasicJsonType& leaf_element) + { +#if JSON_DIAGNOSTICS + std::vector tokens; + for (const auto* current = &leaf_element; current->m_parent != nullptr; current = current->m_parent) + { + switch (current->m_parent->type()) + { + case value_t::array: + { + for (std::size_t i = 0; i < current->m_parent->m_value.array->size(); ++i) + { + if (¤t->m_parent->m_value.array->operator[](i) == current) + { + tokens.emplace_back(std::to_string(i)); + break; + } + } + break; + } + + case value_t::object: + { + for (const auto& element : *current->m_parent->m_value.object) + { + if (&element.second == current) + { + tokens.emplace_back(element.first.c_str()); + break; + } + } + break; + } + + case value_t::null: // LCOV_EXCL_LINE + case value_t::string: // LCOV_EXCL_LINE + case value_t::boolean: // LCOV_EXCL_LINE + case value_t::number_integer: // LCOV_EXCL_LINE + case value_t::number_unsigned: // LCOV_EXCL_LINE + case value_t::number_float: // LCOV_EXCL_LINE + case value_t::binary: // LCOV_EXCL_LINE + case value_t::discarded: // LCOV_EXCL_LINE + default: // LCOV_EXCL_LINE + break; // LCOV_EXCL_LINE + } + } + + if (tokens.empty()) + { + return ""; + } + + return "(" + std::accumulate(tokens.rbegin(), tokens.rend(), std::string{}, + [](const std::string & a, const std::string & b) + { + return a + "/" + detail::escape(b); + }) + ") "; +#else + static_cast(leaf_element); + return ""; +#endif + } + + private: + /// an exception object as storage for error messages + std::runtime_error m; +}; + +/*! +@brief exception indicating a parse error + +This exception is thrown by the library when a parse error occurs. Parse errors +can occur during the deserialization of JSON text, CBOR, MessagePack, as well +as when using JSON Patch. + +Member @a byte holds the byte index of the last read character in the input +file. + +Exceptions have ids 1xx. + +name / id | example message | description +------------------------------ | --------------- | ------------------------- +json.exception.parse_error.101 | parse error at 2: unexpected end of input; expected string literal | This error indicates a syntax error while deserializing a JSON text. The error message describes that an unexpected token (character) was encountered, and the member @a byte indicates the error position. +json.exception.parse_error.102 | parse error at 14: missing or wrong low surrogate | JSON uses the `\uxxxx` format to describe Unicode characters. Code points above above 0xFFFF are split into two `\uxxxx` entries ("surrogate pairs"). This error indicates that the surrogate pair is incomplete or contains an invalid code point. +json.exception.parse_error.103 | parse error: code points above 0x10FFFF are invalid | Unicode supports code points up to 0x10FFFF. Code points above 0x10FFFF are invalid. +json.exception.parse_error.104 | parse error: JSON patch must be an array of objects | [RFC 6902](https://tools.ietf.org/html/rfc6902) requires a JSON Patch document to be a JSON document that represents an array of objects. +json.exception.parse_error.105 | parse error: operation must have string member 'op' | An operation of a JSON Patch document must contain exactly one "op" member, whose value indicates the operation to perform. Its value must be one of "add", "remove", "replace", "move", "copy", or "test"; other values are errors. +json.exception.parse_error.106 | parse error: array index '01' must not begin with '0' | An array index in a JSON Pointer ([RFC 6901](https://tools.ietf.org/html/rfc6901)) may be `0` or any number without a leading `0`. +json.exception.parse_error.107 | parse error: JSON pointer must be empty or begin with '/' - was: 'foo' | A JSON Pointer must be a Unicode string containing a sequence of zero or more reference tokens, each prefixed by a `/` character. +json.exception.parse_error.108 | parse error: escape character '~' must be followed with '0' or '1' | In a JSON Pointer, only `~0` and `~1` are valid escape sequences. +json.exception.parse_error.109 | parse error: array index 'one' is not a number | A JSON Pointer array index must be a number. +json.exception.parse_error.110 | parse error at 1: cannot read 2 bytes from vector | When parsing CBOR or MessagePack, the byte vector ends before the complete value has been read. +json.exception.parse_error.112 | parse error at 1: error reading CBOR; last byte: 0xF8 | Not all types of CBOR or MessagePack are supported. This exception occurs if an unsupported byte was read. +json.exception.parse_error.113 | parse error at 2: expected a CBOR string; last byte: 0x98 | While parsing a map key, a value that is not a string has been read. +json.exception.parse_error.114 | parse error: Unsupported BSON record type 0x0F | The parsing of the corresponding BSON record type is not implemented (yet). +json.exception.parse_error.115 | parse error at byte 5: syntax error while parsing UBJSON high-precision number: invalid number text: 1A | A UBJSON high-precision number could not be parsed. + +@note For an input with n bytes, 1 is the index of the first character and n+1 + is the index of the terminating null byte or the end of file. This also + holds true when reading a byte vector (CBOR or MessagePack). + +@liveexample{The following code shows how a `parse_error` exception can be +caught.,parse_error} + +@sa - @ref exception for the base class of the library exceptions +@sa - @ref invalid_iterator for exceptions indicating errors with iterators +@sa - @ref type_error for exceptions indicating executing a member function with + a wrong type +@sa - @ref out_of_range for exceptions indicating access out of the defined range +@sa - @ref other_error for exceptions indicating other library errors + +@since version 3.0.0 +*/ +class parse_error : public exception +{ + public: + /*! + @brief create a parse error exception + @param[in] id_ the id of the exception + @param[in] pos the position where the error occurred (or with + chars_read_total=0 if the position cannot be + determined) + @param[in] what_arg the explanatory string + @return parse_error object + */ + template + static parse_error create(int id_, const position_t& pos, const std::string& what_arg, const BasicJsonType& context) + { + std::string w = exception::name("parse_error", id_) + "parse error" + + position_string(pos) + ": " + exception::diagnostics(context) + what_arg; + return parse_error(id_, pos.chars_read_total, w.c_str()); + } + + template + static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, const BasicJsonType& context) + { + std::string w = exception::name("parse_error", id_) + "parse error" + + (byte_ != 0 ? (" at byte " + std::to_string(byte_)) : "") + + ": " + exception::diagnostics(context) + what_arg; + return parse_error(id_, byte_, w.c_str()); + } + + /*! + @brief byte index of the parse error + + The byte index of the last read character in the input file. + + @note For an input with n bytes, 1 is the index of the first character and + n+1 is the index of the terminating null byte or the end of file. + This also holds true when reading a byte vector (CBOR or MessagePack). + */ + const std::size_t byte; + + private: + parse_error(int id_, std::size_t byte_, const char* what_arg) + : exception(id_, what_arg), byte(byte_) {} + + static std::string position_string(const position_t& pos) + { + return " at line " + std::to_string(pos.lines_read + 1) + + ", column " + std::to_string(pos.chars_read_current_line); + } +}; + +/*! +@brief exception indicating errors with iterators + +This exception is thrown if iterators passed to a library function do not match +the expected semantics. + +Exceptions have ids 2xx. + +name / id | example message | description +----------------------------------- | --------------- | ------------------------- +json.exception.invalid_iterator.201 | iterators are not compatible | The iterators passed to constructor @ref basic_json(InputIT first, InputIT last) are not compatible, meaning they do not belong to the same container. Therefore, the range (@a first, @a last) is invalid. +json.exception.invalid_iterator.202 | iterator does not fit current value | In an erase or insert function, the passed iterator @a pos does not belong to the JSON value for which the function was called. It hence does not define a valid position for the deletion/insertion. +json.exception.invalid_iterator.203 | iterators do not fit current value | Either iterator passed to function @ref erase(IteratorType first, IteratorType last) does not belong to the JSON value from which values shall be erased. It hence does not define a valid range to delete values from. +json.exception.invalid_iterator.204 | iterators out of range | When an iterator range for a primitive type (number, boolean, or string) is passed to a constructor or an erase function, this range has to be exactly (@ref begin(), @ref end()), because this is the only way the single stored value is expressed. All other ranges are invalid. +json.exception.invalid_iterator.205 | iterator out of range | When an iterator for a primitive type (number, boolean, or string) is passed to an erase function, the iterator has to be the @ref begin() iterator, because it is the only way to address the stored value. All other iterators are invalid. +json.exception.invalid_iterator.206 | cannot construct with iterators from null | The iterators passed to constructor @ref basic_json(InputIT first, InputIT last) belong to a JSON null value and hence to not define a valid range. +json.exception.invalid_iterator.207 | cannot use key() for non-object iterators | The key() member function can only be used on iterators belonging to a JSON object, because other types do not have a concept of a key. +json.exception.invalid_iterator.208 | cannot use operator[] for object iterators | The operator[] to specify a concrete offset cannot be used on iterators belonging to a JSON object, because JSON objects are unordered. +json.exception.invalid_iterator.209 | cannot use offsets with object iterators | The offset operators (+, -, +=, -=) cannot be used on iterators belonging to a JSON object, because JSON objects are unordered. +json.exception.invalid_iterator.210 | iterators do not fit | The iterator range passed to the insert function are not compatible, meaning they do not belong to the same container. Therefore, the range (@a first, @a last) is invalid. +json.exception.invalid_iterator.211 | passed iterators may not belong to container | The iterator range passed to the insert function must not be a subrange of the container to insert to. +json.exception.invalid_iterator.212 | cannot compare iterators of different containers | When two iterators are compared, they must belong to the same container. +json.exception.invalid_iterator.213 | cannot compare order of object iterators | The order of object iterators cannot be compared, because JSON objects are unordered. +json.exception.invalid_iterator.214 | cannot get value | Cannot get value for iterator: Either the iterator belongs to a null value or it is an iterator to a primitive type (number, boolean, or string), but the iterator is different to @ref begin(). + +@liveexample{The following code shows how an `invalid_iterator` exception can be +caught.,invalid_iterator} + +@sa - @ref exception for the base class of the library exceptions +@sa - @ref parse_error for exceptions indicating a parse error +@sa - @ref type_error for exceptions indicating executing a member function with + a wrong type +@sa - @ref out_of_range for exceptions indicating access out of the defined range +@sa - @ref other_error for exceptions indicating other library errors + +@since version 3.0.0 +*/ +class invalid_iterator : public exception +{ + public: + template + static invalid_iterator create(int id_, const std::string& what_arg, const BasicJsonType& context) + { + std::string w = exception::name("invalid_iterator", id_) + exception::diagnostics(context) + what_arg; + return invalid_iterator(id_, w.c_str()); + } + + private: + JSON_HEDLEY_NON_NULL(3) + invalid_iterator(int id_, const char* what_arg) + : exception(id_, what_arg) {} +}; + +/*! +@brief exception indicating executing a member function with a wrong type + +This exception is thrown in case of a type error; that is, a library function is +executed on a JSON value whose type does not match the expected semantics. + +Exceptions have ids 3xx. + +name / id | example message | description +----------------------------- | --------------- | ------------------------- +json.exception.type_error.301 | cannot create object from initializer list | To create an object from an initializer list, the initializer list must consist only of a list of pairs whose first element is a string. When this constraint is violated, an array is created instead. +json.exception.type_error.302 | type must be object, but is array | During implicit or explicit value conversion, the JSON type must be compatible to the target type. For instance, a JSON string can only be converted into string types, but not into numbers or boolean types. +json.exception.type_error.303 | incompatible ReferenceType for get_ref, actual type is object | To retrieve a reference to a value stored in a @ref basic_json object with @ref get_ref, the type of the reference must match the value type. For instance, for a JSON array, the @a ReferenceType must be @ref array_t &. +json.exception.type_error.304 | cannot use at() with string | The @ref at() member functions can only be executed for certain JSON types. +json.exception.type_error.305 | cannot use operator[] with string | The @ref operator[] member functions can only be executed for certain JSON types. +json.exception.type_error.306 | cannot use value() with string | The @ref value() member functions can only be executed for certain JSON types. +json.exception.type_error.307 | cannot use erase() with string | The @ref erase() member functions can only be executed for certain JSON types. +json.exception.type_error.308 | cannot use push_back() with string | The @ref push_back() and @ref operator+= member functions can only be executed for certain JSON types. +json.exception.type_error.309 | cannot use insert() with | The @ref insert() member functions can only be executed for certain JSON types. +json.exception.type_error.310 | cannot use swap() with number | The @ref swap() member functions can only be executed for certain JSON types. +json.exception.type_error.311 | cannot use emplace_back() with string | The @ref emplace_back() member function can only be executed for certain JSON types. +json.exception.type_error.312 | cannot use update() with string | The @ref update() member functions can only be executed for certain JSON types. +json.exception.type_error.313 | invalid value to unflatten | The @ref unflatten function converts an object whose keys are JSON Pointers back into an arbitrary nested JSON value. The JSON Pointers must not overlap, because then the resulting value would not be well defined. +json.exception.type_error.314 | only objects can be unflattened | The @ref unflatten function only works for an object whose keys are JSON Pointers. +json.exception.type_error.315 | values in object must be primitive | The @ref unflatten function only works for an object whose keys are JSON Pointers and whose values are primitive. +json.exception.type_error.316 | invalid UTF-8 byte at index 10: 0x7E | The @ref dump function only works with UTF-8 encoded strings; that is, if you assign a `std::string` to a JSON value, make sure it is UTF-8 encoded. | +json.exception.type_error.317 | JSON value cannot be serialized to requested format | The dynamic type of the object cannot be represented in the requested serialization format (e.g. a raw `true` or `null` JSON object cannot be serialized to BSON) | + +@liveexample{The following code shows how a `type_error` exception can be +caught.,type_error} + +@sa - @ref exception for the base class of the library exceptions +@sa - @ref parse_error for exceptions indicating a parse error +@sa - @ref invalid_iterator for exceptions indicating errors with iterators +@sa - @ref out_of_range for exceptions indicating access out of the defined range +@sa - @ref other_error for exceptions indicating other library errors + +@since version 3.0.0 +*/ +class type_error : public exception +{ + public: + template + static type_error create(int id_, const std::string& what_arg, const BasicJsonType& context) + { + std::string w = exception::name("type_error", id_) + exception::diagnostics(context) + what_arg; + return type_error(id_, w.c_str()); + } + + private: + JSON_HEDLEY_NON_NULL(3) + type_error(int id_, const char* what_arg) : exception(id_, what_arg) {} +}; + +/*! +@brief exception indicating access out of the defined range + +This exception is thrown in case a library function is called on an input +parameter that exceeds the expected range, for instance in case of array +indices or nonexisting object keys. + +Exceptions have ids 4xx. + +name / id | example message | description +------------------------------- | --------------- | ------------------------- +json.exception.out_of_range.401 | array index 3 is out of range | The provided array index @a i is larger than @a size-1. +json.exception.out_of_range.402 | array index '-' (3) is out of range | The special array index `-` in a JSON Pointer never describes a valid element of the array, but the index past the end. That is, it can only be used to add elements at this position, but not to read it. +json.exception.out_of_range.403 | key 'foo' not found | The provided key was not found in the JSON object. +json.exception.out_of_range.404 | unresolved reference token 'foo' | A reference token in a JSON Pointer could not be resolved. +json.exception.out_of_range.405 | JSON pointer has no parent | The JSON Patch operations 'remove' and 'add' can not be applied to the root element of the JSON value. +json.exception.out_of_range.406 | number overflow parsing '10E1000' | A parsed number could not be stored as without changing it to NaN or INF. +json.exception.out_of_range.407 | number overflow serializing '9223372036854775808' | UBJSON and BSON only support integer numbers up to 9223372036854775807. (until version 3.8.0) | +json.exception.out_of_range.408 | excessive array size: 8658170730974374167 | The size (following `#`) of an UBJSON array or object exceeds the maximal capacity. | +json.exception.out_of_range.409 | BSON key cannot contain code point U+0000 (at byte 2) | Key identifiers to be serialized to BSON cannot contain code point U+0000, since the key is stored as zero-terminated c-string | + +@liveexample{The following code shows how an `out_of_range` exception can be +caught.,out_of_range} + +@sa - @ref exception for the base class of the library exceptions +@sa - @ref parse_error for exceptions indicating a parse error +@sa - @ref invalid_iterator for exceptions indicating errors with iterators +@sa - @ref type_error for exceptions indicating executing a member function with + a wrong type +@sa - @ref other_error for exceptions indicating other library errors + +@since version 3.0.0 +*/ +class out_of_range : public exception +{ + public: + template + static out_of_range create(int id_, const std::string& what_arg, const BasicJsonType& context) + { + std::string w = exception::name("out_of_range", id_) + exception::diagnostics(context) + what_arg; + return out_of_range(id_, w.c_str()); + } + + private: + JSON_HEDLEY_NON_NULL(3) + out_of_range(int id_, const char* what_arg) : exception(id_, what_arg) {} +}; + +/*! +@brief exception indicating other library errors + +This exception is thrown in case of errors that cannot be classified with the +other exception types. + +Exceptions have ids 5xx. + +name / id | example message | description +------------------------------ | --------------- | ------------------------- +json.exception.other_error.501 | unsuccessful: {"op":"test","path":"/baz", "value":"bar"} | A JSON Patch operation 'test' failed. The unsuccessful operation is also printed. + +@sa - @ref exception for the base class of the library exceptions +@sa - @ref parse_error for exceptions indicating a parse error +@sa - @ref invalid_iterator for exceptions indicating errors with iterators +@sa - @ref type_error for exceptions indicating executing a member function with + a wrong type +@sa - @ref out_of_range for exceptions indicating access out of the defined range + +@liveexample{The following code shows how an `other_error` exception can be +caught.,other_error} + +@since version 3.0.0 +*/ +class other_error : public exception +{ + public: + template + static other_error create(int id_, const std::string& what_arg, const BasicJsonType& context) + { + std::string w = exception::name("other_error", id_) + exception::diagnostics(context) + what_arg; + return other_error(id_, w.c_str()); + } + + private: + JSON_HEDLEY_NON_NULL(3) + other_error(int id_, const char* what_arg) : exception(id_, what_arg) {} +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + + +#include // size_t +#include // conditional, enable_if, false_type, integral_constant, is_constructible, is_integral, is_same, remove_cv, remove_reference, true_type +#include // index_sequence, make_index_sequence, index_sequence_for + +// #include + + +namespace nlohmann +{ +namespace detail +{ + +template +using uncvref_t = typename std::remove_cv::type>::type; + +#ifdef JSON_HAS_CPP_14 + +// the following utilities are natively available in C++14 +using std::enable_if_t; +using std::index_sequence; +using std::make_index_sequence; +using std::index_sequence_for; + +#else + +// alias templates to reduce boilerplate +template +using enable_if_t = typename std::enable_if::type; + +// The following code is taken from https://github.com/abseil/abseil-cpp/blob/10cb35e459f5ecca5b2ff107635da0bfa41011b4/absl/utility/utility.h +// which is part of Google Abseil (https://github.com/abseil/abseil-cpp), licensed under the Apache License 2.0. + +//// START OF CODE FROM GOOGLE ABSEIL + +// integer_sequence +// +// Class template representing a compile-time integer sequence. An instantiation +// of `integer_sequence` has a sequence of integers encoded in its +// type through its template arguments (which is a common need when +// working with C++11 variadic templates). `absl::integer_sequence` is designed +// to be a drop-in replacement for C++14's `std::integer_sequence`. +// +// Example: +// +// template< class T, T... Ints > +// void user_function(integer_sequence); +// +// int main() +// { +// // user_function's `T` will be deduced to `int` and `Ints...` +// // will be deduced to `0, 1, 2, 3, 4`. +// user_function(make_integer_sequence()); +// } +template +struct integer_sequence +{ + using value_type = T; + static constexpr std::size_t size() noexcept + { + return sizeof...(Ints); + } +}; + +// index_sequence +// +// A helper template for an `integer_sequence` of `size_t`, +// `absl::index_sequence` is designed to be a drop-in replacement for C++14's +// `std::index_sequence`. +template +using index_sequence = integer_sequence; + +namespace utility_internal +{ + +template +struct Extend; + +// Note that SeqSize == sizeof...(Ints). It's passed explicitly for efficiency. +template +struct Extend, SeqSize, 0> +{ + using type = integer_sequence < T, Ints..., (Ints + SeqSize)... >; +}; + +template +struct Extend, SeqSize, 1> +{ + using type = integer_sequence < T, Ints..., (Ints + SeqSize)..., 2 * SeqSize >; +}; + +// Recursion helper for 'make_integer_sequence'. +// 'Gen::type' is an alias for 'integer_sequence'. +template +struct Gen +{ + using type = + typename Extend < typename Gen < T, N / 2 >::type, N / 2, N % 2 >::type; +}; + +template +struct Gen +{ + using type = integer_sequence; +}; + +} // namespace utility_internal + +// Compile-time sequences of integers + +// make_integer_sequence +// +// This template alias is equivalent to +// `integer_sequence`, and is designed to be a drop-in +// replacement for C++14's `std::make_integer_sequence`. +template +using make_integer_sequence = typename utility_internal::Gen::type; + +// make_index_sequence +// +// This template alias is equivalent to `index_sequence<0, 1, ..., N-1>`, +// and is designed to be a drop-in replacement for C++14's +// `std::make_index_sequence`. +template +using make_index_sequence = make_integer_sequence; + +// index_sequence_for +// +// Converts a typename pack into an index sequence of the same length, and +// is designed to be a drop-in replacement for C++14's +// `std::index_sequence_for()` +template +using index_sequence_for = make_index_sequence; + +//// END OF CODE FROM GOOGLE ABSEIL + +#endif + +// dispatch utility (taken from ranges-v3) +template struct priority_tag : priority_tag < N - 1 > {}; +template<> struct priority_tag<0> {}; + +// taken from ranges-v3 +template +struct static_const +{ + static constexpr T value{}; +}; + +template +constexpr T static_const::value; + +} // namespace detail +} // namespace nlohmann + +// #include + + +namespace nlohmann +{ +namespace detail +{ +// dispatching helper struct +template struct identity_tag {}; +} // namespace detail +} // namespace nlohmann + +// #include + + +#include // numeric_limits +#include // false_type, is_constructible, is_integral, is_same, true_type +#include // declval +#include // tuple + +// #include + + +// #include + + +#include // random_access_iterator_tag + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +template +struct iterator_types {}; + +template +struct iterator_types < + It, + void_t> +{ + using difference_type = typename It::difference_type; + using value_type = typename It::value_type; + using pointer = typename It::pointer; + using reference = typename It::reference; + using iterator_category = typename It::iterator_category; +}; + +// This is required as some compilers implement std::iterator_traits in a way that +// doesn't work with SFINAE. See https://github.com/nlohmann/json/issues/1341. +template +struct iterator_traits +{ +}; + +template +struct iterator_traits < T, enable_if_t < !std::is_pointer::value >> + : iterator_types +{ +}; + +template +struct iterator_traits::value>> +{ + using iterator_category = std::random_access_iterator_tag; + using value_type = T; + using difference_type = ptrdiff_t; + using pointer = T*; + using reference = T&; +}; +} // namespace detail +} // namespace nlohmann + +// #include + + +// #include + + +namespace nlohmann +{ +NLOHMANN_CAN_CALL_STD_FUNC_IMPL(begin); +} // namespace nlohmann + +// #include + + +// #include + + +namespace nlohmann +{ +NLOHMANN_CAN_CALL_STD_FUNC_IMPL(end); +} // namespace nlohmann + +// #include + +// #include + +// #include +#ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_ +#define INCLUDE_NLOHMANN_JSON_FWD_HPP_ + +#include // int64_t, uint64_t +#include // map +#include // allocator +#include // string +#include // vector + +/*! +@brief namespace for Niels Lohmann +@see https://github.com/nlohmann +@since version 1.0.0 +*/ +namespace nlohmann +{ +/*! +@brief default JSONSerializer template argument + +This serializer ignores the template arguments and uses ADL +([argument-dependent lookup](https://en.cppreference.com/w/cpp/language/adl)) +for serialization. +*/ +template +struct adl_serializer; + +template class ObjectType = + std::map, + template class ArrayType = std::vector, + class StringType = std::string, class BooleanType = bool, + class NumberIntegerType = std::int64_t, + class NumberUnsignedType = std::uint64_t, + class NumberFloatType = double, + template class AllocatorType = std::allocator, + template class JSONSerializer = + adl_serializer, + class BinaryType = std::vector> +class basic_json; + +/*! +@brief JSON Pointer + +A JSON pointer defines a string syntax for identifying a specific value +within a JSON document. It can be used with functions `at` and +`operator[]`. Furthermore, JSON pointers are the base for JSON patches. + +@sa [RFC 6901](https://tools.ietf.org/html/rfc6901) + +@since version 2.0.0 +*/ +template +class json_pointer; + +/*! +@brief default JSON class + +This type is the default specialization of the @ref basic_json class which +uses the standard template types. + +@since version 1.0.0 +*/ +using json = basic_json<>; + +template +struct ordered_map; + +/*! +@brief ordered JSON class + +This type preserves the insertion order of object keys. + +@since version 3.9.0 +*/ +using ordered_json = basic_json; + +} // namespace nlohmann + +#endif // INCLUDE_NLOHMANN_JSON_FWD_HPP_ + + +namespace nlohmann +{ +/*! +@brief detail namespace with internal helper functions + +This namespace collects functions that should not be exposed, +implementations of some @ref basic_json methods, and meta-programming helpers. + +@since version 2.1.0 +*/ +namespace detail +{ +///////////// +// helpers // +///////////// + +// Note to maintainers: +// +// Every trait in this file expects a non CV-qualified type. +// The only exceptions are in the 'aliases for detected' section +// (i.e. those of the form: decltype(T::member_function(std::declval()))) +// +// In this case, T has to be properly CV-qualified to constraint the function arguments +// (e.g. to_json(BasicJsonType&, const T&)) + +template struct is_basic_json : std::false_type {}; + +NLOHMANN_BASIC_JSON_TPL_DECLARATION +struct is_basic_json : std::true_type {}; + +////////////////////// +// json_ref helpers // +////////////////////// + +template +class json_ref; + +template +struct is_json_ref : std::false_type {}; + +template +struct is_json_ref> : std::true_type {}; + +////////////////////////// +// aliases for detected // +////////////////////////// + +template +using mapped_type_t = typename T::mapped_type; + +template +using key_type_t = typename T::key_type; + +template +using value_type_t = typename T::value_type; + +template +using difference_type_t = typename T::difference_type; + +template +using pointer_t = typename T::pointer; + +template +using reference_t = typename T::reference; + +template +using iterator_category_t = typename T::iterator_category; + +template +using to_json_function = decltype(T::to_json(std::declval()...)); + +template +using from_json_function = decltype(T::from_json(std::declval()...)); + +template +using get_template_function = decltype(std::declval().template get()); + +// trait checking if JSONSerializer::from_json(json const&, udt&) exists +template +struct has_from_json : std::false_type {}; + +// trait checking if j.get is valid +// use this trait instead of std::is_constructible or std::is_convertible, +// both rely on, or make use of implicit conversions, and thus fail when T +// has several constructors/operator= (see https://github.com/nlohmann/json/issues/958) +template +struct is_getable +{ + static constexpr bool value = is_detected::value; +}; + +template +struct has_from_json < BasicJsonType, T, enable_if_t < !is_basic_json::value >> +{ + using serializer = typename BasicJsonType::template json_serializer; + + static constexpr bool value = + is_detected_exact::value; +}; + +// This trait checks if JSONSerializer::from_json(json const&) exists +// this overload is used for non-default-constructible user-defined-types +template +struct has_non_default_from_json : std::false_type {}; + +template +struct has_non_default_from_json < BasicJsonType, T, enable_if_t < !is_basic_json::value >> +{ + using serializer = typename BasicJsonType::template json_serializer; + + static constexpr bool value = + is_detected_exact::value; +}; + +// This trait checks if BasicJsonType::json_serializer::to_json exists +// Do not evaluate the trait when T is a basic_json type, to avoid template instantiation infinite recursion. +template +struct has_to_json : std::false_type {}; + +template +struct has_to_json < BasicJsonType, T, enable_if_t < !is_basic_json::value >> +{ + using serializer = typename BasicJsonType::template json_serializer; + + static constexpr bool value = + is_detected_exact::value; +}; + + +/////////////////// +// is_ functions // +/////////////////// + +// https://en.cppreference.com/w/cpp/types/conjunction +template struct conjunction : std::true_type { }; +template struct conjunction : B1 { }; +template +struct conjunction +: std::conditional, B1>::type {}; + +// https://en.cppreference.com/w/cpp/types/negation +template struct negation : std::integral_constant < bool, !B::value > { }; + +// Reimplementation of is_constructible and is_default_constructible, due to them being broken for +// std::pair and std::tuple until LWG 2367 fix (see https://cplusplus.github.io/LWG/lwg-defects.html#2367). +// This causes compile errors in e.g. clang 3.5 or gcc 4.9. +template +struct is_default_constructible : std::is_default_constructible {}; + +template +struct is_default_constructible> + : conjunction, is_default_constructible> {}; + +template +struct is_default_constructible> + : conjunction, is_default_constructible> {}; + +template +struct is_default_constructible> + : conjunction...> {}; + +template +struct is_default_constructible> + : conjunction...> {}; + + +template +struct is_constructible : std::is_constructible {}; + +template +struct is_constructible> : is_default_constructible> {}; + +template +struct is_constructible> : is_default_constructible> {}; + +template +struct is_constructible> : is_default_constructible> {}; + +template +struct is_constructible> : is_default_constructible> {}; + + +template +struct is_iterator_traits : std::false_type {}; + +template +struct is_iterator_traits> +{ + private: + using traits = iterator_traits; + + public: + static constexpr auto value = + is_detected::value && + is_detected::value && + is_detected::value && + is_detected::value && + is_detected::value; +}; + +template +struct is_range +{ + private: + using t_ref = typename std::add_lvalue_reference::type; + + using iterator = detected_t; + using sentinel = detected_t; + + // to be 100% correct, it should use https://en.cppreference.com/w/cpp/iterator/input_or_output_iterator + // and https://en.cppreference.com/w/cpp/iterator/sentinel_for + // but reimplementing these would be too much work, as a lot of other concepts are used underneath + static constexpr auto is_iterator_begin = + is_iterator_traits>::value; + + public: + static constexpr bool value = !std::is_same::value && !std::is_same::value && is_iterator_begin; +}; + +template +using iterator_t = enable_if_t::value, result_of_begin())>>; + +template +using range_value_t = value_type_t>>; + +// The following implementation of is_complete_type is taken from +// https://blogs.msdn.microsoft.com/vcblog/2015/12/02/partial-support-for-expression-sfinae-in-vs-2015-update-1/ +// and is written by Xiang Fan who agreed to using it in this library. + +template +struct is_complete_type : std::false_type {}; + +template +struct is_complete_type : std::true_type {}; + +template +struct is_compatible_object_type_impl : std::false_type {}; + +template +struct is_compatible_object_type_impl < + BasicJsonType, CompatibleObjectType, + enable_if_t < is_detected::value&& + is_detected::value >> +{ + using object_t = typename BasicJsonType::object_t; + + // macOS's is_constructible does not play well with nonesuch... + static constexpr bool value = + is_constructible::value && + is_constructible::value; +}; + +template +struct is_compatible_object_type + : is_compatible_object_type_impl {}; + +template +struct is_constructible_object_type_impl : std::false_type {}; + +template +struct is_constructible_object_type_impl < + BasicJsonType, ConstructibleObjectType, + enable_if_t < is_detected::value&& + is_detected::value >> +{ + using object_t = typename BasicJsonType::object_t; + + static constexpr bool value = + (is_default_constructible::value && + (std::is_move_assignable::value || + std::is_copy_assignable::value) && + (is_constructible::value && + std::is_same < + typename object_t::mapped_type, + typename ConstructibleObjectType::mapped_type >::value)) || + (has_from_json::value || + has_non_default_from_json < + BasicJsonType, + typename ConstructibleObjectType::mapped_type >::value); +}; + +template +struct is_constructible_object_type + : is_constructible_object_type_impl {}; + +template +struct is_compatible_string_type +{ + static constexpr auto value = + is_constructible::value; +}; + +template +struct is_constructible_string_type +{ + static constexpr auto value = + is_constructible::value; +}; + +template +struct is_compatible_array_type_impl : std::false_type {}; + +template +struct is_compatible_array_type_impl < + BasicJsonType, CompatibleArrayType, + enable_if_t < + is_detected::value&& + is_iterator_traits>>::value&& +// special case for types like std::filesystem::path whose iterator's value_type are themselves +// c.f. https://github.com/nlohmann/json/pull/3073 + !std::is_same>::value >> +{ + static constexpr bool value = + is_constructible>::value; +}; + +template +struct is_compatible_array_type + : is_compatible_array_type_impl {}; + +template +struct is_constructible_array_type_impl : std::false_type {}; + +template +struct is_constructible_array_type_impl < + BasicJsonType, ConstructibleArrayType, + enable_if_t::value >> + : std::true_type {}; + +template +struct is_constructible_array_type_impl < + BasicJsonType, ConstructibleArrayType, + enable_if_t < !std::is_same::value&& + !is_compatible_string_type::value&& + is_default_constructible::value&& +(std::is_move_assignable::value || + std::is_copy_assignable::value)&& +is_detected::value&& +is_iterator_traits>>::value&& +is_detected::value&& +// special case for types like std::filesystem::path whose iterator's value_type are themselves +// c.f. https://github.com/nlohmann/json/pull/3073 +!std::is_same>::value&& + is_complete_type < + detected_t>::value >> +{ + using value_type = range_value_t; + + static constexpr bool value = + std::is_same::value || + has_from_json::value || + has_non_default_from_json < + BasicJsonType, + value_type >::value; +}; + +template +struct is_constructible_array_type + : is_constructible_array_type_impl {}; + +template +struct is_compatible_integer_type_impl : std::false_type {}; + +template +struct is_compatible_integer_type_impl < + RealIntegerType, CompatibleNumberIntegerType, + enable_if_t < std::is_integral::value&& + std::is_integral::value&& + !std::is_same::value >> +{ + // is there an assert somewhere on overflows? + using RealLimits = std::numeric_limits; + using CompatibleLimits = std::numeric_limits; + + static constexpr auto value = + is_constructible::value && + CompatibleLimits::is_integer && + RealLimits::is_signed == CompatibleLimits::is_signed; +}; + +template +struct is_compatible_integer_type + : is_compatible_integer_type_impl {}; + +template +struct is_compatible_type_impl: std::false_type {}; + +template +struct is_compatible_type_impl < + BasicJsonType, CompatibleType, + enable_if_t::value >> +{ + static constexpr bool value = + has_to_json::value; +}; + +template +struct is_compatible_type + : is_compatible_type_impl {}; + +template +struct is_constructible_tuple : std::false_type {}; + +template +struct is_constructible_tuple> : conjunction...> {}; + +// a naive helper to check if a type is an ordered_map (exploits the fact that +// ordered_map inherits capacity() from std::vector) +template +struct is_ordered_map +{ + using one = char; + + struct two + { + char x[2]; // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) + }; + + template static one test( decltype(&C::capacity) ) ; + template static two test(...); + + enum { value = sizeof(test(nullptr)) == sizeof(char) }; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) +}; + +// to avoid useless casts (see https://github.com/nlohmann/json/issues/2893#issuecomment-889152324) +template < typename T, typename U, enable_if_t < !std::is_same::value, int > = 0 > +T conditional_static_cast(U value) +{ + return static_cast(value); +} + +template::value, int> = 0> +T conditional_static_cast(U value) +{ + return value; +} + +} // namespace detail +} // namespace nlohmann + +// #include + + +#ifdef JSON_HAS_CPP_17 + #include +#endif + +namespace nlohmann +{ +namespace detail +{ +template +void from_json(const BasicJsonType& j, typename std::nullptr_t& n) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_null())) + { + JSON_THROW(type_error::create(302, "type must be null, but is " + std::string(j.type_name()), j)); + } + n = nullptr; +} + +// overloads for basic_json template parameters +template < typename BasicJsonType, typename ArithmeticType, + enable_if_t < std::is_arithmetic::value&& + !std::is_same::value, + int > = 0 > +void get_arithmetic_value(const BasicJsonType& j, ArithmeticType& val) +{ + switch (static_cast(j)) + { + case value_t::number_unsigned: + { + val = static_cast(*j.template get_ptr()); + break; + } + case value_t::number_integer: + { + val = static_cast(*j.template get_ptr()); + break; + } + case value_t::number_float: + { + val = static_cast(*j.template get_ptr()); + break; + } + + case value_t::null: + case value_t::object: + case value_t::array: + case value_t::string: + case value_t::boolean: + case value_t::binary: + case value_t::discarded: + default: + JSON_THROW(type_error::create(302, "type must be number, but is " + std::string(j.type_name()), j)); + } +} + +template +void from_json(const BasicJsonType& j, typename BasicJsonType::boolean_t& b) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_boolean())) + { + JSON_THROW(type_error::create(302, "type must be boolean, but is " + std::string(j.type_name()), j)); + } + b = *j.template get_ptr(); +} + +template +void from_json(const BasicJsonType& j, typename BasicJsonType::string_t& s) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_string())) + { + JSON_THROW(type_error::create(302, "type must be string, but is " + std::string(j.type_name()), j)); + } + s = *j.template get_ptr(); +} + +template < + typename BasicJsonType, typename ConstructibleStringType, + enable_if_t < + is_constructible_string_type::value&& + !std::is_same::value, + int > = 0 > +void from_json(const BasicJsonType& j, ConstructibleStringType& s) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_string())) + { + JSON_THROW(type_error::create(302, "type must be string, but is " + std::string(j.type_name()), j)); + } + + s = *j.template get_ptr(); +} + +template +void from_json(const BasicJsonType& j, typename BasicJsonType::number_float_t& val) +{ + get_arithmetic_value(j, val); +} + +template +void from_json(const BasicJsonType& j, typename BasicJsonType::number_unsigned_t& val) +{ + get_arithmetic_value(j, val); +} + +template +void from_json(const BasicJsonType& j, typename BasicJsonType::number_integer_t& val) +{ + get_arithmetic_value(j, val); +} + +template::value, int> = 0> +void from_json(const BasicJsonType& j, EnumType& e) +{ + typename std::underlying_type::type val; + get_arithmetic_value(j, val); + e = static_cast(val); +} + +// forward_list doesn't have an insert method +template::value, int> = 0> +void from_json(const BasicJsonType& j, std::forward_list& l) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + l.clear(); + std::transform(j.rbegin(), j.rend(), + std::front_inserter(l), [](const BasicJsonType & i) + { + return i.template get(); + }); +} + +// valarray doesn't have an insert method +template::value, int> = 0> +void from_json(const BasicJsonType& j, std::valarray& l) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + l.resize(j.size()); + std::transform(j.begin(), j.end(), std::begin(l), + [](const BasicJsonType & elem) + { + return elem.template get(); + }); +} + +template +auto from_json(const BasicJsonType& j, T (&arr)[N]) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) +-> decltype(j.template get(), void()) +{ + for (std::size_t i = 0; i < N; ++i) + { + arr[i] = j.at(i).template get(); + } +} + +template +void from_json_array_impl(const BasicJsonType& j, typename BasicJsonType::array_t& arr, priority_tag<3> /*unused*/) +{ + arr = *j.template get_ptr(); +} + +template +auto from_json_array_impl(const BasicJsonType& j, std::array& arr, + priority_tag<2> /*unused*/) +-> decltype(j.template get(), void()) +{ + for (std::size_t i = 0; i < N; ++i) + { + arr[i] = j.at(i).template get(); + } +} + +template::value, + int> = 0> +auto from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr, priority_tag<1> /*unused*/) +-> decltype( + arr.reserve(std::declval()), + j.template get(), + void()) +{ + using std::end; + + ConstructibleArrayType ret; + ret.reserve(j.size()); + std::transform(j.begin(), j.end(), + std::inserter(ret, end(ret)), [](const BasicJsonType & i) + { + // get() returns *this, this won't call a from_json + // method when value_type is BasicJsonType + return i.template get(); + }); + arr = std::move(ret); +} + +template::value, + int> = 0> +void from_json_array_impl(const BasicJsonType& j, ConstructibleArrayType& arr, + priority_tag<0> /*unused*/) +{ + using std::end; + + ConstructibleArrayType ret; + std::transform( + j.begin(), j.end(), std::inserter(ret, end(ret)), + [](const BasicJsonType & i) + { + // get() returns *this, this won't call a from_json + // method when value_type is BasicJsonType + return i.template get(); + }); + arr = std::move(ret); +} + +template < typename BasicJsonType, typename ConstructibleArrayType, + enable_if_t < + is_constructible_array_type::value&& + !is_constructible_object_type::value&& + !is_constructible_string_type::value&& + !std::is_same::value&& + !is_basic_json::value, + int > = 0 > +auto from_json(const BasicJsonType& j, ConstructibleArrayType& arr) +-> decltype(from_json_array_impl(j, arr, priority_tag<3> {}), +j.template get(), +void()) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + + from_json_array_impl(j, arr, priority_tag<3> {}); +} + +template < typename BasicJsonType, typename T, std::size_t... Idx > +std::array from_json_inplace_array_impl(BasicJsonType&& j, + identity_tag> /*unused*/, index_sequence /*unused*/) +{ + return { { std::forward(j).at(Idx).template get()... } }; +} + +template < typename BasicJsonType, typename T, std::size_t N > +auto from_json(BasicJsonType&& j, identity_tag> tag) +-> decltype(from_json_inplace_array_impl(std::forward(j), tag, make_index_sequence {})) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + + return from_json_inplace_array_impl(std::forward(j), tag, make_index_sequence {}); +} + +template +void from_json(const BasicJsonType& j, typename BasicJsonType::binary_t& bin) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_binary())) + { + JSON_THROW(type_error::create(302, "type must be binary, but is " + std::string(j.type_name()), j)); + } + + bin = *j.template get_ptr(); +} + +template::value, int> = 0> +void from_json(const BasicJsonType& j, ConstructibleObjectType& obj) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_object())) + { + JSON_THROW(type_error::create(302, "type must be object, but is " + std::string(j.type_name()), j)); + } + + ConstructibleObjectType ret; + const auto* inner_object = j.template get_ptr(); + using value_type = typename ConstructibleObjectType::value_type; + std::transform( + inner_object->begin(), inner_object->end(), + std::inserter(ret, ret.begin()), + [](typename BasicJsonType::object_t::value_type const & p) + { + return value_type(p.first, p.second.template get()); + }); + obj = std::move(ret); +} + +// overload for arithmetic types, not chosen for basic_json template arguments +// (BooleanType, etc..); note: Is it really necessary to provide explicit +// overloads for boolean_t etc. in case of a custom BooleanType which is not +// an arithmetic type? +template < typename BasicJsonType, typename ArithmeticType, + enable_if_t < + std::is_arithmetic::value&& + !std::is_same::value&& + !std::is_same::value&& + !std::is_same::value&& + !std::is_same::value, + int > = 0 > +void from_json(const BasicJsonType& j, ArithmeticType& val) +{ + switch (static_cast(j)) + { + case value_t::number_unsigned: + { + val = static_cast(*j.template get_ptr()); + break; + } + case value_t::number_integer: + { + val = static_cast(*j.template get_ptr()); + break; + } + case value_t::number_float: + { + val = static_cast(*j.template get_ptr()); + break; + } + case value_t::boolean: + { + val = static_cast(*j.template get_ptr()); + break; + } + + case value_t::null: + case value_t::object: + case value_t::array: + case value_t::string: + case value_t::binary: + case value_t::discarded: + default: + JSON_THROW(type_error::create(302, "type must be number, but is " + std::string(j.type_name()), j)); + } +} + +template +std::tuple from_json_tuple_impl_base(BasicJsonType&& j, index_sequence /*unused*/) +{ + return std::make_tuple(std::forward(j).at(Idx).template get()...); +} + +template < typename BasicJsonType, class A1, class A2 > +std::pair from_json_tuple_impl(BasicJsonType&& j, identity_tag> /*unused*/, priority_tag<0> /*unused*/) +{ + return {std::forward(j).at(0).template get(), + std::forward(j).at(1).template get()}; +} + +template +void from_json_tuple_impl(BasicJsonType&& j, std::pair& p, priority_tag<1> /*unused*/) +{ + p = from_json_tuple_impl(std::forward(j), identity_tag> {}, priority_tag<0> {}); +} + +template +std::tuple from_json_tuple_impl(BasicJsonType&& j, identity_tag> /*unused*/, priority_tag<2> /*unused*/) +{ + return from_json_tuple_impl_base(std::forward(j), index_sequence_for {}); +} + +template +void from_json_tuple_impl(BasicJsonType&& j, std::tuple& t, priority_tag<3> /*unused*/) +{ + t = from_json_tuple_impl_base(std::forward(j), index_sequence_for {}); +} + +template +auto from_json(BasicJsonType&& j, TupleRelated&& t) +-> decltype(from_json_tuple_impl(std::forward(j), std::forward(t), priority_tag<3> {})) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + + return from_json_tuple_impl(std::forward(j), std::forward(t), priority_tag<3> {}); +} + +template < typename BasicJsonType, typename Key, typename Value, typename Compare, typename Allocator, + typename = enable_if_t < !std::is_constructible < + typename BasicJsonType::string_t, Key >::value >> +void from_json(const BasicJsonType& j, std::map& m) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + m.clear(); + for (const auto& p : j) + { + if (JSON_HEDLEY_UNLIKELY(!p.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(p.type_name()), j)); + } + m.emplace(p.at(0).template get(), p.at(1).template get()); + } +} + +template < typename BasicJsonType, typename Key, typename Value, typename Hash, typename KeyEqual, typename Allocator, + typename = enable_if_t < !std::is_constructible < + typename BasicJsonType::string_t, Key >::value >> +void from_json(const BasicJsonType& j, std::unordered_map& m) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(j.type_name()), j)); + } + m.clear(); + for (const auto& p : j) + { + if (JSON_HEDLEY_UNLIKELY(!p.is_array())) + { + JSON_THROW(type_error::create(302, "type must be array, but is " + std::string(p.type_name()), j)); + } + m.emplace(p.at(0).template get(), p.at(1).template get()); + } +} + +#ifdef JSON_HAS_CPP_17 +template +void from_json(const BasicJsonType& j, std::filesystem::path& p) +{ + if (JSON_HEDLEY_UNLIKELY(!j.is_string())) + { + JSON_THROW(type_error::create(302, "type must be string, but is " + std::string(j.type_name()), j)); + } + p = *j.template get_ptr(); +} +#endif + +struct from_json_fn +{ + template + auto operator()(const BasicJsonType& j, T&& val) const + noexcept(noexcept(from_json(j, std::forward(val)))) + -> decltype(from_json(j, std::forward(val))) + { + return from_json(j, std::forward(val)); + } +}; +} // namespace detail + +/// namespace to hold default `from_json` function +/// to see why this is required: +/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html +namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces) +{ +constexpr const auto& from_json = detail::static_const::value; // NOLINT(misc-definitions-in-headers) +} // namespace +} // namespace nlohmann + +// #include + + +#include // copy +#include // begin, end +#include // string +#include // tuple, get +#include // is_same, is_constructible, is_floating_point, is_enum, underlying_type +#include // move, forward, declval, pair +#include // valarray +#include // vector + +// #include + +// #include + + +#include // size_t +#include // input_iterator_tag +#include // string, to_string +#include // tuple_size, get, tuple_element +#include // move + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +template +void int_to_string( string_type& target, std::size_t value ) +{ + // For ADL + using std::to_string; + target = to_string(value); +} +template class iteration_proxy_value +{ + public: + using difference_type = std::ptrdiff_t; + using value_type = iteration_proxy_value; + using pointer = value_type * ; + using reference = value_type & ; + using iterator_category = std::input_iterator_tag; + using string_type = typename std::remove_cv< typename std::remove_reference().key() ) >::type >::type; + + private: + /// the iterator + IteratorType anchor; + /// an index for arrays (used to create key names) + std::size_t array_index = 0; + /// last stringified array index + mutable std::size_t array_index_last = 0; + /// a string representation of the array index + mutable string_type array_index_str = "0"; + /// an empty string (to return a reference for primitive values) + const string_type empty_str{}; + + public: + explicit iteration_proxy_value(IteratorType it) noexcept + : anchor(std::move(it)) + {} + + /// dereference operator (needed for range-based for) + iteration_proxy_value& operator*() + { + return *this; + } + + /// increment operator (needed for range-based for) + iteration_proxy_value& operator++() + { + ++anchor; + ++array_index; + + return *this; + } + + /// equality operator (needed for InputIterator) + bool operator==(const iteration_proxy_value& o) const + { + return anchor == o.anchor; + } + + /// inequality operator (needed for range-based for) + bool operator!=(const iteration_proxy_value& o) const + { + return anchor != o.anchor; + } + + /// return key of the iterator + const string_type& key() const + { + JSON_ASSERT(anchor.m_object != nullptr); + + switch (anchor.m_object->type()) + { + // use integer array index as key + case value_t::array: + { + if (array_index != array_index_last) + { + int_to_string( array_index_str, array_index ); + array_index_last = array_index; + } + return array_index_str; + } + + // use key from the object + case value_t::object: + return anchor.key(); + + // use an empty key for all primitive types + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + return empty_str; + } + } + + /// return value of the iterator + typename IteratorType::reference value() const + { + return anchor.value(); + } +}; + +/// proxy class for the items() function +template class iteration_proxy +{ + private: + /// the container to iterate + typename IteratorType::reference container; + + public: + /// construct iteration proxy from a container + explicit iteration_proxy(typename IteratorType::reference cont) noexcept + : container(cont) {} + + /// return iterator begin (needed for range-based for) + iteration_proxy_value begin() noexcept + { + return iteration_proxy_value(container.begin()); + } + + /// return iterator end (needed for range-based for) + iteration_proxy_value end() noexcept + { + return iteration_proxy_value(container.end()); + } +}; +// Structured Bindings Support +// For further reference see https://blog.tartanllama.xyz/structured-bindings/ +// And see https://github.com/nlohmann/json/pull/1391 +template = 0> +auto get(const nlohmann::detail::iteration_proxy_value& i) -> decltype(i.key()) +{ + return i.key(); +} +// Structured Bindings Support +// For further reference see https://blog.tartanllama.xyz/structured-bindings/ +// And see https://github.com/nlohmann/json/pull/1391 +template = 0> +auto get(const nlohmann::detail::iteration_proxy_value& i) -> decltype(i.value()) +{ + return i.value(); +} +} // namespace detail +} // namespace nlohmann + +// The Addition to the STD Namespace is required to add +// Structured Bindings Support to the iteration_proxy_value class +// For further reference see https://blog.tartanllama.xyz/structured-bindings/ +// And see https://github.com/nlohmann/json/pull/1391 +namespace std +{ +#if defined(__clang__) + // Fix: https://github.com/nlohmann/json/issues/1401 + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wmismatched-tags" +#endif +template +class tuple_size<::nlohmann::detail::iteration_proxy_value> + : public std::integral_constant {}; + +template +class tuple_element> +{ + public: + using type = decltype( + get(std::declval < + ::nlohmann::detail::iteration_proxy_value> ())); +}; +#if defined(__clang__) + #pragma clang diagnostic pop +#endif +} // namespace std + +// #include + +// #include + +// #include + + +#ifdef JSON_HAS_CPP_17 + #include +#endif + +namespace nlohmann +{ +namespace detail +{ +////////////////// +// constructors // +////////////////// + +/* + * Note all external_constructor<>::construct functions need to call + * j.m_value.destroy(j.m_type) to avoid a memory leak in case j contains an + * allocated value (e.g., a string). See bug issue + * https://github.com/nlohmann/json/issues/2865 for more information. + */ + +template struct external_constructor; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::boolean; + j.m_value = b; + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::string; + j.m_value = s; + j.assert_invariant(); + } + + template + static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::string; + j.m_value = std::move(s); + j.assert_invariant(); + } + + template < typename BasicJsonType, typename CompatibleStringType, + enable_if_t < !std::is_same::value, + int > = 0 > + static void construct(BasicJsonType& j, const CompatibleStringType& str) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::string; + j.m_value.string = j.template create(str); + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::binary; + j.m_value = typename BasicJsonType::binary_t(b); + j.assert_invariant(); + } + + template + static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::binary; + j.m_value = typename BasicJsonType::binary_t(std::move(b)); + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::number_float; + j.m_value = val; + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::number_unsigned; + j.m_value = val; + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::number_integer; + j.m_value = val; + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::array; + j.m_value = arr; + j.set_parents(); + j.assert_invariant(); + } + + template + static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::array; + j.m_value = std::move(arr); + j.set_parents(); + j.assert_invariant(); + } + + template < typename BasicJsonType, typename CompatibleArrayType, + enable_if_t < !std::is_same::value, + int > = 0 > + static void construct(BasicJsonType& j, const CompatibleArrayType& arr) + { + using std::begin; + using std::end; + + j.m_value.destroy(j.m_type); + j.m_type = value_t::array; + j.m_value.array = j.template create(begin(arr), end(arr)); + j.set_parents(); + j.assert_invariant(); + } + + template + static void construct(BasicJsonType& j, const std::vector& arr) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::array; + j.m_value = value_t::array; + j.m_value.array->reserve(arr.size()); + for (const bool x : arr) + { + j.m_value.array->push_back(x); + j.set_parent(j.m_value.array->back()); + } + j.assert_invariant(); + } + + template::value, int> = 0> + static void construct(BasicJsonType& j, const std::valarray& arr) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::array; + j.m_value = value_t::array; + j.m_value.array->resize(arr.size()); + if (arr.size() > 0) + { + std::copy(std::begin(arr), std::end(arr), j.m_value.array->begin()); + } + j.set_parents(); + j.assert_invariant(); + } +}; + +template<> +struct external_constructor +{ + template + static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::object; + j.m_value = obj; + j.set_parents(); + j.assert_invariant(); + } + + template + static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj) + { + j.m_value.destroy(j.m_type); + j.m_type = value_t::object; + j.m_value = std::move(obj); + j.set_parents(); + j.assert_invariant(); + } + + template < typename BasicJsonType, typename CompatibleObjectType, + enable_if_t < !std::is_same::value, int > = 0 > + static void construct(BasicJsonType& j, const CompatibleObjectType& obj) + { + using std::begin; + using std::end; + + j.m_value.destroy(j.m_type); + j.m_type = value_t::object; + j.m_value.object = j.template create(begin(obj), end(obj)); + j.set_parents(); + j.assert_invariant(); + } +}; + +///////////// +// to_json // +///////////// + +template::value, int> = 0> +void to_json(BasicJsonType& j, T b) noexcept +{ + external_constructor::construct(j, b); +} + +template::value, int> = 0> +void to_json(BasicJsonType& j, const CompatibleString& s) +{ + external_constructor::construct(j, s); +} + +template +void to_json(BasicJsonType& j, typename BasicJsonType::string_t&& s) +{ + external_constructor::construct(j, std::move(s)); +} + +template::value, int> = 0> +void to_json(BasicJsonType& j, FloatType val) noexcept +{ + external_constructor::construct(j, static_cast(val)); +} + +template::value, int> = 0> +void to_json(BasicJsonType& j, CompatibleNumberUnsignedType val) noexcept +{ + external_constructor::construct(j, static_cast(val)); +} + +template::value, int> = 0> +void to_json(BasicJsonType& j, CompatibleNumberIntegerType val) noexcept +{ + external_constructor::construct(j, static_cast(val)); +} + +template::value, int> = 0> +void to_json(BasicJsonType& j, EnumType e) noexcept +{ + using underlying_type = typename std::underlying_type::type; + external_constructor::construct(j, static_cast(e)); +} + +template +void to_json(BasicJsonType& j, const std::vector& e) +{ + external_constructor::construct(j, e); +} + +template < typename BasicJsonType, typename CompatibleArrayType, + enable_if_t < is_compatible_array_type::value&& + !is_compatible_object_type::value&& + !is_compatible_string_type::value&& + !std::is_same::value&& + !is_basic_json::value, + int > = 0 > +void to_json(BasicJsonType& j, const CompatibleArrayType& arr) +{ + external_constructor::construct(j, arr); +} + +template +void to_json(BasicJsonType& j, const typename BasicJsonType::binary_t& bin) +{ + external_constructor::construct(j, bin); +} + +template::value, int> = 0> +void to_json(BasicJsonType& j, const std::valarray& arr) +{ + external_constructor::construct(j, std::move(arr)); +} + +template +void to_json(BasicJsonType& j, typename BasicJsonType::array_t&& arr) +{ + external_constructor::construct(j, std::move(arr)); +} + +template < typename BasicJsonType, typename CompatibleObjectType, + enable_if_t < is_compatible_object_type::value&& !is_basic_json::value, int > = 0 > +void to_json(BasicJsonType& j, const CompatibleObjectType& obj) +{ + external_constructor::construct(j, obj); +} + +template +void to_json(BasicJsonType& j, typename BasicJsonType::object_t&& obj) +{ + external_constructor::construct(j, std::move(obj)); +} + +template < + typename BasicJsonType, typename T, std::size_t N, + enable_if_t < !std::is_constructible::value, // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) + int > = 0 > +void to_json(BasicJsonType& j, const T(&arr)[N]) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) +{ + external_constructor::construct(j, arr); +} + +template < typename BasicJsonType, typename T1, typename T2, enable_if_t < std::is_constructible::value&& std::is_constructible::value, int > = 0 > +void to_json(BasicJsonType& j, const std::pair& p) +{ + j = { p.first, p.second }; +} + +// for https://github.com/nlohmann/json/pull/1134 +template>::value, int> = 0> +void to_json(BasicJsonType& j, const T& b) +{ + j = { {b.key(), b.value()} }; +} + +template +void to_json_tuple_impl(BasicJsonType& j, const Tuple& t, index_sequence /*unused*/) +{ + j = { std::get(t)... }; +} + +template::value, int > = 0> +void to_json(BasicJsonType& j, const T& t) +{ + to_json_tuple_impl(j, t, make_index_sequence::value> {}); +} + +#ifdef JSON_HAS_CPP_17 +template +void to_json(BasicJsonType& j, const std::filesystem::path& p) +{ + j = p.string(); +} +#endif + +struct to_json_fn +{ + template + auto operator()(BasicJsonType& j, T&& val) const noexcept(noexcept(to_json(j, std::forward(val)))) + -> decltype(to_json(j, std::forward(val)), void()) + { + return to_json(j, std::forward(val)); + } +}; +} // namespace detail + +/// namespace to hold default `to_json` function +/// to see why this is required: +/// http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2015/n4381.html +namespace // NOLINT(cert-dcl59-cpp,fuchsia-header-anon-namespaces,google-build-namespaces) +{ +constexpr const auto& to_json = detail::static_const::value; // NOLINT(misc-definitions-in-headers) +} // namespace +} // namespace nlohmann + +// #include + +// #include + + +namespace nlohmann +{ + +template +struct adl_serializer +{ + /*! + @brief convert a JSON value to any value type + + This function is usually called by the `get()` function of the + @ref basic_json class (either explicit or via conversion operators). + + @note This function is chosen for default-constructible value types. + + @param[in] j JSON value to read from + @param[in,out] val value to write to + */ + template + static auto from_json(BasicJsonType && j, TargetType& val) noexcept( + noexcept(::nlohmann::from_json(std::forward(j), val))) + -> decltype(::nlohmann::from_json(std::forward(j), val), void()) + { + ::nlohmann::from_json(std::forward(j), val); + } + + /*! + @brief convert a JSON value to any value type + + This function is usually called by the `get()` function of the + @ref basic_json class (either explicit or via conversion operators). + + @note This function is chosen for value types which are not default-constructible. + + @param[in] j JSON value to read from + + @return copy of the JSON value, converted to @a ValueType + */ + template + static auto from_json(BasicJsonType && j) noexcept( + noexcept(::nlohmann::from_json(std::forward(j), detail::identity_tag {}))) + -> decltype(::nlohmann::from_json(std::forward(j), detail::identity_tag {})) + { + return ::nlohmann::from_json(std::forward(j), detail::identity_tag {}); + } + + /*! + @brief convert any value type to a JSON value + + This function is usually called by the constructors of the @ref basic_json + class. + + @param[in,out] j JSON value to write to + @param[in] val value to read from + */ + template + static auto to_json(BasicJsonType& j, TargetType && val) noexcept( + noexcept(::nlohmann::to_json(j, std::forward(val)))) + -> decltype(::nlohmann::to_json(j, std::forward(val)), void()) + { + ::nlohmann::to_json(j, std::forward(val)); + } +}; +} // namespace nlohmann + +// #include + + +#include // uint8_t, uint64_t +#include // tie +#include // move + +namespace nlohmann +{ + +/*! +@brief an internal type for a backed binary type + +This type extends the template parameter @a BinaryType provided to `basic_json` +with a subtype used by BSON and MessagePack. This type exists so that the user +does not have to specify a type themselves with a specific naming scheme in +order to override the binary type. + +@tparam BinaryType container to store bytes (`std::vector` by + default) + +@since version 3.8.0; changed type of subtypes to std::uint64_t in 3.10.0. +*/ +template +class byte_container_with_subtype : public BinaryType +{ + public: + /// the type of the underlying container + using container_type = BinaryType; + /// the type of the subtype + using subtype_type = std::uint64_t; + + byte_container_with_subtype() noexcept(noexcept(container_type())) + : container_type() + {} + + byte_container_with_subtype(const container_type& b) noexcept(noexcept(container_type(b))) + : container_type(b) + {} + + byte_container_with_subtype(container_type&& b) noexcept(noexcept(container_type(std::move(b)))) + : container_type(std::move(b)) + {} + + byte_container_with_subtype(const container_type& b, subtype_type subtype_) noexcept(noexcept(container_type(b))) + : container_type(b) + , m_subtype(subtype_) + , m_has_subtype(true) + {} + + byte_container_with_subtype(container_type&& b, subtype_type subtype_) noexcept(noexcept(container_type(std::move(b)))) + : container_type(std::move(b)) + , m_subtype(subtype_) + , m_has_subtype(true) + {} + + bool operator==(const byte_container_with_subtype& rhs) const + { + return std::tie(static_cast(*this), m_subtype, m_has_subtype) == + std::tie(static_cast(rhs), rhs.m_subtype, rhs.m_has_subtype); + } + + bool operator!=(const byte_container_with_subtype& rhs) const + { + return !(rhs == *this); + } + + /*! + @brief sets the binary subtype + + Sets the binary subtype of the value, also flags a binary JSON value as + having a subtype, which has implications for serialization. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @sa see @ref subtype() -- return the binary subtype + @sa see @ref clear_subtype() -- clears the binary subtype + @sa see @ref has_subtype() -- returns whether or not the binary value has a + subtype + + @since version 3.8.0 + */ + void set_subtype(subtype_type subtype_) noexcept + { + m_subtype = subtype_; + m_has_subtype = true; + } + + /*! + @brief return the binary subtype + + Returns the numerical subtype of the value if it has a subtype. If it does + not have a subtype, this function will return subtype_type(-1) as a sentinel + value. + + @return the numerical subtype of the binary value + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @sa see @ref set_subtype() -- sets the binary subtype + @sa see @ref clear_subtype() -- clears the binary subtype + @sa see @ref has_subtype() -- returns whether or not the binary value has a + subtype + + @since version 3.8.0; fixed return value to properly return + subtype_type(-1) as documented in version 3.10.0 + */ + constexpr subtype_type subtype() const noexcept + { + return m_has_subtype ? m_subtype : subtype_type(-1); + } + + /*! + @brief return whether the value has a subtype + + @return whether the value has a subtype + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @sa see @ref subtype() -- return the binary subtype + @sa see @ref set_subtype() -- sets the binary subtype + @sa see @ref clear_subtype() -- clears the binary subtype + + @since version 3.8.0 + */ + constexpr bool has_subtype() const noexcept + { + return m_has_subtype; + } + + /*! + @brief clears the binary subtype + + Clears the binary subtype and flags the value as not having a subtype, which + has implications for serialization; for instance MessagePack will prefer the + bin family over the ext family. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @sa see @ref subtype() -- return the binary subtype + @sa see @ref set_subtype() -- sets the binary subtype + @sa see @ref has_subtype() -- returns whether or not the binary value has a + subtype + + @since version 3.8.0 + */ + void clear_subtype() noexcept + { + m_subtype = 0; + m_has_subtype = false; + } + + private: + subtype_type m_subtype = 0; + bool m_has_subtype = false; +}; + +} // namespace nlohmann + +// #include + +// #include + +// #include + +// #include + + +#include // uint8_t +#include // size_t +#include // hash + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ + +// boost::hash_combine +inline std::size_t combine(std::size_t seed, std::size_t h) noexcept +{ + seed ^= h + 0x9e3779b9 + (seed << 6U) + (seed >> 2U); + return seed; +} + +/*! +@brief hash a JSON value + +The hash function tries to rely on std::hash where possible. Furthermore, the +type of the JSON value is taken into account to have different hash values for +null, 0, 0U, and false, etc. + +@tparam BasicJsonType basic_json specialization +@param j JSON value to hash +@return hash value of j +*/ +template +std::size_t hash(const BasicJsonType& j) +{ + using string_t = typename BasicJsonType::string_t; + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + + const auto type = static_cast(j.type()); + switch (j.type()) + { + case BasicJsonType::value_t::null: + case BasicJsonType::value_t::discarded: + { + return combine(type, 0); + } + + case BasicJsonType::value_t::object: + { + auto seed = combine(type, j.size()); + for (const auto& element : j.items()) + { + const auto h = std::hash {}(element.key()); + seed = combine(seed, h); + seed = combine(seed, hash(element.value())); + } + return seed; + } + + case BasicJsonType::value_t::array: + { + auto seed = combine(type, j.size()); + for (const auto& element : j) + { + seed = combine(seed, hash(element)); + } + return seed; + } + + case BasicJsonType::value_t::string: + { + const auto h = std::hash {}(j.template get_ref()); + return combine(type, h); + } + + case BasicJsonType::value_t::boolean: + { + const auto h = std::hash {}(j.template get()); + return combine(type, h); + } + + case BasicJsonType::value_t::number_integer: + { + const auto h = std::hash {}(j.template get()); + return combine(type, h); + } + + case BasicJsonType::value_t::number_unsigned: + { + const auto h = std::hash {}(j.template get()); + return combine(type, h); + } + + case BasicJsonType::value_t::number_float: + { + const auto h = std::hash {}(j.template get()); + return combine(type, h); + } + + case BasicJsonType::value_t::binary: + { + auto seed = combine(type, j.get_binary().size()); + const auto h = std::hash {}(j.get_binary().has_subtype()); + seed = combine(seed, h); + seed = combine(seed, static_cast(j.get_binary().subtype())); + for (const auto byte : j.get_binary()) + { + seed = combine(seed, std::hash {}(byte)); + } + return seed; + } + + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + return 0; // LCOV_EXCL_LINE + } +} + +} // namespace detail +} // namespace nlohmann + +// #include + + +#include // generate_n +#include // array +#include // ldexp +#include // size_t +#include // uint8_t, uint16_t, uint32_t, uint64_t +#include // snprintf +#include // memcpy +#include // back_inserter +#include // numeric_limits +#include // char_traits, string +#include // make_pair, move +#include // vector + +// #include + +// #include + + +#include // array +#include // size_t +#include // strlen +#include // begin, end, iterator_traits, random_access_iterator_tag, distance, next +#include // shared_ptr, make_shared, addressof +#include // accumulate +#include // string, char_traits +#include // enable_if, is_base_of, is_pointer, is_integral, remove_pointer +#include // pair, declval + +#ifndef JSON_NO_IO + #include // FILE * + #include // istream +#endif // JSON_NO_IO + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +/// the supported input formats +enum class input_format_t { json, cbor, msgpack, ubjson, bson }; + +//////////////////// +// input adapters // +//////////////////// + +#ifndef JSON_NO_IO +/*! +Input adapter for stdio file access. This adapter read only 1 byte and do not use any + buffer. This adapter is a very low level adapter. +*/ +class file_input_adapter +{ + public: + using char_type = char; + + JSON_HEDLEY_NON_NULL(2) + explicit file_input_adapter(std::FILE* f) noexcept + : m_file(f) + {} + + // make class move-only + file_input_adapter(const file_input_adapter&) = delete; + file_input_adapter(file_input_adapter&&) noexcept = default; + file_input_adapter& operator=(const file_input_adapter&) = delete; + file_input_adapter& operator=(file_input_adapter&&) = delete; + ~file_input_adapter() = default; + + std::char_traits::int_type get_character() noexcept + { + return std::fgetc(m_file); + } + + private: + /// the file pointer to read from + std::FILE* m_file; +}; + + +/*! +Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at +beginning of input. Does not support changing the underlying std::streambuf +in mid-input. Maintains underlying std::istream and std::streambuf to support +subsequent use of standard std::istream operations to process any input +characters following those used in parsing the JSON input. Clears the +std::istream flags; any input errors (e.g., EOF) will be detected by the first +subsequent call for input from the std::istream. +*/ +class input_stream_adapter +{ + public: + using char_type = char; + + ~input_stream_adapter() + { + // clear stream flags; we use underlying streambuf I/O, do not + // maintain ifstream flags, except eof + if (is != nullptr) + { + is->clear(is->rdstate() & std::ios::eofbit); + } + } + + explicit input_stream_adapter(std::istream& i) + : is(&i), sb(i.rdbuf()) + {} + + // delete because of pointer members + input_stream_adapter(const input_stream_adapter&) = delete; + input_stream_adapter& operator=(input_stream_adapter&) = delete; + input_stream_adapter& operator=(input_stream_adapter&&) = delete; + + input_stream_adapter(input_stream_adapter&& rhs) noexcept + : is(rhs.is), sb(rhs.sb) + { + rhs.is = nullptr; + rhs.sb = nullptr; + } + + // std::istream/std::streambuf use std::char_traits::to_int_type, to + // ensure that std::char_traits::eof() and the character 0xFF do not + // end up as the same value, eg. 0xFFFFFFFF. + std::char_traits::int_type get_character() + { + auto res = sb->sbumpc(); + // set eof manually, as we don't use the istream interface. + if (JSON_HEDLEY_UNLIKELY(res == std::char_traits::eof())) + { + is->clear(is->rdstate() | std::ios::eofbit); + } + return res; + } + + private: + /// the associated input stream + std::istream* is = nullptr; + std::streambuf* sb = nullptr; +}; +#endif // JSON_NO_IO + +// General-purpose iterator-based adapter. It might not be as fast as +// theoretically possible for some containers, but it is extremely versatile. +template +class iterator_input_adapter +{ + public: + using char_type = typename std::iterator_traits::value_type; + + iterator_input_adapter(IteratorType first, IteratorType last) + : current(std::move(first)), end(std::move(last)) + {} + + typename std::char_traits::int_type get_character() + { + if (JSON_HEDLEY_LIKELY(current != end)) + { + auto result = std::char_traits::to_int_type(*current); + std::advance(current, 1); + return result; + } + + return std::char_traits::eof(); + } + + private: + IteratorType current; + IteratorType end; + + template + friend struct wide_string_input_helper; + + bool empty() const + { + return current == end; + } +}; + + +template +struct wide_string_input_helper; + +template +struct wide_string_input_helper +{ + // UTF-32 + static void fill_buffer(BaseInputAdapter& input, + std::array::int_type, 4>& utf8_bytes, + size_t& utf8_bytes_index, + size_t& utf8_bytes_filled) + { + utf8_bytes_index = 0; + + if (JSON_HEDLEY_UNLIKELY(input.empty())) + { + utf8_bytes[0] = std::char_traits::eof(); + utf8_bytes_filled = 1; + } + else + { + // get the current character + const auto wc = input.get_character(); + + // UTF-32 to UTF-8 encoding + if (wc < 0x80) + { + utf8_bytes[0] = static_cast::int_type>(wc); + utf8_bytes_filled = 1; + } + else if (wc <= 0x7FF) + { + utf8_bytes[0] = static_cast::int_type>(0xC0u | ((static_cast(wc) >> 6u) & 0x1Fu)); + utf8_bytes[1] = static_cast::int_type>(0x80u | (static_cast(wc) & 0x3Fu)); + utf8_bytes_filled = 2; + } + else if (wc <= 0xFFFF) + { + utf8_bytes[0] = static_cast::int_type>(0xE0u | ((static_cast(wc) >> 12u) & 0x0Fu)); + utf8_bytes[1] = static_cast::int_type>(0x80u | ((static_cast(wc) >> 6u) & 0x3Fu)); + utf8_bytes[2] = static_cast::int_type>(0x80u | (static_cast(wc) & 0x3Fu)); + utf8_bytes_filled = 3; + } + else if (wc <= 0x10FFFF) + { + utf8_bytes[0] = static_cast::int_type>(0xF0u | ((static_cast(wc) >> 18u) & 0x07u)); + utf8_bytes[1] = static_cast::int_type>(0x80u | ((static_cast(wc) >> 12u) & 0x3Fu)); + utf8_bytes[2] = static_cast::int_type>(0x80u | ((static_cast(wc) >> 6u) & 0x3Fu)); + utf8_bytes[3] = static_cast::int_type>(0x80u | (static_cast(wc) & 0x3Fu)); + utf8_bytes_filled = 4; + } + else + { + // unknown character + utf8_bytes[0] = static_cast::int_type>(wc); + utf8_bytes_filled = 1; + } + } + } +}; + +template +struct wide_string_input_helper +{ + // UTF-16 + static void fill_buffer(BaseInputAdapter& input, + std::array::int_type, 4>& utf8_bytes, + size_t& utf8_bytes_index, + size_t& utf8_bytes_filled) + { + utf8_bytes_index = 0; + + if (JSON_HEDLEY_UNLIKELY(input.empty())) + { + utf8_bytes[0] = std::char_traits::eof(); + utf8_bytes_filled = 1; + } + else + { + // get the current character + const auto wc = input.get_character(); + + // UTF-16 to UTF-8 encoding + if (wc < 0x80) + { + utf8_bytes[0] = static_cast::int_type>(wc); + utf8_bytes_filled = 1; + } + else if (wc <= 0x7FF) + { + utf8_bytes[0] = static_cast::int_type>(0xC0u | ((static_cast(wc) >> 6u))); + utf8_bytes[1] = static_cast::int_type>(0x80u | (static_cast(wc) & 0x3Fu)); + utf8_bytes_filled = 2; + } + else if (0xD800 > wc || wc >= 0xE000) + { + utf8_bytes[0] = static_cast::int_type>(0xE0u | ((static_cast(wc) >> 12u))); + utf8_bytes[1] = static_cast::int_type>(0x80u | ((static_cast(wc) >> 6u) & 0x3Fu)); + utf8_bytes[2] = static_cast::int_type>(0x80u | (static_cast(wc) & 0x3Fu)); + utf8_bytes_filled = 3; + } + else + { + if (JSON_HEDLEY_UNLIKELY(!input.empty())) + { + const auto wc2 = static_cast(input.get_character()); + const auto charcode = 0x10000u + (((static_cast(wc) & 0x3FFu) << 10u) | (wc2 & 0x3FFu)); + utf8_bytes[0] = static_cast::int_type>(0xF0u | (charcode >> 18u)); + utf8_bytes[1] = static_cast::int_type>(0x80u | ((charcode >> 12u) & 0x3Fu)); + utf8_bytes[2] = static_cast::int_type>(0x80u | ((charcode >> 6u) & 0x3Fu)); + utf8_bytes[3] = static_cast::int_type>(0x80u | (charcode & 0x3Fu)); + utf8_bytes_filled = 4; + } + else + { + utf8_bytes[0] = static_cast::int_type>(wc); + utf8_bytes_filled = 1; + } + } + } + } +}; + +// Wraps another input apdater to convert wide character types into individual bytes. +template +class wide_string_input_adapter +{ + public: + using char_type = char; + + wide_string_input_adapter(BaseInputAdapter base) + : base_adapter(base) {} + + typename std::char_traits::int_type get_character() noexcept + { + // check if buffer needs to be filled + if (utf8_bytes_index == utf8_bytes_filled) + { + fill_buffer(); + + JSON_ASSERT(utf8_bytes_filled > 0); + JSON_ASSERT(utf8_bytes_index == 0); + } + + // use buffer + JSON_ASSERT(utf8_bytes_filled > 0); + JSON_ASSERT(utf8_bytes_index < utf8_bytes_filled); + return utf8_bytes[utf8_bytes_index++]; + } + + private: + BaseInputAdapter base_adapter; + + template + void fill_buffer() + { + wide_string_input_helper::fill_buffer(base_adapter, utf8_bytes, utf8_bytes_index, utf8_bytes_filled); + } + + /// a buffer for UTF-8 bytes + std::array::int_type, 4> utf8_bytes = {{0, 0, 0, 0}}; + + /// index to the utf8_codes array for the next valid byte + std::size_t utf8_bytes_index = 0; + /// number of valid bytes in the utf8_codes array + std::size_t utf8_bytes_filled = 0; +}; + + +template +struct iterator_input_adapter_factory +{ + using iterator_type = IteratorType; + using char_type = typename std::iterator_traits::value_type; + using adapter_type = iterator_input_adapter; + + static adapter_type create(IteratorType first, IteratorType last) + { + return adapter_type(std::move(first), std::move(last)); + } +}; + +template +struct is_iterator_of_multibyte +{ + using value_type = typename std::iterator_traits::value_type; + enum + { + value = sizeof(value_type) > 1 + }; +}; + +template +struct iterator_input_adapter_factory::value>> +{ + using iterator_type = IteratorType; + using char_type = typename std::iterator_traits::value_type; + using base_adapter_type = iterator_input_adapter; + using adapter_type = wide_string_input_adapter; + + static adapter_type create(IteratorType first, IteratorType last) + { + return adapter_type(base_adapter_type(std::move(first), std::move(last))); + } +}; + +// General purpose iterator-based input +template +typename iterator_input_adapter_factory::adapter_type input_adapter(IteratorType first, IteratorType last) +{ + using factory_type = iterator_input_adapter_factory; + return factory_type::create(first, last); +} + +// Convenience shorthand from container to iterator +// Enables ADL on begin(container) and end(container) +// Encloses the using declarations in namespace for not to leak them to outside scope + +namespace container_input_adapter_factory_impl +{ + +using std::begin; +using std::end; + +template +struct container_input_adapter_factory {}; + +template +struct container_input_adapter_factory< ContainerType, + void_t()), end(std::declval()))>> + { + using adapter_type = decltype(input_adapter(begin(std::declval()), end(std::declval()))); + + static adapter_type create(const ContainerType& container) +{ + return input_adapter(begin(container), end(container)); +} + }; + +} // namespace container_input_adapter_factory_impl + +template +typename container_input_adapter_factory_impl::container_input_adapter_factory::adapter_type input_adapter(const ContainerType& container) +{ + return container_input_adapter_factory_impl::container_input_adapter_factory::create(container); +} + +#ifndef JSON_NO_IO +// Special cases with fast paths +inline file_input_adapter input_adapter(std::FILE* file) +{ + return file_input_adapter(file); +} + +inline input_stream_adapter input_adapter(std::istream& stream) +{ + return input_stream_adapter(stream); +} + +inline input_stream_adapter input_adapter(std::istream&& stream) +{ + return input_stream_adapter(stream); +} +#endif // JSON_NO_IO + +using contiguous_bytes_input_adapter = decltype(input_adapter(std::declval(), std::declval())); + +// Null-delimited strings, and the like. +template < typename CharT, + typename std::enable_if < + std::is_pointer::value&& + !std::is_array::value&& + std::is_integral::type>::value&& + sizeof(typename std::remove_pointer::type) == 1, + int >::type = 0 > +contiguous_bytes_input_adapter input_adapter(CharT b) +{ + auto length = std::strlen(reinterpret_cast(b)); + const auto* ptr = reinterpret_cast(b); + return input_adapter(ptr, ptr + length); +} + +template +auto input_adapter(T (&array)[N]) -> decltype(input_adapter(array, array + N)) // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) +{ + return input_adapter(array, array + N); +} + +// This class only handles inputs of input_buffer_adapter type. +// It's required so that expressions like {ptr, len} can be implicitely casted +// to the correct adapter. +class span_input_adapter +{ + public: + template < typename CharT, + typename std::enable_if < + std::is_pointer::value&& + std::is_integral::type>::value&& + sizeof(typename std::remove_pointer::type) == 1, + int >::type = 0 > + span_input_adapter(CharT b, std::size_t l) + : ia(reinterpret_cast(b), reinterpret_cast(b) + l) {} + + template::iterator_category, std::random_access_iterator_tag>::value, + int>::type = 0> + span_input_adapter(IteratorType first, IteratorType last) + : ia(input_adapter(first, last)) {} + + contiguous_bytes_input_adapter&& get() + { + return std::move(ia); // NOLINT(hicpp-move-const-arg,performance-move-const-arg) + } + + private: + contiguous_bytes_input_adapter ia; +}; +} // namespace detail +} // namespace nlohmann + +// #include + + +#include +#include // string +#include // move +#include // vector + +// #include + +// #include + + +namespace nlohmann +{ + +/*! +@brief SAX interface + +This class describes the SAX interface used by @ref nlohmann::json::sax_parse. +Each function is called in different situations while the input is parsed. The +boolean return value informs the parser whether to continue processing the +input. +*/ +template +struct json_sax +{ + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + + /*! + @brief a null value was read + @return whether parsing should proceed + */ + virtual bool null() = 0; + + /*! + @brief a boolean value was read + @param[in] val boolean value + @return whether parsing should proceed + */ + virtual bool boolean(bool val) = 0; + + /*! + @brief an integer number was read + @param[in] val integer value + @return whether parsing should proceed + */ + virtual bool number_integer(number_integer_t val) = 0; + + /*! + @brief an unsigned integer number was read + @param[in] val unsigned integer value + @return whether parsing should proceed + */ + virtual bool number_unsigned(number_unsigned_t val) = 0; + + /*! + @brief an floating-point number was read + @param[in] val floating-point value + @param[in] s raw token value + @return whether parsing should proceed + */ + virtual bool number_float(number_float_t val, const string_t& s) = 0; + + /*! + @brief a string was read + @param[in] val string value + @return whether parsing should proceed + @note It is safe to move the passed string. + */ + virtual bool string(string_t& val) = 0; + + /*! + @brief a binary string was read + @param[in] val binary value + @return whether parsing should proceed + @note It is safe to move the passed binary. + */ + virtual bool binary(binary_t& val) = 0; + + /*! + @brief the beginning of an object was read + @param[in] elements number of object elements or -1 if unknown + @return whether parsing should proceed + @note binary formats may report the number of elements + */ + virtual bool start_object(std::size_t elements) = 0; + + /*! + @brief an object key was read + @param[in] val object key + @return whether parsing should proceed + @note It is safe to move the passed string. + */ + virtual bool key(string_t& val) = 0; + + /*! + @brief the end of an object was read + @return whether parsing should proceed + */ + virtual bool end_object() = 0; + + /*! + @brief the beginning of an array was read + @param[in] elements number of array elements or -1 if unknown + @return whether parsing should proceed + @note binary formats may report the number of elements + */ + virtual bool start_array(std::size_t elements) = 0; + + /*! + @brief the end of an array was read + @return whether parsing should proceed + */ + virtual bool end_array() = 0; + + /*! + @brief a parse error occurred + @param[in] position the position in the input where the error occurs + @param[in] last_token the last read token + @param[in] ex an exception object describing the error + @return whether parsing should proceed (must return false) + */ + virtual bool parse_error(std::size_t position, + const std::string& last_token, + const detail::exception& ex) = 0; + + json_sax() = default; + json_sax(const json_sax&) = default; + json_sax(json_sax&&) noexcept = default; + json_sax& operator=(const json_sax&) = default; + json_sax& operator=(json_sax&&) noexcept = default; + virtual ~json_sax() = default; +}; + + +namespace detail +{ +/*! +@brief SAX implementation to create a JSON value from SAX events + +This class implements the @ref json_sax interface and processes the SAX events +to create a JSON value which makes it basically a DOM parser. The structure or +hierarchy of the JSON value is managed by the stack `ref_stack` which contains +a pointer to the respective array or object for each recursion depth. + +After successful parsing, the value that is passed by reference to the +constructor contains the parsed value. + +@tparam BasicJsonType the JSON type +*/ +template +class json_sax_dom_parser +{ + public: + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + + /*! + @param[in,out] r reference to a JSON value that is manipulated while + parsing + @param[in] allow_exceptions_ whether parse errors yield exceptions + */ + explicit json_sax_dom_parser(BasicJsonType& r, const bool allow_exceptions_ = true) + : root(r), allow_exceptions(allow_exceptions_) + {} + + // make class move-only + json_sax_dom_parser(const json_sax_dom_parser&) = delete; + json_sax_dom_parser(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + json_sax_dom_parser& operator=(const json_sax_dom_parser&) = delete; + json_sax_dom_parser& operator=(json_sax_dom_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + ~json_sax_dom_parser() = default; + + bool null() + { + handle_value(nullptr); + return true; + } + + bool boolean(bool val) + { + handle_value(val); + return true; + } + + bool number_integer(number_integer_t val) + { + handle_value(val); + return true; + } + + bool number_unsigned(number_unsigned_t val) + { + handle_value(val); + return true; + } + + bool number_float(number_float_t val, const string_t& /*unused*/) + { + handle_value(val); + return true; + } + + bool string(string_t& val) + { + handle_value(val); + return true; + } + + bool binary(binary_t& val) + { + handle_value(std::move(val)); + return true; + } + + bool start_object(std::size_t len) + { + ref_stack.push_back(handle_value(BasicJsonType::value_t::object)); + + if (JSON_HEDLEY_UNLIKELY(len != std::size_t(-1) && len > ref_stack.back()->max_size())) + { + JSON_THROW(out_of_range::create(408, "excessive object size: " + std::to_string(len), *ref_stack.back())); + } + + return true; + } + + bool key(string_t& val) + { + // add null at given key and store the reference for later + object_element = &(ref_stack.back()->m_value.object->operator[](val)); + return true; + } + + bool end_object() + { + ref_stack.back()->set_parents(); + ref_stack.pop_back(); + return true; + } + + bool start_array(std::size_t len) + { + ref_stack.push_back(handle_value(BasicJsonType::value_t::array)); + + if (JSON_HEDLEY_UNLIKELY(len != std::size_t(-1) && len > ref_stack.back()->max_size())) + { + JSON_THROW(out_of_range::create(408, "excessive array size: " + std::to_string(len), *ref_stack.back())); + } + + return true; + } + + bool end_array() + { + ref_stack.back()->set_parents(); + ref_stack.pop_back(); + return true; + } + + template + bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/, + const Exception& ex) + { + errored = true; + static_cast(ex); + if (allow_exceptions) + { + JSON_THROW(ex); + } + return false; + } + + constexpr bool is_errored() const + { + return errored; + } + + private: + /*! + @invariant If the ref stack is empty, then the passed value will be the new + root. + @invariant If the ref stack contains a value, then it is an array or an + object to which we can add elements + */ + template + JSON_HEDLEY_RETURNS_NON_NULL + BasicJsonType* handle_value(Value&& v) + { + if (ref_stack.empty()) + { + root = BasicJsonType(std::forward(v)); + return &root; + } + + JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object()); + + if (ref_stack.back()->is_array()) + { + ref_stack.back()->m_value.array->emplace_back(std::forward(v)); + return &(ref_stack.back()->m_value.array->back()); + } + + JSON_ASSERT(ref_stack.back()->is_object()); + JSON_ASSERT(object_element); + *object_element = BasicJsonType(std::forward(v)); + return object_element; + } + + /// the parsed JSON value + BasicJsonType& root; + /// stack to model hierarchy of values + std::vector ref_stack {}; + /// helper to hold the reference for the next object element + BasicJsonType* object_element = nullptr; + /// whether a syntax error occurred + bool errored = false; + /// whether to throw exceptions in case of errors + const bool allow_exceptions = true; +}; + +template +class json_sax_dom_callback_parser +{ + public: + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + using parser_callback_t = typename BasicJsonType::parser_callback_t; + using parse_event_t = typename BasicJsonType::parse_event_t; + + json_sax_dom_callback_parser(BasicJsonType& r, + const parser_callback_t cb, + const bool allow_exceptions_ = true) + : root(r), callback(cb), allow_exceptions(allow_exceptions_) + { + keep_stack.push_back(true); + } + + // make class move-only + json_sax_dom_callback_parser(const json_sax_dom_callback_parser&) = delete; + json_sax_dom_callback_parser(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + json_sax_dom_callback_parser& operator=(const json_sax_dom_callback_parser&) = delete; + json_sax_dom_callback_parser& operator=(json_sax_dom_callback_parser&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + ~json_sax_dom_callback_parser() = default; + + bool null() + { + handle_value(nullptr); + return true; + } + + bool boolean(bool val) + { + handle_value(val); + return true; + } + + bool number_integer(number_integer_t val) + { + handle_value(val); + return true; + } + + bool number_unsigned(number_unsigned_t val) + { + handle_value(val); + return true; + } + + bool number_float(number_float_t val, const string_t& /*unused*/) + { + handle_value(val); + return true; + } + + bool string(string_t& val) + { + handle_value(val); + return true; + } + + bool binary(binary_t& val) + { + handle_value(std::move(val)); + return true; + } + + bool start_object(std::size_t len) + { + // check callback for object start + const bool keep = callback(static_cast(ref_stack.size()), parse_event_t::object_start, discarded); + keep_stack.push_back(keep); + + auto val = handle_value(BasicJsonType::value_t::object, true); + ref_stack.push_back(val.second); + + // check object limit + if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != std::size_t(-1) && len > ref_stack.back()->max_size())) + { + JSON_THROW(out_of_range::create(408, "excessive object size: " + std::to_string(len), *ref_stack.back())); + } + + return true; + } + + bool key(string_t& val) + { + BasicJsonType k = BasicJsonType(val); + + // check callback for key + const bool keep = callback(static_cast(ref_stack.size()), parse_event_t::key, k); + key_keep_stack.push_back(keep); + + // add discarded value at given key and store the reference for later + if (keep && ref_stack.back()) + { + object_element = &(ref_stack.back()->m_value.object->operator[](val) = discarded); + } + + return true; + } + + bool end_object() + { + if (ref_stack.back()) + { + if (!callback(static_cast(ref_stack.size()) - 1, parse_event_t::object_end, *ref_stack.back())) + { + // discard object + *ref_stack.back() = discarded; + } + else + { + ref_stack.back()->set_parents(); + } + } + + JSON_ASSERT(!ref_stack.empty()); + JSON_ASSERT(!keep_stack.empty()); + ref_stack.pop_back(); + keep_stack.pop_back(); + + if (!ref_stack.empty() && ref_stack.back() && ref_stack.back()->is_structured()) + { + // remove discarded value + for (auto it = ref_stack.back()->begin(); it != ref_stack.back()->end(); ++it) + { + if (it->is_discarded()) + { + ref_stack.back()->erase(it); + break; + } + } + } + + return true; + } + + bool start_array(std::size_t len) + { + const bool keep = callback(static_cast(ref_stack.size()), parse_event_t::array_start, discarded); + keep_stack.push_back(keep); + + auto val = handle_value(BasicJsonType::value_t::array, true); + ref_stack.push_back(val.second); + + // check array limit + if (ref_stack.back() && JSON_HEDLEY_UNLIKELY(len != std::size_t(-1) && len > ref_stack.back()->max_size())) + { + JSON_THROW(out_of_range::create(408, "excessive array size: " + std::to_string(len), *ref_stack.back())); + } + + return true; + } + + bool end_array() + { + bool keep = true; + + if (ref_stack.back()) + { + keep = callback(static_cast(ref_stack.size()) - 1, parse_event_t::array_end, *ref_stack.back()); + if (keep) + { + ref_stack.back()->set_parents(); + } + else + { + // discard array + *ref_stack.back() = discarded; + } + } + + JSON_ASSERT(!ref_stack.empty()); + JSON_ASSERT(!keep_stack.empty()); + ref_stack.pop_back(); + keep_stack.pop_back(); + + // remove discarded value + if (!keep && !ref_stack.empty() && ref_stack.back()->is_array()) + { + ref_stack.back()->m_value.array->pop_back(); + } + + return true; + } + + template + bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/, + const Exception& ex) + { + errored = true; + static_cast(ex); + if (allow_exceptions) + { + JSON_THROW(ex); + } + return false; + } + + constexpr bool is_errored() const + { + return errored; + } + + private: + /*! + @param[in] v value to add to the JSON value we build during parsing + @param[in] skip_callback whether we should skip calling the callback + function; this is required after start_array() and + start_object() SAX events, because otherwise we would call the + callback function with an empty array or object, respectively. + + @invariant If the ref stack is empty, then the passed value will be the new + root. + @invariant If the ref stack contains a value, then it is an array or an + object to which we can add elements + + @return pair of boolean (whether value should be kept) and pointer (to the + passed value in the ref_stack hierarchy; nullptr if not kept) + */ + template + std::pair handle_value(Value&& v, const bool skip_callback = false) + { + JSON_ASSERT(!keep_stack.empty()); + + // do not handle this value if we know it would be added to a discarded + // container + if (!keep_stack.back()) + { + return {false, nullptr}; + } + + // create value + auto value = BasicJsonType(std::forward(v)); + + // check callback + const bool keep = skip_callback || callback(static_cast(ref_stack.size()), parse_event_t::value, value); + + // do not handle this value if we just learnt it shall be discarded + if (!keep) + { + return {false, nullptr}; + } + + if (ref_stack.empty()) + { + root = std::move(value); + return {true, &root}; + } + + // skip this value if we already decided to skip the parent + // (https://github.com/nlohmann/json/issues/971#issuecomment-413678360) + if (!ref_stack.back()) + { + return {false, nullptr}; + } + + // we now only expect arrays and objects + JSON_ASSERT(ref_stack.back()->is_array() || ref_stack.back()->is_object()); + + // array + if (ref_stack.back()->is_array()) + { + ref_stack.back()->m_value.array->emplace_back(std::move(value)); + return {true, &(ref_stack.back()->m_value.array->back())}; + } + + // object + JSON_ASSERT(ref_stack.back()->is_object()); + // check if we should store an element for the current key + JSON_ASSERT(!key_keep_stack.empty()); + const bool store_element = key_keep_stack.back(); + key_keep_stack.pop_back(); + + if (!store_element) + { + return {false, nullptr}; + } + + JSON_ASSERT(object_element); + *object_element = std::move(value); + return {true, object_element}; + } + + /// the parsed JSON value + BasicJsonType& root; + /// stack to model hierarchy of values + std::vector ref_stack {}; + /// stack to manage which values to keep + std::vector keep_stack {}; + /// stack to manage which object keys to keep + std::vector key_keep_stack {}; + /// helper to hold the reference for the next object element + BasicJsonType* object_element = nullptr; + /// whether a syntax error occurred + bool errored = false; + /// callback function + const parser_callback_t callback = nullptr; + /// whether to throw exceptions in case of errors + const bool allow_exceptions = true; + /// a discarded value for the callback + BasicJsonType discarded = BasicJsonType::value_t::discarded; +}; + +template +class json_sax_acceptor +{ + public: + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + + bool null() + { + return true; + } + + bool boolean(bool /*unused*/) + { + return true; + } + + bool number_integer(number_integer_t /*unused*/) + { + return true; + } + + bool number_unsigned(number_unsigned_t /*unused*/) + { + return true; + } + + bool number_float(number_float_t /*unused*/, const string_t& /*unused*/) + { + return true; + } + + bool string(string_t& /*unused*/) + { + return true; + } + + bool binary(binary_t& /*unused*/) + { + return true; + } + + bool start_object(std::size_t /*unused*/ = std::size_t(-1)) + { + return true; + } + + bool key(string_t& /*unused*/) + { + return true; + } + + bool end_object() + { + return true; + } + + bool start_array(std::size_t /*unused*/ = std::size_t(-1)) + { + return true; + } + + bool end_array() + { + return true; + } + + bool parse_error(std::size_t /*unused*/, const std::string& /*unused*/, const detail::exception& /*unused*/) + { + return false; + } +}; +} // namespace detail + +} // namespace nlohmann + +// #include + + +#include // array +#include // localeconv +#include // size_t +#include // snprintf +#include // strtof, strtod, strtold, strtoll, strtoull +#include // initializer_list +#include // char_traits, string +#include // move +#include // vector + +// #include + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +/////////// +// lexer // +/////////// + +template +class lexer_base +{ + public: + /// token types for the parser + enum class token_type + { + uninitialized, ///< indicating the scanner is uninitialized + literal_true, ///< the `true` literal + literal_false, ///< the `false` literal + literal_null, ///< the `null` literal + value_string, ///< a string -- use get_string() for actual value + value_unsigned, ///< an unsigned integer -- use get_number_unsigned() for actual value + value_integer, ///< a signed integer -- use get_number_integer() for actual value + value_float, ///< an floating point number -- use get_number_float() for actual value + begin_array, ///< the character for array begin `[` + begin_object, ///< the character for object begin `{` + end_array, ///< the character for array end `]` + end_object, ///< the character for object end `}` + name_separator, ///< the name separator `:` + value_separator, ///< the value separator `,` + parse_error, ///< indicating a parse error + end_of_input, ///< indicating the end of the input buffer + literal_or_value ///< a literal or the begin of a value (only for diagnostics) + }; + + /// return name of values of type token_type (only used for errors) + JSON_HEDLEY_RETURNS_NON_NULL + JSON_HEDLEY_CONST + static const char* token_type_name(const token_type t) noexcept + { + switch (t) + { + case token_type::uninitialized: + return ""; + case token_type::literal_true: + return "true literal"; + case token_type::literal_false: + return "false literal"; + case token_type::literal_null: + return "null literal"; + case token_type::value_string: + return "string literal"; + case token_type::value_unsigned: + case token_type::value_integer: + case token_type::value_float: + return "number literal"; + case token_type::begin_array: + return "'['"; + case token_type::begin_object: + return "'{'"; + case token_type::end_array: + return "']'"; + case token_type::end_object: + return "'}'"; + case token_type::name_separator: + return "':'"; + case token_type::value_separator: + return "','"; + case token_type::parse_error: + return ""; + case token_type::end_of_input: + return "end of input"; + case token_type::literal_or_value: + return "'[', '{', or a literal"; + // LCOV_EXCL_START + default: // catch non-enum values + return "unknown token"; + // LCOV_EXCL_STOP + } + } +}; +/*! +@brief lexical analysis + +This class organizes the lexical analysis during JSON deserialization. +*/ +template +class lexer : public lexer_base +{ + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using char_type = typename InputAdapterType::char_type; + using char_int_type = typename std::char_traits::int_type; + + public: + using token_type = typename lexer_base::token_type; + + explicit lexer(InputAdapterType&& adapter, bool ignore_comments_ = false) noexcept + : ia(std::move(adapter)) + , ignore_comments(ignore_comments_) + , decimal_point_char(static_cast(get_decimal_point())) + {} + + // delete because of pointer members + lexer(const lexer&) = delete; + lexer(lexer&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + lexer& operator=(lexer&) = delete; + lexer& operator=(lexer&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + ~lexer() = default; + + private: + ///////////////////// + // locales + ///////////////////// + + /// return the locale-dependent decimal point + JSON_HEDLEY_PURE + static char get_decimal_point() noexcept + { + const auto* loc = localeconv(); + JSON_ASSERT(loc != nullptr); + return (loc->decimal_point == nullptr) ? '.' : *(loc->decimal_point); + } + + ///////////////////// + // scan functions + ///////////////////// + + /*! + @brief get codepoint from 4 hex characters following `\u` + + For input "\u c1 c2 c3 c4" the codepoint is: + (c1 * 0x1000) + (c2 * 0x0100) + (c3 * 0x0010) + c4 + = (c1 << 12) + (c2 << 8) + (c3 << 4) + (c4 << 0) + + Furthermore, the possible characters '0'..'9', 'A'..'F', and 'a'..'f' + must be converted to the integers 0x0..0x9, 0xA..0xF, 0xA..0xF, resp. The + conversion is done by subtracting the offset (0x30, 0x37, and 0x57) + between the ASCII value of the character and the desired integer value. + + @return codepoint (0x0000..0xFFFF) or -1 in case of an error (e.g. EOF or + non-hex character) + */ + int get_codepoint() + { + // this function only makes sense after reading `\u` + JSON_ASSERT(current == 'u'); + int codepoint = 0; + + const auto factors = { 12u, 8u, 4u, 0u }; + for (const auto factor : factors) + { + get(); + + if (current >= '0' && current <= '9') + { + codepoint += static_cast((static_cast(current) - 0x30u) << factor); + } + else if (current >= 'A' && current <= 'F') + { + codepoint += static_cast((static_cast(current) - 0x37u) << factor); + } + else if (current >= 'a' && current <= 'f') + { + codepoint += static_cast((static_cast(current) - 0x57u) << factor); + } + else + { + return -1; + } + } + + JSON_ASSERT(0x0000 <= codepoint && codepoint <= 0xFFFF); + return codepoint; + } + + /*! + @brief check if the next byte(s) are inside a given range + + Adds the current byte and, for each passed range, reads a new byte and + checks if it is inside the range. If a violation was detected, set up an + error message and return false. Otherwise, return true. + + @param[in] ranges list of integers; interpreted as list of pairs of + inclusive lower and upper bound, respectively + + @pre The passed list @a ranges must have 2, 4, or 6 elements; that is, + 1, 2, or 3 pairs. This precondition is enforced by an assertion. + + @return true if and only if no range violation was detected + */ + bool next_byte_in_range(std::initializer_list ranges) + { + JSON_ASSERT(ranges.size() == 2 || ranges.size() == 4 || ranges.size() == 6); + add(current); + + for (auto range = ranges.begin(); range != ranges.end(); ++range) + { + get(); + if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) + { + add(current); + } + else + { + error_message = "invalid string: ill-formed UTF-8 byte"; + return false; + } + } + + return true; + } + + /*! + @brief scan a string literal + + This function scans a string according to Sect. 7 of RFC 8259. While + scanning, bytes are escaped and copied into buffer token_buffer. Then the + function returns successfully, token_buffer is *not* null-terminated (as it + may contain \0 bytes), and token_buffer.size() is the number of bytes in the + string. + + @return token_type::value_string if string could be successfully scanned, + token_type::parse_error otherwise + + @note In case of errors, variable error_message contains a textual + description. + */ + token_type scan_string() + { + // reset token_buffer (ignore opening quote) + reset(); + + // we entered the function by reading an open quote + JSON_ASSERT(current == '\"'); + + while (true) + { + // get next character + switch (get()) + { + // end of file while parsing string + case std::char_traits::eof(): + { + error_message = "invalid string: missing closing quote"; + return token_type::parse_error; + } + + // closing quote + case '\"': + { + return token_type::value_string; + } + + // escapes + case '\\': + { + switch (get()) + { + // quotation mark + case '\"': + add('\"'); + break; + // reverse solidus + case '\\': + add('\\'); + break; + // solidus + case '/': + add('/'); + break; + // backspace + case 'b': + add('\b'); + break; + // form feed + case 'f': + add('\f'); + break; + // line feed + case 'n': + add('\n'); + break; + // carriage return + case 'r': + add('\r'); + break; + // tab + case 't': + add('\t'); + break; + + // unicode escapes + case 'u': + { + const int codepoint1 = get_codepoint(); + int codepoint = codepoint1; // start with codepoint1 + + if (JSON_HEDLEY_UNLIKELY(codepoint1 == -1)) + { + error_message = "invalid string: '\\u' must be followed by 4 hex digits"; + return token_type::parse_error; + } + + // check if code point is a high surrogate + if (0xD800 <= codepoint1 && codepoint1 <= 0xDBFF) + { + // expect next \uxxxx entry + if (JSON_HEDLEY_LIKELY(get() == '\\' && get() == 'u')) + { + const int codepoint2 = get_codepoint(); + + if (JSON_HEDLEY_UNLIKELY(codepoint2 == -1)) + { + error_message = "invalid string: '\\u' must be followed by 4 hex digits"; + return token_type::parse_error; + } + + // check if codepoint2 is a low surrogate + if (JSON_HEDLEY_LIKELY(0xDC00 <= codepoint2 && codepoint2 <= 0xDFFF)) + { + // overwrite codepoint + codepoint = static_cast( + // high surrogate occupies the most significant 22 bits + (static_cast(codepoint1) << 10u) + // low surrogate occupies the least significant 15 bits + + static_cast(codepoint2) + // there is still the 0xD800, 0xDC00 and 0x10000 noise + // in the result so we have to subtract with: + // (0xD800 << 10) + DC00 - 0x10000 = 0x35FDC00 + - 0x35FDC00u); + } + else + { + error_message = "invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF"; + return token_type::parse_error; + } + } + else + { + error_message = "invalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF"; + return token_type::parse_error; + } + } + else + { + if (JSON_HEDLEY_UNLIKELY(0xDC00 <= codepoint1 && codepoint1 <= 0xDFFF)) + { + error_message = "invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF"; + return token_type::parse_error; + } + } + + // result of the above calculation yields a proper codepoint + JSON_ASSERT(0x00 <= codepoint && codepoint <= 0x10FFFF); + + // translate codepoint into bytes + if (codepoint < 0x80) + { + // 1-byte characters: 0xxxxxxx (ASCII) + add(static_cast(codepoint)); + } + else if (codepoint <= 0x7FF) + { + // 2-byte characters: 110xxxxx 10xxxxxx + add(static_cast(0xC0u | (static_cast(codepoint) >> 6u))); + add(static_cast(0x80u | (static_cast(codepoint) & 0x3Fu))); + } + else if (codepoint <= 0xFFFF) + { + // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx + add(static_cast(0xE0u | (static_cast(codepoint) >> 12u))); + add(static_cast(0x80u | ((static_cast(codepoint) >> 6u) & 0x3Fu))); + add(static_cast(0x80u | (static_cast(codepoint) & 0x3Fu))); + } + else + { + // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx + add(static_cast(0xF0u | (static_cast(codepoint) >> 18u))); + add(static_cast(0x80u | ((static_cast(codepoint) >> 12u) & 0x3Fu))); + add(static_cast(0x80u | ((static_cast(codepoint) >> 6u) & 0x3Fu))); + add(static_cast(0x80u | (static_cast(codepoint) & 0x3Fu))); + } + + break; + } + + // other characters after escape + default: + error_message = "invalid string: forbidden character after backslash"; + return token_type::parse_error; + } + + break; + } + + // invalid control characters + case 0x00: + { + error_message = "invalid string: control character U+0000 (NUL) must be escaped to \\u0000"; + return token_type::parse_error; + } + + case 0x01: + { + error_message = "invalid string: control character U+0001 (SOH) must be escaped to \\u0001"; + return token_type::parse_error; + } + + case 0x02: + { + error_message = "invalid string: control character U+0002 (STX) must be escaped to \\u0002"; + return token_type::parse_error; + } + + case 0x03: + { + error_message = "invalid string: control character U+0003 (ETX) must be escaped to \\u0003"; + return token_type::parse_error; + } + + case 0x04: + { + error_message = "invalid string: control character U+0004 (EOT) must be escaped to \\u0004"; + return token_type::parse_error; + } + + case 0x05: + { + error_message = "invalid string: control character U+0005 (ENQ) must be escaped to \\u0005"; + return token_type::parse_error; + } + + case 0x06: + { + error_message = "invalid string: control character U+0006 (ACK) must be escaped to \\u0006"; + return token_type::parse_error; + } + + case 0x07: + { + error_message = "invalid string: control character U+0007 (BEL) must be escaped to \\u0007"; + return token_type::parse_error; + } + + case 0x08: + { + error_message = "invalid string: control character U+0008 (BS) must be escaped to \\u0008 or \\b"; + return token_type::parse_error; + } + + case 0x09: + { + error_message = "invalid string: control character U+0009 (HT) must be escaped to \\u0009 or \\t"; + return token_type::parse_error; + } + + case 0x0A: + { + error_message = "invalid string: control character U+000A (LF) must be escaped to \\u000A or \\n"; + return token_type::parse_error; + } + + case 0x0B: + { + error_message = "invalid string: control character U+000B (VT) must be escaped to \\u000B"; + return token_type::parse_error; + } + + case 0x0C: + { + error_message = "invalid string: control character U+000C (FF) must be escaped to \\u000C or \\f"; + return token_type::parse_error; + } + + case 0x0D: + { + error_message = "invalid string: control character U+000D (CR) must be escaped to \\u000D or \\r"; + return token_type::parse_error; + } + + case 0x0E: + { + error_message = "invalid string: control character U+000E (SO) must be escaped to \\u000E"; + return token_type::parse_error; + } + + case 0x0F: + { + error_message = "invalid string: control character U+000F (SI) must be escaped to \\u000F"; + return token_type::parse_error; + } + + case 0x10: + { + error_message = "invalid string: control character U+0010 (DLE) must be escaped to \\u0010"; + return token_type::parse_error; + } + + case 0x11: + { + error_message = "invalid string: control character U+0011 (DC1) must be escaped to \\u0011"; + return token_type::parse_error; + } + + case 0x12: + { + error_message = "invalid string: control character U+0012 (DC2) must be escaped to \\u0012"; + return token_type::parse_error; + } + + case 0x13: + { + error_message = "invalid string: control character U+0013 (DC3) must be escaped to \\u0013"; + return token_type::parse_error; + } + + case 0x14: + { + error_message = "invalid string: control character U+0014 (DC4) must be escaped to \\u0014"; + return token_type::parse_error; + } + + case 0x15: + { + error_message = "invalid string: control character U+0015 (NAK) must be escaped to \\u0015"; + return token_type::parse_error; + } + + case 0x16: + { + error_message = "invalid string: control character U+0016 (SYN) must be escaped to \\u0016"; + return token_type::parse_error; + } + + case 0x17: + { + error_message = "invalid string: control character U+0017 (ETB) must be escaped to \\u0017"; + return token_type::parse_error; + } + + case 0x18: + { + error_message = "invalid string: control character U+0018 (CAN) must be escaped to \\u0018"; + return token_type::parse_error; + } + + case 0x19: + { + error_message = "invalid string: control character U+0019 (EM) must be escaped to \\u0019"; + return token_type::parse_error; + } + + case 0x1A: + { + error_message = "invalid string: control character U+001A (SUB) must be escaped to \\u001A"; + return token_type::parse_error; + } + + case 0x1B: + { + error_message = "invalid string: control character U+001B (ESC) must be escaped to \\u001B"; + return token_type::parse_error; + } + + case 0x1C: + { + error_message = "invalid string: control character U+001C (FS) must be escaped to \\u001C"; + return token_type::parse_error; + } + + case 0x1D: + { + error_message = "invalid string: control character U+001D (GS) must be escaped to \\u001D"; + return token_type::parse_error; + } + + case 0x1E: + { + error_message = "invalid string: control character U+001E (RS) must be escaped to \\u001E"; + return token_type::parse_error; + } + + case 0x1F: + { + error_message = "invalid string: control character U+001F (US) must be escaped to \\u001F"; + return token_type::parse_error; + } + + // U+0020..U+007F (except U+0022 (quote) and U+005C (backspace)) + case 0x20: + case 0x21: + case 0x23: + case 0x24: + case 0x25: + case 0x26: + case 0x27: + case 0x28: + case 0x29: + case 0x2A: + case 0x2B: + case 0x2C: + case 0x2D: + case 0x2E: + case 0x2F: + case 0x30: + case 0x31: + case 0x32: + case 0x33: + case 0x34: + case 0x35: + case 0x36: + case 0x37: + case 0x38: + case 0x39: + case 0x3A: + case 0x3B: + case 0x3C: + case 0x3D: + case 0x3E: + case 0x3F: + case 0x40: + case 0x41: + case 0x42: + case 0x43: + case 0x44: + case 0x45: + case 0x46: + case 0x47: + case 0x48: + case 0x49: + case 0x4A: + case 0x4B: + case 0x4C: + case 0x4D: + case 0x4E: + case 0x4F: + case 0x50: + case 0x51: + case 0x52: + case 0x53: + case 0x54: + case 0x55: + case 0x56: + case 0x57: + case 0x58: + case 0x59: + case 0x5A: + case 0x5B: + case 0x5D: + case 0x5E: + case 0x5F: + case 0x60: + case 0x61: + case 0x62: + case 0x63: + case 0x64: + case 0x65: + case 0x66: + case 0x67: + case 0x68: + case 0x69: + case 0x6A: + case 0x6B: + case 0x6C: + case 0x6D: + case 0x6E: + case 0x6F: + case 0x70: + case 0x71: + case 0x72: + case 0x73: + case 0x74: + case 0x75: + case 0x76: + case 0x77: + case 0x78: + case 0x79: + case 0x7A: + case 0x7B: + case 0x7C: + case 0x7D: + case 0x7E: + case 0x7F: + { + add(current); + break; + } + + // U+0080..U+07FF: bytes C2..DF 80..BF + case 0xC2: + case 0xC3: + case 0xC4: + case 0xC5: + case 0xC6: + case 0xC7: + case 0xC8: + case 0xC9: + case 0xCA: + case 0xCB: + case 0xCC: + case 0xCD: + case 0xCE: + case 0xCF: + case 0xD0: + case 0xD1: + case 0xD2: + case 0xD3: + case 0xD4: + case 0xD5: + case 0xD6: + case 0xD7: + case 0xD8: + case 0xD9: + case 0xDA: + case 0xDB: + case 0xDC: + case 0xDD: + case 0xDE: + case 0xDF: + { + if (JSON_HEDLEY_UNLIKELY(!next_byte_in_range({0x80, 0xBF}))) + { + return token_type::parse_error; + } + break; + } + + // U+0800..U+0FFF: bytes E0 A0..BF 80..BF + case 0xE0: + { + if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0xA0, 0xBF, 0x80, 0xBF})))) + { + return token_type::parse_error; + } + break; + } + + // U+1000..U+CFFF: bytes E1..EC 80..BF 80..BF + // U+E000..U+FFFF: bytes EE..EF 80..BF 80..BF + case 0xE1: + case 0xE2: + case 0xE3: + case 0xE4: + case 0xE5: + case 0xE6: + case 0xE7: + case 0xE8: + case 0xE9: + case 0xEA: + case 0xEB: + case 0xEC: + case 0xEE: + case 0xEF: + { + if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0xBF, 0x80, 0xBF})))) + { + return token_type::parse_error; + } + break; + } + + // U+D000..U+D7FF: bytes ED 80..9F 80..BF + case 0xED: + { + if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0x9F, 0x80, 0xBF})))) + { + return token_type::parse_error; + } + break; + } + + // U+10000..U+3FFFF F0 90..BF 80..BF 80..BF + case 0xF0: + { + if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x90, 0xBF, 0x80, 0xBF, 0x80, 0xBF})))) + { + return token_type::parse_error; + } + break; + } + + // U+40000..U+FFFFF F1..F3 80..BF 80..BF 80..BF + case 0xF1: + case 0xF2: + case 0xF3: + { + if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0xBF, 0x80, 0xBF, 0x80, 0xBF})))) + { + return token_type::parse_error; + } + break; + } + + // U+100000..U+10FFFF F4 80..8F 80..BF 80..BF + case 0xF4: + { + if (JSON_HEDLEY_UNLIKELY(!(next_byte_in_range({0x80, 0x8F, 0x80, 0xBF, 0x80, 0xBF})))) + { + return token_type::parse_error; + } + break; + } + + // remaining bytes (80..C1 and F5..FF) are ill-formed + default: + { + error_message = "invalid string: ill-formed UTF-8 byte"; + return token_type::parse_error; + } + } + } + } + + /*! + * @brief scan a comment + * @return whether comment could be scanned successfully + */ + bool scan_comment() + { + switch (get()) + { + // single-line comments skip input until a newline or EOF is read + case '/': + { + while (true) + { + switch (get()) + { + case '\n': + case '\r': + case std::char_traits::eof(): + case '\0': + return true; + + default: + break; + } + } + } + + // multi-line comments skip input until */ is read + case '*': + { + while (true) + { + switch (get()) + { + case std::char_traits::eof(): + case '\0': + { + error_message = "invalid comment; missing closing '*/'"; + return false; + } + + case '*': + { + switch (get()) + { + case '/': + return true; + + default: + { + unget(); + continue; + } + } + } + + default: + continue; + } + } + } + + // unexpected character after reading '/' + default: + { + error_message = "invalid comment; expecting '/' or '*' after '/'"; + return false; + } + } + } + + JSON_HEDLEY_NON_NULL(2) + static void strtof(float& f, const char* str, char** endptr) noexcept + { + f = std::strtof(str, endptr); + } + + JSON_HEDLEY_NON_NULL(2) + static void strtof(double& f, const char* str, char** endptr) noexcept + { + f = std::strtod(str, endptr); + } + + JSON_HEDLEY_NON_NULL(2) + static void strtof(long double& f, const char* str, char** endptr) noexcept + { + f = std::strtold(str, endptr); + } + + /*! + @brief scan a number literal + + This function scans a string according to Sect. 6 of RFC 8259. + + The function is realized with a deterministic finite state machine derived + from the grammar described in RFC 8259. Starting in state "init", the + input is read and used to determined the next state. Only state "done" + accepts the number. State "error" is a trap state to model errors. In the + table below, "anything" means any character but the ones listed before. + + state | 0 | 1-9 | e E | + | - | . | anything + ---------|----------|----------|----------|---------|---------|----------|----------- + init | zero | any1 | [error] | [error] | minus | [error] | [error] + minus | zero | any1 | [error] | [error] | [error] | [error] | [error] + zero | done | done | exponent | done | done | decimal1 | done + any1 | any1 | any1 | exponent | done | done | decimal1 | done + decimal1 | decimal2 | decimal2 | [error] | [error] | [error] | [error] | [error] + decimal2 | decimal2 | decimal2 | exponent | done | done | done | done + exponent | any2 | any2 | [error] | sign | sign | [error] | [error] + sign | any2 | any2 | [error] | [error] | [error] | [error] | [error] + any2 | any2 | any2 | done | done | done | done | done + + The state machine is realized with one label per state (prefixed with + "scan_number_") and `goto` statements between them. The state machine + contains cycles, but any cycle can be left when EOF is read. Therefore, + the function is guaranteed to terminate. + + During scanning, the read bytes are stored in token_buffer. This string is + then converted to a signed integer, an unsigned integer, or a + floating-point number. + + @return token_type::value_unsigned, token_type::value_integer, or + token_type::value_float if number could be successfully scanned, + token_type::parse_error otherwise + + @note The scanner is independent of the current locale. Internally, the + locale's decimal point is used instead of `.` to work with the + locale-dependent converters. + */ + token_type scan_number() // lgtm [cpp/use-of-goto] + { + // reset token_buffer to store the number's bytes + reset(); + + // the type of the parsed number; initially set to unsigned; will be + // changed if minus sign, decimal point or exponent is read + token_type number_type = token_type::value_unsigned; + + // state (init): we just found out we need to scan a number + switch (current) + { + case '-': + { + add(current); + goto scan_number_minus; + } + + case '0': + { + add(current); + goto scan_number_zero; + } + + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_any1; + } + + // all other characters are rejected outside scan_number() + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + +scan_number_minus: + // state: we just parsed a leading minus sign + number_type = token_type::value_integer; + switch (get()) + { + case '0': + { + add(current); + goto scan_number_zero; + } + + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_any1; + } + + default: + { + error_message = "invalid number; expected digit after '-'"; + return token_type::parse_error; + } + } + +scan_number_zero: + // state: we just parse a zero (maybe with a leading minus sign) + switch (get()) + { + case '.': + { + add(decimal_point_char); + goto scan_number_decimal1; + } + + case 'e': + case 'E': + { + add(current); + goto scan_number_exponent; + } + + default: + goto scan_number_done; + } + +scan_number_any1: + // state: we just parsed a number 0-9 (maybe with a leading minus sign) + switch (get()) + { + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_any1; + } + + case '.': + { + add(decimal_point_char); + goto scan_number_decimal1; + } + + case 'e': + case 'E': + { + add(current); + goto scan_number_exponent; + } + + default: + goto scan_number_done; + } + +scan_number_decimal1: + // state: we just parsed a decimal point + number_type = token_type::value_float; + switch (get()) + { + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_decimal2; + } + + default: + { + error_message = "invalid number; expected digit after '.'"; + return token_type::parse_error; + } + } + +scan_number_decimal2: + // we just parsed at least one number after a decimal point + switch (get()) + { + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_decimal2; + } + + case 'e': + case 'E': + { + add(current); + goto scan_number_exponent; + } + + default: + goto scan_number_done; + } + +scan_number_exponent: + // we just parsed an exponent + number_type = token_type::value_float; + switch (get()) + { + case '+': + case '-': + { + add(current); + goto scan_number_sign; + } + + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_any2; + } + + default: + { + error_message = + "invalid number; expected '+', '-', or digit after exponent"; + return token_type::parse_error; + } + } + +scan_number_sign: + // we just parsed an exponent sign + switch (get()) + { + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_any2; + } + + default: + { + error_message = "invalid number; expected digit after exponent sign"; + return token_type::parse_error; + } + } + +scan_number_any2: + // we just parsed a number after the exponent or exponent sign + switch (get()) + { + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + { + add(current); + goto scan_number_any2; + } + + default: + goto scan_number_done; + } + +scan_number_done: + // unget the character after the number (we only read it to know that + // we are done scanning a number) + unget(); + + char* endptr = nullptr; // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + errno = 0; + + // try to parse integers first and fall back to floats + if (number_type == token_type::value_unsigned) + { + const auto x = std::strtoull(token_buffer.data(), &endptr, 10); + + // we checked the number format before + JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size()); + + if (errno == 0) + { + value_unsigned = static_cast(x); + if (value_unsigned == x) + { + return token_type::value_unsigned; + } + } + } + else if (number_type == token_type::value_integer) + { + const auto x = std::strtoll(token_buffer.data(), &endptr, 10); + + // we checked the number format before + JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size()); + + if (errno == 0) + { + value_integer = static_cast(x); + if (value_integer == x) + { + return token_type::value_integer; + } + } + } + + // this code is reached if we parse a floating-point number or if an + // integer conversion above failed + strtof(value_float, token_buffer.data(), &endptr); + + // we checked the number format before + JSON_ASSERT(endptr == token_buffer.data() + token_buffer.size()); + + return token_type::value_float; + } + + /*! + @param[in] literal_text the literal text to expect + @param[in] length the length of the passed literal text + @param[in] return_type the token type to return on success + */ + JSON_HEDLEY_NON_NULL(2) + token_type scan_literal(const char_type* literal_text, const std::size_t length, + token_type return_type) + { + JSON_ASSERT(std::char_traits::to_char_type(current) == literal_text[0]); + for (std::size_t i = 1; i < length; ++i) + { + if (JSON_HEDLEY_UNLIKELY(std::char_traits::to_char_type(get()) != literal_text[i])) + { + error_message = "invalid literal"; + return token_type::parse_error; + } + } + return return_type; + } + + ///////////////////// + // input management + ///////////////////// + + /// reset token_buffer; current character is beginning of token + void reset() noexcept + { + token_buffer.clear(); + token_string.clear(); + token_string.push_back(std::char_traits::to_char_type(current)); + } + + /* + @brief get next character from the input + + This function provides the interface to the used input adapter. It does + not throw in case the input reached EOF, but returns a + `std::char_traits::eof()` in that case. Stores the scanned characters + for use in error messages. + + @return character read from the input + */ + char_int_type get() + { + ++position.chars_read_total; + ++position.chars_read_current_line; + + if (next_unget) + { + // just reset the next_unget variable and work with current + next_unget = false; + } + else + { + current = ia.get_character(); + } + + if (JSON_HEDLEY_LIKELY(current != std::char_traits::eof())) + { + token_string.push_back(std::char_traits::to_char_type(current)); + } + + if (current == '\n') + { + ++position.lines_read; + position.chars_read_current_line = 0; + } + + return current; + } + + /*! + @brief unget current character (read it again on next get) + + We implement unget by setting variable next_unget to true. The input is not + changed - we just simulate ungetting by modifying chars_read_total, + chars_read_current_line, and token_string. The next call to get() will + behave as if the unget character is read again. + */ + void unget() + { + next_unget = true; + + --position.chars_read_total; + + // in case we "unget" a newline, we have to also decrement the lines_read + if (position.chars_read_current_line == 0) + { + if (position.lines_read > 0) + { + --position.lines_read; + } + } + else + { + --position.chars_read_current_line; + } + + if (JSON_HEDLEY_LIKELY(current != std::char_traits::eof())) + { + JSON_ASSERT(!token_string.empty()); + token_string.pop_back(); + } + } + + /// add a character to token_buffer + void add(char_int_type c) + { + token_buffer.push_back(static_cast(c)); + } + + public: + ///////////////////// + // value getters + ///////////////////// + + /// return integer value + constexpr number_integer_t get_number_integer() const noexcept + { + return value_integer; + } + + /// return unsigned integer value + constexpr number_unsigned_t get_number_unsigned() const noexcept + { + return value_unsigned; + } + + /// return floating-point value + constexpr number_float_t get_number_float() const noexcept + { + return value_float; + } + + /// return current string value (implicitly resets the token; useful only once) + string_t& get_string() + { + return token_buffer; + } + + ///////////////////// + // diagnostics + ///////////////////// + + /// return position of last read token + constexpr position_t get_position() const noexcept + { + return position; + } + + /// return the last read token (for errors only). Will never contain EOF + /// (an arbitrary value that is not a valid char value, often -1), because + /// 255 may legitimately occur. May contain NUL, which should be escaped. + std::string get_token_string() const + { + // escape control characters + std::string result; + for (const auto c : token_string) + { + if (static_cast(c) <= '\x1F') + { + // escape control characters + std::array cs{{}}; + (std::snprintf)(cs.data(), cs.size(), "", static_cast(c)); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + result += cs.data(); + } + else + { + // add character as is + result.push_back(static_cast(c)); + } + } + + return result; + } + + /// return syntax error message + JSON_HEDLEY_RETURNS_NON_NULL + constexpr const char* get_error_message() const noexcept + { + return error_message; + } + + ///////////////////// + // actual scanner + ///////////////////// + + /*! + @brief skip the UTF-8 byte order mark + @return true iff there is no BOM or the correct BOM has been skipped + */ + bool skip_bom() + { + if (get() == 0xEF) + { + // check if we completely parse the BOM + return get() == 0xBB && get() == 0xBF; + } + + // the first character is not the beginning of the BOM; unget it to + // process is later + unget(); + return true; + } + + void skip_whitespace() + { + do + { + get(); + } + while (current == ' ' || current == '\t' || current == '\n' || current == '\r'); + } + + token_type scan() + { + // initially, skip the BOM + if (position.chars_read_total == 0 && !skip_bom()) + { + error_message = "invalid BOM; must be 0xEF 0xBB 0xBF if given"; + return token_type::parse_error; + } + + // read next character and ignore whitespace + skip_whitespace(); + + // ignore comments + while (ignore_comments && current == '/') + { + if (!scan_comment()) + { + return token_type::parse_error; + } + + // skip following whitespace + skip_whitespace(); + } + + switch (current) + { + // structural characters + case '[': + return token_type::begin_array; + case ']': + return token_type::end_array; + case '{': + return token_type::begin_object; + case '}': + return token_type::end_object; + case ':': + return token_type::name_separator; + case ',': + return token_type::value_separator; + + // literals + case 't': + { + std::array true_literal = {{char_type('t'), char_type('r'), char_type('u'), char_type('e')}}; + return scan_literal(true_literal.data(), true_literal.size(), token_type::literal_true); + } + case 'f': + { + std::array false_literal = {{char_type('f'), char_type('a'), char_type('l'), char_type('s'), char_type('e')}}; + return scan_literal(false_literal.data(), false_literal.size(), token_type::literal_false); + } + case 'n': + { + std::array null_literal = {{char_type('n'), char_type('u'), char_type('l'), char_type('l')}}; + return scan_literal(null_literal.data(), null_literal.size(), token_type::literal_null); + } + + // string + case '\"': + return scan_string(); + + // number + case '-': + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + return scan_number(); + + // end of input (the null byte is needed when parsing from + // string literals) + case '\0': + case std::char_traits::eof(): + return token_type::end_of_input; + + // error + default: + error_message = "invalid literal"; + return token_type::parse_error; + } + } + + private: + /// input adapter + InputAdapterType ia; + + /// whether comments should be ignored (true) or signaled as errors (false) + const bool ignore_comments = false; + + /// the current character + char_int_type current = std::char_traits::eof(); + + /// whether the next get() call should just return current + bool next_unget = false; + + /// the start position of the current token + position_t position {}; + + /// raw input token string (for error messages) + std::vector token_string {}; + + /// buffer for variable-length tokens (numbers, strings) + string_t token_buffer {}; + + /// a description of occurred lexer errors + const char* error_message = ""; + + // number values + number_integer_t value_integer = 0; + number_unsigned_t value_unsigned = 0; + number_float_t value_float = 0; + + /// the decimal point + const char_int_type decimal_point_char = '.'; +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + + +#include // size_t +#include // declval +#include // string + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +template +using null_function_t = decltype(std::declval().null()); + +template +using boolean_function_t = + decltype(std::declval().boolean(std::declval())); + +template +using number_integer_function_t = + decltype(std::declval().number_integer(std::declval())); + +template +using number_unsigned_function_t = + decltype(std::declval().number_unsigned(std::declval())); + +template +using number_float_function_t = decltype(std::declval().number_float( + std::declval(), std::declval())); + +template +using string_function_t = + decltype(std::declval().string(std::declval())); + +template +using binary_function_t = + decltype(std::declval().binary(std::declval())); + +template +using start_object_function_t = + decltype(std::declval().start_object(std::declval())); + +template +using key_function_t = + decltype(std::declval().key(std::declval())); + +template +using end_object_function_t = decltype(std::declval().end_object()); + +template +using start_array_function_t = + decltype(std::declval().start_array(std::declval())); + +template +using end_array_function_t = decltype(std::declval().end_array()); + +template +using parse_error_function_t = decltype(std::declval().parse_error( + std::declval(), std::declval(), + std::declval())); + +template +struct is_sax +{ + private: + static_assert(is_basic_json::value, + "BasicJsonType must be of type basic_json<...>"); + + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + using exception_t = typename BasicJsonType::exception; + + public: + static constexpr bool value = + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value && + is_detected_exact::value; +}; + +template +struct is_sax_static_asserts +{ + private: + static_assert(is_basic_json::value, + "BasicJsonType must be of type basic_json<...>"); + + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + using exception_t = typename BasicJsonType::exception; + + public: + static_assert(is_detected_exact::value, + "Missing/invalid function: bool null()"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool boolean(bool)"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool boolean(bool)"); + static_assert( + is_detected_exact::value, + "Missing/invalid function: bool number_integer(number_integer_t)"); + static_assert( + is_detected_exact::value, + "Missing/invalid function: bool number_unsigned(number_unsigned_t)"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool number_float(number_float_t, const string_t&)"); + static_assert( + is_detected_exact::value, + "Missing/invalid function: bool string(string_t&)"); + static_assert( + is_detected_exact::value, + "Missing/invalid function: bool binary(binary_t&)"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool start_object(std::size_t)"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool key(string_t&)"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool end_object()"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool start_array(std::size_t)"); + static_assert(is_detected_exact::value, + "Missing/invalid function: bool end_array()"); + static_assert( + is_detected_exact::value, + "Missing/invalid function: bool parse_error(std::size_t, const " + "std::string&, const exception&)"); +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ + +/// how to treat CBOR tags +enum class cbor_tag_handler_t +{ + error, ///< throw a parse_error exception in case of a tag + ignore, ///< ignore tags + store ///< store tags as binary type +}; + +/*! +@brief determine system byte order + +@return true if and only if system's byte order is little endian + +@note from https://stackoverflow.com/a/1001328/266378 +*/ +static inline bool little_endianess(int num = 1) noexcept +{ + return *reinterpret_cast(&num) == 1; +} + + +/////////////////// +// binary reader // +/////////////////// + +/*! +@brief deserialization of CBOR, MessagePack, and UBJSON values +*/ +template> +class binary_reader +{ + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + using json_sax_t = SAX; + using char_type = typename InputAdapterType::char_type; + using char_int_type = typename std::char_traits::int_type; + + public: + /*! + @brief create a binary reader + + @param[in] adapter input adapter to read from + */ + explicit binary_reader(InputAdapterType&& adapter) noexcept : ia(std::move(adapter)) + { + (void)detail::is_sax_static_asserts {}; + } + + // make class move-only + binary_reader(const binary_reader&) = delete; + binary_reader(binary_reader&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + binary_reader& operator=(const binary_reader&) = delete; + binary_reader& operator=(binary_reader&&) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor) + ~binary_reader() = default; + + /*! + @param[in] format the binary format to parse + @param[in] sax_ a SAX event processor + @param[in] strict whether to expect the input to be consumed completed + @param[in] tag_handler how to treat CBOR tags + + @return whether parsing was successful + */ + JSON_HEDLEY_NON_NULL(3) + bool sax_parse(const input_format_t format, + json_sax_t* sax_, + const bool strict = true, + const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error) + { + sax = sax_; + bool result = false; + + switch (format) + { + case input_format_t::bson: + result = parse_bson_internal(); + break; + + case input_format_t::cbor: + result = parse_cbor_internal(true, tag_handler); + break; + + case input_format_t::msgpack: + result = parse_msgpack_internal(); + break; + + case input_format_t::ubjson: + result = parse_ubjson_internal(); + break; + + case input_format_t::json: // LCOV_EXCL_LINE + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + + // strict mode: next byte must be EOF + if (result && strict) + { + if (format == input_format_t::ubjson) + { + get_ignore_noop(); + } + else + { + get(); + } + + if (JSON_HEDLEY_UNLIKELY(current != std::char_traits::eof())) + { + return sax->parse_error(chars_read, get_token_string(), + parse_error::create(110, chars_read, exception_message(format, "expected end of input; last byte: 0x" + get_token_string(), "value"), BasicJsonType())); + } + } + + return result; + } + + private: + ////////// + // BSON // + ////////// + + /*! + @brief Reads in a BSON-object and passes it to the SAX-parser. + @return whether a valid BSON-value was passed to the SAX parser + */ + bool parse_bson_internal() + { + std::int32_t document_size{}; + get_number(input_format_t::bson, document_size); + + if (JSON_HEDLEY_UNLIKELY(!sax->start_object(std::size_t(-1)))) + { + return false; + } + + if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_list(/*is_array*/false))) + { + return false; + } + + return sax->end_object(); + } + + /*! + @brief Parses a C-style string from the BSON input. + @param[in,out] result A reference to the string variable where the read + string is to be stored. + @return `true` if the \x00-byte indicating the end of the string was + encountered before the EOF; false` indicates an unexpected EOF. + */ + bool get_bson_cstr(string_t& result) + { + auto out = std::back_inserter(result); + while (true) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::bson, "cstring"))) + { + return false; + } + if (current == 0x00) + { + return true; + } + *out++ = static_cast(current); + } + } + + /*! + @brief Parses a zero-terminated string of length @a len from the BSON + input. + @param[in] len The length (including the zero-byte at the end) of the + string to be read. + @param[in,out] result A reference to the string variable where the read + string is to be stored. + @tparam NumberType The type of the length @a len + @pre len >= 1 + @return `true` if the string was successfully parsed + */ + template + bool get_bson_string(const NumberType len, string_t& result) + { + if (JSON_HEDLEY_UNLIKELY(len < 1)) + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::bson, "string length must be at least 1, is " + std::to_string(len), "string"), BasicJsonType())); + } + + return get_string(input_format_t::bson, len - static_cast(1), result) && get() != std::char_traits::eof(); + } + + /*! + @brief Parses a byte array input of length @a len from the BSON input. + @param[in] len The length of the byte array to be read. + @param[in,out] result A reference to the binary variable where the read + array is to be stored. + @tparam NumberType The type of the length @a len + @pre len >= 0 + @return `true` if the byte array was successfully parsed + */ + template + bool get_bson_binary(const NumberType len, binary_t& result) + { + if (JSON_HEDLEY_UNLIKELY(len < 0)) + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::bson, "byte array length cannot be negative, is " + std::to_string(len), "binary"), BasicJsonType())); + } + + // All BSON binary values have a subtype + std::uint8_t subtype{}; + get_number(input_format_t::bson, subtype); + result.set_subtype(subtype); + + return get_binary(input_format_t::bson, len, result); + } + + /*! + @brief Read a BSON document element of the given @a element_type. + @param[in] element_type The BSON element type, c.f. http://bsonspec.org/spec.html + @param[in] element_type_parse_position The position in the input stream, + where the `element_type` was read. + @warning Not all BSON element types are supported yet. An unsupported + @a element_type will give rise to a parse_error.114: + Unsupported BSON record type 0x... + @return whether a valid BSON-object/array was passed to the SAX parser + */ + bool parse_bson_element_internal(const char_int_type element_type, + const std::size_t element_type_parse_position) + { + switch (element_type) + { + case 0x01: // double + { + double number{}; + return get_number(input_format_t::bson, number) && sax->number_float(static_cast(number), ""); + } + + case 0x02: // string + { + std::int32_t len{}; + string_t value; + return get_number(input_format_t::bson, len) && get_bson_string(len, value) && sax->string(value); + } + + case 0x03: // object + { + return parse_bson_internal(); + } + + case 0x04: // array + { + return parse_bson_array(); + } + + case 0x05: // binary + { + std::int32_t len{}; + binary_t value; + return get_number(input_format_t::bson, len) && get_bson_binary(len, value) && sax->binary(value); + } + + case 0x08: // boolean + { + return sax->boolean(get() != 0); + } + + case 0x0A: // null + { + return sax->null(); + } + + case 0x10: // int32 + { + std::int32_t value{}; + return get_number(input_format_t::bson, value) && sax->number_integer(value); + } + + case 0x12: // int64 + { + std::int64_t value{}; + return get_number(input_format_t::bson, value) && sax->number_integer(value); + } + + default: // anything else not supported (yet) + { + std::array cr{{}}; + (std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast(element_type)); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + return sax->parse_error(element_type_parse_position, std::string(cr.data()), parse_error::create(114, element_type_parse_position, "Unsupported BSON record type 0x" + std::string(cr.data()), BasicJsonType())); + } + } + } + + /*! + @brief Read a BSON element list (as specified in the BSON-spec) + + The same binary layout is used for objects and arrays, hence it must be + indicated with the argument @a is_array which one is expected + (true --> array, false --> object). + + @param[in] is_array Determines if the element list being read is to be + treated as an object (@a is_array == false), or as an + array (@a is_array == true). + @return whether a valid BSON-object/array was passed to the SAX parser + */ + bool parse_bson_element_list(const bool is_array) + { + string_t key; + + while (auto element_type = get()) + { + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::bson, "element list"))) + { + return false; + } + + const std::size_t element_type_parse_position = chars_read; + if (JSON_HEDLEY_UNLIKELY(!get_bson_cstr(key))) + { + return false; + } + + if (!is_array && !sax->key(key)) + { + return false; + } + + if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_internal(element_type, element_type_parse_position))) + { + return false; + } + + // get_bson_cstr only appends + key.clear(); + } + + return true; + } + + /*! + @brief Reads an array from the BSON input and passes it to the SAX-parser. + @return whether a valid BSON-array was passed to the SAX parser + */ + bool parse_bson_array() + { + std::int32_t document_size{}; + get_number(input_format_t::bson, document_size); + + if (JSON_HEDLEY_UNLIKELY(!sax->start_array(std::size_t(-1)))) + { + return false; + } + + if (JSON_HEDLEY_UNLIKELY(!parse_bson_element_list(/*is_array*/true))) + { + return false; + } + + return sax->end_array(); + } + + ////////// + // CBOR // + ////////// + + /*! + @param[in] get_char whether a new character should be retrieved from the + input (true) or whether the last read character should + be considered instead (false) + @param[in] tag_handler how CBOR tags should be treated + + @return whether a valid CBOR value was passed to the SAX parser + */ + bool parse_cbor_internal(const bool get_char, + const cbor_tag_handler_t tag_handler) + { + switch (get_char ? get() : current) + { + // EOF + case std::char_traits::eof(): + return unexpect_eof(input_format_t::cbor, "value"); + + // Integer 0x00..0x17 (0..23) + case 0x00: + case 0x01: + case 0x02: + case 0x03: + case 0x04: + case 0x05: + case 0x06: + case 0x07: + case 0x08: + case 0x09: + case 0x0A: + case 0x0B: + case 0x0C: + case 0x0D: + case 0x0E: + case 0x0F: + case 0x10: + case 0x11: + case 0x12: + case 0x13: + case 0x14: + case 0x15: + case 0x16: + case 0x17: + return sax->number_unsigned(static_cast(current)); + + case 0x18: // Unsigned integer (one-byte uint8_t follows) + { + std::uint8_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_unsigned(number); + } + + case 0x19: // Unsigned integer (two-byte uint16_t follows) + { + std::uint16_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_unsigned(number); + } + + case 0x1A: // Unsigned integer (four-byte uint32_t follows) + { + std::uint32_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_unsigned(number); + } + + case 0x1B: // Unsigned integer (eight-byte uint64_t follows) + { + std::uint64_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_unsigned(number); + } + + // Negative integer -1-0x00..-1-0x17 (-1..-24) + case 0x20: + case 0x21: + case 0x22: + case 0x23: + case 0x24: + case 0x25: + case 0x26: + case 0x27: + case 0x28: + case 0x29: + case 0x2A: + case 0x2B: + case 0x2C: + case 0x2D: + case 0x2E: + case 0x2F: + case 0x30: + case 0x31: + case 0x32: + case 0x33: + case 0x34: + case 0x35: + case 0x36: + case 0x37: + return sax->number_integer(static_cast(0x20 - 1 - current)); + + case 0x38: // Negative integer (one-byte uint8_t follows) + { + std::uint8_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast(-1) - number); + } + + case 0x39: // Negative integer -1-n (two-byte uint16_t follows) + { + std::uint16_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast(-1) - number); + } + + case 0x3A: // Negative integer -1-n (four-byte uint32_t follows) + { + std::uint32_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast(-1) - number); + } + + case 0x3B: // Negative integer -1-n (eight-byte uint64_t follows) + { + std::uint64_t number{}; + return get_number(input_format_t::cbor, number) && sax->number_integer(static_cast(-1) + - static_cast(number)); + } + + // Binary data (0x00..0x17 bytes follow) + case 0x40: + case 0x41: + case 0x42: + case 0x43: + case 0x44: + case 0x45: + case 0x46: + case 0x47: + case 0x48: + case 0x49: + case 0x4A: + case 0x4B: + case 0x4C: + case 0x4D: + case 0x4E: + case 0x4F: + case 0x50: + case 0x51: + case 0x52: + case 0x53: + case 0x54: + case 0x55: + case 0x56: + case 0x57: + case 0x58: // Binary data (one-byte uint8_t for n follows) + case 0x59: // Binary data (two-byte uint16_t for n follow) + case 0x5A: // Binary data (four-byte uint32_t for n follow) + case 0x5B: // Binary data (eight-byte uint64_t for n follow) + case 0x5F: // Binary data (indefinite length) + { + binary_t b; + return get_cbor_binary(b) && sax->binary(b); + } + + // UTF-8 string (0x00..0x17 bytes follow) + case 0x60: + case 0x61: + case 0x62: + case 0x63: + case 0x64: + case 0x65: + case 0x66: + case 0x67: + case 0x68: + case 0x69: + case 0x6A: + case 0x6B: + case 0x6C: + case 0x6D: + case 0x6E: + case 0x6F: + case 0x70: + case 0x71: + case 0x72: + case 0x73: + case 0x74: + case 0x75: + case 0x76: + case 0x77: + case 0x78: // UTF-8 string (one-byte uint8_t for n follows) + case 0x79: // UTF-8 string (two-byte uint16_t for n follow) + case 0x7A: // UTF-8 string (four-byte uint32_t for n follow) + case 0x7B: // UTF-8 string (eight-byte uint64_t for n follow) + case 0x7F: // UTF-8 string (indefinite length) + { + string_t s; + return get_cbor_string(s) && sax->string(s); + } + + // array (0x00..0x17 data items follow) + case 0x80: + case 0x81: + case 0x82: + case 0x83: + case 0x84: + case 0x85: + case 0x86: + case 0x87: + case 0x88: + case 0x89: + case 0x8A: + case 0x8B: + case 0x8C: + case 0x8D: + case 0x8E: + case 0x8F: + case 0x90: + case 0x91: + case 0x92: + case 0x93: + case 0x94: + case 0x95: + case 0x96: + case 0x97: + return get_cbor_array(static_cast(static_cast(current) & 0x1Fu), tag_handler); + + case 0x98: // array (one-byte uint8_t for n follows) + { + std::uint8_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast(len), tag_handler); + } + + case 0x99: // array (two-byte uint16_t for n follow) + { + std::uint16_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast(len), tag_handler); + } + + case 0x9A: // array (four-byte uint32_t for n follow) + { + std::uint32_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_array(static_cast(len), tag_handler); + } + + case 0x9B: // array (eight-byte uint64_t for n follow) + { + std::uint64_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_array(detail::conditional_static_cast(len), tag_handler); + } + + case 0x9F: // array (indefinite length) + return get_cbor_array(std::size_t(-1), tag_handler); + + // map (0x00..0x17 pairs of data items follow) + case 0xA0: + case 0xA1: + case 0xA2: + case 0xA3: + case 0xA4: + case 0xA5: + case 0xA6: + case 0xA7: + case 0xA8: + case 0xA9: + case 0xAA: + case 0xAB: + case 0xAC: + case 0xAD: + case 0xAE: + case 0xAF: + case 0xB0: + case 0xB1: + case 0xB2: + case 0xB3: + case 0xB4: + case 0xB5: + case 0xB6: + case 0xB7: + return get_cbor_object(static_cast(static_cast(current) & 0x1Fu), tag_handler); + + case 0xB8: // map (one-byte uint8_t for n follows) + { + std::uint8_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast(len), tag_handler); + } + + case 0xB9: // map (two-byte uint16_t for n follow) + { + std::uint16_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast(len), tag_handler); + } + + case 0xBA: // map (four-byte uint32_t for n follow) + { + std::uint32_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_object(static_cast(len), tag_handler); + } + + case 0xBB: // map (eight-byte uint64_t for n follow) + { + std::uint64_t len{}; + return get_number(input_format_t::cbor, len) && get_cbor_object(detail::conditional_static_cast(len), tag_handler); + } + + case 0xBF: // map (indefinite length) + return get_cbor_object(std::size_t(-1), tag_handler); + + case 0xC6: // tagged item + case 0xC7: + case 0xC8: + case 0xC9: + case 0xCA: + case 0xCB: + case 0xCC: + case 0xCD: + case 0xCE: + case 0xCF: + case 0xD0: + case 0xD1: + case 0xD2: + case 0xD3: + case 0xD4: + case 0xD8: // tagged item (1 bytes follow) + case 0xD9: // tagged item (2 bytes follow) + case 0xDA: // tagged item (4 bytes follow) + case 0xDB: // tagged item (8 bytes follow) + { + switch (tag_handler) + { + case cbor_tag_handler_t::error: + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::cbor, "invalid byte: 0x" + last_token, "value"), BasicJsonType())); + } + + case cbor_tag_handler_t::ignore: + { + // ignore binary subtype + switch (current) + { + case 0xD8: + { + std::uint8_t subtype_to_ignore{}; + get_number(input_format_t::cbor, subtype_to_ignore); + break; + } + case 0xD9: + { + std::uint16_t subtype_to_ignore{}; + get_number(input_format_t::cbor, subtype_to_ignore); + break; + } + case 0xDA: + { + std::uint32_t subtype_to_ignore{}; + get_number(input_format_t::cbor, subtype_to_ignore); + break; + } + case 0xDB: + { + std::uint64_t subtype_to_ignore{}; + get_number(input_format_t::cbor, subtype_to_ignore); + break; + } + default: + break; + } + return parse_cbor_internal(true, tag_handler); + } + + case cbor_tag_handler_t::store: + { + binary_t b; + // use binary subtype and store in binary container + switch (current) + { + case 0xD8: + { + std::uint8_t subtype{}; + get_number(input_format_t::cbor, subtype); + b.set_subtype(detail::conditional_static_cast(subtype)); + break; + } + case 0xD9: + { + std::uint16_t subtype{}; + get_number(input_format_t::cbor, subtype); + b.set_subtype(detail::conditional_static_cast(subtype)); + break; + } + case 0xDA: + { + std::uint32_t subtype{}; + get_number(input_format_t::cbor, subtype); + b.set_subtype(detail::conditional_static_cast(subtype)); + break; + } + case 0xDB: + { + std::uint64_t subtype{}; + get_number(input_format_t::cbor, subtype); + b.set_subtype(detail::conditional_static_cast(subtype)); + break; + } + default: + return parse_cbor_internal(true, tag_handler); + } + get(); + return get_cbor_binary(b) && sax->binary(b); + } + + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + return false; // LCOV_EXCL_LINE + } + } + + case 0xF4: // false + return sax->boolean(false); + + case 0xF5: // true + return sax->boolean(true); + + case 0xF6: // null + return sax->null(); + + case 0xF9: // Half-Precision Float (two-byte IEEE 754) + { + const auto byte1_raw = get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "number"))) + { + return false; + } + const auto byte2_raw = get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "number"))) + { + return false; + } + + const auto byte1 = static_cast(byte1_raw); + const auto byte2 = static_cast(byte2_raw); + + // code from RFC 7049, Appendix D, Figure 3: + // As half-precision floating-point numbers were only added + // to IEEE 754 in 2008, today's programming platforms often + // still only have limited support for them. It is very + // easy to include at least decoding support for them even + // without such support. An example of a small decoder for + // half-precision floating-point numbers in the C language + // is shown in Fig. 3. + const auto half = static_cast((byte1 << 8u) + byte2); + const double val = [&half] + { + const int exp = (half >> 10u) & 0x1Fu; + const unsigned int mant = half & 0x3FFu; + JSON_ASSERT(0 <= exp&& exp <= 32); + JSON_ASSERT(mant <= 1024); + switch (exp) + { + case 0: + return std::ldexp(mant, -24); + case 31: + return (mant == 0) + ? std::numeric_limits::infinity() + : std::numeric_limits::quiet_NaN(); + default: + return std::ldexp(mant + 1024, exp - 25); + } + }(); + return sax->number_float((half & 0x8000u) != 0 + ? static_cast(-val) + : static_cast(val), ""); + } + + case 0xFA: // Single-Precision Float (four-byte IEEE 754) + { + float number{}; + return get_number(input_format_t::cbor, number) && sax->number_float(static_cast(number), ""); + } + + case 0xFB: // Double-Precision Float (eight-byte IEEE 754) + { + double number{}; + return get_number(input_format_t::cbor, number) && sax->number_float(static_cast(number), ""); + } + + default: // anything else (0xFF is handled inside the other types) + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::cbor, "invalid byte: 0x" + last_token, "value"), BasicJsonType())); + } + } + } + + /*! + @brief reads a CBOR string + + This function first reads starting bytes to determine the expected + string length and then copies this number of bytes into a string. + Additionally, CBOR's strings with indefinite lengths are supported. + + @param[out] result created string + + @return whether string creation completed + */ + bool get_cbor_string(string_t& result) + { + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "string"))) + { + return false; + } + + switch (current) + { + // UTF-8 string (0x00..0x17 bytes follow) + case 0x60: + case 0x61: + case 0x62: + case 0x63: + case 0x64: + case 0x65: + case 0x66: + case 0x67: + case 0x68: + case 0x69: + case 0x6A: + case 0x6B: + case 0x6C: + case 0x6D: + case 0x6E: + case 0x6F: + case 0x70: + case 0x71: + case 0x72: + case 0x73: + case 0x74: + case 0x75: + case 0x76: + case 0x77: + { + return get_string(input_format_t::cbor, static_cast(current) & 0x1Fu, result); + } + + case 0x78: // UTF-8 string (one-byte uint8_t for n follows) + { + std::uint8_t len{}; + return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result); + } + + case 0x79: // UTF-8 string (two-byte uint16_t for n follow) + { + std::uint16_t len{}; + return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result); + } + + case 0x7A: // UTF-8 string (four-byte uint32_t for n follow) + { + std::uint32_t len{}; + return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result); + } + + case 0x7B: // UTF-8 string (eight-byte uint64_t for n follow) + { + std::uint64_t len{}; + return get_number(input_format_t::cbor, len) && get_string(input_format_t::cbor, len, result); + } + + case 0x7F: // UTF-8 string (indefinite length) + { + while (get() != 0xFF) + { + string_t chunk; + if (!get_cbor_string(chunk)) + { + return false; + } + result.append(chunk); + } + return true; + } + + default: + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format_t::cbor, "expected length specification (0x60-0x7B) or indefinite string type (0x7F); last byte: 0x" + last_token, "string"), BasicJsonType())); + } + } + } + + /*! + @brief reads a CBOR byte array + + This function first reads starting bytes to determine the expected + byte array length and then copies this number of bytes into the byte array. + Additionally, CBOR's byte arrays with indefinite lengths are supported. + + @param[out] result created byte array + + @return whether byte array creation completed + */ + bool get_cbor_binary(binary_t& result) + { + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::cbor, "binary"))) + { + return false; + } + + switch (current) + { + // Binary data (0x00..0x17 bytes follow) + case 0x40: + case 0x41: + case 0x42: + case 0x43: + case 0x44: + case 0x45: + case 0x46: + case 0x47: + case 0x48: + case 0x49: + case 0x4A: + case 0x4B: + case 0x4C: + case 0x4D: + case 0x4E: + case 0x4F: + case 0x50: + case 0x51: + case 0x52: + case 0x53: + case 0x54: + case 0x55: + case 0x56: + case 0x57: + { + return get_binary(input_format_t::cbor, static_cast(current) & 0x1Fu, result); + } + + case 0x58: // Binary data (one-byte uint8_t for n follows) + { + std::uint8_t len{}; + return get_number(input_format_t::cbor, len) && + get_binary(input_format_t::cbor, len, result); + } + + case 0x59: // Binary data (two-byte uint16_t for n follow) + { + std::uint16_t len{}; + return get_number(input_format_t::cbor, len) && + get_binary(input_format_t::cbor, len, result); + } + + case 0x5A: // Binary data (four-byte uint32_t for n follow) + { + std::uint32_t len{}; + return get_number(input_format_t::cbor, len) && + get_binary(input_format_t::cbor, len, result); + } + + case 0x5B: // Binary data (eight-byte uint64_t for n follow) + { + std::uint64_t len{}; + return get_number(input_format_t::cbor, len) && + get_binary(input_format_t::cbor, len, result); + } + + case 0x5F: // Binary data (indefinite length) + { + while (get() != 0xFF) + { + binary_t chunk; + if (!get_cbor_binary(chunk)) + { + return false; + } + result.insert(result.end(), chunk.begin(), chunk.end()); + } + return true; + } + + default: + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format_t::cbor, "expected length specification (0x40-0x5B) or indefinite binary array type (0x5F); last byte: 0x" + last_token, "binary"), BasicJsonType())); + } + } + } + + /*! + @param[in] len the length of the array or std::size_t(-1) for an + array of indefinite size + @param[in] tag_handler how CBOR tags should be treated + @return whether array creation completed + */ + bool get_cbor_array(const std::size_t len, + const cbor_tag_handler_t tag_handler) + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_array(len))) + { + return false; + } + + if (len != std::size_t(-1)) + { + for (std::size_t i = 0; i < len; ++i) + { + if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler))) + { + return false; + } + } + } + else + { + while (get() != 0xFF) + { + if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(false, tag_handler))) + { + return false; + } + } + } + + return sax->end_array(); + } + + /*! + @param[in] len the length of the object or std::size_t(-1) for an + object of indefinite size + @param[in] tag_handler how CBOR tags should be treated + @return whether object creation completed + */ + bool get_cbor_object(const std::size_t len, + const cbor_tag_handler_t tag_handler) + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_object(len))) + { + return false; + } + + if (len != 0) + { + string_t key; + if (len != std::size_t(-1)) + { + for (std::size_t i = 0; i < len; ++i) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!get_cbor_string(key) || !sax->key(key))) + { + return false; + } + + if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler))) + { + return false; + } + key.clear(); + } + } + else + { + while (get() != 0xFF) + { + if (JSON_HEDLEY_UNLIKELY(!get_cbor_string(key) || !sax->key(key))) + { + return false; + } + + if (JSON_HEDLEY_UNLIKELY(!parse_cbor_internal(true, tag_handler))) + { + return false; + } + key.clear(); + } + } + } + + return sax->end_object(); + } + + ///////////// + // MsgPack // + ///////////// + + /*! + @return whether a valid MessagePack value was passed to the SAX parser + */ + bool parse_msgpack_internal() + { + switch (get()) + { + // EOF + case std::char_traits::eof(): + return unexpect_eof(input_format_t::msgpack, "value"); + + // positive fixint + case 0x00: + case 0x01: + case 0x02: + case 0x03: + case 0x04: + case 0x05: + case 0x06: + case 0x07: + case 0x08: + case 0x09: + case 0x0A: + case 0x0B: + case 0x0C: + case 0x0D: + case 0x0E: + case 0x0F: + case 0x10: + case 0x11: + case 0x12: + case 0x13: + case 0x14: + case 0x15: + case 0x16: + case 0x17: + case 0x18: + case 0x19: + case 0x1A: + case 0x1B: + case 0x1C: + case 0x1D: + case 0x1E: + case 0x1F: + case 0x20: + case 0x21: + case 0x22: + case 0x23: + case 0x24: + case 0x25: + case 0x26: + case 0x27: + case 0x28: + case 0x29: + case 0x2A: + case 0x2B: + case 0x2C: + case 0x2D: + case 0x2E: + case 0x2F: + case 0x30: + case 0x31: + case 0x32: + case 0x33: + case 0x34: + case 0x35: + case 0x36: + case 0x37: + case 0x38: + case 0x39: + case 0x3A: + case 0x3B: + case 0x3C: + case 0x3D: + case 0x3E: + case 0x3F: + case 0x40: + case 0x41: + case 0x42: + case 0x43: + case 0x44: + case 0x45: + case 0x46: + case 0x47: + case 0x48: + case 0x49: + case 0x4A: + case 0x4B: + case 0x4C: + case 0x4D: + case 0x4E: + case 0x4F: + case 0x50: + case 0x51: + case 0x52: + case 0x53: + case 0x54: + case 0x55: + case 0x56: + case 0x57: + case 0x58: + case 0x59: + case 0x5A: + case 0x5B: + case 0x5C: + case 0x5D: + case 0x5E: + case 0x5F: + case 0x60: + case 0x61: + case 0x62: + case 0x63: + case 0x64: + case 0x65: + case 0x66: + case 0x67: + case 0x68: + case 0x69: + case 0x6A: + case 0x6B: + case 0x6C: + case 0x6D: + case 0x6E: + case 0x6F: + case 0x70: + case 0x71: + case 0x72: + case 0x73: + case 0x74: + case 0x75: + case 0x76: + case 0x77: + case 0x78: + case 0x79: + case 0x7A: + case 0x7B: + case 0x7C: + case 0x7D: + case 0x7E: + case 0x7F: + return sax->number_unsigned(static_cast(current)); + + // fixmap + case 0x80: + case 0x81: + case 0x82: + case 0x83: + case 0x84: + case 0x85: + case 0x86: + case 0x87: + case 0x88: + case 0x89: + case 0x8A: + case 0x8B: + case 0x8C: + case 0x8D: + case 0x8E: + case 0x8F: + return get_msgpack_object(static_cast(static_cast(current) & 0x0Fu)); + + // fixarray + case 0x90: + case 0x91: + case 0x92: + case 0x93: + case 0x94: + case 0x95: + case 0x96: + case 0x97: + case 0x98: + case 0x99: + case 0x9A: + case 0x9B: + case 0x9C: + case 0x9D: + case 0x9E: + case 0x9F: + return get_msgpack_array(static_cast(static_cast(current) & 0x0Fu)); + + // fixstr + case 0xA0: + case 0xA1: + case 0xA2: + case 0xA3: + case 0xA4: + case 0xA5: + case 0xA6: + case 0xA7: + case 0xA8: + case 0xA9: + case 0xAA: + case 0xAB: + case 0xAC: + case 0xAD: + case 0xAE: + case 0xAF: + case 0xB0: + case 0xB1: + case 0xB2: + case 0xB3: + case 0xB4: + case 0xB5: + case 0xB6: + case 0xB7: + case 0xB8: + case 0xB9: + case 0xBA: + case 0xBB: + case 0xBC: + case 0xBD: + case 0xBE: + case 0xBF: + case 0xD9: // str 8 + case 0xDA: // str 16 + case 0xDB: // str 32 + { + string_t s; + return get_msgpack_string(s) && sax->string(s); + } + + case 0xC0: // nil + return sax->null(); + + case 0xC2: // false + return sax->boolean(false); + + case 0xC3: // true + return sax->boolean(true); + + case 0xC4: // bin 8 + case 0xC5: // bin 16 + case 0xC6: // bin 32 + case 0xC7: // ext 8 + case 0xC8: // ext 16 + case 0xC9: // ext 32 + case 0xD4: // fixext 1 + case 0xD5: // fixext 2 + case 0xD6: // fixext 4 + case 0xD7: // fixext 8 + case 0xD8: // fixext 16 + { + binary_t b; + return get_msgpack_binary(b) && sax->binary(b); + } + + case 0xCA: // float 32 + { + float number{}; + return get_number(input_format_t::msgpack, number) && sax->number_float(static_cast(number), ""); + } + + case 0xCB: // float 64 + { + double number{}; + return get_number(input_format_t::msgpack, number) && sax->number_float(static_cast(number), ""); + } + + case 0xCC: // uint 8 + { + std::uint8_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number); + } + + case 0xCD: // uint 16 + { + std::uint16_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number); + } + + case 0xCE: // uint 32 + { + std::uint32_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number); + } + + case 0xCF: // uint 64 + { + std::uint64_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_unsigned(number); + } + + case 0xD0: // int 8 + { + std::int8_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_integer(number); + } + + case 0xD1: // int 16 + { + std::int16_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_integer(number); + } + + case 0xD2: // int 32 + { + std::int32_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_integer(number); + } + + case 0xD3: // int 64 + { + std::int64_t number{}; + return get_number(input_format_t::msgpack, number) && sax->number_integer(number); + } + + case 0xDC: // array 16 + { + std::uint16_t len{}; + return get_number(input_format_t::msgpack, len) && get_msgpack_array(static_cast(len)); + } + + case 0xDD: // array 32 + { + std::uint32_t len{}; + return get_number(input_format_t::msgpack, len) && get_msgpack_array(static_cast(len)); + } + + case 0xDE: // map 16 + { + std::uint16_t len{}; + return get_number(input_format_t::msgpack, len) && get_msgpack_object(static_cast(len)); + } + + case 0xDF: // map 32 + { + std::uint32_t len{}; + return get_number(input_format_t::msgpack, len) && get_msgpack_object(static_cast(len)); + } + + // negative fixint + case 0xE0: + case 0xE1: + case 0xE2: + case 0xE3: + case 0xE4: + case 0xE5: + case 0xE6: + case 0xE7: + case 0xE8: + case 0xE9: + case 0xEA: + case 0xEB: + case 0xEC: + case 0xED: + case 0xEE: + case 0xEF: + case 0xF0: + case 0xF1: + case 0xF2: + case 0xF3: + case 0xF4: + case 0xF5: + case 0xF6: + case 0xF7: + case 0xF8: + case 0xF9: + case 0xFA: + case 0xFB: + case 0xFC: + case 0xFD: + case 0xFE: + case 0xFF: + return sax->number_integer(static_cast(current)); + + default: // anything else + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::msgpack, "invalid byte: 0x" + last_token, "value"), BasicJsonType())); + } + } + } + + /*! + @brief reads a MessagePack string + + This function first reads starting bytes to determine the expected + string length and then copies this number of bytes into a string. + + @param[out] result created string + + @return whether string creation completed + */ + bool get_msgpack_string(string_t& result) + { + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::msgpack, "string"))) + { + return false; + } + + switch (current) + { + // fixstr + case 0xA0: + case 0xA1: + case 0xA2: + case 0xA3: + case 0xA4: + case 0xA5: + case 0xA6: + case 0xA7: + case 0xA8: + case 0xA9: + case 0xAA: + case 0xAB: + case 0xAC: + case 0xAD: + case 0xAE: + case 0xAF: + case 0xB0: + case 0xB1: + case 0xB2: + case 0xB3: + case 0xB4: + case 0xB5: + case 0xB6: + case 0xB7: + case 0xB8: + case 0xB9: + case 0xBA: + case 0xBB: + case 0xBC: + case 0xBD: + case 0xBE: + case 0xBF: + { + return get_string(input_format_t::msgpack, static_cast(current) & 0x1Fu, result); + } + + case 0xD9: // str 8 + { + std::uint8_t len{}; + return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result); + } + + case 0xDA: // str 16 + { + std::uint16_t len{}; + return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result); + } + + case 0xDB: // str 32 + { + std::uint32_t len{}; + return get_number(input_format_t::msgpack, len) && get_string(input_format_t::msgpack, len, result); + } + + default: + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format_t::msgpack, "expected length specification (0xA0-0xBF, 0xD9-0xDB); last byte: 0x" + last_token, "string"), BasicJsonType())); + } + } + } + + /*! + @brief reads a MessagePack byte array + + This function first reads starting bytes to determine the expected + byte array length and then copies this number of bytes into a byte array. + + @param[out] result created byte array + + @return whether byte array creation completed + */ + bool get_msgpack_binary(binary_t& result) + { + // helper function to set the subtype + auto assign_and_return_true = [&result](std::int8_t subtype) + { + result.set_subtype(static_cast(subtype)); + return true; + }; + + switch (current) + { + case 0xC4: // bin 8 + { + std::uint8_t len{}; + return get_number(input_format_t::msgpack, len) && + get_binary(input_format_t::msgpack, len, result); + } + + case 0xC5: // bin 16 + { + std::uint16_t len{}; + return get_number(input_format_t::msgpack, len) && + get_binary(input_format_t::msgpack, len, result); + } + + case 0xC6: // bin 32 + { + std::uint32_t len{}; + return get_number(input_format_t::msgpack, len) && + get_binary(input_format_t::msgpack, len, result); + } + + case 0xC7: // ext 8 + { + std::uint8_t len{}; + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, len) && + get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, len, result) && + assign_and_return_true(subtype); + } + + case 0xC8: // ext 16 + { + std::uint16_t len{}; + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, len) && + get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, len, result) && + assign_and_return_true(subtype); + } + + case 0xC9: // ext 32 + { + std::uint32_t len{}; + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, len) && + get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, len, result) && + assign_and_return_true(subtype); + } + + case 0xD4: // fixext 1 + { + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, 1, result) && + assign_and_return_true(subtype); + } + + case 0xD5: // fixext 2 + { + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, 2, result) && + assign_and_return_true(subtype); + } + + case 0xD6: // fixext 4 + { + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, 4, result) && + assign_and_return_true(subtype); + } + + case 0xD7: // fixext 8 + { + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, 8, result) && + assign_and_return_true(subtype); + } + + case 0xD8: // fixext 16 + { + std::int8_t subtype{}; + return get_number(input_format_t::msgpack, subtype) && + get_binary(input_format_t::msgpack, 16, result) && + assign_and_return_true(subtype); + } + + default: // LCOV_EXCL_LINE + return false; // LCOV_EXCL_LINE + } + } + + /*! + @param[in] len the length of the array + @return whether array creation completed + */ + bool get_msgpack_array(const std::size_t len) + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_array(len))) + { + return false; + } + + for (std::size_t i = 0; i < len; ++i) + { + if (JSON_HEDLEY_UNLIKELY(!parse_msgpack_internal())) + { + return false; + } + } + + return sax->end_array(); + } + + /*! + @param[in] len the length of the object + @return whether object creation completed + */ + bool get_msgpack_object(const std::size_t len) + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_object(len))) + { + return false; + } + + string_t key; + for (std::size_t i = 0; i < len; ++i) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!get_msgpack_string(key) || !sax->key(key))) + { + return false; + } + + if (JSON_HEDLEY_UNLIKELY(!parse_msgpack_internal())) + { + return false; + } + key.clear(); + } + + return sax->end_object(); + } + + //////////// + // UBJSON // + //////////// + + /*! + @param[in] get_char whether a new character should be retrieved from the + input (true, default) or whether the last read + character should be considered instead + + @return whether a valid UBJSON value was passed to the SAX parser + */ + bool parse_ubjson_internal(const bool get_char = true) + { + return get_ubjson_value(get_char ? get_ignore_noop() : current); + } + + /*! + @brief reads a UBJSON string + + This function is either called after reading the 'S' byte explicitly + indicating a string, or in case of an object key where the 'S' byte can be + left out. + + @param[out] result created string + @param[in] get_char whether a new character should be retrieved from the + input (true, default) or whether the last read + character should be considered instead + + @return whether string creation completed + */ + bool get_ubjson_string(string_t& result, const bool get_char = true) + { + if (get_char) + { + get(); // TODO(niels): may we ignore N here? + } + + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::ubjson, "value"))) + { + return false; + } + + switch (current) + { + case 'U': + { + std::uint8_t len{}; + return get_number(input_format_t::ubjson, len) && get_string(input_format_t::ubjson, len, result); + } + + case 'i': + { + std::int8_t len{}; + return get_number(input_format_t::ubjson, len) && get_string(input_format_t::ubjson, len, result); + } + + case 'I': + { + std::int16_t len{}; + return get_number(input_format_t::ubjson, len) && get_string(input_format_t::ubjson, len, result); + } + + case 'l': + { + std::int32_t len{}; + return get_number(input_format_t::ubjson, len) && get_string(input_format_t::ubjson, len, result); + } + + case 'L': + { + std::int64_t len{}; + return get_number(input_format_t::ubjson, len) && get_string(input_format_t::ubjson, len, result); + } + + default: + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format_t::ubjson, "expected length type specification (U, i, I, l, L); last byte: 0x" + last_token, "string"), BasicJsonType())); + } + } + + /*! + @param[out] result determined size + @return whether size determination completed + */ + bool get_ubjson_size_value(std::size_t& result) + { + switch (get_ignore_noop()) + { + case 'U': + { + std::uint8_t number{}; + if (JSON_HEDLEY_UNLIKELY(!get_number(input_format_t::ubjson, number))) + { + return false; + } + result = static_cast(number); + return true; + } + + case 'i': + { + std::int8_t number{}; + if (JSON_HEDLEY_UNLIKELY(!get_number(input_format_t::ubjson, number))) + { + return false; + } + result = static_cast(number); // NOLINT(bugprone-signed-char-misuse,cert-str34-c): number is not a char + return true; + } + + case 'I': + { + std::int16_t number{}; + if (JSON_HEDLEY_UNLIKELY(!get_number(input_format_t::ubjson, number))) + { + return false; + } + result = static_cast(number); + return true; + } + + case 'l': + { + std::int32_t number{}; + if (JSON_HEDLEY_UNLIKELY(!get_number(input_format_t::ubjson, number))) + { + return false; + } + result = static_cast(number); + return true; + } + + case 'L': + { + std::int64_t number{}; + if (JSON_HEDLEY_UNLIKELY(!get_number(input_format_t::ubjson, number))) + { + return false; + } + result = static_cast(number); + return true; + } + + default: + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format_t::ubjson, "expected length type specification (U, i, I, l, L) after '#'; last byte: 0x" + last_token, "size"), BasicJsonType())); + } + } + } + + /*! + @brief determine the type and size for a container + + In the optimized UBJSON format, a type and a size can be provided to allow + for a more compact representation. + + @param[out] result pair of the size and the type + + @return whether pair creation completed + */ + bool get_ubjson_size_type(std::pair& result) + { + result.first = string_t::npos; // size + result.second = 0; // type + + get_ignore_noop(); + + if (current == '$') + { + result.second = get(); // must not ignore 'N', because 'N' maybe the type + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::ubjson, "type"))) + { + return false; + } + + get_ignore_noop(); + if (JSON_HEDLEY_UNLIKELY(current != '#')) + { + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::ubjson, "value"))) + { + return false; + } + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::ubjson, "expected '#' after type information; last byte: 0x" + last_token, "size"), BasicJsonType())); + } + + return get_ubjson_size_value(result.first); + } + + if (current == '#') + { + return get_ubjson_size_value(result.first); + } + + return true; + } + + /*! + @param prefix the previously read or set type prefix + @return whether value creation completed + */ + bool get_ubjson_value(const char_int_type prefix) + { + switch (prefix) + { + case std::char_traits::eof(): // EOF + return unexpect_eof(input_format_t::ubjson, "value"); + + case 'T': // true + return sax->boolean(true); + case 'F': // false + return sax->boolean(false); + + case 'Z': // null + return sax->null(); + + case 'U': + { + std::uint8_t number{}; + return get_number(input_format_t::ubjson, number) && sax->number_unsigned(number); + } + + case 'i': + { + std::int8_t number{}; + return get_number(input_format_t::ubjson, number) && sax->number_integer(number); + } + + case 'I': + { + std::int16_t number{}; + return get_number(input_format_t::ubjson, number) && sax->number_integer(number); + } + + case 'l': + { + std::int32_t number{}; + return get_number(input_format_t::ubjson, number) && sax->number_integer(number); + } + + case 'L': + { + std::int64_t number{}; + return get_number(input_format_t::ubjson, number) && sax->number_integer(number); + } + + case 'd': + { + float number{}; + return get_number(input_format_t::ubjson, number) && sax->number_float(static_cast(number), ""); + } + + case 'D': + { + double number{}; + return get_number(input_format_t::ubjson, number) && sax->number_float(static_cast(number), ""); + } + + case 'H': + { + return get_ubjson_high_precision_number(); + } + + case 'C': // char + { + get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::ubjson, "char"))) + { + return false; + } + if (JSON_HEDLEY_UNLIKELY(current > 127)) + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(113, chars_read, exception_message(input_format_t::ubjson, "byte after 'C' must be in range 0x00..0x7F; last byte: 0x" + last_token, "char"), BasicJsonType())); + } + string_t s(1, static_cast(current)); + return sax->string(s); + } + + case 'S': // string + { + string_t s; + return get_ubjson_string(s) && sax->string(s); + } + + case '[': // array + return get_ubjson_array(); + + case '{': // object + return get_ubjson_object(); + + default: // anything else + { + auto last_token = get_token_string(); + return sax->parse_error(chars_read, last_token, parse_error::create(112, chars_read, exception_message(input_format_t::ubjson, "invalid byte: 0x" + last_token, "value"), BasicJsonType())); + } + } + } + + /*! + @return whether array creation completed + */ + bool get_ubjson_array() + { + std::pair size_and_type; + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type))) + { + return false; + } + + if (size_and_type.first != string_t::npos) + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_array(size_and_type.first))) + { + return false; + } + + if (size_and_type.second != 0) + { + if (size_and_type.second != 'N') + { + for (std::size_t i = 0; i < size_and_type.first; ++i) + { + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second))) + { + return false; + } + } + } + } + else + { + for (std::size_t i = 0; i < size_and_type.first; ++i) + { + if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal())) + { + return false; + } + } + } + } + else + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_array(std::size_t(-1)))) + { + return false; + } + + while (current != ']') + { + if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal(false))) + { + return false; + } + get_ignore_noop(); + } + } + + return sax->end_array(); + } + + /*! + @return whether object creation completed + */ + bool get_ubjson_object() + { + std::pair size_and_type; + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_size_type(size_and_type))) + { + return false; + } + + string_t key; + if (size_and_type.first != string_t::npos) + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_object(size_and_type.first))) + { + return false; + } + + if (size_and_type.second != 0) + { + for (std::size_t i = 0; i < size_and_type.first; ++i) + { + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key) || !sax->key(key))) + { + return false; + } + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_value(size_and_type.second))) + { + return false; + } + key.clear(); + } + } + else + { + for (std::size_t i = 0; i < size_and_type.first; ++i) + { + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key) || !sax->key(key))) + { + return false; + } + if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal())) + { + return false; + } + key.clear(); + } + } + } + else + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_object(std::size_t(-1)))) + { + return false; + } + + while (current != '}') + { + if (JSON_HEDLEY_UNLIKELY(!get_ubjson_string(key, false) || !sax->key(key))) + { + return false; + } + if (JSON_HEDLEY_UNLIKELY(!parse_ubjson_internal())) + { + return false; + } + get_ignore_noop(); + key.clear(); + } + } + + return sax->end_object(); + } + + // Note, no reader for UBJSON binary types is implemented because they do + // not exist + + bool get_ubjson_high_precision_number() + { + // get size of following number string + std::size_t size{}; + auto res = get_ubjson_size_value(size); + if (JSON_HEDLEY_UNLIKELY(!res)) + { + return res; + } + + // get number string + std::vector number_vector; + for (std::size_t i = 0; i < size; ++i) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(input_format_t::ubjson, "number"))) + { + return false; + } + number_vector.push_back(static_cast(current)); + } + + // parse number string + using ia_type = decltype(detail::input_adapter(number_vector)); + auto number_lexer = detail::lexer(detail::input_adapter(number_vector), false); + const auto result_number = number_lexer.scan(); + const auto number_string = number_lexer.get_token_string(); + const auto result_remainder = number_lexer.scan(); + + using token_type = typename detail::lexer_base::token_type; + + if (JSON_HEDLEY_UNLIKELY(result_remainder != token_type::end_of_input)) + { + return sax->parse_error(chars_read, number_string, parse_error::create(115, chars_read, exception_message(input_format_t::ubjson, "invalid number text: " + number_lexer.get_token_string(), "high-precision number"), BasicJsonType())); + } + + switch (result_number) + { + case token_type::value_integer: + return sax->number_integer(number_lexer.get_number_integer()); + case token_type::value_unsigned: + return sax->number_unsigned(number_lexer.get_number_unsigned()); + case token_type::value_float: + return sax->number_float(number_lexer.get_number_float(), std::move(number_string)); + case token_type::uninitialized: + case token_type::literal_true: + case token_type::literal_false: + case token_type::literal_null: + case token_type::value_string: + case token_type::begin_array: + case token_type::begin_object: + case token_type::end_array: + case token_type::end_object: + case token_type::name_separator: + case token_type::value_separator: + case token_type::parse_error: + case token_type::end_of_input: + case token_type::literal_or_value: + default: + return sax->parse_error(chars_read, number_string, parse_error::create(115, chars_read, exception_message(input_format_t::ubjson, "invalid number text: " + number_lexer.get_token_string(), "high-precision number"), BasicJsonType())); + } + } + + /////////////////////// + // Utility functions // + /////////////////////// + + /*! + @brief get next character from the input + + This function provides the interface to the used input adapter. It does + not throw in case the input reached EOF, but returns a -'ve valued + `std::char_traits::eof()` in that case. + + @return character read from the input + */ + char_int_type get() + { + ++chars_read; + return current = ia.get_character(); + } + + /*! + @return character read from the input after ignoring all 'N' entries + */ + char_int_type get_ignore_noop() + { + do + { + get(); + } + while (current == 'N'); + + return current; + } + + /* + @brief read a number from the input + + @tparam NumberType the type of the number + @param[in] format the current format (for diagnostics) + @param[out] result number of type @a NumberType + + @return whether conversion completed + + @note This function needs to respect the system's endianess, because + bytes in CBOR, MessagePack, and UBJSON are stored in network order + (big endian) and therefore need reordering on little endian systems. + */ + template + bool get_number(const input_format_t format, NumberType& result) + { + // step 1: read input into array with system's byte order + std::array vec{}; + for (std::size_t i = 0; i < sizeof(NumberType); ++i) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "number"))) + { + return false; + } + + // reverse byte order prior to conversion if necessary + if (is_little_endian != InputIsLittleEndian) + { + vec[sizeof(NumberType) - i - 1] = static_cast(current); + } + else + { + vec[i] = static_cast(current); // LCOV_EXCL_LINE + } + } + + // step 2: convert array into number of type T and return + std::memcpy(&result, vec.data(), sizeof(NumberType)); + return true; + } + + /*! + @brief create a string by reading characters from the input + + @tparam NumberType the type of the number + @param[in] format the current format (for diagnostics) + @param[in] len number of characters to read + @param[out] result string created by reading @a len bytes + + @return whether string creation completed + + @note We can not reserve @a len bytes for the result, because @a len + may be too large. Usually, @ref unexpect_eof() detects the end of + the input before we run out of string memory. + */ + template + bool get_string(const input_format_t format, + const NumberType len, + string_t& result) + { + bool success = true; + for (NumberType i = 0; i < len; i++) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "string"))) + { + success = false; + break; + } + result.push_back(static_cast(current)); + } + return success; + } + + /*! + @brief create a byte array by reading bytes from the input + + @tparam NumberType the type of the number + @param[in] format the current format (for diagnostics) + @param[in] len number of bytes to read + @param[out] result byte array created by reading @a len bytes + + @return whether byte array creation completed + + @note We can not reserve @a len bytes for the result, because @a len + may be too large. Usually, @ref unexpect_eof() detects the end of + the input before we run out of memory. + */ + template + bool get_binary(const input_format_t format, + const NumberType len, + binary_t& result) + { + bool success = true; + for (NumberType i = 0; i < len; i++) + { + get(); + if (JSON_HEDLEY_UNLIKELY(!unexpect_eof(format, "binary"))) + { + success = false; + break; + } + result.push_back(static_cast(current)); + } + return success; + } + + /*! + @param[in] format the current format (for diagnostics) + @param[in] context further context information (for diagnostics) + @return whether the last read character is not EOF + */ + JSON_HEDLEY_NON_NULL(3) + bool unexpect_eof(const input_format_t format, const char* context) const + { + if (JSON_HEDLEY_UNLIKELY(current == std::char_traits::eof())) + { + return sax->parse_error(chars_read, "", + parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), BasicJsonType())); + } + return true; + } + + /*! + @return a string representation of the last read byte + */ + std::string get_token_string() const + { + std::array cr{{}}; + (std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast(current)); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + return std::string{cr.data()}; + } + + /*! + @param[in] format the current format + @param[in] detail a detailed error message + @param[in] context further context information + @return a message string to use in the parse_error exceptions + */ + std::string exception_message(const input_format_t format, + const std::string& detail, + const std::string& context) const + { + std::string error_msg = "syntax error while parsing "; + + switch (format) + { + case input_format_t::cbor: + error_msg += "CBOR"; + break; + + case input_format_t::msgpack: + error_msg += "MessagePack"; + break; + + case input_format_t::ubjson: + error_msg += "UBJSON"; + break; + + case input_format_t::bson: + error_msg += "BSON"; + break; + + case input_format_t::json: // LCOV_EXCL_LINE + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + + return error_msg + " " + context + ": " + detail; + } + + private: + /// input adapter + InputAdapterType ia; + + /// the current character + char_int_type current = std::char_traits::eof(); + + /// the number of characters read + std::size_t chars_read = 0; + + /// whether we can assume little endianess + const bool is_little_endian = little_endianess(); + + /// the SAX parser + json_sax_t* sax = nullptr; +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + +// #include + + +#include // isfinite +#include // uint8_t +#include // function +#include // string +#include // move +#include // vector + +// #include + +// #include + +// #include + +// #include + +// #include + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +//////////// +// parser // +//////////// + +enum class parse_event_t : std::uint8_t +{ + /// the parser read `{` and started to process a JSON object + object_start, + /// the parser read `}` and finished processing a JSON object + object_end, + /// the parser read `[` and started to process a JSON array + array_start, + /// the parser read `]` and finished processing a JSON array + array_end, + /// the parser read a key of a value in an object + key, + /// the parser finished reading a JSON value + value +}; + +template +using parser_callback_t = + std::function; + +/*! +@brief syntax analysis + +This class implements a recursive descent parser. +*/ +template +class parser +{ + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using number_float_t = typename BasicJsonType::number_float_t; + using string_t = typename BasicJsonType::string_t; + using lexer_t = lexer; + using token_type = typename lexer_t::token_type; + + public: + /// a parser reading from an input adapter + explicit parser(InputAdapterType&& adapter, + const parser_callback_t cb = nullptr, + const bool allow_exceptions_ = true, + const bool skip_comments = false) + : callback(cb) + , m_lexer(std::move(adapter), skip_comments) + , allow_exceptions(allow_exceptions_) + { + // read first token + get_token(); + } + + /*! + @brief public parser interface + + @param[in] strict whether to expect the last token to be EOF + @param[in,out] result parsed JSON value + + @throw parse_error.101 in case of an unexpected token + @throw parse_error.102 if to_unicode fails or surrogate error + @throw parse_error.103 if to_unicode fails + */ + void parse(const bool strict, BasicJsonType& result) + { + if (callback) + { + json_sax_dom_callback_parser sdp(result, callback, allow_exceptions); + sax_parse_internal(&sdp); + + // in strict mode, input must be completely read + if (strict && (get_token() != token_type::end_of_input)) + { + sdp.parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), + exception_message(token_type::end_of_input, "value"), BasicJsonType())); + } + + // in case of an error, return discarded value + if (sdp.is_errored()) + { + result = value_t::discarded; + return; + } + + // set top-level value to null if it was discarded by the callback + // function + if (result.is_discarded()) + { + result = nullptr; + } + } + else + { + json_sax_dom_parser sdp(result, allow_exceptions); + sax_parse_internal(&sdp); + + // in strict mode, input must be completely read + if (strict && (get_token() != token_type::end_of_input)) + { + sdp.parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), BasicJsonType())); + } + + // in case of an error, return discarded value + if (sdp.is_errored()) + { + result = value_t::discarded; + return; + } + } + + result.assert_invariant(); + } + + /*! + @brief public accept interface + + @param[in] strict whether to expect the last token to be EOF + @return whether the input is a proper JSON text + */ + bool accept(const bool strict = true) + { + json_sax_acceptor sax_acceptor; + return sax_parse(&sax_acceptor, strict); + } + + template + JSON_HEDLEY_NON_NULL(2) + bool sax_parse(SAX* sax, const bool strict = true) + { + (void)detail::is_sax_static_asserts {}; + const bool result = sax_parse_internal(sax); + + // strict mode: next byte must be EOF + if (result && strict && (get_token() != token_type::end_of_input)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_of_input, "value"), BasicJsonType())); + } + + return result; + } + + private: + template + JSON_HEDLEY_NON_NULL(2) + bool sax_parse_internal(SAX* sax) + { + // stack to remember the hierarchy of structured values we are parsing + // true = array; false = object + std::vector states; + // value to avoid a goto (see comment where set to true) + bool skip_to_state_evaluation = false; + + while (true) + { + if (!skip_to_state_evaluation) + { + // invariant: get_token() was called before each iteration + switch (last_token) + { + case token_type::begin_object: + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_object(std::size_t(-1)))) + { + return false; + } + + // closing } -> we are done + if (get_token() == token_type::end_object) + { + if (JSON_HEDLEY_UNLIKELY(!sax->end_object())) + { + return false; + } + break; + } + + // parse key + if (JSON_HEDLEY_UNLIKELY(last_token != token_type::value_string)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), BasicJsonType())); + } + if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string()))) + { + return false; + } + + // parse separator (:) + if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), BasicJsonType())); + } + + // remember we are now inside an object + states.push_back(false); + + // parse values + get_token(); + continue; + } + + case token_type::begin_array: + { + if (JSON_HEDLEY_UNLIKELY(!sax->start_array(std::size_t(-1)))) + { + return false; + } + + // closing ] -> we are done + if (get_token() == token_type::end_array) + { + if (JSON_HEDLEY_UNLIKELY(!sax->end_array())) + { + return false; + } + break; + } + + // remember we are now inside an array + states.push_back(true); + + // parse values (no need to call get_token) + continue; + } + + case token_type::value_float: + { + const auto res = m_lexer.get_number_float(); + + if (JSON_HEDLEY_UNLIKELY(!std::isfinite(res))) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + out_of_range::create(406, "number overflow parsing '" + m_lexer.get_token_string() + "'", BasicJsonType())); + } + + if (JSON_HEDLEY_UNLIKELY(!sax->number_float(res, m_lexer.get_string()))) + { + return false; + } + + break; + } + + case token_type::literal_false: + { + if (JSON_HEDLEY_UNLIKELY(!sax->boolean(false))) + { + return false; + } + break; + } + + case token_type::literal_null: + { + if (JSON_HEDLEY_UNLIKELY(!sax->null())) + { + return false; + } + break; + } + + case token_type::literal_true: + { + if (JSON_HEDLEY_UNLIKELY(!sax->boolean(true))) + { + return false; + } + break; + } + + case token_type::value_integer: + { + if (JSON_HEDLEY_UNLIKELY(!sax->number_integer(m_lexer.get_number_integer()))) + { + return false; + } + break; + } + + case token_type::value_string: + { + if (JSON_HEDLEY_UNLIKELY(!sax->string(m_lexer.get_string()))) + { + return false; + } + break; + } + + case token_type::value_unsigned: + { + if (JSON_HEDLEY_UNLIKELY(!sax->number_unsigned(m_lexer.get_number_unsigned()))) + { + return false; + } + break; + } + + case token_type::parse_error: + { + // using "uninitialized" to avoid "expected" message + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), BasicJsonType())); + } + + case token_type::uninitialized: + case token_type::end_array: + case token_type::end_object: + case token_type::name_separator: + case token_type::value_separator: + case token_type::end_of_input: + case token_type::literal_or_value: + default: // the last token was unexpected + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), BasicJsonType())); + } + } + } + else + { + skip_to_state_evaluation = false; + } + + // we reached this line after we successfully parsed a value + if (states.empty()) + { + // empty stack: we reached the end of the hierarchy: done + return true; + } + + if (states.back()) // array + { + // comma -> next value + if (get_token() == token_type::value_separator) + { + // parse a new value + get_token(); + continue; + } + + // closing ] + if (JSON_HEDLEY_LIKELY(last_token == token_type::end_array)) + { + if (JSON_HEDLEY_UNLIKELY(!sax->end_array())) + { + return false; + } + + // We are done with this array. Before we can parse a + // new value, we need to evaluate the new state first. + // By setting skip_to_state_evaluation to false, we + // are effectively jumping to the beginning of this if. + JSON_ASSERT(!states.empty()); + states.pop_back(); + skip_to_state_evaluation = true; + continue; + } + + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_array, "array"), BasicJsonType())); + } + + // states.back() is false -> object + + // comma -> next value + if (get_token() == token_type::value_separator) + { + // parse key + if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::value_string)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::value_string, "object key"), BasicJsonType())); + } + + if (JSON_HEDLEY_UNLIKELY(!sax->key(m_lexer.get_string()))) + { + return false; + } + + // parse separator (:) + if (JSON_HEDLEY_UNLIKELY(get_token() != token_type::name_separator)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::name_separator, "object separator"), BasicJsonType())); + } + + // parse values + get_token(); + continue; + } + + // closing } + if (JSON_HEDLEY_LIKELY(last_token == token_type::end_object)) + { + if (JSON_HEDLEY_UNLIKELY(!sax->end_object())) + { + return false; + } + + // We are done with this object. Before we can parse a + // new value, we need to evaluate the new state first. + // By setting skip_to_state_evaluation to false, we + // are effectively jumping to the beginning of this if. + JSON_ASSERT(!states.empty()); + states.pop_back(); + skip_to_state_evaluation = true; + continue; + } + + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::end_object, "object"), BasicJsonType())); + } + } + + /// get next token from lexer + token_type get_token() + { + return last_token = m_lexer.scan(); + } + + std::string exception_message(const token_type expected, const std::string& context) + { + std::string error_msg = "syntax error "; + + if (!context.empty()) + { + error_msg += "while parsing " + context + " "; + } + + error_msg += "- "; + + if (last_token == token_type::parse_error) + { + error_msg += std::string(m_lexer.get_error_message()) + "; last read: '" + + m_lexer.get_token_string() + "'"; + } + else + { + error_msg += "unexpected " + std::string(lexer_t::token_type_name(last_token)); + } + + if (expected != token_type::uninitialized) + { + error_msg += "; expected " + std::string(lexer_t::token_type_name(expected)); + } + + return error_msg; + } + + private: + /// callback function + const parser_callback_t callback = nullptr; + /// the type of the last read token + token_type last_token = token_type::uninitialized; + /// the lexer + lexer_t m_lexer; + /// whether to throw exceptions in case of errors + const bool allow_exceptions = true; +}; + +} // namespace detail +} // namespace nlohmann + +// #include + + +// #include + + +#include // ptrdiff_t +#include // numeric_limits + +// #include + + +namespace nlohmann +{ +namespace detail +{ +/* +@brief an iterator for primitive JSON types + +This class models an iterator for primitive JSON types (boolean, number, +string). It's only purpose is to allow the iterator/const_iterator classes +to "iterate" over primitive values. Internally, the iterator is modeled by +a `difference_type` variable. Value begin_value (`0`) models the begin, +end_value (`1`) models past the end. +*/ +class primitive_iterator_t +{ + private: + using difference_type = std::ptrdiff_t; + static constexpr difference_type begin_value = 0; + static constexpr difference_type end_value = begin_value + 1; + + JSON_PRIVATE_UNLESS_TESTED: + /// iterator as signed integer type + difference_type m_it = (std::numeric_limits::min)(); + + public: + constexpr difference_type get_value() const noexcept + { + return m_it; + } + + /// set iterator to a defined beginning + void set_begin() noexcept + { + m_it = begin_value; + } + + /// set iterator to a defined past the end + void set_end() noexcept + { + m_it = end_value; + } + + /// return whether the iterator can be dereferenced + constexpr bool is_begin() const noexcept + { + return m_it == begin_value; + } + + /// return whether the iterator is at end + constexpr bool is_end() const noexcept + { + return m_it == end_value; + } + + friend constexpr bool operator==(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept + { + return lhs.m_it == rhs.m_it; + } + + friend constexpr bool operator<(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept + { + return lhs.m_it < rhs.m_it; + } + + primitive_iterator_t operator+(difference_type n) noexcept + { + auto result = *this; + result += n; + return result; + } + + friend constexpr difference_type operator-(primitive_iterator_t lhs, primitive_iterator_t rhs) noexcept + { + return lhs.m_it - rhs.m_it; + } + + primitive_iterator_t& operator++() noexcept + { + ++m_it; + return *this; + } + + primitive_iterator_t const operator++(int) noexcept // NOLINT(readability-const-return-type) + { + auto result = *this; + ++m_it; + return result; + } + + primitive_iterator_t& operator--() noexcept + { + --m_it; + return *this; + } + + primitive_iterator_t const operator--(int) noexcept // NOLINT(readability-const-return-type) + { + auto result = *this; + --m_it; + return result; + } + + primitive_iterator_t& operator+=(difference_type n) noexcept + { + m_it += n; + return *this; + } + + primitive_iterator_t& operator-=(difference_type n) noexcept + { + m_it -= n; + return *this; + } +}; +} // namespace detail +} // namespace nlohmann + + +namespace nlohmann +{ +namespace detail +{ +/*! +@brief an iterator value + +@note This structure could easily be a union, but MSVC currently does not allow +unions members with complex constructors, see https://github.com/nlohmann/json/pull/105. +*/ +template struct internal_iterator +{ + /// iterator for JSON objects + typename BasicJsonType::object_t::iterator object_iterator {}; + /// iterator for JSON arrays + typename BasicJsonType::array_t::iterator array_iterator {}; + /// generic iterator for all other types + primitive_iterator_t primitive_iterator {}; +}; +} // namespace detail +} // namespace nlohmann + +// #include + + +#include // iterator, random_access_iterator_tag, bidirectional_iterator_tag, advance, next +#include // conditional, is_const, remove_const + +// #include + +// #include + +// #include + +// #include + +// #include + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +// forward declare, to be able to friend it later on +template class iteration_proxy; +template class iteration_proxy_value; + +/*! +@brief a template for a bidirectional iterator for the @ref basic_json class +This class implements a both iterators (iterator and const_iterator) for the +@ref basic_json class. +@note An iterator is called *initialized* when a pointer to a JSON value has + been set (e.g., by a constructor or a copy assignment). If the iterator is + default-constructed, it is *uninitialized* and most methods are undefined. + **The library uses assertions to detect calls on uninitialized iterators.** +@requirement The class satisfies the following concept requirements: +- +[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator): + The iterator that can be moved can be moved in both directions (i.e. + incremented and decremented). +@since version 1.0.0, simplified in version 2.0.9, change to bidirectional + iterators in version 3.0.0 (see https://github.com/nlohmann/json/issues/593) +*/ +template +class iter_impl +{ + /// the iterator with BasicJsonType of different const-ness + using other_iter_impl = iter_impl::value, typename std::remove_const::type, const BasicJsonType>::type>; + /// allow basic_json to access private members + friend other_iter_impl; + friend BasicJsonType; + friend iteration_proxy; + friend iteration_proxy_value; + + using object_t = typename BasicJsonType::object_t; + using array_t = typename BasicJsonType::array_t; + // make sure BasicJsonType is basic_json or const basic_json + static_assert(is_basic_json::type>::value, + "iter_impl only accepts (const) basic_json"); + + public: + + /// The std::iterator class template (used as a base class to provide typedefs) is deprecated in C++17. + /// The C++ Standard has never required user-defined iterators to derive from std::iterator. + /// A user-defined iterator should provide publicly accessible typedefs named + /// iterator_category, value_type, difference_type, pointer, and reference. + /// Note that value_type is required to be non-const, even for constant iterators. + using iterator_category = std::bidirectional_iterator_tag; + + /// the type of the values when the iterator is dereferenced + using value_type = typename BasicJsonType::value_type; + /// a type to represent differences between iterators + using difference_type = typename BasicJsonType::difference_type; + /// defines a pointer to the type iterated over (value_type) + using pointer = typename std::conditional::value, + typename BasicJsonType::const_pointer, + typename BasicJsonType::pointer>::type; + /// defines a reference to the type iterated over (value_type) + using reference = + typename std::conditional::value, + typename BasicJsonType::const_reference, + typename BasicJsonType::reference>::type; + + iter_impl() = default; + ~iter_impl() = default; + iter_impl(iter_impl&&) noexcept = default; + iter_impl& operator=(iter_impl&&) noexcept = default; + + /*! + @brief constructor for a given JSON instance + @param[in] object pointer to a JSON object for this iterator + @pre object != nullptr + @post The iterator is initialized; i.e. `m_object != nullptr`. + */ + explicit iter_impl(pointer object) noexcept : m_object(object) + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + m_it.object_iterator = typename object_t::iterator(); + break; + } + + case value_t::array: + { + m_it.array_iterator = typename array_t::iterator(); + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + m_it.primitive_iterator = primitive_iterator_t(); + break; + } + } + } + + /*! + @note The conventional copy constructor and copy assignment are implicitly + defined. Combined with the following converting constructor and + assignment, they support: (1) copy from iterator to iterator, (2) + copy from const iterator to const iterator, and (3) conversion from + iterator to const iterator. However conversion from const iterator + to iterator is not defined. + */ + + /*! + @brief const copy constructor + @param[in] other const iterator to copy from + @note This copy constructor had to be defined explicitly to circumvent a bug + occurring on msvc v19.0 compiler (VS 2015) debug build. For more + information refer to: https://github.com/nlohmann/json/issues/1608 + */ + iter_impl(const iter_impl& other) noexcept + : m_object(other.m_object), m_it(other.m_it) + {} + + /*! + @brief converting assignment + @param[in] other const iterator to copy from + @return const/non-const iterator + @note It is not checked whether @a other is initialized. + */ + iter_impl& operator=(const iter_impl& other) noexcept + { + if (&other != this) + { + m_object = other.m_object; + m_it = other.m_it; + } + return *this; + } + + /*! + @brief converting constructor + @param[in] other non-const iterator to copy from + @note It is not checked whether @a other is initialized. + */ + iter_impl(const iter_impl::type>& other) noexcept + : m_object(other.m_object), m_it(other.m_it) + {} + + /*! + @brief converting assignment + @param[in] other non-const iterator to copy from + @return const/non-const iterator + @note It is not checked whether @a other is initialized. + */ + iter_impl& operator=(const iter_impl::type>& other) noexcept // NOLINT(cert-oop54-cpp) + { + m_object = other.m_object; + m_it = other.m_it; + return *this; + } + + JSON_PRIVATE_UNLESS_TESTED: + /*! + @brief set the iterator to the first value + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + void set_begin() noexcept + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + m_it.object_iterator = m_object->m_value.object->begin(); + break; + } + + case value_t::array: + { + m_it.array_iterator = m_object->m_value.array->begin(); + break; + } + + case value_t::null: + { + // set to end so begin()==end() is true: null is empty + m_it.primitive_iterator.set_end(); + break; + } + + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + m_it.primitive_iterator.set_begin(); + break; + } + } + } + + /*! + @brief set the iterator past the last value + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + void set_end() noexcept + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + m_it.object_iterator = m_object->m_value.object->end(); + break; + } + + case value_t::array: + { + m_it.array_iterator = m_object->m_value.array->end(); + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + m_it.primitive_iterator.set_end(); + break; + } + } + } + + public: + /*! + @brief return a reference to the value pointed to by the iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + reference operator*() const + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end()); + return m_it.object_iterator->second; + } + + case value_t::array: + { + JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end()); + return *m_it.array_iterator; + } + + case value_t::null: + JSON_THROW(invalid_iterator::create(214, "cannot get value", *m_object)); + + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin())) + { + return *m_object; + } + + JSON_THROW(invalid_iterator::create(214, "cannot get value", *m_object)); + } + } + } + + /*! + @brief dereference the iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + pointer operator->() const + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end()); + return &(m_it.object_iterator->second); + } + + case value_t::array: + { + JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end()); + return &*m_it.array_iterator; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.is_begin())) + { + return m_object; + } + + JSON_THROW(invalid_iterator::create(214, "cannot get value", *m_object)); + } + } + } + + /*! + @brief post-increment (it++) + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl const operator++(int) // NOLINT(readability-const-return-type) + { + auto result = *this; + ++(*this); + return result; + } + + /*! + @brief pre-increment (++it) + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl& operator++() + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + std::advance(m_it.object_iterator, 1); + break; + } + + case value_t::array: + { + std::advance(m_it.array_iterator, 1); + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + ++m_it.primitive_iterator; + break; + } + } + + return *this; + } + + /*! + @brief post-decrement (it--) + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl const operator--(int) // NOLINT(readability-const-return-type) + { + auto result = *this; + --(*this); + return result; + } + + /*! + @brief pre-decrement (--it) + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl& operator--() + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + { + std::advance(m_it.object_iterator, -1); + break; + } + + case value_t::array: + { + std::advance(m_it.array_iterator, -1); + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + --m_it.primitive_iterator; + break; + } + } + + return *this; + } + + /*! + @brief comparison: equal + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + template < typename IterImpl, detail::enable_if_t < (std::is_same::value || std::is_same::value), std::nullptr_t > = nullptr > + bool operator==(const IterImpl& other) const + { + // if objects are not the same, the comparison is undefined + if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object)) + { + JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", *m_object)); + } + + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + return (m_it.object_iterator == other.m_it.object_iterator); + + case value_t::array: + return (m_it.array_iterator == other.m_it.array_iterator); + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + return (m_it.primitive_iterator == other.m_it.primitive_iterator); + } + } + + /*! + @brief comparison: not equal + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + template < typename IterImpl, detail::enable_if_t < (std::is_same::value || std::is_same::value), std::nullptr_t > = nullptr > + bool operator!=(const IterImpl& other) const + { + return !operator==(other); + } + + /*! + @brief comparison: smaller + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + bool operator<(const iter_impl& other) const + { + // if objects are not the same, the comparison is undefined + if (JSON_HEDLEY_UNLIKELY(m_object != other.m_object)) + { + JSON_THROW(invalid_iterator::create(212, "cannot compare iterators of different containers", *m_object)); + } + + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", *m_object)); + + case value_t::array: + return (m_it.array_iterator < other.m_it.array_iterator); + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + return (m_it.primitive_iterator < other.m_it.primitive_iterator); + } + } + + /*! + @brief comparison: less than or equal + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + bool operator<=(const iter_impl& other) const + { + return !other.operator < (*this); + } + + /*! + @brief comparison: greater than + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + bool operator>(const iter_impl& other) const + { + return !operator<=(other); + } + + /*! + @brief comparison: greater than or equal + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + bool operator>=(const iter_impl& other) const + { + return !operator<(other); + } + + /*! + @brief add to iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl& operator+=(difference_type i) + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", *m_object)); + + case value_t::array: + { + std::advance(m_it.array_iterator, i); + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + m_it.primitive_iterator += i; + break; + } + } + + return *this; + } + + /*! + @brief subtract from iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl& operator-=(difference_type i) + { + return operator+=(-i); + } + + /*! + @brief add to iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl operator+(difference_type i) const + { + auto result = *this; + result += i; + return result; + } + + /*! + @brief addition of distance and iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + friend iter_impl operator+(difference_type i, const iter_impl& it) + { + auto result = it; + result += i; + return result; + } + + /*! + @brief subtract from iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + iter_impl operator-(difference_type i) const + { + auto result = *this; + result -= i; + return result; + } + + /*! + @brief return difference + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + difference_type operator-(const iter_impl& other) const + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", *m_object)); + + case value_t::array: + return m_it.array_iterator - other.m_it.array_iterator; + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + return m_it.primitive_iterator - other.m_it.primitive_iterator; + } + } + + /*! + @brief access to successor + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + reference operator[](difference_type n) const + { + JSON_ASSERT(m_object != nullptr); + + switch (m_object->m_type) + { + case value_t::object: + JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", *m_object)); + + case value_t::array: + return *std::next(m_it.array_iterator, n); + + case value_t::null: + JSON_THROW(invalid_iterator::create(214, "cannot get value", *m_object)); + + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + if (JSON_HEDLEY_LIKELY(m_it.primitive_iterator.get_value() == -n)) + { + return *m_object; + } + + JSON_THROW(invalid_iterator::create(214, "cannot get value", *m_object)); + } + } + } + + /*! + @brief return the key of an object iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + const typename object_t::key_type& key() const + { + JSON_ASSERT(m_object != nullptr); + + if (JSON_HEDLEY_LIKELY(m_object->is_object())) + { + return m_it.object_iterator->first; + } + + JSON_THROW(invalid_iterator::create(207, "cannot use key() for non-object iterators", *m_object)); + } + + /*! + @brief return the value of an iterator + @pre The iterator is initialized; i.e. `m_object != nullptr`. + */ + reference value() const + { + return operator*(); + } + + JSON_PRIVATE_UNLESS_TESTED: + /// associated JSON instance + pointer m_object = nullptr; + /// the actual iterator of the associated instance + internal_iterator::type> m_it {}; +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + + +#include // ptrdiff_t +#include // reverse_iterator +#include // declval + +namespace nlohmann +{ +namespace detail +{ +////////////////////// +// reverse_iterator // +////////////////////// + +/*! +@brief a template for a reverse iterator class + +@tparam Base the base iterator type to reverse. Valid types are @ref +iterator (to create @ref reverse_iterator) and @ref const_iterator (to +create @ref const_reverse_iterator). + +@requirement The class satisfies the following concept requirements: +- +[BidirectionalIterator](https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator): + The iterator that can be moved can be moved in both directions (i.e. + incremented and decremented). +- [OutputIterator](https://en.cppreference.com/w/cpp/named_req/OutputIterator): + It is possible to write to the pointed-to element (only if @a Base is + @ref iterator). + +@since version 1.0.0 +*/ +template +class json_reverse_iterator : public std::reverse_iterator +{ + public: + using difference_type = std::ptrdiff_t; + /// shortcut to the reverse iterator adapter + using base_iterator = std::reverse_iterator; + /// the reference type for the pointed-to element + using reference = typename Base::reference; + + /// create reverse iterator from iterator + explicit json_reverse_iterator(const typename base_iterator::iterator_type& it) noexcept + : base_iterator(it) {} + + /// create reverse iterator from base class + explicit json_reverse_iterator(const base_iterator& it) noexcept : base_iterator(it) {} + + /// post-increment (it++) + json_reverse_iterator const operator++(int) // NOLINT(readability-const-return-type) + { + return static_cast(base_iterator::operator++(1)); + } + + /// pre-increment (++it) + json_reverse_iterator& operator++() + { + return static_cast(base_iterator::operator++()); + } + + /// post-decrement (it--) + json_reverse_iterator const operator--(int) // NOLINT(readability-const-return-type) + { + return static_cast(base_iterator::operator--(1)); + } + + /// pre-decrement (--it) + json_reverse_iterator& operator--() + { + return static_cast(base_iterator::operator--()); + } + + /// add to iterator + json_reverse_iterator& operator+=(difference_type i) + { + return static_cast(base_iterator::operator+=(i)); + } + + /// add to iterator + json_reverse_iterator operator+(difference_type i) const + { + return static_cast(base_iterator::operator+(i)); + } + + /// subtract from iterator + json_reverse_iterator operator-(difference_type i) const + { + return static_cast(base_iterator::operator-(i)); + } + + /// return difference + difference_type operator-(const json_reverse_iterator& other) const + { + return base_iterator(*this) - base_iterator(other); + } + + /// access to successor + reference operator[](difference_type n) const + { + return *(this->operator+(n)); + } + + /// return the key of an object iterator + auto key() const -> decltype(std::declval().key()) + { + auto it = --this->base(); + return it.key(); + } + + /// return the value of an iterator + reference value() const + { + auto it = --this->base(); + return it.operator * (); + } +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + + +#include // all_of +#include // isdigit +#include // max +#include // accumulate +#include // string +#include // move +#include // vector + +// #include + +// #include + +// #include + +// #include + + +namespace nlohmann +{ +template +class json_pointer +{ + // allow basic_json to access private members + NLOHMANN_BASIC_JSON_TPL_DECLARATION + friend class basic_json; + + public: + /*! + @brief create JSON pointer + + Create a JSON pointer according to the syntax described in + [Section 3 of RFC6901](https://tools.ietf.org/html/rfc6901#section-3). + + @param[in] s string representing the JSON pointer; if omitted, the empty + string is assumed which references the whole JSON value + + @throw parse_error.107 if the given JSON pointer @a s is nonempty and does + not begin with a slash (`/`); see example below + + @throw parse_error.108 if a tilde (`~`) in the given JSON pointer @a s is + not followed by `0` (representing `~`) or `1` (representing `/`); see + example below + + @liveexample{The example shows the construction several valid JSON pointers + as well as the exceptional behavior.,json_pointer} + + @since version 2.0.0 + */ + explicit json_pointer(const std::string& s = "") + : reference_tokens(split(s)) + {} + + /*! + @brief return a string representation of the JSON pointer + + @invariant For each JSON pointer `ptr`, it holds: + @code {.cpp} + ptr == json_pointer(ptr.to_string()); + @endcode + + @return a string representation of the JSON pointer + + @liveexample{The example shows the result of `to_string`.,json_pointer__to_string} + + @since version 2.0.0 + */ + std::string to_string() const + { + return std::accumulate(reference_tokens.begin(), reference_tokens.end(), + std::string{}, + [](const std::string & a, const std::string & b) + { + return a + "/" + detail::escape(b); + }); + } + + /// @copydoc to_string() + operator std::string() const + { + return to_string(); + } + + /*! + @brief append another JSON pointer at the end of this JSON pointer + + @param[in] ptr JSON pointer to append + @return JSON pointer with @a ptr appended + + @liveexample{The example shows the usage of `operator/=`.,json_pointer__operator_add} + + @complexity Linear in the length of @a ptr. + + @sa see @ref operator/=(std::string) to append a reference token + @sa see @ref operator/=(std::size_t) to append an array index + @sa see @ref operator/(const json_pointer&, const json_pointer&) for a binary operator + + @since version 3.6.0 + */ + json_pointer& operator/=(const json_pointer& ptr) + { + reference_tokens.insert(reference_tokens.end(), + ptr.reference_tokens.begin(), + ptr.reference_tokens.end()); + return *this; + } + + /*! + @brief append an unescaped reference token at the end of this JSON pointer + + @param[in] token reference token to append + @return JSON pointer with @a token appended without escaping @a token + + @liveexample{The example shows the usage of `operator/=`.,json_pointer__operator_add} + + @complexity Amortized constant. + + @sa see @ref operator/=(const json_pointer&) to append a JSON pointer + @sa see @ref operator/=(std::size_t) to append an array index + @sa see @ref operator/(const json_pointer&, std::size_t) for a binary operator + + @since version 3.6.0 + */ + json_pointer& operator/=(std::string token) + { + push_back(std::move(token)); + return *this; + } + + /*! + @brief append an array index at the end of this JSON pointer + + @param[in] array_idx array index to append + @return JSON pointer with @a array_idx appended + + @liveexample{The example shows the usage of `operator/=`.,json_pointer__operator_add} + + @complexity Amortized constant. + + @sa see @ref operator/=(const json_pointer&) to append a JSON pointer + @sa see @ref operator/=(std::string) to append a reference token + @sa see @ref operator/(const json_pointer&, std::string) for a binary operator + + @since version 3.6.0 + */ + json_pointer& operator/=(std::size_t array_idx) + { + return *this /= std::to_string(array_idx); + } + + /*! + @brief create a new JSON pointer by appending the right JSON pointer at the end of the left JSON pointer + + @param[in] lhs JSON pointer + @param[in] rhs JSON pointer + @return a new JSON pointer with @a rhs appended to @a lhs + + @liveexample{The example shows the usage of `operator/`.,json_pointer__operator_add_binary} + + @complexity Linear in the length of @a lhs and @a rhs. + + @sa see @ref operator/=(const json_pointer&) to append a JSON pointer + + @since version 3.6.0 + */ + friend json_pointer operator/(const json_pointer& lhs, + const json_pointer& rhs) + { + return json_pointer(lhs) /= rhs; + } + + /*! + @brief create a new JSON pointer by appending the unescaped token at the end of the JSON pointer + + @param[in] ptr JSON pointer + @param[in] token reference token + @return a new JSON pointer with unescaped @a token appended to @a ptr + + @liveexample{The example shows the usage of `operator/`.,json_pointer__operator_add_binary} + + @complexity Linear in the length of @a ptr. + + @sa see @ref operator/=(std::string) to append a reference token + + @since version 3.6.0 + */ + friend json_pointer operator/(const json_pointer& ptr, std::string token) // NOLINT(performance-unnecessary-value-param) + { + return json_pointer(ptr) /= std::move(token); + } + + /*! + @brief create a new JSON pointer by appending the array-index-token at the end of the JSON pointer + + @param[in] ptr JSON pointer + @param[in] array_idx array index + @return a new JSON pointer with @a array_idx appended to @a ptr + + @liveexample{The example shows the usage of `operator/`.,json_pointer__operator_add_binary} + + @complexity Linear in the length of @a ptr. + + @sa see @ref operator/=(std::size_t) to append an array index + + @since version 3.6.0 + */ + friend json_pointer operator/(const json_pointer& ptr, std::size_t array_idx) + { + return json_pointer(ptr) /= array_idx; + } + + /*! + @brief returns the parent of this JSON pointer + + @return parent of this JSON pointer; in case this JSON pointer is the root, + the root itself is returned + + @complexity Linear in the length of the JSON pointer. + + @liveexample{The example shows the result of `parent_pointer` for different + JSON Pointers.,json_pointer__parent_pointer} + + @since version 3.6.0 + */ + json_pointer parent_pointer() const + { + if (empty()) + { + return *this; + } + + json_pointer res = *this; + res.pop_back(); + return res; + } + + /*! + @brief remove last reference token + + @pre not `empty()` + + @liveexample{The example shows the usage of `pop_back`.,json_pointer__pop_back} + + @complexity Constant. + + @throw out_of_range.405 if JSON pointer has no parent + + @since version 3.6.0 + */ + void pop_back() + { + if (JSON_HEDLEY_UNLIKELY(empty())) + { + JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", BasicJsonType())); + } + + reference_tokens.pop_back(); + } + + /*! + @brief return last reference token + + @pre not `empty()` + @return last reference token + + @liveexample{The example shows the usage of `back`.,json_pointer__back} + + @complexity Constant. + + @throw out_of_range.405 if JSON pointer has no parent + + @since version 3.6.0 + */ + const std::string& back() const + { + if (JSON_HEDLEY_UNLIKELY(empty())) + { + JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", BasicJsonType())); + } + + return reference_tokens.back(); + } + + /*! + @brief append an unescaped token at the end of the reference pointer + + @param[in] token token to add + + @complexity Amortized constant. + + @liveexample{The example shows the result of `push_back` for different + JSON Pointers.,json_pointer__push_back} + + @since version 3.6.0 + */ + void push_back(const std::string& token) + { + reference_tokens.push_back(token); + } + + /// @copydoc push_back(const std::string&) + void push_back(std::string&& token) + { + reference_tokens.push_back(std::move(token)); + } + + /*! + @brief return whether pointer points to the root document + + @return true iff the JSON pointer points to the root document + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @liveexample{The example shows the result of `empty` for different JSON + Pointers.,json_pointer__empty} + + @since version 3.6.0 + */ + bool empty() const noexcept + { + return reference_tokens.empty(); + } + + private: + /*! + @param[in] s reference token to be converted into an array index + + @return integer representation of @a s + + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index begins not with a digit + @throw out_of_range.404 if string @a s could not be converted to an integer + @throw out_of_range.410 if an array index exceeds size_type + */ + static typename BasicJsonType::size_type array_index(const std::string& s) + { + using size_type = typename BasicJsonType::size_type; + + // error condition (cf. RFC 6901, Sect. 4) + if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && s[0] == '0')) + { + JSON_THROW(detail::parse_error::create(106, 0, "array index '" + s + "' must not begin with '0'", BasicJsonType())); + } + + // error condition (cf. RFC 6901, Sect. 4) + if (JSON_HEDLEY_UNLIKELY(s.size() > 1 && !(s[0] >= '1' && s[0] <= '9'))) + { + JSON_THROW(detail::parse_error::create(109, 0, "array index '" + s + "' is not a number", BasicJsonType())); + } + + std::size_t processed_chars = 0; + unsigned long long res = 0; // NOLINT(runtime/int) + JSON_TRY + { + res = std::stoull(s, &processed_chars); + } + JSON_CATCH(std::out_of_range&) + { + JSON_THROW(detail::out_of_range::create(404, "unresolved reference token '" + s + "'", BasicJsonType())); + } + + // check if the string was completely read + if (JSON_HEDLEY_UNLIKELY(processed_chars != s.size())) + { + JSON_THROW(detail::out_of_range::create(404, "unresolved reference token '" + s + "'", BasicJsonType())); + } + + // only triggered on special platforms (like 32bit), see also + // https://github.com/nlohmann/json/pull/2203 + if (res >= static_cast((std::numeric_limits::max)())) // NOLINT(runtime/int) + { + JSON_THROW(detail::out_of_range::create(410, "array index " + s + " exceeds size_type", BasicJsonType())); // LCOV_EXCL_LINE + } + + return static_cast(res); + } + + JSON_PRIVATE_UNLESS_TESTED: + json_pointer top() const + { + if (JSON_HEDLEY_UNLIKELY(empty())) + { + JSON_THROW(detail::out_of_range::create(405, "JSON pointer has no parent", BasicJsonType())); + } + + json_pointer result = *this; + result.reference_tokens = {reference_tokens[0]}; + return result; + } + + private: + /*! + @brief create and return a reference to the pointed to value + + @complexity Linear in the number of reference tokens. + + @throw parse_error.109 if array index is not a number + @throw type_error.313 if value cannot be unflattened + */ + BasicJsonType& get_and_create(BasicJsonType& j) const + { + auto* result = &j; + + // in case no reference tokens exist, return a reference to the JSON value + // j which will be overwritten by a primitive value + for (const auto& reference_token : reference_tokens) + { + switch (result->type()) + { + case detail::value_t::null: + { + if (reference_token == "0") + { + // start a new array if reference token is 0 + result = &result->operator[](0); + } + else + { + // start a new object otherwise + result = &result->operator[](reference_token); + } + break; + } + + case detail::value_t::object: + { + // create an entry in the object + result = &result->operator[](reference_token); + break; + } + + case detail::value_t::array: + { + // create an entry in the array + result = &result->operator[](array_index(reference_token)); + break; + } + + /* + The following code is only reached if there exists a reference + token _and_ the current value is primitive. In this case, we have + an error situation, because primitive values may only occur as + single value; that is, with an empty list of reference tokens. + */ + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + JSON_THROW(detail::type_error::create(313, "invalid value to unflatten", j)); + } + } + + return *result; + } + + /*! + @brief return a reference to the pointed to value + + @note This version does not throw if a value is not present, but tries to + create nested values instead. For instance, calling this function + with pointer `"/this/that"` on a null value is equivalent to calling + `operator[]("this").operator[]("that")` on that value, effectively + changing the null value to an object. + + @param[in] ptr a JSON value + + @return reference to the JSON value pointed to by the JSON pointer + + @complexity Linear in the length of the JSON pointer. + + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + @throw out_of_range.404 if the JSON pointer can not be resolved + */ + BasicJsonType& get_unchecked(BasicJsonType* ptr) const + { + for (const auto& reference_token : reference_tokens) + { + // convert null values to arrays or objects before continuing + if (ptr->is_null()) + { + // check if reference token is a number + const bool nums = + std::all_of(reference_token.begin(), reference_token.end(), + [](const unsigned char x) + { + return std::isdigit(x); + }); + + // change value to array for numbers or "-" or to object otherwise + *ptr = (nums || reference_token == "-") + ? detail::value_t::array + : detail::value_t::object; + } + + switch (ptr->type()) + { + case detail::value_t::object: + { + // use unchecked object access + ptr = &ptr->operator[](reference_token); + break; + } + + case detail::value_t::array: + { + if (reference_token == "-") + { + // explicitly treat "-" as index beyond the end + ptr = &ptr->operator[](ptr->m_value.array->size()); + } + else + { + // convert array index to number; unchecked access + ptr = &ptr->operator[](array_index(reference_token)); + } + break; + } + + case detail::value_t::null: + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + JSON_THROW(detail::out_of_range::create(404, "unresolved reference token '" + reference_token + "'", *ptr)); + } + } + + return *ptr; + } + + /*! + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + @throw out_of_range.402 if the array index '-' is used + @throw out_of_range.404 if the JSON pointer can not be resolved + */ + BasicJsonType& get_checked(BasicJsonType* ptr) const + { + for (const auto& reference_token : reference_tokens) + { + switch (ptr->type()) + { + case detail::value_t::object: + { + // note: at performs range check + ptr = &ptr->at(reference_token); + break; + } + + case detail::value_t::array: + { + if (JSON_HEDLEY_UNLIKELY(reference_token == "-")) + { + // "-" always fails the range check + JSON_THROW(detail::out_of_range::create(402, + "array index '-' (" + std::to_string(ptr->m_value.array->size()) + + ") is out of range", *ptr)); + } + + // note: at performs range check + ptr = &ptr->at(array_index(reference_token)); + break; + } + + case detail::value_t::null: + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + JSON_THROW(detail::out_of_range::create(404, "unresolved reference token '" + reference_token + "'", *ptr)); + } + } + + return *ptr; + } + + /*! + @brief return a const reference to the pointed to value + + @param[in] ptr a JSON value + + @return const reference to the JSON value pointed to by the JSON + pointer + + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + @throw out_of_range.402 if the array index '-' is used + @throw out_of_range.404 if the JSON pointer can not be resolved + */ + const BasicJsonType& get_unchecked(const BasicJsonType* ptr) const + { + for (const auto& reference_token : reference_tokens) + { + switch (ptr->type()) + { + case detail::value_t::object: + { + // use unchecked object access + ptr = &ptr->operator[](reference_token); + break; + } + + case detail::value_t::array: + { + if (JSON_HEDLEY_UNLIKELY(reference_token == "-")) + { + // "-" cannot be used for const access + JSON_THROW(detail::out_of_range::create(402, "array index '-' (" + std::to_string(ptr->m_value.array->size()) + ") is out of range", *ptr)); + } + + // use unchecked array access + ptr = &ptr->operator[](array_index(reference_token)); + break; + } + + case detail::value_t::null: + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + JSON_THROW(detail::out_of_range::create(404, "unresolved reference token '" + reference_token + "'", *ptr)); + } + } + + return *ptr; + } + + /*! + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + @throw out_of_range.402 if the array index '-' is used + @throw out_of_range.404 if the JSON pointer can not be resolved + */ + const BasicJsonType& get_checked(const BasicJsonType* ptr) const + { + for (const auto& reference_token : reference_tokens) + { + switch (ptr->type()) + { + case detail::value_t::object: + { + // note: at performs range check + ptr = &ptr->at(reference_token); + break; + } + + case detail::value_t::array: + { + if (JSON_HEDLEY_UNLIKELY(reference_token == "-")) + { + // "-" always fails the range check + JSON_THROW(detail::out_of_range::create(402, + "array index '-' (" + std::to_string(ptr->m_value.array->size()) + + ") is out of range", *ptr)); + } + + // note: at performs range check + ptr = &ptr->at(array_index(reference_token)); + break; + } + + case detail::value_t::null: + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + JSON_THROW(detail::out_of_range::create(404, "unresolved reference token '" + reference_token + "'", *ptr)); + } + } + + return *ptr; + } + + /*! + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + */ + bool contains(const BasicJsonType* ptr) const + { + for (const auto& reference_token : reference_tokens) + { + switch (ptr->type()) + { + case detail::value_t::object: + { + if (!ptr->contains(reference_token)) + { + // we did not find the key in the object + return false; + } + + ptr = &ptr->operator[](reference_token); + break; + } + + case detail::value_t::array: + { + if (JSON_HEDLEY_UNLIKELY(reference_token == "-")) + { + // "-" always fails the range check + return false; + } + if (JSON_HEDLEY_UNLIKELY(reference_token.size() == 1 && !("0" <= reference_token && reference_token <= "9"))) + { + // invalid char + return false; + } + if (JSON_HEDLEY_UNLIKELY(reference_token.size() > 1)) + { + if (JSON_HEDLEY_UNLIKELY(!('1' <= reference_token[0] && reference_token[0] <= '9'))) + { + // first char should be between '1' and '9' + return false; + } + for (std::size_t i = 1; i < reference_token.size(); i++) + { + if (JSON_HEDLEY_UNLIKELY(!('0' <= reference_token[i] && reference_token[i] <= '9'))) + { + // other char should be between '0' and '9' + return false; + } + } + } + + const auto idx = array_index(reference_token); + if (idx >= ptr->size()) + { + // index out of range + return false; + } + + ptr = &ptr->operator[](idx); + break; + } + + case detail::value_t::null: + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + { + // we do not expect primitive values if there is still a + // reference token to process + return false; + } + } + } + + // no reference token left means we found a primitive value + return true; + } + + /*! + @brief split the string input to reference tokens + + @note This function is only called by the json_pointer constructor. + All exceptions below are documented there. + + @throw parse_error.107 if the pointer is not empty or begins with '/' + @throw parse_error.108 if character '~' is not followed by '0' or '1' + */ + static std::vector split(const std::string& reference_string) + { + std::vector result; + + // special case: empty reference string -> no reference tokens + if (reference_string.empty()) + { + return result; + } + + // check if nonempty reference string begins with slash + if (JSON_HEDLEY_UNLIKELY(reference_string[0] != '/')) + { + JSON_THROW(detail::parse_error::create(107, 1, "JSON pointer must be empty or begin with '/' - was: '" + reference_string + "'", BasicJsonType())); + } + + // extract the reference tokens: + // - slash: position of the last read slash (or end of string) + // - start: position after the previous slash + for ( + // search for the first slash after the first character + std::size_t slash = reference_string.find_first_of('/', 1), + // set the beginning of the first reference token + start = 1; + // we can stop if start == 0 (if slash == std::string::npos) + start != 0; + // set the beginning of the next reference token + // (will eventually be 0 if slash == std::string::npos) + start = (slash == std::string::npos) ? 0 : slash + 1, + // find next slash + slash = reference_string.find_first_of('/', start)) + { + // use the text between the beginning of the reference token + // (start) and the last slash (slash). + auto reference_token = reference_string.substr(start, slash - start); + + // check reference tokens are properly escaped + for (std::size_t pos = reference_token.find_first_of('~'); + pos != std::string::npos; + pos = reference_token.find_first_of('~', pos + 1)) + { + JSON_ASSERT(reference_token[pos] == '~'); + + // ~ must be followed by 0 or 1 + if (JSON_HEDLEY_UNLIKELY(pos == reference_token.size() - 1 || + (reference_token[pos + 1] != '0' && + reference_token[pos + 1] != '1'))) + { + JSON_THROW(detail::parse_error::create(108, 0, "escape character '~' must be followed with '0' or '1'", BasicJsonType())); + } + } + + // finally, store the reference token + detail::unescape(reference_token); + result.push_back(reference_token); + } + + return result; + } + + private: + /*! + @param[in] reference_string the reference string to the current value + @param[in] value the value to consider + @param[in,out] result the result object to insert values to + + @note Empty objects or arrays are flattened to `null`. + */ + static void flatten(const std::string& reference_string, + const BasicJsonType& value, + BasicJsonType& result) + { + switch (value.type()) + { + case detail::value_t::array: + { + if (value.m_value.array->empty()) + { + // flatten empty array as null + result[reference_string] = nullptr; + } + else + { + // iterate array and use index as reference string + for (std::size_t i = 0; i < value.m_value.array->size(); ++i) + { + flatten(reference_string + "/" + std::to_string(i), + value.m_value.array->operator[](i), result); + } + } + break; + } + + case detail::value_t::object: + { + if (value.m_value.object->empty()) + { + // flatten empty object as null + result[reference_string] = nullptr; + } + else + { + // iterate object and use keys as reference string + for (const auto& element : *value.m_value.object) + { + flatten(reference_string + "/" + detail::escape(element.first), element.second, result); + } + } + break; + } + + case detail::value_t::null: + case detail::value_t::string: + case detail::value_t::boolean: + case detail::value_t::number_integer: + case detail::value_t::number_unsigned: + case detail::value_t::number_float: + case detail::value_t::binary: + case detail::value_t::discarded: + default: + { + // add primitive value with its reference string + result[reference_string] = value; + break; + } + } + } + + /*! + @param[in] value flattened JSON + + @return unflattened JSON + + @throw parse_error.109 if array index is not a number + @throw type_error.314 if value is not an object + @throw type_error.315 if object values are not primitive + @throw type_error.313 if value cannot be unflattened + */ + static BasicJsonType + unflatten(const BasicJsonType& value) + { + if (JSON_HEDLEY_UNLIKELY(!value.is_object())) + { + JSON_THROW(detail::type_error::create(314, "only objects can be unflattened", value)); + } + + BasicJsonType result; + + // iterate the JSON object values + for (const auto& element : *value.m_value.object) + { + if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive())) + { + JSON_THROW(detail::type_error::create(315, "values in object must be primitive", element.second)); + } + + // assign value to reference pointed to by JSON pointer; Note that if + // the JSON pointer is "" (i.e., points to the whole value), function + // get_and_create returns a reference to result itself. An assignment + // will then create a primitive value. + json_pointer(element.first).get_and_create(result) = element.second; + } + + return result; + } + + /*! + @brief compares two JSON pointers for equality + + @param[in] lhs JSON pointer to compare + @param[in] rhs JSON pointer to compare + @return whether @a lhs is equal to @a rhs + + @complexity Linear in the length of the JSON pointer + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + */ + friend bool operator==(json_pointer const& lhs, + json_pointer const& rhs) noexcept + { + return lhs.reference_tokens == rhs.reference_tokens; + } + + /*! + @brief compares two JSON pointers for inequality + + @param[in] lhs JSON pointer to compare + @param[in] rhs JSON pointer to compare + @return whether @a lhs is not equal @a rhs + + @complexity Linear in the length of the JSON pointer + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + */ + friend bool operator!=(json_pointer const& lhs, + json_pointer const& rhs) noexcept + { + return !(lhs == rhs); + } + + /// the reference tokens + std::vector reference_tokens; +}; +} // namespace nlohmann + +// #include + + +#include +#include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +template +class json_ref +{ + public: + using value_type = BasicJsonType; + + json_ref(value_type&& value) + : owned_value(std::move(value)) + {} + + json_ref(const value_type& value) + : value_ref(&value) + {} + + json_ref(std::initializer_list init) + : owned_value(init) + {} + + template < + class... Args, + enable_if_t::value, int> = 0 > + json_ref(Args && ... args) + : owned_value(std::forward(args)...) + {} + + // class should be movable only + json_ref(json_ref&&) noexcept = default; + json_ref(const json_ref&) = delete; + json_ref& operator=(const json_ref&) = delete; + json_ref& operator=(json_ref&&) = delete; + ~json_ref() = default; + + value_type moved_or_copied() const + { + if (value_ref == nullptr) + { + return std::move(owned_value); + } + return *value_ref; + } + + value_type const& operator*() const + { + return value_ref ? *value_ref : owned_value; + } + + value_type const* operator->() const + { + return &** this; + } + + private: + mutable value_type owned_value = nullptr; + value_type const* value_ref = nullptr; +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + +// #include + +// #include + +// #include + + +#include // reverse +#include // array +#include // isnan, isinf +#include // uint8_t, uint16_t, uint32_t, uint64_t +#include // memcpy +#include // numeric_limits +#include // string +#include // move + +// #include + +// #include + +// #include + + +#include // copy +#include // size_t +#include // back_inserter +#include // shared_ptr, make_shared +#include // basic_string +#include // vector + +#ifndef JSON_NO_IO + #include // streamsize + #include // basic_ostream +#endif // JSON_NO_IO + +// #include + + +namespace nlohmann +{ +namespace detail +{ +/// abstract output adapter interface +template struct output_adapter_protocol +{ + virtual void write_character(CharType c) = 0; + virtual void write_characters(const CharType* s, std::size_t length) = 0; + virtual ~output_adapter_protocol() = default; + + output_adapter_protocol() = default; + output_adapter_protocol(const output_adapter_protocol&) = default; + output_adapter_protocol(output_adapter_protocol&&) noexcept = default; + output_adapter_protocol& operator=(const output_adapter_protocol&) = default; + output_adapter_protocol& operator=(output_adapter_protocol&&) noexcept = default; +}; + +/// a type to simplify interfaces +template +using output_adapter_t = std::shared_ptr>; + +/// output adapter for byte vectors +template> +class output_vector_adapter : public output_adapter_protocol +{ + public: + explicit output_vector_adapter(std::vector& vec) noexcept + : v(vec) + {} + + void write_character(CharType c) override + { + v.push_back(c); + } + + JSON_HEDLEY_NON_NULL(2) + void write_characters(const CharType* s, std::size_t length) override + { + std::copy(s, s + length, std::back_inserter(v)); + } + + private: + std::vector& v; +}; + +#ifndef JSON_NO_IO +/// output adapter for output streams +template +class output_stream_adapter : public output_adapter_protocol +{ + public: + explicit output_stream_adapter(std::basic_ostream& s) noexcept + : stream(s) + {} + + void write_character(CharType c) override + { + stream.put(c); + } + + JSON_HEDLEY_NON_NULL(2) + void write_characters(const CharType* s, std::size_t length) override + { + stream.write(s, static_cast(length)); + } + + private: + std::basic_ostream& stream; +}; +#endif // JSON_NO_IO + +/// output adapter for basic_string +template> +class output_string_adapter : public output_adapter_protocol +{ + public: + explicit output_string_adapter(StringType& s) noexcept + : str(s) + {} + + void write_character(CharType c) override + { + str.push_back(c); + } + + JSON_HEDLEY_NON_NULL(2) + void write_characters(const CharType* s, std::size_t length) override + { + str.append(s, length); + } + + private: + StringType& str; +}; + +template> +class output_adapter +{ + public: + template> + output_adapter(std::vector& vec) + : oa(std::make_shared>(vec)) {} + +#ifndef JSON_NO_IO + output_adapter(std::basic_ostream& s) + : oa(std::make_shared>(s)) {} +#endif // JSON_NO_IO + + output_adapter(StringType& s) + : oa(std::make_shared>(s)) {} + + operator output_adapter_t() + { + return oa; + } + + private: + output_adapter_t oa = nullptr; +}; +} // namespace detail +} // namespace nlohmann + + +namespace nlohmann +{ +namespace detail +{ +/////////////////// +// binary writer // +/////////////////// + +/*! +@brief serialization to CBOR and MessagePack values +*/ +template +class binary_writer +{ + using string_t = typename BasicJsonType::string_t; + using binary_t = typename BasicJsonType::binary_t; + using number_float_t = typename BasicJsonType::number_float_t; + + public: + /*! + @brief create a binary writer + + @param[in] adapter output adapter to write to + */ + explicit binary_writer(output_adapter_t adapter) : oa(std::move(adapter)) + { + JSON_ASSERT(oa); + } + + /*! + @param[in] j JSON value to serialize + @pre j.type() == value_t::object + */ + void write_bson(const BasicJsonType& j) + { + switch (j.type()) + { + case value_t::object: + { + write_bson_object(*j.m_value.object); + break; + } + + case value_t::null: + case value_t::array: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + JSON_THROW(type_error::create(317, "to serialize to BSON, top-level type must be object, but is " + std::string(j.type_name()), j)); + } + } + } + + /*! + @param[in] j JSON value to serialize + */ + void write_cbor(const BasicJsonType& j) + { + switch (j.type()) + { + case value_t::null: + { + oa->write_character(to_char_type(0xF6)); + break; + } + + case value_t::boolean: + { + oa->write_character(j.m_value.boolean + ? to_char_type(0xF5) + : to_char_type(0xF4)); + break; + } + + case value_t::number_integer: + { + if (j.m_value.number_integer >= 0) + { + // CBOR does not differentiate between positive signed + // integers and unsigned integers. Therefore, we used the + // code from the value_t::number_unsigned case here. + if (j.m_value.number_integer <= 0x17) + { + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x18)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x19)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x1A)); + write_number(static_cast(j.m_value.number_integer)); + } + else + { + oa->write_character(to_char_type(0x1B)); + write_number(static_cast(j.m_value.number_integer)); + } + } + else + { + // The conversions below encode the sign in the first + // byte, and the value is converted to a positive number. + const auto positive_number = -1 - j.m_value.number_integer; + if (j.m_value.number_integer >= -24) + { + write_number(static_cast(0x20 + positive_number)); + } + else if (positive_number <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x38)); + write_number(static_cast(positive_number)); + } + else if (positive_number <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x39)); + write_number(static_cast(positive_number)); + } + else if (positive_number <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x3A)); + write_number(static_cast(positive_number)); + } + else + { + oa->write_character(to_char_type(0x3B)); + write_number(static_cast(positive_number)); + } + } + break; + } + + case value_t::number_unsigned: + { + if (j.m_value.number_unsigned <= 0x17) + { + write_number(static_cast(j.m_value.number_unsigned)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x18)); + write_number(static_cast(j.m_value.number_unsigned)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x19)); + write_number(static_cast(j.m_value.number_unsigned)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x1A)); + write_number(static_cast(j.m_value.number_unsigned)); + } + else + { + oa->write_character(to_char_type(0x1B)); + write_number(static_cast(j.m_value.number_unsigned)); + } + break; + } + + case value_t::number_float: + { + if (std::isnan(j.m_value.number_float)) + { + // NaN is 0xf97e00 in CBOR + oa->write_character(to_char_type(0xF9)); + oa->write_character(to_char_type(0x7E)); + oa->write_character(to_char_type(0x00)); + } + else if (std::isinf(j.m_value.number_float)) + { + // Infinity is 0xf97c00, -Infinity is 0xf9fc00 + oa->write_character(to_char_type(0xf9)); + oa->write_character(j.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC)); + oa->write_character(to_char_type(0x00)); + } + else + { + write_compact_float(j.m_value.number_float, detail::input_format_t::cbor); + } + break; + } + + case value_t::string: + { + // step 1: write control byte and the string length + const auto N = j.m_value.string->size(); + if (N <= 0x17) + { + write_number(static_cast(0x60 + N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x78)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x79)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x7A)); + write_number(static_cast(N)); + } + // LCOV_EXCL_START + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x7B)); + write_number(static_cast(N)); + } + // LCOV_EXCL_STOP + + // step 2: write the string + oa->write_characters( + reinterpret_cast(j.m_value.string->c_str()), + j.m_value.string->size()); + break; + } + + case value_t::array: + { + // step 1: write control byte and the array size + const auto N = j.m_value.array->size(); + if (N <= 0x17) + { + write_number(static_cast(0x80 + N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x98)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x99)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x9A)); + write_number(static_cast(N)); + } + // LCOV_EXCL_START + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x9B)); + write_number(static_cast(N)); + } + // LCOV_EXCL_STOP + + // step 2: write each element + for (const auto& el : *j.m_value.array) + { + write_cbor(el); + } + break; + } + + case value_t::binary: + { + if (j.m_value.binary->has_subtype()) + { + if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + { + write_number(static_cast(0xd8)); + write_number(static_cast(j.m_value.binary->subtype())); + } + else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + { + write_number(static_cast(0xd9)); + write_number(static_cast(j.m_value.binary->subtype())); + } + else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + { + write_number(static_cast(0xda)); + write_number(static_cast(j.m_value.binary->subtype())); + } + else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + { + write_number(static_cast(0xdb)); + write_number(static_cast(j.m_value.binary->subtype())); + } + } + + // step 1: write control byte and the binary array size + const auto N = j.m_value.binary->size(); + if (N <= 0x17) + { + write_number(static_cast(0x40 + N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x58)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x59)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x5A)); + write_number(static_cast(N)); + } + // LCOV_EXCL_START + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0x5B)); + write_number(static_cast(N)); + } + // LCOV_EXCL_STOP + + // step 2: write each element + oa->write_characters( + reinterpret_cast(j.m_value.binary->data()), + N); + + break; + } + + case value_t::object: + { + // step 1: write control byte and the object size + const auto N = j.m_value.object->size(); + if (N <= 0x17) + { + write_number(static_cast(0xA0 + N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0xB8)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0xB9)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0xBA)); + write_number(static_cast(N)); + } + // LCOV_EXCL_START + else if (N <= (std::numeric_limits::max)()) + { + oa->write_character(to_char_type(0xBB)); + write_number(static_cast(N)); + } + // LCOV_EXCL_STOP + + // step 2: write each element + for (const auto& el : *j.m_value.object) + { + write_cbor(el.first); + write_cbor(el.second); + } + break; + } + + case value_t::discarded: + default: + break; + } + } + + /*! + @param[in] j JSON value to serialize + */ + void write_msgpack(const BasicJsonType& j) + { + switch (j.type()) + { + case value_t::null: // nil + { + oa->write_character(to_char_type(0xC0)); + break; + } + + case value_t::boolean: // true and false + { + oa->write_character(j.m_value.boolean + ? to_char_type(0xC3) + : to_char_type(0xC2)); + break; + } + + case value_t::number_integer: + { + if (j.m_value.number_integer >= 0) + { + // MessagePack does not differentiate between positive + // signed integers and unsigned integers. Therefore, we used + // the code from the value_t::number_unsigned case here. + if (j.m_value.number_unsigned < 128) + { + // positive fixnum + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 8 + oa->write_character(to_char_type(0xCC)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 16 + oa->write_character(to_char_type(0xCD)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 32 + oa->write_character(to_char_type(0xCE)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 64 + oa->write_character(to_char_type(0xCF)); + write_number(static_cast(j.m_value.number_integer)); + } + } + else + { + if (j.m_value.number_integer >= -32) + { + // negative fixnum + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_value.number_integer <= (std::numeric_limits::max)()) + { + // int 8 + oa->write_character(to_char_type(0xD0)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_value.number_integer <= (std::numeric_limits::max)()) + { + // int 16 + oa->write_character(to_char_type(0xD1)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_value.number_integer <= (std::numeric_limits::max)()) + { + // int 32 + oa->write_character(to_char_type(0xD2)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_value.number_integer <= (std::numeric_limits::max)()) + { + // int 64 + oa->write_character(to_char_type(0xD3)); + write_number(static_cast(j.m_value.number_integer)); + } + } + break; + } + + case value_t::number_unsigned: + { + if (j.m_value.number_unsigned < 128) + { + // positive fixnum + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 8 + oa->write_character(to_char_type(0xCC)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 16 + oa->write_character(to_char_type(0xCD)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 32 + oa->write_character(to_char_type(0xCE)); + write_number(static_cast(j.m_value.number_integer)); + } + else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + { + // uint 64 + oa->write_character(to_char_type(0xCF)); + write_number(static_cast(j.m_value.number_integer)); + } + break; + } + + case value_t::number_float: + { + write_compact_float(j.m_value.number_float, detail::input_format_t::msgpack); + break; + } + + case value_t::string: + { + // step 1: write control byte and the string length + const auto N = j.m_value.string->size(); + if (N <= 31) + { + // fixstr + write_number(static_cast(0xA0 | N)); + } + else if (N <= (std::numeric_limits::max)()) + { + // str 8 + oa->write_character(to_char_type(0xD9)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + // str 16 + oa->write_character(to_char_type(0xDA)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + // str 32 + oa->write_character(to_char_type(0xDB)); + write_number(static_cast(N)); + } + + // step 2: write the string + oa->write_characters( + reinterpret_cast(j.m_value.string->c_str()), + j.m_value.string->size()); + break; + } + + case value_t::array: + { + // step 1: write control byte and the array size + const auto N = j.m_value.array->size(); + if (N <= 15) + { + // fixarray + write_number(static_cast(0x90 | N)); + } + else if (N <= (std::numeric_limits::max)()) + { + // array 16 + oa->write_character(to_char_type(0xDC)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + // array 32 + oa->write_character(to_char_type(0xDD)); + write_number(static_cast(N)); + } + + // step 2: write each element + for (const auto& el : *j.m_value.array) + { + write_msgpack(el); + } + break; + } + + case value_t::binary: + { + // step 0: determine if the binary type has a set subtype to + // determine whether or not to use the ext or fixext types + const bool use_ext = j.m_value.binary->has_subtype(); + + // step 1: write control byte and the byte string length + const auto N = j.m_value.binary->size(); + if (N <= (std::numeric_limits::max)()) + { + std::uint8_t output_type{}; + bool fixed = true; + if (use_ext) + { + switch (N) + { + case 1: + output_type = 0xD4; // fixext 1 + break; + case 2: + output_type = 0xD5; // fixext 2 + break; + case 4: + output_type = 0xD6; // fixext 4 + break; + case 8: + output_type = 0xD7; // fixext 8 + break; + case 16: + output_type = 0xD8; // fixext 16 + break; + default: + output_type = 0xC7; // ext 8 + fixed = false; + break; + } + + } + else + { + output_type = 0xC4; // bin 8 + fixed = false; + } + + oa->write_character(to_char_type(output_type)); + if (!fixed) + { + write_number(static_cast(N)); + } + } + else if (N <= (std::numeric_limits::max)()) + { + std::uint8_t output_type = use_ext + ? 0xC8 // ext 16 + : 0xC5; // bin 16 + + oa->write_character(to_char_type(output_type)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + std::uint8_t output_type = use_ext + ? 0xC9 // ext 32 + : 0xC6; // bin 32 + + oa->write_character(to_char_type(output_type)); + write_number(static_cast(N)); + } + + // step 1.5: if this is an ext type, write the subtype + if (use_ext) + { + write_number(static_cast(j.m_value.binary->subtype())); + } + + // step 2: write the byte string + oa->write_characters( + reinterpret_cast(j.m_value.binary->data()), + N); + + break; + } + + case value_t::object: + { + // step 1: write control byte and the object size + const auto N = j.m_value.object->size(); + if (N <= 15) + { + // fixmap + write_number(static_cast(0x80 | (N & 0xF))); + } + else if (N <= (std::numeric_limits::max)()) + { + // map 16 + oa->write_character(to_char_type(0xDE)); + write_number(static_cast(N)); + } + else if (N <= (std::numeric_limits::max)()) + { + // map 32 + oa->write_character(to_char_type(0xDF)); + write_number(static_cast(N)); + } + + // step 2: write each element + for (const auto& el : *j.m_value.object) + { + write_msgpack(el.first); + write_msgpack(el.second); + } + break; + } + + case value_t::discarded: + default: + break; + } + } + + /*! + @param[in] j JSON value to serialize + @param[in] use_count whether to use '#' prefixes (optimized format) + @param[in] use_type whether to use '$' prefixes (optimized format) + @param[in] add_prefix whether prefixes need to be used for this value + */ + void write_ubjson(const BasicJsonType& j, const bool use_count, + const bool use_type, const bool add_prefix = true) + { + switch (j.type()) + { + case value_t::null: + { + if (add_prefix) + { + oa->write_character(to_char_type('Z')); + } + break; + } + + case value_t::boolean: + { + if (add_prefix) + { + oa->write_character(j.m_value.boolean + ? to_char_type('T') + : to_char_type('F')); + } + break; + } + + case value_t::number_integer: + { + write_number_with_ubjson_prefix(j.m_value.number_integer, add_prefix); + break; + } + + case value_t::number_unsigned: + { + write_number_with_ubjson_prefix(j.m_value.number_unsigned, add_prefix); + break; + } + + case value_t::number_float: + { + write_number_with_ubjson_prefix(j.m_value.number_float, add_prefix); + break; + } + + case value_t::string: + { + if (add_prefix) + { + oa->write_character(to_char_type('S')); + } + write_number_with_ubjson_prefix(j.m_value.string->size(), true); + oa->write_characters( + reinterpret_cast(j.m_value.string->c_str()), + j.m_value.string->size()); + break; + } + + case value_t::array: + { + if (add_prefix) + { + oa->write_character(to_char_type('[')); + } + + bool prefix_required = true; + if (use_type && !j.m_value.array->empty()) + { + JSON_ASSERT(use_count); + const CharType first_prefix = ubjson_prefix(j.front()); + const bool same_prefix = std::all_of(j.begin() + 1, j.end(), + [this, first_prefix](const BasicJsonType & v) + { + return ubjson_prefix(v) == first_prefix; + }); + + if (same_prefix) + { + prefix_required = false; + oa->write_character(to_char_type('$')); + oa->write_character(first_prefix); + } + } + + if (use_count) + { + oa->write_character(to_char_type('#')); + write_number_with_ubjson_prefix(j.m_value.array->size(), true); + } + + for (const auto& el : *j.m_value.array) + { + write_ubjson(el, use_count, use_type, prefix_required); + } + + if (!use_count) + { + oa->write_character(to_char_type(']')); + } + + break; + } + + case value_t::binary: + { + if (add_prefix) + { + oa->write_character(to_char_type('[')); + } + + if (use_type && !j.m_value.binary->empty()) + { + JSON_ASSERT(use_count); + oa->write_character(to_char_type('$')); + oa->write_character('U'); + } + + if (use_count) + { + oa->write_character(to_char_type('#')); + write_number_with_ubjson_prefix(j.m_value.binary->size(), true); + } + + if (use_type) + { + oa->write_characters( + reinterpret_cast(j.m_value.binary->data()), + j.m_value.binary->size()); + } + else + { + for (size_t i = 0; i < j.m_value.binary->size(); ++i) + { + oa->write_character(to_char_type('U')); + oa->write_character(j.m_value.binary->data()[i]); + } + } + + if (!use_count) + { + oa->write_character(to_char_type(']')); + } + + break; + } + + case value_t::object: + { + if (add_prefix) + { + oa->write_character(to_char_type('{')); + } + + bool prefix_required = true; + if (use_type && !j.m_value.object->empty()) + { + JSON_ASSERT(use_count); + const CharType first_prefix = ubjson_prefix(j.front()); + const bool same_prefix = std::all_of(j.begin(), j.end(), + [this, first_prefix](const BasicJsonType & v) + { + return ubjson_prefix(v) == first_prefix; + }); + + if (same_prefix) + { + prefix_required = false; + oa->write_character(to_char_type('$')); + oa->write_character(first_prefix); + } + } + + if (use_count) + { + oa->write_character(to_char_type('#')); + write_number_with_ubjson_prefix(j.m_value.object->size(), true); + } + + for (const auto& el : *j.m_value.object) + { + write_number_with_ubjson_prefix(el.first.size(), true); + oa->write_characters( + reinterpret_cast(el.first.c_str()), + el.first.size()); + write_ubjson(el.second, use_count, use_type, prefix_required); + } + + if (!use_count) + { + oa->write_character(to_char_type('}')); + } + + break; + } + + case value_t::discarded: + default: + break; + } + } + + private: + ////////// + // BSON // + ////////// + + /*! + @return The size of a BSON document entry header, including the id marker + and the entry name size (and its null-terminator). + */ + static std::size_t calc_bson_entry_header_size(const string_t& name, const BasicJsonType& j) + { + const auto it = name.find(static_cast(0)); + if (JSON_HEDLEY_UNLIKELY(it != BasicJsonType::string_t::npos)) + { + JSON_THROW(out_of_range::create(409, "BSON key cannot contain code point U+0000 (at byte " + std::to_string(it) + ")", j)); + static_cast(j); + } + + return /*id*/ 1ul + name.size() + /*zero-terminator*/1u; + } + + /*! + @brief Writes the given @a element_type and @a name to the output adapter + */ + void write_bson_entry_header(const string_t& name, + const std::uint8_t element_type) + { + oa->write_character(to_char_type(element_type)); // boolean + oa->write_characters( + reinterpret_cast(name.c_str()), + name.size() + 1u); + } + + /*! + @brief Writes a BSON element with key @a name and boolean value @a value + */ + void write_bson_boolean(const string_t& name, + const bool value) + { + write_bson_entry_header(name, 0x08); + oa->write_character(value ? to_char_type(0x01) : to_char_type(0x00)); + } + + /*! + @brief Writes a BSON element with key @a name and double value @a value + */ + void write_bson_double(const string_t& name, + const double value) + { + write_bson_entry_header(name, 0x01); + write_number(value); + } + + /*! + @return The size of the BSON-encoded string in @a value + */ + static std::size_t calc_bson_string_size(const string_t& value) + { + return sizeof(std::int32_t) + value.size() + 1ul; + } + + /*! + @brief Writes a BSON element with key @a name and string value @a value + */ + void write_bson_string(const string_t& name, + const string_t& value) + { + write_bson_entry_header(name, 0x02); + + write_number(static_cast(value.size() + 1ul)); + oa->write_characters( + reinterpret_cast(value.c_str()), + value.size() + 1); + } + + /*! + @brief Writes a BSON element with key @a name and null value + */ + void write_bson_null(const string_t& name) + { + write_bson_entry_header(name, 0x0A); + } + + /*! + @return The size of the BSON-encoded integer @a value + */ + static std::size_t calc_bson_integer_size(const std::int64_t value) + { + return (std::numeric_limits::min)() <= value && value <= (std::numeric_limits::max)() + ? sizeof(std::int32_t) + : sizeof(std::int64_t); + } + + /*! + @brief Writes a BSON element with key @a name and integer @a value + */ + void write_bson_integer(const string_t& name, + const std::int64_t value) + { + if ((std::numeric_limits::min)() <= value && value <= (std::numeric_limits::max)()) + { + write_bson_entry_header(name, 0x10); // int32 + write_number(static_cast(value)); + } + else + { + write_bson_entry_header(name, 0x12); // int64 + write_number(static_cast(value)); + } + } + + /*! + @return The size of the BSON-encoded unsigned integer in @a j + */ + static constexpr std::size_t calc_bson_unsigned_size(const std::uint64_t value) noexcept + { + return (value <= static_cast((std::numeric_limits::max)())) + ? sizeof(std::int32_t) + : sizeof(std::int64_t); + } + + /*! + @brief Writes a BSON element with key @a name and unsigned @a value + */ + void write_bson_unsigned(const string_t& name, + const BasicJsonType& j) + { + if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + write_bson_entry_header(name, 0x10 /* int32 */); + write_number(static_cast(j.m_value.number_unsigned)); + } + else if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + write_bson_entry_header(name, 0x12 /* int64 */); + write_number(static_cast(j.m_value.number_unsigned)); + } + else + { + JSON_THROW(out_of_range::create(407, "integer number " + std::to_string(j.m_value.number_unsigned) + " cannot be represented by BSON as it does not fit int64", j)); + } + } + + /*! + @brief Writes a BSON element with key @a name and object @a value + */ + void write_bson_object_entry(const string_t& name, + const typename BasicJsonType::object_t& value) + { + write_bson_entry_header(name, 0x03); // object + write_bson_object(value); + } + + /*! + @return The size of the BSON-encoded array @a value + */ + static std::size_t calc_bson_array_size(const typename BasicJsonType::array_t& value) + { + std::size_t array_index = 0ul; + + const std::size_t embedded_document_size = std::accumulate(std::begin(value), std::end(value), std::size_t(0), [&array_index](std::size_t result, const typename BasicJsonType::array_t::value_type & el) + { + return result + calc_bson_element_size(std::to_string(array_index++), el); + }); + + return sizeof(std::int32_t) + embedded_document_size + 1ul; + } + + /*! + @return The size of the BSON-encoded binary array @a value + */ + static std::size_t calc_bson_binary_size(const typename BasicJsonType::binary_t& value) + { + return sizeof(std::int32_t) + value.size() + 1ul; + } + + /*! + @brief Writes a BSON element with key @a name and array @a value + */ + void write_bson_array(const string_t& name, + const typename BasicJsonType::array_t& value) + { + write_bson_entry_header(name, 0x04); // array + write_number(static_cast(calc_bson_array_size(value))); + + std::size_t array_index = 0ul; + + for (const auto& el : value) + { + write_bson_element(std::to_string(array_index++), el); + } + + oa->write_character(to_char_type(0x00)); + } + + /*! + @brief Writes a BSON element with key @a name and binary value @a value + */ + void write_bson_binary(const string_t& name, + const binary_t& value) + { + write_bson_entry_header(name, 0x05); + + write_number(static_cast(value.size())); + write_number(value.has_subtype() ? static_cast(value.subtype()) : std::uint8_t(0x00)); + + oa->write_characters(reinterpret_cast(value.data()), value.size()); + } + + /*! + @brief Calculates the size necessary to serialize the JSON value @a j with its @a name + @return The calculated size for the BSON document entry for @a j with the given @a name. + */ + static std::size_t calc_bson_element_size(const string_t& name, + const BasicJsonType& j) + { + const auto header_size = calc_bson_entry_header_size(name, j); + switch (j.type()) + { + case value_t::object: + return header_size + calc_bson_object_size(*j.m_value.object); + + case value_t::array: + return header_size + calc_bson_array_size(*j.m_value.array); + + case value_t::binary: + return header_size + calc_bson_binary_size(*j.m_value.binary); + + case value_t::boolean: + return header_size + 1ul; + + case value_t::number_float: + return header_size + 8ul; + + case value_t::number_integer: + return header_size + calc_bson_integer_size(j.m_value.number_integer); + + case value_t::number_unsigned: + return header_size + calc_bson_unsigned_size(j.m_value.number_unsigned); + + case value_t::string: + return header_size + calc_bson_string_size(*j.m_value.string); + + case value_t::null: + return header_size + 0ul; + + // LCOV_EXCL_START + case value_t::discarded: + default: + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) + return 0ul; + // LCOV_EXCL_STOP + } + } + + /*! + @brief Serializes the JSON value @a j to BSON and associates it with the + key @a name. + @param name The name to associate with the JSON entity @a j within the + current BSON document + */ + void write_bson_element(const string_t& name, + const BasicJsonType& j) + { + switch (j.type()) + { + case value_t::object: + return write_bson_object_entry(name, *j.m_value.object); + + case value_t::array: + return write_bson_array(name, *j.m_value.array); + + case value_t::binary: + return write_bson_binary(name, *j.m_value.binary); + + case value_t::boolean: + return write_bson_boolean(name, j.m_value.boolean); + + case value_t::number_float: + return write_bson_double(name, j.m_value.number_float); + + case value_t::number_integer: + return write_bson_integer(name, j.m_value.number_integer); + + case value_t::number_unsigned: + return write_bson_unsigned(name, j); + + case value_t::string: + return write_bson_string(name, *j.m_value.string); + + case value_t::null: + return write_bson_null(name); + + // LCOV_EXCL_START + case value_t::discarded: + default: + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) + return; + // LCOV_EXCL_STOP + } + } + + /*! + @brief Calculates the size of the BSON serialization of the given + JSON-object @a j. + @param[in] value JSON value to serialize + @pre value.type() == value_t::object + */ + static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value) + { + std::size_t document_size = std::accumulate(value.begin(), value.end(), std::size_t(0), + [](size_t result, const typename BasicJsonType::object_t::value_type & el) + { + return result += calc_bson_element_size(el.first, el.second); + }); + + return sizeof(std::int32_t) + document_size + 1ul; + } + + /*! + @param[in] value JSON value to serialize + @pre value.type() == value_t::object + */ + void write_bson_object(const typename BasicJsonType::object_t& value) + { + write_number(static_cast(calc_bson_object_size(value))); + + for (const auto& el : value) + { + write_bson_element(el.first, el.second); + } + + oa->write_character(to_char_type(0x00)); + } + + ////////// + // CBOR // + ////////// + + static constexpr CharType get_cbor_float_prefix(float /*unused*/) + { + return to_char_type(0xFA); // Single-Precision Float + } + + static constexpr CharType get_cbor_float_prefix(double /*unused*/) + { + return to_char_type(0xFB); // Double-Precision Float + } + + ///////////// + // MsgPack // + ///////////// + + static constexpr CharType get_msgpack_float_prefix(float /*unused*/) + { + return to_char_type(0xCA); // float 32 + } + + static constexpr CharType get_msgpack_float_prefix(double /*unused*/) + { + return to_char_type(0xCB); // float 64 + } + + //////////// + // UBJSON // + //////////// + + // UBJSON: write number (floating point) + template::value, int>::type = 0> + void write_number_with_ubjson_prefix(const NumberType n, + const bool add_prefix) + { + if (add_prefix) + { + oa->write_character(get_ubjson_float_prefix(n)); + } + write_number(n); + } + + // UBJSON: write number (unsigned integer) + template::value, int>::type = 0> + void write_number_with_ubjson_prefix(const NumberType n, + const bool add_prefix) + { + if (n <= static_cast((std::numeric_limits::max)())) + { + if (add_prefix) + { + oa->write_character(to_char_type('i')); // int8 + } + write_number(static_cast(n)); + } + else if (n <= (std::numeric_limits::max)()) + { + if (add_prefix) + { + oa->write_character(to_char_type('U')); // uint8 + } + write_number(static_cast(n)); + } + else if (n <= static_cast((std::numeric_limits::max)())) + { + if (add_prefix) + { + oa->write_character(to_char_type('I')); // int16 + } + write_number(static_cast(n)); + } + else if (n <= static_cast((std::numeric_limits::max)())) + { + if (add_prefix) + { + oa->write_character(to_char_type('l')); // int32 + } + write_number(static_cast(n)); + } + else if (n <= static_cast((std::numeric_limits::max)())) + { + if (add_prefix) + { + oa->write_character(to_char_type('L')); // int64 + } + write_number(static_cast(n)); + } + else + { + if (add_prefix) + { + oa->write_character(to_char_type('H')); // high-precision number + } + + const auto number = BasicJsonType(n).dump(); + write_number_with_ubjson_prefix(number.size(), true); + for (std::size_t i = 0; i < number.size(); ++i) + { + oa->write_character(to_char_type(static_cast(number[i]))); + } + } + } + + // UBJSON: write number (signed integer) + template < typename NumberType, typename std::enable_if < + std::is_signed::value&& + !std::is_floating_point::value, int >::type = 0 > + void write_number_with_ubjson_prefix(const NumberType n, + const bool add_prefix) + { + if ((std::numeric_limits::min)() <= n && n <= (std::numeric_limits::max)()) + { + if (add_prefix) + { + oa->write_character(to_char_type('i')); // int8 + } + write_number(static_cast(n)); + } + else if (static_cast((std::numeric_limits::min)()) <= n && n <= static_cast((std::numeric_limits::max)())) + { + if (add_prefix) + { + oa->write_character(to_char_type('U')); // uint8 + } + write_number(static_cast(n)); + } + else if ((std::numeric_limits::min)() <= n && n <= (std::numeric_limits::max)()) + { + if (add_prefix) + { + oa->write_character(to_char_type('I')); // int16 + } + write_number(static_cast(n)); + } + else if ((std::numeric_limits::min)() <= n && n <= (std::numeric_limits::max)()) + { + if (add_prefix) + { + oa->write_character(to_char_type('l')); // int32 + } + write_number(static_cast(n)); + } + else if ((std::numeric_limits::min)() <= n && n <= (std::numeric_limits::max)()) + { + if (add_prefix) + { + oa->write_character(to_char_type('L')); // int64 + } + write_number(static_cast(n)); + } + // LCOV_EXCL_START + else + { + if (add_prefix) + { + oa->write_character(to_char_type('H')); // high-precision number + } + + const auto number = BasicJsonType(n).dump(); + write_number_with_ubjson_prefix(number.size(), true); + for (std::size_t i = 0; i < number.size(); ++i) + { + oa->write_character(to_char_type(static_cast(number[i]))); + } + } + // LCOV_EXCL_STOP + } + + /*! + @brief determine the type prefix of container values + */ + CharType ubjson_prefix(const BasicJsonType& j) const noexcept + { + switch (j.type()) + { + case value_t::null: + return 'Z'; + + case value_t::boolean: + return j.m_value.boolean ? 'T' : 'F'; + + case value_t::number_integer: + { + if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + { + return 'i'; + } + if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + { + return 'U'; + } + if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + { + return 'I'; + } + if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + { + return 'l'; + } + if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + { + return 'L'; + } + // anything else is treated as high-precision number + return 'H'; // LCOV_EXCL_LINE + } + + case value_t::number_unsigned: + { + if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + return 'i'; + } + if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + return 'U'; + } + if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + return 'I'; + } + if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + return 'l'; + } + if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + { + return 'L'; + } + // anything else is treated as high-precision number + return 'H'; // LCOV_EXCL_LINE + } + + case value_t::number_float: + return get_ubjson_float_prefix(j.m_value.number_float); + + case value_t::string: + return 'S'; + + case value_t::array: // fallthrough + case value_t::binary: + return '['; + + case value_t::object: + return '{'; + + case value_t::discarded: + default: // discarded values + return 'N'; + } + } + + static constexpr CharType get_ubjson_float_prefix(float /*unused*/) + { + return 'd'; // float 32 + } + + static constexpr CharType get_ubjson_float_prefix(double /*unused*/) + { + return 'D'; // float 64 + } + + /////////////////////// + // Utility functions // + /////////////////////// + + /* + @brief write a number to output input + @param[in] n number of type @a NumberType + @tparam NumberType the type of the number + @tparam OutputIsLittleEndian Set to true if output data is + required to be little endian + + @note This function needs to respect the system's endianess, because bytes + in CBOR, MessagePack, and UBJSON are stored in network order (big + endian) and therefore need reordering on little endian systems. + */ + template + void write_number(const NumberType n) + { + // step 1: write number to array of length NumberType + std::array vec{}; + std::memcpy(vec.data(), &n, sizeof(NumberType)); + + // step 2: write array to output (with possible reordering) + if (is_little_endian != OutputIsLittleEndian) + { + // reverse byte order prior to conversion if necessary + std::reverse(vec.begin(), vec.end()); + } + + oa->write_characters(vec.data(), sizeof(NumberType)); + } + + void write_compact_float(const number_float_t n, detail::input_format_t format) + { +#ifdef __GNUC__ +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wfloat-equal" +#endif + if (static_cast(n) >= static_cast(std::numeric_limits::lowest()) && + static_cast(n) <= static_cast((std::numeric_limits::max)()) && + static_cast(static_cast(n)) == static_cast(n)) + { + oa->write_character(format == detail::input_format_t::cbor + ? get_cbor_float_prefix(static_cast(n)) + : get_msgpack_float_prefix(static_cast(n))); + write_number(static_cast(n)); + } + else + { + oa->write_character(format == detail::input_format_t::cbor + ? get_cbor_float_prefix(n) + : get_msgpack_float_prefix(n)); + write_number(n); + } +#ifdef __GNUC__ +#pragma GCC diagnostic pop +#endif + } + + public: + // The following to_char_type functions are implement the conversion + // between uint8_t and CharType. In case CharType is not unsigned, + // such a conversion is required to allow values greater than 128. + // See for a discussion. + template < typename C = CharType, + enable_if_t < std::is_signed::value && std::is_signed::value > * = nullptr > + static constexpr CharType to_char_type(std::uint8_t x) noexcept + { + return *reinterpret_cast(&x); + } + + template < typename C = CharType, + enable_if_t < std::is_signed::value && std::is_unsigned::value > * = nullptr > + static CharType to_char_type(std::uint8_t x) noexcept + { + static_assert(sizeof(std::uint8_t) == sizeof(CharType), "size of CharType must be equal to std::uint8_t"); + static_assert(std::is_trivial::value, "CharType must be trivial"); + CharType result; + std::memcpy(&result, &x, sizeof(x)); + return result; + } + + template::value>* = nullptr> + static constexpr CharType to_char_type(std::uint8_t x) noexcept + { + return x; + } + + template < typename InputCharType, typename C = CharType, + enable_if_t < + std::is_signed::value && + std::is_signed::value && + std::is_same::type>::value + > * = nullptr > + static constexpr CharType to_char_type(InputCharType x) noexcept + { + return x; + } + + private: + /// whether we can assume little endianess + const bool is_little_endian = little_endianess(); + + /// the output + output_adapter_t oa = nullptr; +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + + +#include // reverse, remove, fill, find, none_of +#include // array +#include // localeconv, lconv +#include // labs, isfinite, isnan, signbit +#include // size_t, ptrdiff_t +#include // uint8_t +#include // snprintf +#include // numeric_limits +#include // string, char_traits +#include // is_same +#include // move + +// #include + + +#include // array +#include // signbit, isfinite +#include // intN_t, uintN_t +#include // memcpy, memmove +#include // numeric_limits +#include // conditional + +// #include + + +namespace nlohmann +{ +namespace detail +{ + +/*! +@brief implements the Grisu2 algorithm for binary to decimal floating-point +conversion. + +This implementation is a slightly modified version of the reference +implementation which may be obtained from +http://florian.loitsch.com/publications (bench.tar.gz). + +The code is distributed under the MIT license, Copyright (c) 2009 Florian Loitsch. + +For a detailed description of the algorithm see: + +[1] Loitsch, "Printing Floating-Point Numbers Quickly and Accurately with + Integers", Proceedings of the ACM SIGPLAN 2010 Conference on Programming + Language Design and Implementation, PLDI 2010 +[2] Burger, Dybvig, "Printing Floating-Point Numbers Quickly and Accurately", + Proceedings of the ACM SIGPLAN 1996 Conference on Programming Language + Design and Implementation, PLDI 1996 +*/ +namespace dtoa_impl +{ + +template +Target reinterpret_bits(const Source source) +{ + static_assert(sizeof(Target) == sizeof(Source), "size mismatch"); + + Target target; + std::memcpy(&target, &source, sizeof(Source)); + return target; +} + +struct diyfp // f * 2^e +{ + static constexpr int kPrecision = 64; // = q + + std::uint64_t f = 0; + int e = 0; + + constexpr diyfp(std::uint64_t f_, int e_) noexcept : f(f_), e(e_) {} + + /*! + @brief returns x - y + @pre x.e == y.e and x.f >= y.f + */ + static diyfp sub(const diyfp& x, const diyfp& y) noexcept + { + JSON_ASSERT(x.e == y.e); + JSON_ASSERT(x.f >= y.f); + + return {x.f - y.f, x.e}; + } + + /*! + @brief returns x * y + @note The result is rounded. (Only the upper q bits are returned.) + */ + static diyfp mul(const diyfp& x, const diyfp& y) noexcept + { + static_assert(kPrecision == 64, "internal error"); + + // Computes: + // f = round((x.f * y.f) / 2^q) + // e = x.e + y.e + q + + // Emulate the 64-bit * 64-bit multiplication: + // + // p = u * v + // = (u_lo + 2^32 u_hi) (v_lo + 2^32 v_hi) + // = (u_lo v_lo ) + 2^32 ((u_lo v_hi ) + (u_hi v_lo )) + 2^64 (u_hi v_hi ) + // = (p0 ) + 2^32 ((p1 ) + (p2 )) + 2^64 (p3 ) + // = (p0_lo + 2^32 p0_hi) + 2^32 ((p1_lo + 2^32 p1_hi) + (p2_lo + 2^32 p2_hi)) + 2^64 (p3 ) + // = (p0_lo ) + 2^32 (p0_hi + p1_lo + p2_lo ) + 2^64 (p1_hi + p2_hi + p3) + // = (p0_lo ) + 2^32 (Q ) + 2^64 (H ) + // = (p0_lo ) + 2^32 (Q_lo + 2^32 Q_hi ) + 2^64 (H ) + // + // (Since Q might be larger than 2^32 - 1) + // + // = (p0_lo + 2^32 Q_lo) + 2^64 (Q_hi + H) + // + // (Q_hi + H does not overflow a 64-bit int) + // + // = p_lo + 2^64 p_hi + + const std::uint64_t u_lo = x.f & 0xFFFFFFFFu; + const std::uint64_t u_hi = x.f >> 32u; + const std::uint64_t v_lo = y.f & 0xFFFFFFFFu; + const std::uint64_t v_hi = y.f >> 32u; + + const std::uint64_t p0 = u_lo * v_lo; + const std::uint64_t p1 = u_lo * v_hi; + const std::uint64_t p2 = u_hi * v_lo; + const std::uint64_t p3 = u_hi * v_hi; + + const std::uint64_t p0_hi = p0 >> 32u; + const std::uint64_t p1_lo = p1 & 0xFFFFFFFFu; + const std::uint64_t p1_hi = p1 >> 32u; + const std::uint64_t p2_lo = p2 & 0xFFFFFFFFu; + const std::uint64_t p2_hi = p2 >> 32u; + + std::uint64_t Q = p0_hi + p1_lo + p2_lo; + + // The full product might now be computed as + // + // p_hi = p3 + p2_hi + p1_hi + (Q >> 32) + // p_lo = p0_lo + (Q << 32) + // + // But in this particular case here, the full p_lo is not required. + // Effectively we only need to add the highest bit in p_lo to p_hi (and + // Q_hi + 1 does not overflow). + + Q += std::uint64_t{1} << (64u - 32u - 1u); // round, ties up + + const std::uint64_t h = p3 + p2_hi + p1_hi + (Q >> 32u); + + return {h, x.e + y.e + 64}; + } + + /*! + @brief normalize x such that the significand is >= 2^(q-1) + @pre x.f != 0 + */ + static diyfp normalize(diyfp x) noexcept + { + JSON_ASSERT(x.f != 0); + + while ((x.f >> 63u) == 0) + { + x.f <<= 1u; + x.e--; + } + + return x; + } + + /*! + @brief normalize x such that the result has the exponent E + @pre e >= x.e and the upper e - x.e bits of x.f must be zero. + */ + static diyfp normalize_to(const diyfp& x, const int target_exponent) noexcept + { + const int delta = x.e - target_exponent; + + JSON_ASSERT(delta >= 0); + JSON_ASSERT(((x.f << delta) >> delta) == x.f); + + return {x.f << delta, target_exponent}; + } +}; + +struct boundaries +{ + diyfp w; + diyfp minus; + diyfp plus; +}; + +/*! +Compute the (normalized) diyfp representing the input number 'value' and its +boundaries. + +@pre value must be finite and positive +*/ +template +boundaries compute_boundaries(FloatType value) +{ + JSON_ASSERT(std::isfinite(value)); + JSON_ASSERT(value > 0); + + // Convert the IEEE representation into a diyfp. + // + // If v is denormal: + // value = 0.F * 2^(1 - bias) = ( F) * 2^(1 - bias - (p-1)) + // If v is normalized: + // value = 1.F * 2^(E - bias) = (2^(p-1) + F) * 2^(E - bias - (p-1)) + + static_assert(std::numeric_limits::is_iec559, + "internal error: dtoa_short requires an IEEE-754 floating-point implementation"); + + constexpr int kPrecision = std::numeric_limits::digits; // = p (includes the hidden bit) + constexpr int kBias = std::numeric_limits::max_exponent - 1 + (kPrecision - 1); + constexpr int kMinExp = 1 - kBias; + constexpr std::uint64_t kHiddenBit = std::uint64_t{1} << (kPrecision - 1); // = 2^(p-1) + + using bits_type = typename std::conditional::type; + + const auto bits = static_cast(reinterpret_bits(value)); + const std::uint64_t E = bits >> (kPrecision - 1); + const std::uint64_t F = bits & (kHiddenBit - 1); + + const bool is_denormal = E == 0; + const diyfp v = is_denormal + ? diyfp(F, kMinExp) + : diyfp(F + kHiddenBit, static_cast(E) - kBias); + + // Compute the boundaries m- and m+ of the floating-point value + // v = f * 2^e. + // + // Determine v- and v+, the floating-point predecessor and successor if v, + // respectively. + // + // v- = v - 2^e if f != 2^(p-1) or e == e_min (A) + // = v - 2^(e-1) if f == 2^(p-1) and e > e_min (B) + // + // v+ = v + 2^e + // + // Let m- = (v- + v) / 2 and m+ = (v + v+) / 2. All real numbers _strictly_ + // between m- and m+ round to v, regardless of how the input rounding + // algorithm breaks ties. + // + // ---+-------------+-------------+-------------+-------------+--- (A) + // v- m- v m+ v+ + // + // -----------------+------+------+-------------+-------------+--- (B) + // v- m- v m+ v+ + + const bool lower_boundary_is_closer = F == 0 && E > 1; + const diyfp m_plus = diyfp(2 * v.f + 1, v.e - 1); + const diyfp m_minus = lower_boundary_is_closer + ? diyfp(4 * v.f - 1, v.e - 2) // (B) + : diyfp(2 * v.f - 1, v.e - 1); // (A) + + // Determine the normalized w+ = m+. + const diyfp w_plus = diyfp::normalize(m_plus); + + // Determine w- = m- such that e_(w-) = e_(w+). + const diyfp w_minus = diyfp::normalize_to(m_minus, w_plus.e); + + return {diyfp::normalize(v), w_minus, w_plus}; +} + +// Given normalized diyfp w, Grisu needs to find a (normalized) cached +// power-of-ten c, such that the exponent of the product c * w = f * 2^e lies +// within a certain range [alpha, gamma] (Definition 3.2 from [1]) +// +// alpha <= e = e_c + e_w + q <= gamma +// +// or +// +// f_c * f_w * 2^alpha <= f_c 2^(e_c) * f_w 2^(e_w) * 2^q +// <= f_c * f_w * 2^gamma +// +// Since c and w are normalized, i.e. 2^(q-1) <= f < 2^q, this implies +// +// 2^(q-1) * 2^(q-1) * 2^alpha <= c * w * 2^q < 2^q * 2^q * 2^gamma +// +// or +// +// 2^(q - 2 + alpha) <= c * w < 2^(q + gamma) +// +// The choice of (alpha,gamma) determines the size of the table and the form of +// the digit generation procedure. Using (alpha,gamma)=(-60,-32) works out well +// in practice: +// +// The idea is to cut the number c * w = f * 2^e into two parts, which can be +// processed independently: An integral part p1, and a fractional part p2: +// +// f * 2^e = ( (f div 2^-e) * 2^-e + (f mod 2^-e) ) * 2^e +// = (f div 2^-e) + (f mod 2^-e) * 2^e +// = p1 + p2 * 2^e +// +// The conversion of p1 into decimal form requires a series of divisions and +// modulos by (a power of) 10. These operations are faster for 32-bit than for +// 64-bit integers, so p1 should ideally fit into a 32-bit integer. This can be +// achieved by choosing +// +// -e >= 32 or e <= -32 := gamma +// +// In order to convert the fractional part +// +// p2 * 2^e = p2 / 2^-e = d[-1] / 10^1 + d[-2] / 10^2 + ... +// +// into decimal form, the fraction is repeatedly multiplied by 10 and the digits +// d[-i] are extracted in order: +// +// (10 * p2) div 2^-e = d[-1] +// (10 * p2) mod 2^-e = d[-2] / 10^1 + ... +// +// The multiplication by 10 must not overflow. It is sufficient to choose +// +// 10 * p2 < 16 * p2 = 2^4 * p2 <= 2^64. +// +// Since p2 = f mod 2^-e < 2^-e, +// +// -e <= 60 or e >= -60 := alpha + +constexpr int kAlpha = -60; +constexpr int kGamma = -32; + +struct cached_power // c = f * 2^e ~= 10^k +{ + std::uint64_t f; + int e; + int k; +}; + +/*! +For a normalized diyfp w = f * 2^e, this function returns a (normalized) cached +power-of-ten c = f_c * 2^e_c, such that the exponent of the product w * c +satisfies (Definition 3.2 from [1]) + + alpha <= e_c + e + q <= gamma. +*/ +inline cached_power get_cached_power_for_binary_exponent(int e) +{ + // Now + // + // alpha <= e_c + e + q <= gamma (1) + // ==> f_c * 2^alpha <= c * 2^e * 2^q + // + // and since the c's are normalized, 2^(q-1) <= f_c, + // + // ==> 2^(q - 1 + alpha) <= c * 2^(e + q) + // ==> 2^(alpha - e - 1) <= c + // + // If c were an exact power of ten, i.e. c = 10^k, one may determine k as + // + // k = ceil( log_10( 2^(alpha - e - 1) ) ) + // = ceil( (alpha - e - 1) * log_10(2) ) + // + // From the paper: + // "In theory the result of the procedure could be wrong since c is rounded, + // and the computation itself is approximated [...]. In practice, however, + // this simple function is sufficient." + // + // For IEEE double precision floating-point numbers converted into + // normalized diyfp's w = f * 2^e, with q = 64, + // + // e >= -1022 (min IEEE exponent) + // -52 (p - 1) + // -52 (p - 1, possibly normalize denormal IEEE numbers) + // -11 (normalize the diyfp) + // = -1137 + // + // and + // + // e <= +1023 (max IEEE exponent) + // -52 (p - 1) + // -11 (normalize the diyfp) + // = 960 + // + // This binary exponent range [-1137,960] results in a decimal exponent + // range [-307,324]. One does not need to store a cached power for each + // k in this range. For each such k it suffices to find a cached power + // such that the exponent of the product lies in [alpha,gamma]. + // This implies that the difference of the decimal exponents of adjacent + // table entries must be less than or equal to + // + // floor( (gamma - alpha) * log_10(2) ) = 8. + // + // (A smaller distance gamma-alpha would require a larger table.) + + // NB: + // Actually this function returns c, such that -60 <= e_c + e + 64 <= -34. + + constexpr int kCachedPowersMinDecExp = -300; + constexpr int kCachedPowersDecStep = 8; + + static constexpr std::array kCachedPowers = + { + { + { 0xAB70FE17C79AC6CA, -1060, -300 }, + { 0xFF77B1FCBEBCDC4F, -1034, -292 }, + { 0xBE5691EF416BD60C, -1007, -284 }, + { 0x8DD01FAD907FFC3C, -980, -276 }, + { 0xD3515C2831559A83, -954, -268 }, + { 0x9D71AC8FADA6C9B5, -927, -260 }, + { 0xEA9C227723EE8BCB, -901, -252 }, + { 0xAECC49914078536D, -874, -244 }, + { 0x823C12795DB6CE57, -847, -236 }, + { 0xC21094364DFB5637, -821, -228 }, + { 0x9096EA6F3848984F, -794, -220 }, + { 0xD77485CB25823AC7, -768, -212 }, + { 0xA086CFCD97BF97F4, -741, -204 }, + { 0xEF340A98172AACE5, -715, -196 }, + { 0xB23867FB2A35B28E, -688, -188 }, + { 0x84C8D4DFD2C63F3B, -661, -180 }, + { 0xC5DD44271AD3CDBA, -635, -172 }, + { 0x936B9FCEBB25C996, -608, -164 }, + { 0xDBAC6C247D62A584, -582, -156 }, + { 0xA3AB66580D5FDAF6, -555, -148 }, + { 0xF3E2F893DEC3F126, -529, -140 }, + { 0xB5B5ADA8AAFF80B8, -502, -132 }, + { 0x87625F056C7C4A8B, -475, -124 }, + { 0xC9BCFF6034C13053, -449, -116 }, + { 0x964E858C91BA2655, -422, -108 }, + { 0xDFF9772470297EBD, -396, -100 }, + { 0xA6DFBD9FB8E5B88F, -369, -92 }, + { 0xF8A95FCF88747D94, -343, -84 }, + { 0xB94470938FA89BCF, -316, -76 }, + { 0x8A08F0F8BF0F156B, -289, -68 }, + { 0xCDB02555653131B6, -263, -60 }, + { 0x993FE2C6D07B7FAC, -236, -52 }, + { 0xE45C10C42A2B3B06, -210, -44 }, + { 0xAA242499697392D3, -183, -36 }, + { 0xFD87B5F28300CA0E, -157, -28 }, + { 0xBCE5086492111AEB, -130, -20 }, + { 0x8CBCCC096F5088CC, -103, -12 }, + { 0xD1B71758E219652C, -77, -4 }, + { 0x9C40000000000000, -50, 4 }, + { 0xE8D4A51000000000, -24, 12 }, + { 0xAD78EBC5AC620000, 3, 20 }, + { 0x813F3978F8940984, 30, 28 }, + { 0xC097CE7BC90715B3, 56, 36 }, + { 0x8F7E32CE7BEA5C70, 83, 44 }, + { 0xD5D238A4ABE98068, 109, 52 }, + { 0x9F4F2726179A2245, 136, 60 }, + { 0xED63A231D4C4FB27, 162, 68 }, + { 0xB0DE65388CC8ADA8, 189, 76 }, + { 0x83C7088E1AAB65DB, 216, 84 }, + { 0xC45D1DF942711D9A, 242, 92 }, + { 0x924D692CA61BE758, 269, 100 }, + { 0xDA01EE641A708DEA, 295, 108 }, + { 0xA26DA3999AEF774A, 322, 116 }, + { 0xF209787BB47D6B85, 348, 124 }, + { 0xB454E4A179DD1877, 375, 132 }, + { 0x865B86925B9BC5C2, 402, 140 }, + { 0xC83553C5C8965D3D, 428, 148 }, + { 0x952AB45CFA97A0B3, 455, 156 }, + { 0xDE469FBD99A05FE3, 481, 164 }, + { 0xA59BC234DB398C25, 508, 172 }, + { 0xF6C69A72A3989F5C, 534, 180 }, + { 0xB7DCBF5354E9BECE, 561, 188 }, + { 0x88FCF317F22241E2, 588, 196 }, + { 0xCC20CE9BD35C78A5, 614, 204 }, + { 0x98165AF37B2153DF, 641, 212 }, + { 0xE2A0B5DC971F303A, 667, 220 }, + { 0xA8D9D1535CE3B396, 694, 228 }, + { 0xFB9B7CD9A4A7443C, 720, 236 }, + { 0xBB764C4CA7A44410, 747, 244 }, + { 0x8BAB8EEFB6409C1A, 774, 252 }, + { 0xD01FEF10A657842C, 800, 260 }, + { 0x9B10A4E5E9913129, 827, 268 }, + { 0xE7109BFBA19C0C9D, 853, 276 }, + { 0xAC2820D9623BF429, 880, 284 }, + { 0x80444B5E7AA7CF85, 907, 292 }, + { 0xBF21E44003ACDD2D, 933, 300 }, + { 0x8E679C2F5E44FF8F, 960, 308 }, + { 0xD433179D9C8CB841, 986, 316 }, + { 0x9E19DB92B4E31BA9, 1013, 324 }, + } + }; + + // This computation gives exactly the same results for k as + // k = ceil((kAlpha - e - 1) * 0.30102999566398114) + // for |e| <= 1500, but doesn't require floating-point operations. + // NB: log_10(2) ~= 78913 / 2^18 + JSON_ASSERT(e >= -1500); + JSON_ASSERT(e <= 1500); + const int f = kAlpha - e - 1; + const int k = (f * 78913) / (1 << 18) + static_cast(f > 0); + + const int index = (-kCachedPowersMinDecExp + k + (kCachedPowersDecStep - 1)) / kCachedPowersDecStep; + JSON_ASSERT(index >= 0); + JSON_ASSERT(static_cast(index) < kCachedPowers.size()); + + const cached_power cached = kCachedPowers[static_cast(index)]; + JSON_ASSERT(kAlpha <= cached.e + e + 64); + JSON_ASSERT(kGamma >= cached.e + e + 64); + + return cached; +} + +/*! +For n != 0, returns k, such that pow10 := 10^(k-1) <= n < 10^k. +For n == 0, returns 1 and sets pow10 := 1. +*/ +inline int find_largest_pow10(const std::uint32_t n, std::uint32_t& pow10) +{ + // LCOV_EXCL_START + if (n >= 1000000000) + { + pow10 = 1000000000; + return 10; + } + // LCOV_EXCL_STOP + if (n >= 100000000) + { + pow10 = 100000000; + return 9; + } + if (n >= 10000000) + { + pow10 = 10000000; + return 8; + } + if (n >= 1000000) + { + pow10 = 1000000; + return 7; + } + if (n >= 100000) + { + pow10 = 100000; + return 6; + } + if (n >= 10000) + { + pow10 = 10000; + return 5; + } + if (n >= 1000) + { + pow10 = 1000; + return 4; + } + if (n >= 100) + { + pow10 = 100; + return 3; + } + if (n >= 10) + { + pow10 = 10; + return 2; + } + + pow10 = 1; + return 1; +} + +inline void grisu2_round(char* buf, int len, std::uint64_t dist, std::uint64_t delta, + std::uint64_t rest, std::uint64_t ten_k) +{ + JSON_ASSERT(len >= 1); + JSON_ASSERT(dist <= delta); + JSON_ASSERT(rest <= delta); + JSON_ASSERT(ten_k > 0); + + // <--------------------------- delta ----> + // <---- dist ---------> + // --------------[------------------+-------------------]-------------- + // M- w M+ + // + // ten_k + // <------> + // <---- rest ----> + // --------------[------------------+----+--------------]-------------- + // w V + // = buf * 10^k + // + // ten_k represents a unit-in-the-last-place in the decimal representation + // stored in buf. + // Decrement buf by ten_k while this takes buf closer to w. + + // The tests are written in this order to avoid overflow in unsigned + // integer arithmetic. + + while (rest < dist + && delta - rest >= ten_k + && (rest + ten_k < dist || dist - rest > rest + ten_k - dist)) + { + JSON_ASSERT(buf[len - 1] != '0'); + buf[len - 1]--; + rest += ten_k; + } +} + +/*! +Generates V = buffer * 10^decimal_exponent, such that M- <= V <= M+. +M- and M+ must be normalized and share the same exponent -60 <= e <= -32. +*/ +inline void grisu2_digit_gen(char* buffer, int& length, int& decimal_exponent, + diyfp M_minus, diyfp w, diyfp M_plus) +{ + static_assert(kAlpha >= -60, "internal error"); + static_assert(kGamma <= -32, "internal error"); + + // Generates the digits (and the exponent) of a decimal floating-point + // number V = buffer * 10^decimal_exponent in the range [M-, M+]. The diyfp's + // w, M- and M+ share the same exponent e, which satisfies alpha <= e <= gamma. + // + // <--------------------------- delta ----> + // <---- dist ---------> + // --------------[------------------+-------------------]-------------- + // M- w M+ + // + // Grisu2 generates the digits of M+ from left to right and stops as soon as + // V is in [M-,M+]. + + JSON_ASSERT(M_plus.e >= kAlpha); + JSON_ASSERT(M_plus.e <= kGamma); + + std::uint64_t delta = diyfp::sub(M_plus, M_minus).f; // (significand of (M+ - M-), implicit exponent is e) + std::uint64_t dist = diyfp::sub(M_plus, w ).f; // (significand of (M+ - w ), implicit exponent is e) + + // Split M+ = f * 2^e into two parts p1 and p2 (note: e < 0): + // + // M+ = f * 2^e + // = ((f div 2^-e) * 2^-e + (f mod 2^-e)) * 2^e + // = ((p1 ) * 2^-e + (p2 )) * 2^e + // = p1 + p2 * 2^e + + const diyfp one(std::uint64_t{1} << -M_plus.e, M_plus.e); + + auto p1 = static_cast(M_plus.f >> -one.e); // p1 = f div 2^-e (Since -e >= 32, p1 fits into a 32-bit int.) + std::uint64_t p2 = M_plus.f & (one.f - 1); // p2 = f mod 2^-e + + // 1) + // + // Generate the digits of the integral part p1 = d[n-1]...d[1]d[0] + + JSON_ASSERT(p1 > 0); + + std::uint32_t pow10{}; + const int k = find_largest_pow10(p1, pow10); + + // 10^(k-1) <= p1 < 10^k, pow10 = 10^(k-1) + // + // p1 = (p1 div 10^(k-1)) * 10^(k-1) + (p1 mod 10^(k-1)) + // = (d[k-1] ) * 10^(k-1) + (p1 mod 10^(k-1)) + // + // M+ = p1 + p2 * 2^e + // = d[k-1] * 10^(k-1) + (p1 mod 10^(k-1)) + p2 * 2^e + // = d[k-1] * 10^(k-1) + ((p1 mod 10^(k-1)) * 2^-e + p2) * 2^e + // = d[k-1] * 10^(k-1) + ( rest) * 2^e + // + // Now generate the digits d[n] of p1 from left to right (n = k-1,...,0) + // + // p1 = d[k-1]...d[n] * 10^n + d[n-1]...d[0] + // + // but stop as soon as + // + // rest * 2^e = (d[n-1]...d[0] * 2^-e + p2) * 2^e <= delta * 2^e + + int n = k; + while (n > 0) + { + // Invariants: + // M+ = buffer * 10^n + (p1 + p2 * 2^e) (buffer = 0 for n = k) + // pow10 = 10^(n-1) <= p1 < 10^n + // + const std::uint32_t d = p1 / pow10; // d = p1 div 10^(n-1) + const std::uint32_t r = p1 % pow10; // r = p1 mod 10^(n-1) + // + // M+ = buffer * 10^n + (d * 10^(n-1) + r) + p2 * 2^e + // = (buffer * 10 + d) * 10^(n-1) + (r + p2 * 2^e) + // + JSON_ASSERT(d <= 9); + buffer[length++] = static_cast('0' + d); // buffer := buffer * 10 + d + // + // M+ = buffer * 10^(n-1) + (r + p2 * 2^e) + // + p1 = r; + n--; + // + // M+ = buffer * 10^n + (p1 + p2 * 2^e) + // pow10 = 10^n + // + + // Now check if enough digits have been generated. + // Compute + // + // p1 + p2 * 2^e = (p1 * 2^-e + p2) * 2^e = rest * 2^e + // + // Note: + // Since rest and delta share the same exponent e, it suffices to + // compare the significands. + const std::uint64_t rest = (std::uint64_t{p1} << -one.e) + p2; + if (rest <= delta) + { + // V = buffer * 10^n, with M- <= V <= M+. + + decimal_exponent += n; + + // We may now just stop. But instead look if the buffer could be + // decremented to bring V closer to w. + // + // pow10 = 10^n is now 1 ulp in the decimal representation V. + // The rounding procedure works with diyfp's with an implicit + // exponent of e. + // + // 10^n = (10^n * 2^-e) * 2^e = ulp * 2^e + // + const std::uint64_t ten_n = std::uint64_t{pow10} << -one.e; + grisu2_round(buffer, length, dist, delta, rest, ten_n); + + return; + } + + pow10 /= 10; + // + // pow10 = 10^(n-1) <= p1 < 10^n + // Invariants restored. + } + + // 2) + // + // The digits of the integral part have been generated: + // + // M+ = d[k-1]...d[1]d[0] + p2 * 2^e + // = buffer + p2 * 2^e + // + // Now generate the digits of the fractional part p2 * 2^e. + // + // Note: + // No decimal point is generated: the exponent is adjusted instead. + // + // p2 actually represents the fraction + // + // p2 * 2^e + // = p2 / 2^-e + // = d[-1] / 10^1 + d[-2] / 10^2 + ... + // + // Now generate the digits d[-m] of p1 from left to right (m = 1,2,...) + // + // p2 * 2^e = d[-1]d[-2]...d[-m] * 10^-m + // + 10^-m * (d[-m-1] / 10^1 + d[-m-2] / 10^2 + ...) + // + // using + // + // 10^m * p2 = ((10^m * p2) div 2^-e) * 2^-e + ((10^m * p2) mod 2^-e) + // = ( d) * 2^-e + ( r) + // + // or + // 10^m * p2 * 2^e = d + r * 2^e + // + // i.e. + // + // M+ = buffer + p2 * 2^e + // = buffer + 10^-m * (d + r * 2^e) + // = (buffer * 10^m + d) * 10^-m + 10^-m * r * 2^e + // + // and stop as soon as 10^-m * r * 2^e <= delta * 2^e + + JSON_ASSERT(p2 > delta); + + int m = 0; + for (;;) + { + // Invariant: + // M+ = buffer * 10^-m + 10^-m * (d[-m-1] / 10 + d[-m-2] / 10^2 + ...) * 2^e + // = buffer * 10^-m + 10^-m * (p2 ) * 2^e + // = buffer * 10^-m + 10^-m * (1/10 * (10 * p2) ) * 2^e + // = buffer * 10^-m + 10^-m * (1/10 * ((10*p2 div 2^-e) * 2^-e + (10*p2 mod 2^-e)) * 2^e + // + JSON_ASSERT(p2 <= (std::numeric_limits::max)() / 10); + p2 *= 10; + const std::uint64_t d = p2 >> -one.e; // d = (10 * p2) div 2^-e + const std::uint64_t r = p2 & (one.f - 1); // r = (10 * p2) mod 2^-e + // + // M+ = buffer * 10^-m + 10^-m * (1/10 * (d * 2^-e + r) * 2^e + // = buffer * 10^-m + 10^-m * (1/10 * (d + r * 2^e)) + // = (buffer * 10 + d) * 10^(-m-1) + 10^(-m-1) * r * 2^e + // + JSON_ASSERT(d <= 9); + buffer[length++] = static_cast('0' + d); // buffer := buffer * 10 + d + // + // M+ = buffer * 10^(-m-1) + 10^(-m-1) * r * 2^e + // + p2 = r; + m++; + // + // M+ = buffer * 10^-m + 10^-m * p2 * 2^e + // Invariant restored. + + // Check if enough digits have been generated. + // + // 10^-m * p2 * 2^e <= delta * 2^e + // p2 * 2^e <= 10^m * delta * 2^e + // p2 <= 10^m * delta + delta *= 10; + dist *= 10; + if (p2 <= delta) + { + break; + } + } + + // V = buffer * 10^-m, with M- <= V <= M+. + + decimal_exponent -= m; + + // 1 ulp in the decimal representation is now 10^-m. + // Since delta and dist are now scaled by 10^m, we need to do the + // same with ulp in order to keep the units in sync. + // + // 10^m * 10^-m = 1 = 2^-e * 2^e = ten_m * 2^e + // + const std::uint64_t ten_m = one.f; + grisu2_round(buffer, length, dist, delta, p2, ten_m); + + // By construction this algorithm generates the shortest possible decimal + // number (Loitsch, Theorem 6.2) which rounds back to w. + // For an input number of precision p, at least + // + // N = 1 + ceil(p * log_10(2)) + // + // decimal digits are sufficient to identify all binary floating-point + // numbers (Matula, "In-and-Out conversions"). + // This implies that the algorithm does not produce more than N decimal + // digits. + // + // N = 17 for p = 53 (IEEE double precision) + // N = 9 for p = 24 (IEEE single precision) +} + +/*! +v = buf * 10^decimal_exponent +len is the length of the buffer (number of decimal digits) +The buffer must be large enough, i.e. >= max_digits10. +*/ +JSON_HEDLEY_NON_NULL(1) +inline void grisu2(char* buf, int& len, int& decimal_exponent, + diyfp m_minus, diyfp v, diyfp m_plus) +{ + JSON_ASSERT(m_plus.e == m_minus.e); + JSON_ASSERT(m_plus.e == v.e); + + // --------(-----------------------+-----------------------)-------- (A) + // m- v m+ + // + // --------------------(-----------+-----------------------)-------- (B) + // m- v m+ + // + // First scale v (and m- and m+) such that the exponent is in the range + // [alpha, gamma]. + + const cached_power cached = get_cached_power_for_binary_exponent(m_plus.e); + + const diyfp c_minus_k(cached.f, cached.e); // = c ~= 10^-k + + // The exponent of the products is = v.e + c_minus_k.e + q and is in the range [alpha,gamma] + const diyfp w = diyfp::mul(v, c_minus_k); + const diyfp w_minus = diyfp::mul(m_minus, c_minus_k); + const diyfp w_plus = diyfp::mul(m_plus, c_minus_k); + + // ----(---+---)---------------(---+---)---------------(---+---)---- + // w- w w+ + // = c*m- = c*v = c*m+ + // + // diyfp::mul rounds its result and c_minus_k is approximated too. w, w- and + // w+ are now off by a small amount. + // In fact: + // + // w - v * 10^k < 1 ulp + // + // To account for this inaccuracy, add resp. subtract 1 ulp. + // + // --------+---[---------------(---+---)---------------]---+-------- + // w- M- w M+ w+ + // + // Now any number in [M-, M+] (bounds included) will round to w when input, + // regardless of how the input rounding algorithm breaks ties. + // + // And digit_gen generates the shortest possible such number in [M-, M+]. + // Note that this does not mean that Grisu2 always generates the shortest + // possible number in the interval (m-, m+). + const diyfp M_minus(w_minus.f + 1, w_minus.e); + const diyfp M_plus (w_plus.f - 1, w_plus.e ); + + decimal_exponent = -cached.k; // = -(-k) = k + + grisu2_digit_gen(buf, len, decimal_exponent, M_minus, w, M_plus); +} + +/*! +v = buf * 10^decimal_exponent +len is the length of the buffer (number of decimal digits) +The buffer must be large enough, i.e. >= max_digits10. +*/ +template +JSON_HEDLEY_NON_NULL(1) +void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value) +{ + static_assert(diyfp::kPrecision >= std::numeric_limits::digits + 3, + "internal error: not enough precision"); + + JSON_ASSERT(std::isfinite(value)); + JSON_ASSERT(value > 0); + + // If the neighbors (and boundaries) of 'value' are always computed for double-precision + // numbers, all float's can be recovered using strtod (and strtof). However, the resulting + // decimal representations are not exactly "short". + // + // The documentation for 'std::to_chars' (https://en.cppreference.com/w/cpp/utility/to_chars) + // says "value is converted to a string as if by std::sprintf in the default ("C") locale" + // and since sprintf promotes float's to double's, I think this is exactly what 'std::to_chars' + // does. + // On the other hand, the documentation for 'std::to_chars' requires that "parsing the + // representation using the corresponding std::from_chars function recovers value exactly". That + // indicates that single precision floating-point numbers should be recovered using + // 'std::strtof'. + // + // NB: If the neighbors are computed for single-precision numbers, there is a single float + // (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision + // value is off by 1 ulp. +#if 0 + const boundaries w = compute_boundaries(static_cast(value)); +#else + const boundaries w = compute_boundaries(value); +#endif + + grisu2(buf, len, decimal_exponent, w.minus, w.w, w.plus); +} + +/*! +@brief appends a decimal representation of e to buf +@return a pointer to the element following the exponent. +@pre -1000 < e < 1000 +*/ +JSON_HEDLEY_NON_NULL(1) +JSON_HEDLEY_RETURNS_NON_NULL +inline char* append_exponent(char* buf, int e) +{ + JSON_ASSERT(e > -1000); + JSON_ASSERT(e < 1000); + + if (e < 0) + { + e = -e; + *buf++ = '-'; + } + else + { + *buf++ = '+'; + } + + auto k = static_cast(e); + if (k < 10) + { + // Always print at least two digits in the exponent. + // This is for compatibility with printf("%g"). + *buf++ = '0'; + *buf++ = static_cast('0' + k); + } + else if (k < 100) + { + *buf++ = static_cast('0' + k / 10); + k %= 10; + *buf++ = static_cast('0' + k); + } + else + { + *buf++ = static_cast('0' + k / 100); + k %= 100; + *buf++ = static_cast('0' + k / 10); + k %= 10; + *buf++ = static_cast('0' + k); + } + + return buf; +} + +/*! +@brief prettify v = buf * 10^decimal_exponent + +If v is in the range [10^min_exp, 10^max_exp) it will be printed in fixed-point +notation. Otherwise it will be printed in exponential notation. + +@pre min_exp < 0 +@pre max_exp > 0 +*/ +JSON_HEDLEY_NON_NULL(1) +JSON_HEDLEY_RETURNS_NON_NULL +inline char* format_buffer(char* buf, int len, int decimal_exponent, + int min_exp, int max_exp) +{ + JSON_ASSERT(min_exp < 0); + JSON_ASSERT(max_exp > 0); + + const int k = len; + const int n = len + decimal_exponent; + + // v = buf * 10^(n-k) + // k is the length of the buffer (number of decimal digits) + // n is the position of the decimal point relative to the start of the buffer. + + if (k <= n && n <= max_exp) + { + // digits[000] + // len <= max_exp + 2 + + std::memset(buf + k, '0', static_cast(n) - static_cast(k)); + // Make it look like a floating-point number (#362, #378) + buf[n + 0] = '.'; + buf[n + 1] = '0'; + return buf + (static_cast(n) + 2); + } + + if (0 < n && n <= max_exp) + { + // dig.its + // len <= max_digits10 + 1 + + JSON_ASSERT(k > n); + + std::memmove(buf + (static_cast(n) + 1), buf + n, static_cast(k) - static_cast(n)); + buf[n] = '.'; + return buf + (static_cast(k) + 1U); + } + + if (min_exp < n && n <= 0) + { + // 0.[000]digits + // len <= 2 + (-min_exp - 1) + max_digits10 + + std::memmove(buf + (2 + static_cast(-n)), buf, static_cast(k)); + buf[0] = '0'; + buf[1] = '.'; + std::memset(buf + 2, '0', static_cast(-n)); + return buf + (2U + static_cast(-n) + static_cast(k)); + } + + if (k == 1) + { + // dE+123 + // len <= 1 + 5 + + buf += 1; + } + else + { + // d.igitsE+123 + // len <= max_digits10 + 1 + 5 + + std::memmove(buf + 2, buf + 1, static_cast(k) - 1); + buf[1] = '.'; + buf += 1 + static_cast(k); + } + + *buf++ = 'e'; + return append_exponent(buf, n - 1); +} + +} // namespace dtoa_impl + +/*! +@brief generates a decimal representation of the floating-point number value in [first, last). + +The format of the resulting decimal representation is similar to printf's %g +format. Returns an iterator pointing past-the-end of the decimal representation. + +@note The input number must be finite, i.e. NaN's and Inf's are not supported. +@note The buffer must be large enough. +@note The result is NOT null-terminated. +*/ +template +JSON_HEDLEY_NON_NULL(1, 2) +JSON_HEDLEY_RETURNS_NON_NULL +char* to_chars(char* first, const char* last, FloatType value) +{ + static_cast(last); // maybe unused - fix warning + JSON_ASSERT(std::isfinite(value)); + + // Use signbit(value) instead of (value < 0) since signbit works for -0. + if (std::signbit(value)) + { + value = -value; + *first++ = '-'; + } + +#ifdef __GNUC__ +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wfloat-equal" +#endif + if (value == 0) // +-0 + { + *first++ = '0'; + // Make it look like a floating-point number (#362, #378) + *first++ = '.'; + *first++ = '0'; + return first; + } +#ifdef __GNUC__ +#pragma GCC diagnostic pop +#endif + + JSON_ASSERT(last - first >= std::numeric_limits::max_digits10); + + // Compute v = buffer * 10^decimal_exponent. + // The decimal digits are stored in the buffer, which needs to be interpreted + // as an unsigned decimal integer. + // len is the length of the buffer, i.e. the number of decimal digits. + int len = 0; + int decimal_exponent = 0; + dtoa_impl::grisu2(first, len, decimal_exponent, value); + + JSON_ASSERT(len <= std::numeric_limits::max_digits10); + + // Format the buffer like printf("%.*g", prec, value) + constexpr int kMinExp = -4; + // Use digits10 here to increase compatibility with version 2. + constexpr int kMaxExp = std::numeric_limits::digits10; + + JSON_ASSERT(last - first >= kMaxExp + 2); + JSON_ASSERT(last - first >= 2 + (-kMinExp - 1) + std::numeric_limits::max_digits10); + JSON_ASSERT(last - first >= std::numeric_limits::max_digits10 + 6); + + return dtoa_impl::format_buffer(first, len, decimal_exponent, kMinExp, kMaxExp); +} + +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + +// #include + +// #include + +// #include + +// #include + + +namespace nlohmann +{ +namespace detail +{ +/////////////////// +// serialization // +/////////////////// + +/// how to treat decoding errors +enum class error_handler_t +{ + strict, ///< throw a type_error exception in case of invalid UTF-8 + replace, ///< replace invalid UTF-8 sequences with U+FFFD + ignore ///< ignore invalid UTF-8 sequences +}; + +template +class serializer +{ + using string_t = typename BasicJsonType::string_t; + using number_float_t = typename BasicJsonType::number_float_t; + using number_integer_t = typename BasicJsonType::number_integer_t; + using number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using binary_char_t = typename BasicJsonType::binary_t::value_type; + static constexpr std::uint8_t UTF8_ACCEPT = 0; + static constexpr std::uint8_t UTF8_REJECT = 1; + + public: + /*! + @param[in] s output stream to serialize to + @param[in] ichar indentation character to use + @param[in] error_handler_ how to react on decoding errors + */ + serializer(output_adapter_t s, const char ichar, + error_handler_t error_handler_ = error_handler_t::strict) + : o(std::move(s)) + , loc(std::localeconv()) + , thousands_sep(loc->thousands_sep == nullptr ? '\0' : std::char_traits::to_char_type(* (loc->thousands_sep))) + , decimal_point(loc->decimal_point == nullptr ? '\0' : std::char_traits::to_char_type(* (loc->decimal_point))) + , indent_char(ichar) + , indent_string(512, indent_char) + , error_handler(error_handler_) + {} + + // delete because of pointer members + serializer(const serializer&) = delete; + serializer& operator=(const serializer&) = delete; + serializer(serializer&&) = delete; + serializer& operator=(serializer&&) = delete; + ~serializer() = default; + + /*! + @brief internal implementation of the serialization function + + This function is called by the public member function dump and organizes + the serialization internally. The indentation level is propagated as + additional parameter. In case of arrays and objects, the function is + called recursively. + + - strings and object keys are escaped using `escape_string()` + - integer numbers are converted implicitly via `operator<<` + - floating-point numbers are converted to a string using `"%g"` format + - binary values are serialized as objects containing the subtype and the + byte array + + @param[in] val value to serialize + @param[in] pretty_print whether the output shall be pretty-printed + @param[in] ensure_ascii If @a ensure_ascii is true, all non-ASCII characters + in the output are escaped with `\uXXXX` sequences, and the result consists + of ASCII characters only. + @param[in] indent_step the indent level + @param[in] current_indent the current indent level (only used internally) + */ + void dump(const BasicJsonType& val, + const bool pretty_print, + const bool ensure_ascii, + const unsigned int indent_step, + const unsigned int current_indent = 0) + { + switch (val.m_type) + { + case value_t::object: + { + if (val.m_value.object->empty()) + { + o->write_characters("{}", 2); + return; + } + + if (pretty_print) + { + o->write_characters("{\n", 2); + + // variable to hold indentation for recursive calls + const auto new_indent = current_indent + indent_step; + if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent)) + { + indent_string.resize(indent_string.size() * 2, ' '); + } + + // first n-1 elements + auto i = val.m_value.object->cbegin(); + for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i) + { + o->write_characters(indent_string.c_str(), new_indent); + o->write_character('\"'); + dump_escaped(i->first, ensure_ascii); + o->write_characters("\": ", 3); + dump(i->second, true, ensure_ascii, indent_step, new_indent); + o->write_characters(",\n", 2); + } + + // last element + JSON_ASSERT(i != val.m_value.object->cend()); + JSON_ASSERT(std::next(i) == val.m_value.object->cend()); + o->write_characters(indent_string.c_str(), new_indent); + o->write_character('\"'); + dump_escaped(i->first, ensure_ascii); + o->write_characters("\": ", 3); + dump(i->second, true, ensure_ascii, indent_step, new_indent); + + o->write_character('\n'); + o->write_characters(indent_string.c_str(), current_indent); + o->write_character('}'); + } + else + { + o->write_character('{'); + + // first n-1 elements + auto i = val.m_value.object->cbegin(); + for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i) + { + o->write_character('\"'); + dump_escaped(i->first, ensure_ascii); + o->write_characters("\":", 2); + dump(i->second, false, ensure_ascii, indent_step, current_indent); + o->write_character(','); + } + + // last element + JSON_ASSERT(i != val.m_value.object->cend()); + JSON_ASSERT(std::next(i) == val.m_value.object->cend()); + o->write_character('\"'); + dump_escaped(i->first, ensure_ascii); + o->write_characters("\":", 2); + dump(i->second, false, ensure_ascii, indent_step, current_indent); + + o->write_character('}'); + } + + return; + } + + case value_t::array: + { + if (val.m_value.array->empty()) + { + o->write_characters("[]", 2); + return; + } + + if (pretty_print) + { + o->write_characters("[\n", 2); + + // variable to hold indentation for recursive calls + const auto new_indent = current_indent + indent_step; + if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent)) + { + indent_string.resize(indent_string.size() * 2, ' '); + } + + // first n-1 elements + for (auto i = val.m_value.array->cbegin(); + i != val.m_value.array->cend() - 1; ++i) + { + o->write_characters(indent_string.c_str(), new_indent); + dump(*i, true, ensure_ascii, indent_step, new_indent); + o->write_characters(",\n", 2); + } + + // last element + JSON_ASSERT(!val.m_value.array->empty()); + o->write_characters(indent_string.c_str(), new_indent); + dump(val.m_value.array->back(), true, ensure_ascii, indent_step, new_indent); + + o->write_character('\n'); + o->write_characters(indent_string.c_str(), current_indent); + o->write_character(']'); + } + else + { + o->write_character('['); + + // first n-1 elements + for (auto i = val.m_value.array->cbegin(); + i != val.m_value.array->cend() - 1; ++i) + { + dump(*i, false, ensure_ascii, indent_step, current_indent); + o->write_character(','); + } + + // last element + JSON_ASSERT(!val.m_value.array->empty()); + dump(val.m_value.array->back(), false, ensure_ascii, indent_step, current_indent); + + o->write_character(']'); + } + + return; + } + + case value_t::string: + { + o->write_character('\"'); + dump_escaped(*val.m_value.string, ensure_ascii); + o->write_character('\"'); + return; + } + + case value_t::binary: + { + if (pretty_print) + { + o->write_characters("{\n", 2); + + // variable to hold indentation for recursive calls + const auto new_indent = current_indent + indent_step; + if (JSON_HEDLEY_UNLIKELY(indent_string.size() < new_indent)) + { + indent_string.resize(indent_string.size() * 2, ' '); + } + + o->write_characters(indent_string.c_str(), new_indent); + + o->write_characters("\"bytes\": [", 10); + + if (!val.m_value.binary->empty()) + { + for (auto i = val.m_value.binary->cbegin(); + i != val.m_value.binary->cend() - 1; ++i) + { + dump_integer(*i); + o->write_characters(", ", 2); + } + dump_integer(val.m_value.binary->back()); + } + + o->write_characters("],\n", 3); + o->write_characters(indent_string.c_str(), new_indent); + + o->write_characters("\"subtype\": ", 11); + if (val.m_value.binary->has_subtype()) + { + dump_integer(val.m_value.binary->subtype()); + } + else + { + o->write_characters("null", 4); + } + o->write_character('\n'); + o->write_characters(indent_string.c_str(), current_indent); + o->write_character('}'); + } + else + { + o->write_characters("{\"bytes\":[", 10); + + if (!val.m_value.binary->empty()) + { + for (auto i = val.m_value.binary->cbegin(); + i != val.m_value.binary->cend() - 1; ++i) + { + dump_integer(*i); + o->write_character(','); + } + dump_integer(val.m_value.binary->back()); + } + + o->write_characters("],\"subtype\":", 12); + if (val.m_value.binary->has_subtype()) + { + dump_integer(val.m_value.binary->subtype()); + o->write_character('}'); + } + else + { + o->write_characters("null}", 5); + } + } + return; + } + + case value_t::boolean: + { + if (val.m_value.boolean) + { + o->write_characters("true", 4); + } + else + { + o->write_characters("false", 5); + } + return; + } + + case value_t::number_integer: + { + dump_integer(val.m_value.number_integer); + return; + } + + case value_t::number_unsigned: + { + dump_integer(val.m_value.number_unsigned); + return; + } + + case value_t::number_float: + { + dump_float(val.m_value.number_float); + return; + } + + case value_t::discarded: + { + o->write_characters("", 11); + return; + } + + case value_t::null: + { + o->write_characters("null", 4); + return; + } + + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + } + + JSON_PRIVATE_UNLESS_TESTED: + /*! + @brief dump escaped string + + Escape a string by replacing certain special characters by a sequence of an + escape character (backslash) and another character and other control + characters by a sequence of "\u" followed by a four-digit hex + representation. The escaped string is written to output stream @a o. + + @param[in] s the string to escape + @param[in] ensure_ascii whether to escape non-ASCII characters with + \uXXXX sequences + + @complexity Linear in the length of string @a s. + */ + void dump_escaped(const string_t& s, const bool ensure_ascii) + { + std::uint32_t codepoint{}; + std::uint8_t state = UTF8_ACCEPT; + std::size_t bytes = 0; // number of bytes written to string_buffer + + // number of bytes written at the point of the last valid byte + std::size_t bytes_after_last_accept = 0; + std::size_t undumped_chars = 0; + + for (std::size_t i = 0; i < s.size(); ++i) + { + const auto byte = static_cast(s[i]); + + switch (decode(state, codepoint, byte)) + { + case UTF8_ACCEPT: // decode found a new code point + { + switch (codepoint) + { + case 0x08: // backspace + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = 'b'; + break; + } + + case 0x09: // horizontal tab + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = 't'; + break; + } + + case 0x0A: // newline + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = 'n'; + break; + } + + case 0x0C: // formfeed + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = 'f'; + break; + } + + case 0x0D: // carriage return + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = 'r'; + break; + } + + case 0x22: // quotation mark + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = '\"'; + break; + } + + case 0x5C: // reverse solidus + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = '\\'; + break; + } + + default: + { + // escape control characters (0x00..0x1F) or, if + // ensure_ascii parameter is used, non-ASCII characters + if ((codepoint <= 0x1F) || (ensure_ascii && (codepoint >= 0x7F))) + { + if (codepoint <= 0xFFFF) + { + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + (std::snprintf)(string_buffer.data() + bytes, 7, "\\u%04x", + static_cast(codepoint)); + bytes += 6; + } + else + { + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + (std::snprintf)(string_buffer.data() + bytes, 13, "\\u%04x\\u%04x", + static_cast(0xD7C0u + (codepoint >> 10u)), + static_cast(0xDC00u + (codepoint & 0x3FFu))); + bytes += 12; + } + } + else + { + // copy byte to buffer (all previous bytes + // been copied have in default case above) + string_buffer[bytes++] = s[i]; + } + break; + } + } + + // write buffer and reset index; there must be 13 bytes + // left, as this is the maximal number of bytes to be + // written ("\uxxxx\uxxxx\0") for one code point + if (string_buffer.size() - bytes < 13) + { + o->write_characters(string_buffer.data(), bytes); + bytes = 0; + } + + // remember the byte position of this accept + bytes_after_last_accept = bytes; + undumped_chars = 0; + break; + } + + case UTF8_REJECT: // decode found invalid UTF-8 byte + { + switch (error_handler) + { + case error_handler_t::strict: + { + std::string sn(9, '\0'); + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + (std::snprintf)(&sn[0], sn.size(), "%.2X", byte); + JSON_THROW(type_error::create(316, "invalid UTF-8 byte at index " + std::to_string(i) + ": 0x" + sn, BasicJsonType())); + } + + case error_handler_t::ignore: + case error_handler_t::replace: + { + // in case we saw this character the first time, we + // would like to read it again, because the byte + // may be OK for itself, but just not OK for the + // previous sequence + if (undumped_chars > 0) + { + --i; + } + + // reset length buffer to the last accepted index; + // thus removing/ignoring the invalid characters + bytes = bytes_after_last_accept; + + if (error_handler == error_handler_t::replace) + { + // add a replacement character + if (ensure_ascii) + { + string_buffer[bytes++] = '\\'; + string_buffer[bytes++] = 'u'; + string_buffer[bytes++] = 'f'; + string_buffer[bytes++] = 'f'; + string_buffer[bytes++] = 'f'; + string_buffer[bytes++] = 'd'; + } + else + { + string_buffer[bytes++] = detail::binary_writer::to_char_type('\xEF'); + string_buffer[bytes++] = detail::binary_writer::to_char_type('\xBF'); + string_buffer[bytes++] = detail::binary_writer::to_char_type('\xBD'); + } + + // write buffer and reset index; there must be 13 bytes + // left, as this is the maximal number of bytes to be + // written ("\uxxxx\uxxxx\0") for one code point + if (string_buffer.size() - bytes < 13) + { + o->write_characters(string_buffer.data(), bytes); + bytes = 0; + } + + bytes_after_last_accept = bytes; + } + + undumped_chars = 0; + + // continue processing the string + state = UTF8_ACCEPT; + break; + } + + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + break; + } + + default: // decode found yet incomplete multi-byte code point + { + if (!ensure_ascii) + { + // code point will not be escaped - copy byte to buffer + string_buffer[bytes++] = s[i]; + } + ++undumped_chars; + break; + } + } + } + + // we finished processing the string + if (JSON_HEDLEY_LIKELY(state == UTF8_ACCEPT)) + { + // write buffer + if (bytes > 0) + { + o->write_characters(string_buffer.data(), bytes); + } + } + else + { + // we finish reading, but do not accept: string was incomplete + switch (error_handler) + { + case error_handler_t::strict: + { + std::string sn(9, '\0'); + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + (std::snprintf)(&sn[0], sn.size(), "%.2X", static_cast(s.back())); + JSON_THROW(type_error::create(316, "incomplete UTF-8 string; last byte: 0x" + sn, BasicJsonType())); + } + + case error_handler_t::ignore: + { + // write all accepted bytes + o->write_characters(string_buffer.data(), bytes_after_last_accept); + break; + } + + case error_handler_t::replace: + { + // write all accepted bytes + o->write_characters(string_buffer.data(), bytes_after_last_accept); + // add a replacement character + if (ensure_ascii) + { + o->write_characters("\\ufffd", 6); + } + else + { + o->write_characters("\xEF\xBF\xBD", 3); + } + break; + } + + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + } + } + + private: + /*! + @brief count digits + + Count the number of decimal (base 10) digits for an input unsigned integer. + + @param[in] x unsigned integer number to count its digits + @return number of decimal digits + */ + inline unsigned int count_digits(number_unsigned_t x) noexcept + { + unsigned int n_digits = 1; + for (;;) + { + if (x < 10) + { + return n_digits; + } + if (x < 100) + { + return n_digits + 1; + } + if (x < 1000) + { + return n_digits + 2; + } + if (x < 10000) + { + return n_digits + 3; + } + x = x / 10000u; + n_digits += 4; + } + } + + /*! + @brief dump an integer + + Dump a given integer to output stream @a o. Works internally with + @a number_buffer. + + @param[in] x integer number (signed or unsigned) to dump + @tparam NumberType either @a number_integer_t or @a number_unsigned_t + */ + template < typename NumberType, detail::enable_if_t < + std::is_integral::value || + std::is_same::value || + std::is_same::value || + std::is_same::value, + int > = 0 > + void dump_integer(NumberType x) + { + static constexpr std::array, 100> digits_to_99 + { + { + {{'0', '0'}}, {{'0', '1'}}, {{'0', '2'}}, {{'0', '3'}}, {{'0', '4'}}, {{'0', '5'}}, {{'0', '6'}}, {{'0', '7'}}, {{'0', '8'}}, {{'0', '9'}}, + {{'1', '0'}}, {{'1', '1'}}, {{'1', '2'}}, {{'1', '3'}}, {{'1', '4'}}, {{'1', '5'}}, {{'1', '6'}}, {{'1', '7'}}, {{'1', '8'}}, {{'1', '9'}}, + {{'2', '0'}}, {{'2', '1'}}, {{'2', '2'}}, {{'2', '3'}}, {{'2', '4'}}, {{'2', '5'}}, {{'2', '6'}}, {{'2', '7'}}, {{'2', '8'}}, {{'2', '9'}}, + {{'3', '0'}}, {{'3', '1'}}, {{'3', '2'}}, {{'3', '3'}}, {{'3', '4'}}, {{'3', '5'}}, {{'3', '6'}}, {{'3', '7'}}, {{'3', '8'}}, {{'3', '9'}}, + {{'4', '0'}}, {{'4', '1'}}, {{'4', '2'}}, {{'4', '3'}}, {{'4', '4'}}, {{'4', '5'}}, {{'4', '6'}}, {{'4', '7'}}, {{'4', '8'}}, {{'4', '9'}}, + {{'5', '0'}}, {{'5', '1'}}, {{'5', '2'}}, {{'5', '3'}}, {{'5', '4'}}, {{'5', '5'}}, {{'5', '6'}}, {{'5', '7'}}, {{'5', '8'}}, {{'5', '9'}}, + {{'6', '0'}}, {{'6', '1'}}, {{'6', '2'}}, {{'6', '3'}}, {{'6', '4'}}, {{'6', '5'}}, {{'6', '6'}}, {{'6', '7'}}, {{'6', '8'}}, {{'6', '9'}}, + {{'7', '0'}}, {{'7', '1'}}, {{'7', '2'}}, {{'7', '3'}}, {{'7', '4'}}, {{'7', '5'}}, {{'7', '6'}}, {{'7', '7'}}, {{'7', '8'}}, {{'7', '9'}}, + {{'8', '0'}}, {{'8', '1'}}, {{'8', '2'}}, {{'8', '3'}}, {{'8', '4'}}, {{'8', '5'}}, {{'8', '6'}}, {{'8', '7'}}, {{'8', '8'}}, {{'8', '9'}}, + {{'9', '0'}}, {{'9', '1'}}, {{'9', '2'}}, {{'9', '3'}}, {{'9', '4'}}, {{'9', '5'}}, {{'9', '6'}}, {{'9', '7'}}, {{'9', '8'}}, {{'9', '9'}}, + } + }; + + // special case for "0" + if (x == 0) + { + o->write_character('0'); + return; + } + + // use a pointer to fill the buffer + auto buffer_ptr = number_buffer.begin(); // NOLINT(llvm-qualified-auto,readability-qualified-auto,cppcoreguidelines-pro-type-vararg,hicpp-vararg) + + const bool is_negative = std::is_signed::value && !(x >= 0); // see issue #755 + number_unsigned_t abs_value; + + unsigned int n_chars{}; + + if (is_negative) + { + *buffer_ptr = '-'; + abs_value = remove_sign(static_cast(x)); + + // account one more byte for the minus sign + n_chars = 1 + count_digits(abs_value); + } + else + { + abs_value = static_cast(x); + n_chars = count_digits(abs_value); + } + + // spare 1 byte for '\0' + JSON_ASSERT(n_chars < number_buffer.size() - 1); + + // jump to the end to generate the string from backward + // so we later avoid reversing the result + buffer_ptr += n_chars; + + // Fast int2ascii implementation inspired by "Fastware" talk by Andrei Alexandrescu + // See: https://www.youtube.com/watch?v=o4-CwDo2zpg + while (abs_value >= 100) + { + const auto digits_index = static_cast((abs_value % 100)); + abs_value /= 100; + *(--buffer_ptr) = digits_to_99[digits_index][1]; + *(--buffer_ptr) = digits_to_99[digits_index][0]; + } + + if (abs_value >= 10) + { + const auto digits_index = static_cast(abs_value); + *(--buffer_ptr) = digits_to_99[digits_index][1]; + *(--buffer_ptr) = digits_to_99[digits_index][0]; + } + else + { + *(--buffer_ptr) = static_cast('0' + abs_value); + } + + o->write_characters(number_buffer.data(), n_chars); + } + + /*! + @brief dump a floating-point number + + Dump a given floating-point number to output stream @a o. Works internally + with @a number_buffer. + + @param[in] x floating-point number to dump + */ + void dump_float(number_float_t x) + { + // NaN / inf + if (!std::isfinite(x)) + { + o->write_characters("null", 4); + return; + } + + // If number_float_t is an IEEE-754 single or double precision number, + // use the Grisu2 algorithm to produce short numbers which are + // guaranteed to round-trip, using strtof and strtod, resp. + // + // NB: The test below works if == . + static constexpr bool is_ieee_single_or_double + = (std::numeric_limits::is_iec559 && std::numeric_limits::digits == 24 && std::numeric_limits::max_exponent == 128) || + (std::numeric_limits::is_iec559 && std::numeric_limits::digits == 53 && std::numeric_limits::max_exponent == 1024); + + dump_float(x, std::integral_constant()); + } + + void dump_float(number_float_t x, std::true_type /*is_ieee_single_or_double*/) + { + auto* begin = number_buffer.data(); + auto* end = ::nlohmann::detail::to_chars(begin, begin + number_buffer.size(), x); + + o->write_characters(begin, static_cast(end - begin)); + } + + void dump_float(number_float_t x, std::false_type /*is_ieee_single_or_double*/) + { + // get number of digits for a float -> text -> float round-trip + static constexpr auto d = std::numeric_limits::max_digits10; + + // the actual conversion + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg,hicpp-vararg) + std::ptrdiff_t len = (std::snprintf)(number_buffer.data(), number_buffer.size(), "%.*g", d, x); + + // negative value indicates an error + JSON_ASSERT(len > 0); + // check if buffer was large enough + JSON_ASSERT(static_cast(len) < number_buffer.size()); + + // erase thousands separator + if (thousands_sep != '\0') + { + // NOLINTNEXTLINE(readability-qualified-auto,llvm-qualified-auto): std::remove returns an iterator, see https://github.com/nlohmann/json/issues/3081 + const auto end = std::remove(number_buffer.begin(), number_buffer.begin() + len, thousands_sep); + std::fill(end, number_buffer.end(), '\0'); + JSON_ASSERT((end - number_buffer.begin()) <= len); + len = (end - number_buffer.begin()); + } + + // convert decimal point to '.' + if (decimal_point != '\0' && decimal_point != '.') + { + // NOLINTNEXTLINE(readability-qualified-auto,llvm-qualified-auto): std::find returns an iterator, see https://github.com/nlohmann/json/issues/3081 + const auto dec_pos = std::find(number_buffer.begin(), number_buffer.end(), decimal_point); + if (dec_pos != number_buffer.end()) + { + *dec_pos = '.'; + } + } + + o->write_characters(number_buffer.data(), static_cast(len)); + + // determine if need to append ".0" + const bool value_is_int_like = + std::none_of(number_buffer.begin(), number_buffer.begin() + len + 1, + [](char c) + { + return c == '.' || c == 'e'; + }); + + if (value_is_int_like) + { + o->write_characters(".0", 2); + } + } + + /*! + @brief check whether a string is UTF-8 encoded + + The function checks each byte of a string whether it is UTF-8 encoded. The + result of the check is stored in the @a state parameter. The function must + be called initially with state 0 (accept). State 1 means the string must + be rejected, because the current byte is not allowed. If the string is + completely processed, but the state is non-zero, the string ended + prematurely; that is, the last byte indicated more bytes should have + followed. + + @param[in,out] state the state of the decoding + @param[in,out] codep codepoint (valid only if resulting state is UTF8_ACCEPT) + @param[in] byte next byte to decode + @return new state + + @note The function has been edited: a std::array is used. + + @copyright Copyright (c) 2008-2009 Bjoern Hoehrmann + @sa http://bjoern.hoehrmann.de/utf-8/decoder/dfa/ + */ + static std::uint8_t decode(std::uint8_t& state, std::uint32_t& codep, const std::uint8_t byte) noexcept + { + static const std::array utf8d = + { + { + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 00..1F + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 20..3F + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 40..5F + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // 60..7F + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, // 80..9F + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, // A0..BF + 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, // C0..DF + 0xA, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x3, 0x4, 0x3, 0x3, // E0..EF + 0xB, 0x6, 0x6, 0x6, 0x5, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, 0x8, // F0..FF + 0x0, 0x1, 0x2, 0x3, 0x5, 0x8, 0x7, 0x1, 0x1, 0x1, 0x4, 0x6, 0x1, 0x1, 0x1, 0x1, // s0..s0 + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, // s1..s2 + 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, // s3..s4 + 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, // s5..s6 + 1, 3, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 // s7..s8 + } + }; + + JSON_ASSERT(byte < utf8d.size()); + const std::uint8_t type = utf8d[byte]; + + codep = (state != UTF8_ACCEPT) + ? (byte & 0x3fu) | (codep << 6u) + : (0xFFu >> type) & (byte); + + std::size_t index = 256u + static_cast(state) * 16u + static_cast(type); + JSON_ASSERT(index < 400); + state = utf8d[index]; + return state; + } + + /* + * Overload to make the compiler happy while it is instantiating + * dump_integer for number_unsigned_t. + * Must never be called. + */ + number_unsigned_t remove_sign(number_unsigned_t x) + { + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + return x; // LCOV_EXCL_LINE + } + + /* + * Helper function for dump_integer + * + * This function takes a negative signed integer and returns its absolute + * value as unsigned integer. The plus/minus shuffling is necessary as we can + * not directly remove the sign of an arbitrary signed integer as the + * absolute values of INT_MIN and INT_MAX are usually not the same. See + * #1708 for details. + */ + inline number_unsigned_t remove_sign(number_integer_t x) noexcept + { + JSON_ASSERT(x < 0 && x < (std::numeric_limits::max)()); // NOLINT(misc-redundant-expression) + return static_cast(-(x + 1)) + 1; + } + + private: + /// the output of the serializer + output_adapter_t o = nullptr; + + /// a (hopefully) large enough character buffer + std::array number_buffer{{}}; + + /// the locale + const std::lconv* loc = nullptr; + /// the locale's thousand separator character + const char thousands_sep = '\0'; + /// the locale's decimal point character + const char decimal_point = '\0'; + + /// string buffer + std::array string_buffer{{}}; + + /// the indentation character + const char indent_char; + /// the indentation string + string_t indent_string; + + /// error_handler how to react on decoding errors + const error_handler_t error_handler; +}; +} // namespace detail +} // namespace nlohmann + +// #include + +// #include + +// #include + + +#include // less +#include // initializer_list +#include // input_iterator_tag, iterator_traits +#include // allocator +#include // for out_of_range +#include // enable_if, is_convertible +#include // pair +#include // vector + +// #include + + +namespace nlohmann +{ + +/// ordered_map: a minimal map-like container that preserves insertion order +/// for use within nlohmann::basic_json +template , + class Allocator = std::allocator>> + struct ordered_map : std::vector, Allocator> +{ + using key_type = Key; + using mapped_type = T; + using Container = std::vector, Allocator>; + using typename Container::iterator; + using typename Container::const_iterator; + using typename Container::size_type; + using typename Container::value_type; + + // Explicit constructors instead of `using Container::Container` + // otherwise older compilers choke on it (GCC <= 5.5, xcode <= 9.4) + ordered_map(const Allocator& alloc = Allocator()) : Container{alloc} {} + template + ordered_map(It first, It last, const Allocator& alloc = Allocator()) + : Container{first, last, alloc} {} + ordered_map(std::initializer_list init, const Allocator& alloc = Allocator() ) + : Container{init, alloc} {} + + std::pair emplace(const key_type& key, T&& t) + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + return {it, false}; + } + } + Container::emplace_back(key, t); + return {--this->end(), true}; + } + + T& operator[](const Key& key) + { + return emplace(key, T{}).first->second; + } + + const T& operator[](const Key& key) const + { + return at(key); + } + + T& at(const Key& key) + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + return it->second; + } + } + + JSON_THROW(std::out_of_range("key not found")); + } + + const T& at(const Key& key) const + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + return it->second; + } + } + + JSON_THROW(std::out_of_range("key not found")); + } + + size_type erase(const Key& key) + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + // Since we cannot move const Keys, re-construct them in place + for (auto next = it; ++next != this->end(); ++it) + { + it->~value_type(); // Destroy but keep allocation + new (&*it) value_type{std::move(*next)}; + } + Container::pop_back(); + return 1; + } + } + return 0; + } + + iterator erase(iterator pos) + { + auto it = pos; + + // Since we cannot move const Keys, re-construct them in place + for (auto next = it; ++next != this->end(); ++it) + { + it->~value_type(); // Destroy but keep allocation + new (&*it) value_type{std::move(*next)}; + } + Container::pop_back(); + return pos; + } + + size_type count(const Key& key) const + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + return 1; + } + } + return 0; + } + + iterator find(const Key& key) + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + return it; + } + } + return Container::end(); + } + + const_iterator find(const Key& key) const + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == key) + { + return it; + } + } + return Container::end(); + } + + std::pair insert( value_type&& value ) + { + return emplace(value.first, std::move(value.second)); + } + + std::pair insert( const value_type& value ) + { + for (auto it = this->begin(); it != this->end(); ++it) + { + if (it->first == value.first) + { + return {it, false}; + } + } + Container::push_back(value); + return {--this->end(), true}; + } + + template + using require_input_iter = typename std::enable_if::iterator_category, + std::input_iterator_tag>::value>::type; + + template> + void insert(InputIt first, InputIt last) + { + for (auto it = first; it != last; ++it) + { + insert(*it); + } + } +}; + +} // namespace nlohmann + + +#if defined(JSON_HAS_CPP_17) + #include +#endif + +/*! +@brief namespace for Niels Lohmann +@see https://github.com/nlohmann +@since version 1.0.0 +*/ +namespace nlohmann +{ + +/*! +@brief a class to store JSON values + +@tparam ObjectType type for JSON objects (`std::map` by default; will be used +in @ref object_t) +@tparam ArrayType type for JSON arrays (`std::vector` by default; will be used +in @ref array_t) +@tparam StringType type for JSON strings and object keys (`std::string` by +default; will be used in @ref string_t) +@tparam BooleanType type for JSON booleans (`bool` by default; will be used +in @ref boolean_t) +@tparam NumberIntegerType type for JSON integer numbers (`int64_t` by +default; will be used in @ref number_integer_t) +@tparam NumberUnsignedType type for JSON unsigned integer numbers (@c +`uint64_t` by default; will be used in @ref number_unsigned_t) +@tparam NumberFloatType type for JSON floating-point numbers (`double` by +default; will be used in @ref number_float_t) +@tparam BinaryType type for packed binary data for compatibility with binary +serialization formats (`std::vector` by default; will be used in +@ref binary_t) +@tparam AllocatorType type of the allocator to use (`std::allocator` by +default) +@tparam JSONSerializer the serializer to resolve internal calls to `to_json()` +and `from_json()` (@ref adl_serializer by default) + +@requirement The class satisfies the following concept requirements: +- Basic + - [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible): + JSON values can be default constructed. The result will be a JSON null + value. + - [MoveConstructible](https://en.cppreference.com/w/cpp/named_req/MoveConstructible): + A JSON value can be constructed from an rvalue argument. + - [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible): + A JSON value can be copy-constructed from an lvalue expression. + - [MoveAssignable](https://en.cppreference.com/w/cpp/named_req/MoveAssignable): + A JSON value van be assigned from an rvalue argument. + - [CopyAssignable](https://en.cppreference.com/w/cpp/named_req/CopyAssignable): + A JSON value can be copy-assigned from an lvalue expression. + - [Destructible](https://en.cppreference.com/w/cpp/named_req/Destructible): + JSON values can be destructed. +- Layout + - [StandardLayoutType](https://en.cppreference.com/w/cpp/named_req/StandardLayoutType): + JSON values have + [standard layout](https://en.cppreference.com/w/cpp/language/data_members#Standard_layout): + All non-static data members are private and standard layout types, the + class has no virtual functions or (virtual) base classes. +- Library-wide + - [EqualityComparable](https://en.cppreference.com/w/cpp/named_req/EqualityComparable): + JSON values can be compared with `==`, see @ref + operator==(const_reference,const_reference). + - [LessThanComparable](https://en.cppreference.com/w/cpp/named_req/LessThanComparable): + JSON values can be compared with `<`, see @ref + operator<(const_reference,const_reference). + - [Swappable](https://en.cppreference.com/w/cpp/named_req/Swappable): + Any JSON lvalue or rvalue of can be swapped with any lvalue or rvalue of + other compatible types, using unqualified function call @ref swap(). + - [NullablePointer](https://en.cppreference.com/w/cpp/named_req/NullablePointer): + JSON values can be compared against `std::nullptr_t` objects which are used + to model the `null` value. +- Container + - [Container](https://en.cppreference.com/w/cpp/named_req/Container): + JSON values can be used like STL containers and provide iterator access. + - [ReversibleContainer](https://en.cppreference.com/w/cpp/named_req/ReversibleContainer); + JSON values can be used like STL containers and provide reverse iterator + access. + +@invariant The member variables @a m_value and @a m_type have the following +relationship: +- If `m_type == value_t::object`, then `m_value.object != nullptr`. +- If `m_type == value_t::array`, then `m_value.array != nullptr`. +- If `m_type == value_t::string`, then `m_value.string != nullptr`. +The invariants are checked by member function assert_invariant(). + +@internal +@note ObjectType trick from https://stackoverflow.com/a/9860911 +@endinternal + +@see [RFC 8259: The JavaScript Object Notation (JSON) Data Interchange +Format](https://tools.ietf.org/html/rfc8259) + +@since version 1.0.0 + +@nosubgrouping +*/ +NLOHMANN_BASIC_JSON_TPL_DECLARATION +class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions) +{ + private: + template friend struct detail::external_constructor; + friend ::nlohmann::json_pointer; + + template + friend class ::nlohmann::detail::parser; + friend ::nlohmann::detail::serializer; + template + friend class ::nlohmann::detail::iter_impl; + template + friend class ::nlohmann::detail::binary_writer; + template + friend class ::nlohmann::detail::binary_reader; + template + friend class ::nlohmann::detail::json_sax_dom_parser; + template + friend class ::nlohmann::detail::json_sax_dom_callback_parser; + friend class ::nlohmann::detail::exception; + + /// workaround type for MSVC + using basic_json_t = NLOHMANN_BASIC_JSON_TPL; + + JSON_PRIVATE_UNLESS_TESTED: + // convenience aliases for types residing in namespace detail; + using lexer = ::nlohmann::detail::lexer_base; + + template + static ::nlohmann::detail::parser parser( + InputAdapterType adapter, + detail::parser_callback_tcb = nullptr, + const bool allow_exceptions = true, + const bool ignore_comments = false + ) + { + return ::nlohmann::detail::parser(std::move(adapter), + std::move(cb), allow_exceptions, ignore_comments); + } + + private: + using primitive_iterator_t = ::nlohmann::detail::primitive_iterator_t; + template + using internal_iterator = ::nlohmann::detail::internal_iterator; + template + using iter_impl = ::nlohmann::detail::iter_impl; + template + using iteration_proxy = ::nlohmann::detail::iteration_proxy; + template using json_reverse_iterator = ::nlohmann::detail::json_reverse_iterator; + + template + using output_adapter_t = ::nlohmann::detail::output_adapter_t; + + template + using binary_reader = ::nlohmann::detail::binary_reader; + template using binary_writer = ::nlohmann::detail::binary_writer; + + JSON_PRIVATE_UNLESS_TESTED: + using serializer = ::nlohmann::detail::serializer; + + public: + using value_t = detail::value_t; + /// JSON Pointer, see @ref nlohmann::json_pointer + using json_pointer = ::nlohmann::json_pointer; + template + using json_serializer = JSONSerializer; + /// how to treat decoding errors + using error_handler_t = detail::error_handler_t; + /// how to treat CBOR tags + using cbor_tag_handler_t = detail::cbor_tag_handler_t; + /// helper type for initializer lists of basic_json values + using initializer_list_t = std::initializer_list>; + + using input_format_t = detail::input_format_t; + /// SAX interface type, see @ref nlohmann::json_sax + using json_sax_t = json_sax; + + //////////////// + // exceptions // + //////////////// + + /// @name exceptions + /// Classes to implement user-defined exceptions. + /// @{ + + /// @copydoc detail::exception + using exception = detail::exception; + /// @copydoc detail::parse_error + using parse_error = detail::parse_error; + /// @copydoc detail::invalid_iterator + using invalid_iterator = detail::invalid_iterator; + /// @copydoc detail::type_error + using type_error = detail::type_error; + /// @copydoc detail::out_of_range + using out_of_range = detail::out_of_range; + /// @copydoc detail::other_error + using other_error = detail::other_error; + + /// @} + + + ///////////////////// + // container types // + ///////////////////// + + /// @name container types + /// The canonic container types to use @ref basic_json like any other STL + /// container. + /// @{ + + /// the type of elements in a basic_json container + using value_type = basic_json; + + /// the type of an element reference + using reference = value_type&; + /// the type of an element const reference + using const_reference = const value_type&; + + /// a type to represent differences between iterators + using difference_type = std::ptrdiff_t; + /// a type to represent container sizes + using size_type = std::size_t; + + /// the allocator type + using allocator_type = AllocatorType; + + /// the type of an element pointer + using pointer = typename std::allocator_traits::pointer; + /// the type of an element const pointer + using const_pointer = typename std::allocator_traits::const_pointer; + + /// an iterator for a basic_json container + using iterator = iter_impl; + /// a const iterator for a basic_json container + using const_iterator = iter_impl; + /// a reverse iterator for a basic_json container + using reverse_iterator = json_reverse_iterator; + /// a const reverse iterator for a basic_json container + using const_reverse_iterator = json_reverse_iterator; + + /// @} + + + /*! + @brief returns the allocator associated with the container + */ + static allocator_type get_allocator() + { + return allocator_type(); + } + + /*! + @brief returns version information on the library + + This function returns a JSON object with information about the library, + including the version number and information on the platform and compiler. + + @return JSON object holding version information + key | description + ----------- | --------------- + `compiler` | Information on the used compiler. It is an object with the following keys: `c++` (the used C++ standard), `family` (the compiler family; possible values are `clang`, `icc`, `gcc`, `ilecpp`, `msvc`, `pgcpp`, `sunpro`, and `unknown`), and `version` (the compiler version). + `copyright` | The copyright line for the library as string. + `name` | The name of the library as string. + `platform` | The used platform as string. Possible values are `win32`, `linux`, `apple`, `unix`, and `unknown`. + `url` | The URL of the project as string. + `version` | The version of the library. It is an object with the following keys: `major`, `minor`, and `patch` as defined by [Semantic Versioning](http://semver.org), and `string` (the version string). + + @liveexample{The following code shows an example output of the `meta()` + function.,meta} + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @complexity Constant. + + @since 2.1.0 + */ + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json meta() + { + basic_json result; + + result["copyright"] = "(C) 2013-2021 Niels Lohmann"; + result["name"] = "JSON for Modern C++"; + result["url"] = "https://github.com/nlohmann/json"; + result["version"]["string"] = + std::to_string(NLOHMANN_JSON_VERSION_MAJOR) + "." + + std::to_string(NLOHMANN_JSON_VERSION_MINOR) + "." + + std::to_string(NLOHMANN_JSON_VERSION_PATCH); + result["version"]["major"] = NLOHMANN_JSON_VERSION_MAJOR; + result["version"]["minor"] = NLOHMANN_JSON_VERSION_MINOR; + result["version"]["patch"] = NLOHMANN_JSON_VERSION_PATCH; + +#ifdef _WIN32 + result["platform"] = "win32"; +#elif defined __linux__ + result["platform"] = "linux"; +#elif defined __APPLE__ + result["platform"] = "apple"; +#elif defined __unix__ + result["platform"] = "unix"; +#else + result["platform"] = "unknown"; +#endif + +#if defined(__ICC) || defined(__INTEL_COMPILER) + result["compiler"] = {{"family", "icc"}, {"version", __INTEL_COMPILER}}; +#elif defined(__clang__) + result["compiler"] = {{"family", "clang"}, {"version", __clang_version__}}; +#elif defined(__GNUC__) || defined(__GNUG__) + result["compiler"] = {{"family", "gcc"}, {"version", std::to_string(__GNUC__) + "." + std::to_string(__GNUC_MINOR__) + "." + std::to_string(__GNUC_PATCHLEVEL__)}}; +#elif defined(__HP_cc) || defined(__HP_aCC) + result["compiler"] = "hp" +#elif defined(__IBMCPP__) + result["compiler"] = {{"family", "ilecpp"}, {"version", __IBMCPP__}}; +#elif defined(_MSC_VER) + result["compiler"] = {{"family", "msvc"}, {"version", _MSC_VER}}; +#elif defined(__PGI) + result["compiler"] = {{"family", "pgcpp"}, {"version", __PGI}}; +#elif defined(__SUNPRO_CC) + result["compiler"] = {{"family", "sunpro"}, {"version", __SUNPRO_CC}}; +#else + result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}}; +#endif + +#ifdef __cplusplus + result["compiler"]["c++"] = std::to_string(__cplusplus); +#else + result["compiler"]["c++"] = "unknown"; +#endif + return result; + } + + + /////////////////////////// + // JSON value data types // + /////////////////////////// + + /// @name JSON value data types + /// The data types to store a JSON value. These types are derived from + /// the template arguments passed to class @ref basic_json. + /// @{ + +#if defined(JSON_HAS_CPP_14) + // Use transparent comparator if possible, combined with perfect forwarding + // on find() and count() calls prevents unnecessary string construction. + using object_comparator_t = std::less<>; +#else + using object_comparator_t = std::less; +#endif + + /*! + @brief a type for an object + + [RFC 8259](https://tools.ietf.org/html/rfc8259) describes JSON objects as follows: + > An object is an unordered collection of zero or more name/value pairs, + > where a name is a string and a value is a string, number, boolean, null, + > object, or array. + + To store objects in C++, a type is defined by the template parameters + described below. + + @tparam ObjectType the container to store objects (e.g., `std::map` or + `std::unordered_map`) + @tparam StringType the type of the keys or names (e.g., `std::string`). + The comparison function `std::less` is used to order elements + inside the container. + @tparam AllocatorType the allocator to use for objects (e.g., + `std::allocator`) + + #### Default type + + With the default values for @a ObjectType (`std::map`), @a StringType + (`std::string`), and @a AllocatorType (`std::allocator`), the default + value for @a object_t is: + + @code {.cpp} + std::map< + std::string, // key_type + basic_json, // value_type + std::less, // key_compare + std::allocator> // allocator_type + > + @endcode + + #### Behavior + + The choice of @a object_t influences the behavior of the JSON class. With + the default type, objects have the following behavior: + + - When all names are unique, objects will be interoperable in the sense + that all software implementations receiving that object will agree on + the name-value mappings. + - When the names within an object are not unique, it is unspecified which + one of the values for a given key will be chosen. For instance, + `{"key": 2, "key": 1}` could be equal to either `{"key": 1}` or + `{"key": 2}`. + - Internally, name/value pairs are stored in lexicographical order of the + names. Objects will also be serialized (see @ref dump) in this order. + For instance, `{"b": 1, "a": 2}` and `{"a": 2, "b": 1}` will be stored + and serialized as `{"a": 2, "b": 1}`. + - When comparing objects, the order of the name/value pairs is irrelevant. + This makes objects interoperable in the sense that they will not be + affected by these differences. For instance, `{"b": 1, "a": 2}` and + `{"a": 2, "b": 1}` will be treated as equal. + + #### Limits + + [RFC 8259](https://tools.ietf.org/html/rfc8259) specifies: + > An implementation may set limits on the maximum depth of nesting. + + In this class, the object's limit of nesting is not explicitly constrained. + However, a maximum depth of nesting may be introduced by the compiler or + runtime environment. A theoretical limit can be queried by calling the + @ref max_size function of a JSON object. + + #### Storage + + Objects are stored as pointers in a @ref basic_json type. That is, for any + access to object values, a pointer of type `object_t*` must be + dereferenced. + + @sa see @ref array_t -- type for an array value + + @since version 1.0.0 + + @note The order name/value pairs are added to the object is *not* + preserved by the library. Therefore, iterating an object may return + name/value pairs in a different order than they were originally stored. In + fact, keys will be traversed in alphabetical order as `std::map` with + `std::less` is used by default. Please note this behavior conforms to [RFC + 8259](https://tools.ietf.org/html/rfc8259), because any order implements the + specified "unordered" nature of JSON objects. + */ + using object_t = ObjectType>>; + + /*! + @brief a type for an array + + [RFC 8259](https://tools.ietf.org/html/rfc8259) describes JSON arrays as follows: + > An array is an ordered sequence of zero or more values. + + To store objects in C++, a type is defined by the template parameters + explained below. + + @tparam ArrayType container type to store arrays (e.g., `std::vector` or + `std::list`) + @tparam AllocatorType allocator to use for arrays (e.g., `std::allocator`) + + #### Default type + + With the default values for @a ArrayType (`std::vector`) and @a + AllocatorType (`std::allocator`), the default value for @a array_t is: + + @code {.cpp} + std::vector< + basic_json, // value_type + std::allocator // allocator_type + > + @endcode + + #### Limits + + [RFC 8259](https://tools.ietf.org/html/rfc8259) specifies: + > An implementation may set limits on the maximum depth of nesting. + + In this class, the array's limit of nesting is not explicitly constrained. + However, a maximum depth of nesting may be introduced by the compiler or + runtime environment. A theoretical limit can be queried by calling the + @ref max_size function of a JSON array. + + #### Storage + + Arrays are stored as pointers in a @ref basic_json type. That is, for any + access to array values, a pointer of type `array_t*` must be dereferenced. + + @sa see @ref object_t -- type for an object value + + @since version 1.0.0 + */ + using array_t = ArrayType>; + + /*! + @brief a type for a string + + [RFC 8259](https://tools.ietf.org/html/rfc8259) describes JSON strings as follows: + > A string is a sequence of zero or more Unicode characters. + + To store objects in C++, a type is defined by the template parameter + described below. Unicode values are split by the JSON class into + byte-sized characters during deserialization. + + @tparam StringType the container to store strings (e.g., `std::string`). + Note this container is used for keys/names in objects, see @ref object_t. + + #### Default type + + With the default values for @a StringType (`std::string`), the default + value for @a string_t is: + + @code {.cpp} + std::string + @endcode + + #### Encoding + + Strings are stored in UTF-8 encoding. Therefore, functions like + `std::string::size()` or `std::string::length()` return the number of + bytes in the string rather than the number of characters or glyphs. + + #### String comparison + + [RFC 8259](https://tools.ietf.org/html/rfc8259) states: + > Software implementations are typically required to test names of object + > members for equality. Implementations that transform the textual + > representation into sequences of Unicode code units and then perform the + > comparison numerically, code unit by code unit, are interoperable in the + > sense that implementations will agree in all cases on equality or + > inequality of two strings. For example, implementations that compare + > strings with escaped characters unconverted may incorrectly find that + > `"a\\b"` and `"a\u005Cb"` are not equal. + + This implementation is interoperable as it does compare strings code unit + by code unit. + + #### Storage + + String values are stored as pointers in a @ref basic_json type. That is, + for any access to string values, a pointer of type `string_t*` must be + dereferenced. + + @since version 1.0.0 + */ + using string_t = StringType; + + /*! + @brief a type for a boolean + + [RFC 8259](https://tools.ietf.org/html/rfc8259) implicitly describes a boolean as a + type which differentiates the two literals `true` and `false`. + + To store objects in C++, a type is defined by the template parameter @a + BooleanType which chooses the type to use. + + #### Default type + + With the default values for @a BooleanType (`bool`), the default value for + @a boolean_t is: + + @code {.cpp} + bool + @endcode + + #### Storage + + Boolean values are stored directly inside a @ref basic_json type. + + @since version 1.0.0 + */ + using boolean_t = BooleanType; + + /*! + @brief a type for a number (integer) + + [RFC 8259](https://tools.ietf.org/html/rfc8259) describes numbers as follows: + > The representation of numbers is similar to that used in most + > programming languages. A number is represented in base 10 using decimal + > digits. It contains an integer component that may be prefixed with an + > optional minus sign, which may be followed by a fraction part and/or an + > exponent part. Leading zeros are not allowed. (...) Numeric values that + > cannot be represented in the grammar below (such as Infinity and NaN) + > are not permitted. + + This description includes both integer and floating-point numbers. + However, C++ allows more precise storage if it is known whether the number + is a signed integer, an unsigned integer or a floating-point number. + Therefore, three different types, @ref number_integer_t, @ref + number_unsigned_t and @ref number_float_t are used. + + To store integer numbers in C++, a type is defined by the template + parameter @a NumberIntegerType which chooses the type to use. + + #### Default type + + With the default values for @a NumberIntegerType (`int64_t`), the default + value for @a number_integer_t is: + + @code {.cpp} + int64_t + @endcode + + #### Default behavior + + - The restrictions about leading zeros is not enforced in C++. Instead, + leading zeros in integer literals lead to an interpretation as octal + number. Internally, the value will be stored as decimal number. For + instance, the C++ integer literal `010` will be serialized to `8`. + During deserialization, leading zeros yield an error. + - Not-a-number (NaN) values will be serialized to `null`. + + #### Limits + + [RFC 8259](https://tools.ietf.org/html/rfc8259) specifies: + > An implementation may set limits on the range and precision of numbers. + + When the default type is used, the maximal integer number that can be + stored is `9223372036854775807` (INT64_MAX) and the minimal integer number + that can be stored is `-9223372036854775808` (INT64_MIN). Integer numbers + that are out of range will yield over/underflow when used in a + constructor. During deserialization, too large or small integer numbers + will be automatically be stored as @ref number_unsigned_t or @ref + number_float_t. + + [RFC 8259](https://tools.ietf.org/html/rfc8259) further states: + > Note that when such software is used, numbers that are integers and are + > in the range \f$[-2^{53}+1, 2^{53}-1]\f$ are interoperable in the sense + > that implementations will agree exactly on their numeric values. + + As this range is a subrange of the exactly supported range [INT64_MIN, + INT64_MAX], this class's integer type is interoperable. + + #### Storage + + Integer number values are stored directly inside a @ref basic_json type. + + @sa see @ref number_float_t -- type for number values (floating-point) + + @sa see @ref number_unsigned_t -- type for number values (unsigned integer) + + @since version 1.0.0 + */ + using number_integer_t = NumberIntegerType; + + /*! + @brief a type for a number (unsigned) + + [RFC 8259](https://tools.ietf.org/html/rfc8259) describes numbers as follows: + > The representation of numbers is similar to that used in most + > programming languages. A number is represented in base 10 using decimal + > digits. It contains an integer component that may be prefixed with an + > optional minus sign, which may be followed by a fraction part and/or an + > exponent part. Leading zeros are not allowed. (...) Numeric values that + > cannot be represented in the grammar below (such as Infinity and NaN) + > are not permitted. + + This description includes both integer and floating-point numbers. + However, C++ allows more precise storage if it is known whether the number + is a signed integer, an unsigned integer or a floating-point number. + Therefore, three different types, @ref number_integer_t, @ref + number_unsigned_t and @ref number_float_t are used. + + To store unsigned integer numbers in C++, a type is defined by the + template parameter @a NumberUnsignedType which chooses the type to use. + + #### Default type + + With the default values for @a NumberUnsignedType (`uint64_t`), the + default value for @a number_unsigned_t is: + + @code {.cpp} + uint64_t + @endcode + + #### Default behavior + + - The restrictions about leading zeros is not enforced in C++. Instead, + leading zeros in integer literals lead to an interpretation as octal + number. Internally, the value will be stored as decimal number. For + instance, the C++ integer literal `010` will be serialized to `8`. + During deserialization, leading zeros yield an error. + - Not-a-number (NaN) values will be serialized to `null`. + + #### Limits + + [RFC 8259](https://tools.ietf.org/html/rfc8259) specifies: + > An implementation may set limits on the range and precision of numbers. + + When the default type is used, the maximal integer number that can be + stored is `18446744073709551615` (UINT64_MAX) and the minimal integer + number that can be stored is `0`. Integer numbers that are out of range + will yield over/underflow when used in a constructor. During + deserialization, too large or small integer numbers will be automatically + be stored as @ref number_integer_t or @ref number_float_t. + + [RFC 8259](https://tools.ietf.org/html/rfc8259) further states: + > Note that when such software is used, numbers that are integers and are + > in the range \f$[-2^{53}+1, 2^{53}-1]\f$ are interoperable in the sense + > that implementations will agree exactly on their numeric values. + + As this range is a subrange (when considered in conjunction with the + number_integer_t type) of the exactly supported range [0, UINT64_MAX], + this class's integer type is interoperable. + + #### Storage + + Integer number values are stored directly inside a @ref basic_json type. + + @sa see @ref number_float_t -- type for number values (floating-point) + @sa see @ref number_integer_t -- type for number values (integer) + + @since version 2.0.0 + */ + using number_unsigned_t = NumberUnsignedType; + + /*! + @brief a type for a number (floating-point) + + [RFC 8259](https://tools.ietf.org/html/rfc8259) describes numbers as follows: + > The representation of numbers is similar to that used in most + > programming languages. A number is represented in base 10 using decimal + > digits. It contains an integer component that may be prefixed with an + > optional minus sign, which may be followed by a fraction part and/or an + > exponent part. Leading zeros are not allowed. (...) Numeric values that + > cannot be represented in the grammar below (such as Infinity and NaN) + > are not permitted. + + This description includes both integer and floating-point numbers. + However, C++ allows more precise storage if it is known whether the number + is a signed integer, an unsigned integer or a floating-point number. + Therefore, three different types, @ref number_integer_t, @ref + number_unsigned_t and @ref number_float_t are used. + + To store floating-point numbers in C++, a type is defined by the template + parameter @a NumberFloatType which chooses the type to use. + + #### Default type + + With the default values for @a NumberFloatType (`double`), the default + value for @a number_float_t is: + + @code {.cpp} + double + @endcode + + #### Default behavior + + - The restrictions about leading zeros is not enforced in C++. Instead, + leading zeros in floating-point literals will be ignored. Internally, + the value will be stored as decimal number. For instance, the C++ + floating-point literal `01.2` will be serialized to `1.2`. During + deserialization, leading zeros yield an error. + - Not-a-number (NaN) values will be serialized to `null`. + + #### Limits + + [RFC 8259](https://tools.ietf.org/html/rfc8259) states: + > This specification allows implementations to set limits on the range and + > precision of numbers accepted. Since software that implements IEEE + > 754-2008 binary64 (double precision) numbers is generally available and + > widely used, good interoperability can be achieved by implementations + > that expect no more precision or range than these provide, in the sense + > that implementations will approximate JSON numbers within the expected + > precision. + + This implementation does exactly follow this approach, as it uses double + precision floating-point numbers. Note values smaller than + `-1.79769313486232e+308` and values greater than `1.79769313486232e+308` + will be stored as NaN internally and be serialized to `null`. + + #### Storage + + Floating-point number values are stored directly inside a @ref basic_json + type. + + @sa see @ref number_integer_t -- type for number values (integer) + + @sa see @ref number_unsigned_t -- type for number values (unsigned integer) + + @since version 1.0.0 + */ + using number_float_t = NumberFloatType; + + /*! + @brief a type for a packed binary type + + This type is a type designed to carry binary data that appears in various + serialized formats, such as CBOR's Major Type 2, MessagePack's bin, and + BSON's generic binary subtype. This type is NOT a part of standard JSON and + exists solely for compatibility with these binary types. As such, it is + simply defined as an ordered sequence of zero or more byte values. + + Additionally, as an implementation detail, the subtype of the binary data is + carried around as a `std::uint8_t`, which is compatible with both of the + binary data formats that use binary subtyping, (though the specific + numbering is incompatible with each other, and it is up to the user to + translate between them). + + [CBOR's RFC 7049](https://tools.ietf.org/html/rfc7049) describes this type + as: + > Major type 2: a byte string. The string's length in bytes is represented + > following the rules for positive integers (major type 0). + + [MessagePack's documentation on the bin type + family](https://github.com/msgpack/msgpack/blob/master/spec.md#bin-format-family) + describes this type as: + > Bin format family stores an byte array in 2, 3, or 5 bytes of extra bytes + > in addition to the size of the byte array. + + [BSON's specifications](http://bsonspec.org/spec.html) describe several + binary types; however, this type is intended to represent the generic binary + type which has the description: + > Generic binary subtype - This is the most commonly used binary subtype and + > should be the 'default' for drivers and tools. + + None of these impose any limitations on the internal representation other + than the basic unit of storage be some type of array whose parts are + decomposable into bytes. + + The default representation of this binary format is a + `std::vector`, which is a very common way to represent a byte + array in modern C++. + + #### Default type + + The default values for @a BinaryType is `std::vector` + + #### Storage + + Binary Arrays are stored as pointers in a @ref basic_json type. That is, + for any access to array values, a pointer of the type `binary_t*` must be + dereferenced. + + #### Notes on subtypes + + - CBOR + - Binary values are represented as byte strings. Subtypes are serialized + as tagged values. + - MessagePack + - If a subtype is given and the binary array contains exactly 1, 2, 4, 8, + or 16 elements, the fixext family (fixext1, fixext2, fixext4, fixext8) + is used. For other sizes, the ext family (ext8, ext16, ext32) is used. + The subtype is then added as singed 8-bit integer. + - If no subtype is given, the bin family (bin8, bin16, bin32) is used. + - BSON + - If a subtype is given, it is used and added as unsigned 8-bit integer. + - If no subtype is given, the generic binary subtype 0x00 is used. + + @sa see @ref binary -- create a binary array + + @since version 3.8.0 + */ + using binary_t = nlohmann::byte_container_with_subtype; + /// @} + + private: + + /// helper for exception-safe object creation + template + JSON_HEDLEY_RETURNS_NON_NULL + static T* create(Args&& ... args) + { + AllocatorType alloc; + using AllocatorTraits = std::allocator_traits>; + + auto deleter = [&](T * obj) + { + AllocatorTraits::deallocate(alloc, obj, 1); + }; + std::unique_ptr obj(AllocatorTraits::allocate(alloc, 1), deleter); + AllocatorTraits::construct(alloc, obj.get(), std::forward(args)...); + JSON_ASSERT(obj != nullptr); + return obj.release(); + } + + //////////////////////// + // JSON value storage // + //////////////////////// + + JSON_PRIVATE_UNLESS_TESTED: + /*! + @brief a JSON value + + The actual storage for a JSON value of the @ref basic_json class. This + union combines the different storage types for the JSON value types + defined in @ref value_t. + + JSON type | value_t type | used type + --------- | --------------- | ------------------------ + object | object | pointer to @ref object_t + array | array | pointer to @ref array_t + string | string | pointer to @ref string_t + boolean | boolean | @ref boolean_t + number | number_integer | @ref number_integer_t + number | number_unsigned | @ref number_unsigned_t + number | number_float | @ref number_float_t + binary | binary | pointer to @ref binary_t + null | null | *no value is stored* + + @note Variable-length types (objects, arrays, and strings) are stored as + pointers. The size of the union should not exceed 64 bits if the default + value types are used. + + @since version 1.0.0 + */ + union json_value + { + /// object (stored with pointer to save storage) + object_t* object; + /// array (stored with pointer to save storage) + array_t* array; + /// string (stored with pointer to save storage) + string_t* string; + /// binary (stored with pointer to save storage) + binary_t* binary; + /// boolean + boolean_t boolean; + /// number (integer) + number_integer_t number_integer; + /// number (unsigned integer) + number_unsigned_t number_unsigned; + /// number (floating-point) + number_float_t number_float; + + /// default constructor (for null values) + json_value() = default; + /// constructor for booleans + json_value(boolean_t v) noexcept : boolean(v) {} + /// constructor for numbers (integer) + json_value(number_integer_t v) noexcept : number_integer(v) {} + /// constructor for numbers (unsigned) + json_value(number_unsigned_t v) noexcept : number_unsigned(v) {} + /// constructor for numbers (floating-point) + json_value(number_float_t v) noexcept : number_float(v) {} + /// constructor for empty values of a given type + json_value(value_t t) + { + switch (t) + { + case value_t::object: + { + object = create(); + break; + } + + case value_t::array: + { + array = create(); + break; + } + + case value_t::string: + { + string = create(""); + break; + } + + case value_t::binary: + { + binary = create(); + break; + } + + case value_t::boolean: + { + boolean = boolean_t(false); + break; + } + + case value_t::number_integer: + { + number_integer = number_integer_t(0); + break; + } + + case value_t::number_unsigned: + { + number_unsigned = number_unsigned_t(0); + break; + } + + case value_t::number_float: + { + number_float = number_float_t(0.0); + break; + } + + case value_t::null: + { + object = nullptr; // silence warning, see #821 + break; + } + + case value_t::discarded: + default: + { + object = nullptr; // silence warning, see #821 + if (JSON_HEDLEY_UNLIKELY(t == value_t::null)) + { + JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.10.4", basic_json())); // LCOV_EXCL_LINE + } + break; + } + } + } + + /// constructor for strings + json_value(const string_t& value) + { + string = create(value); + } + + /// constructor for rvalue strings + json_value(string_t&& value) + { + string = create(std::move(value)); + } + + /// constructor for objects + json_value(const object_t& value) + { + object = create(value); + } + + /// constructor for rvalue objects + json_value(object_t&& value) + { + object = create(std::move(value)); + } + + /// constructor for arrays + json_value(const array_t& value) + { + array = create(value); + } + + /// constructor for rvalue arrays + json_value(array_t&& value) + { + array = create(std::move(value)); + } + + /// constructor for binary arrays + json_value(const typename binary_t::container_type& value) + { + binary = create(value); + } + + /// constructor for rvalue binary arrays + json_value(typename binary_t::container_type&& value) + { + binary = create(std::move(value)); + } + + /// constructor for binary arrays (internal type) + json_value(const binary_t& value) + { + binary = create(value); + } + + /// constructor for rvalue binary arrays (internal type) + json_value(binary_t&& value) + { + binary = create(std::move(value)); + } + + void destroy(value_t t) + { + if (t == value_t::array || t == value_t::object) + { + // flatten the current json_value to a heap-allocated stack + std::vector stack; + + // move the top-level items to stack + if (t == value_t::array) + { + stack.reserve(array->size()); + std::move(array->begin(), array->end(), std::back_inserter(stack)); + } + else + { + stack.reserve(object->size()); + for (auto&& it : *object) + { + stack.push_back(std::move(it.second)); + } + } + + while (!stack.empty()) + { + // move the last item to local variable to be processed + basic_json current_item(std::move(stack.back())); + stack.pop_back(); + + // if current_item is array/object, move + // its children to the stack to be processed later + if (current_item.is_array()) + { + std::move(current_item.m_value.array->begin(), current_item.m_value.array->end(), std::back_inserter(stack)); + + current_item.m_value.array->clear(); + } + else if (current_item.is_object()) + { + for (auto&& it : *current_item.m_value.object) + { + stack.push_back(std::move(it.second)); + } + + current_item.m_value.object->clear(); + } + + // it's now safe that current_item get destructed + // since it doesn't have any children + } + } + + switch (t) + { + case value_t::object: + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, object); + std::allocator_traits::deallocate(alloc, object, 1); + break; + } + + case value_t::array: + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, array); + std::allocator_traits::deallocate(alloc, array, 1); + break; + } + + case value_t::string: + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, string); + std::allocator_traits::deallocate(alloc, string, 1); + break; + } + + case value_t::binary: + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, binary); + std::allocator_traits::deallocate(alloc, binary, 1); + break; + } + + case value_t::null: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::discarded: + default: + { + break; + } + } + } + }; + + private: + /*! + @brief checks the class invariants + + This function asserts the class invariants. It needs to be called at the + end of every constructor to make sure that created objects respect the + invariant. Furthermore, it has to be called each time the type of a JSON + value is changed, because the invariant expresses a relationship between + @a m_type and @a m_value. + + Furthermore, the parent relation is checked for arrays and objects: If + @a check_parents true and the value is an array or object, then the + container's elements must have the current value as parent. + + @param[in] check_parents whether the parent relation should be checked. + The value is true by default and should only be set to false + during destruction of objects when the invariant does not + need to hold. + */ + void assert_invariant(bool check_parents = true) const noexcept + { + JSON_ASSERT(m_type != value_t::object || m_value.object != nullptr); + JSON_ASSERT(m_type != value_t::array || m_value.array != nullptr); + JSON_ASSERT(m_type != value_t::string || m_value.string != nullptr); + JSON_ASSERT(m_type != value_t::binary || m_value.binary != nullptr); + +#if JSON_DIAGNOSTICS + JSON_TRY + { + // cppcheck-suppress assertWithSideEffect + JSON_ASSERT(!check_parents || !is_structured() || std::all_of(begin(), end(), [this](const basic_json & j) + { + return j.m_parent == this; + })); + } + JSON_CATCH(...) {} // LCOV_EXCL_LINE +#endif + static_cast(check_parents); + } + + void set_parents() + { +#if JSON_DIAGNOSTICS + switch (m_type) + { + case value_t::array: + { + for (auto& element : *m_value.array) + { + element.m_parent = this; + } + break; + } + + case value_t::object: + { + for (auto& element : *m_value.object) + { + element.second.m_parent = this; + } + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + break; + } +#endif + } + + iterator set_parents(iterator it, typename iterator::difference_type count) + { +#if JSON_DIAGNOSTICS + for (typename iterator::difference_type i = 0; i < count; ++i) + { + (it + i)->m_parent = this; + } +#else + static_cast(count); +#endif + return it; + } + + reference set_parent(reference j, std::size_t old_capacity = std::size_t(-1)) + { +#if JSON_DIAGNOSTICS + if (old_capacity != std::size_t(-1)) + { + // see https://github.com/nlohmann/json/issues/2838 + JSON_ASSERT(type() == value_t::array); + if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity)) + { + // capacity has changed: update all parents + set_parents(); + return j; + } + } + + // ordered_json uses a vector internally, so pointers could have + // been invalidated; see https://github.com/nlohmann/json/issues/2962 +#ifdef JSON_HEDLEY_MSVC_VERSION +#pragma warning(push ) +#pragma warning(disable : 4127) // ignore warning to replace if with if constexpr +#endif + if (detail::is_ordered_map::value) + { + set_parents(); + return j; + } +#ifdef JSON_HEDLEY_MSVC_VERSION +#pragma warning( pop ) +#endif + + j.m_parent = this; +#else + static_cast(j); + static_cast(old_capacity); +#endif + return j; + } + + public: + ////////////////////////// + // JSON parser callback // + ////////////////////////// + + /*! + @brief parser event types + + The parser callback distinguishes the following events: + - `object_start`: the parser read `{` and started to process a JSON object + - `key`: the parser read a key of a value in an object + - `object_end`: the parser read `}` and finished processing a JSON object + - `array_start`: the parser read `[` and started to process a JSON array + - `array_end`: the parser read `]` and finished processing a JSON array + - `value`: the parser finished reading a JSON value + + @image html callback_events.png "Example when certain parse events are triggered" + + @sa see @ref parser_callback_t for more information and examples + */ + using parse_event_t = detail::parse_event_t; + + /*! + @brief per-element parser callback type + + With a parser callback function, the result of parsing a JSON text can be + influenced. When passed to @ref parse, it is called on certain events + (passed as @ref parse_event_t via parameter @a event) with a set recursion + depth @a depth and context JSON value @a parsed. The return value of the + callback function is a boolean indicating whether the element that emitted + the callback shall be kept or not. + + We distinguish six scenarios (determined by the event type) in which the + callback function can be called. The following table describes the values + of the parameters @a depth, @a event, and @a parsed. + + parameter @a event | description | parameter @a depth | parameter @a parsed + ------------------ | ----------- | ------------------ | ------------------- + parse_event_t::object_start | the parser read `{` and started to process a JSON object | depth of the parent of the JSON object | a JSON value with type discarded + parse_event_t::key | the parser read a key of a value in an object | depth of the currently parsed JSON object | a JSON string containing the key + parse_event_t::object_end | the parser read `}` and finished processing a JSON object | depth of the parent of the JSON object | the parsed JSON object + parse_event_t::array_start | the parser read `[` and started to process a JSON array | depth of the parent of the JSON array | a JSON value with type discarded + parse_event_t::array_end | the parser read `]` and finished processing a JSON array | depth of the parent of the JSON array | the parsed JSON array + parse_event_t::value | the parser finished reading a JSON value | depth of the value | the parsed JSON value + + @image html callback_events.png "Example when certain parse events are triggered" + + Discarding a value (i.e., returning `false`) has different effects + depending on the context in which function was called: + + - Discarded values in structured types are skipped. That is, the parser + will behave as if the discarded value was never read. + - In case a value outside a structured type is skipped, it is replaced + with `null`. This case happens if the top-level element is skipped. + + @param[in] depth the depth of the recursion during parsing + + @param[in] event an event of type parse_event_t indicating the context in + the callback function has been called + + @param[in,out] parsed the current intermediate parse result; note that + writing to this value has no effect for parse_event_t::key events + + @return Whether the JSON value which called the function during parsing + should be kept (`true`) or not (`false`). In the latter case, it is either + skipped completely or replaced by an empty discarded object. + + @sa see @ref parse for examples + + @since version 1.0.0 + */ + using parser_callback_t = detail::parser_callback_t; + + ////////////////// + // constructors // + ////////////////// + + /// @name constructors and destructors + /// Constructors of class @ref basic_json, copy/move constructor, copy + /// assignment, static functions creating objects, and the destructor. + /// @{ + + /*! + @brief create an empty value with a given type + + Create an empty JSON value with a given type. The value will be default + initialized with an empty value which depends on the type: + + Value type | initial value + ----------- | ------------- + null | `null` + boolean | `false` + string | `""` + number | `0` + object | `{}` + array | `[]` + binary | empty array + + @param[in] v the type of the value to create + + @complexity Constant. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @liveexample{The following code shows the constructor for different @ref + value_t values,basic_json__value_t} + + @sa see @ref clear() -- restores the postcondition of this constructor + + @since version 1.0.0 + */ + basic_json(const value_t v) + : m_type(v), m_value(v) + { + assert_invariant(); + } + + /*! + @brief create a null object + + Create a `null` JSON value. It either takes a null pointer as parameter + (explicitly creating `null`) or no parameter (implicitly creating `null`). + The passed null pointer itself is not read -- it is only used to choose + the right constructor. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this constructor never throws + exceptions. + + @liveexample{The following code shows the constructor with and without a + null pointer parameter.,basic_json__nullptr_t} + + @since version 1.0.0 + */ + basic_json(std::nullptr_t = nullptr) noexcept + : basic_json(value_t::null) + { + assert_invariant(); + } + + /*! + @brief create a JSON value + + This is a "catch all" constructor for all compatible JSON types; that is, + types for which a `to_json()` method exists. The constructor forwards the + parameter @a val to that method (to `json_serializer::to_json` method + with `U = uncvref_t`, to be exact). + + Template type @a CompatibleType includes, but is not limited to, the + following types: + - **arrays**: @ref array_t and all kinds of compatible containers such as + `std::vector`, `std::deque`, `std::list`, `std::forward_list`, + `std::array`, `std::valarray`, `std::set`, `std::unordered_set`, + `std::multiset`, and `std::unordered_multiset` with a `value_type` from + which a @ref basic_json value can be constructed. + - **objects**: @ref object_t and all kinds of compatible associative + containers such as `std::map`, `std::unordered_map`, `std::multimap`, + and `std::unordered_multimap` with a `key_type` compatible to + @ref string_t and a `value_type` from which a @ref basic_json value can + be constructed. + - **strings**: @ref string_t, string literals, and all compatible string + containers can be used. + - **numbers**: @ref number_integer_t, @ref number_unsigned_t, + @ref number_float_t, and all convertible number types such as `int`, + `size_t`, `int64_t`, `float` or `double` can be used. + - **boolean**: @ref boolean_t / `bool` can be used. + - **binary**: @ref binary_t / `std::vector` may be used, + unfortunately because string literals cannot be distinguished from binary + character arrays by the C++ type system, all types compatible with `const + char*` will be directed to the string constructor instead. This is both + for backwards compatibility, and due to the fact that a binary type is not + a standard JSON type. + + See the examples below. + + @tparam CompatibleType a type such that: + - @a CompatibleType is not derived from `std::istream`, + - @a CompatibleType is not @ref basic_json (to avoid hijacking copy/move + constructors), + - @a CompatibleType is not a different @ref basic_json type (i.e. with different template arguments) + - @a CompatibleType is not a @ref basic_json nested type (e.g., + @ref json_pointer, @ref iterator, etc ...) + - `json_serializer` has a `to_json(basic_json_t&, CompatibleType&&)` method + + @tparam U = `uncvref_t` + + @param[in] val the value to be forwarded to the respective constructor + + @complexity Usually linear in the size of the passed @a val, also + depending on the implementation of the called `to_json()` + method. + + @exceptionsafety Depends on the called constructor. For types directly + supported by the library (i.e., all types for which no `to_json()` function + was provided), strong guarantee holds: if an exception is thrown, there are + no changes to any JSON value. + + @liveexample{The following code shows the constructor with several + compatible types.,basic_json__CompatibleType} + + @since version 2.1.0 + */ + template < typename CompatibleType, + typename U = detail::uncvref_t, + detail::enable_if_t < + !detail::is_basic_json::value && detail::is_compatible_type::value, int > = 0 > + basic_json(CompatibleType && val) noexcept(noexcept( // NOLINT(bugprone-forwarding-reference-overload,bugprone-exception-escape) + JSONSerializer::to_json(std::declval(), + std::forward(val)))) + { + JSONSerializer::to_json(*this, std::forward(val)); + set_parents(); + assert_invariant(); + } + + /*! + @brief create a JSON value from an existing one + + This is a constructor for existing @ref basic_json types. + It does not hijack copy/move constructors, since the parameter has different + template arguments than the current ones. + + The constructor tries to convert the internal @ref m_value of the parameter. + + @tparam BasicJsonType a type such that: + - @a BasicJsonType is a @ref basic_json type. + - @a BasicJsonType has different template arguments than @ref basic_json_t. + + @param[in] val the @ref basic_json value to be converted. + + @complexity Usually linear in the size of the passed @a val, also + depending on the implementation of the called `to_json()` + method. + + @exceptionsafety Depends on the called constructor. For types directly + supported by the library (i.e., all types for which no `to_json()` function + was provided), strong guarantee holds: if an exception is thrown, there are + no changes to any JSON value. + + @since version 3.2.0 + */ + template < typename BasicJsonType, + detail::enable_if_t < + detail::is_basic_json::value&& !std::is_same::value, int > = 0 > + basic_json(const BasicJsonType& val) + { + using other_boolean_t = typename BasicJsonType::boolean_t; + using other_number_float_t = typename BasicJsonType::number_float_t; + using other_number_integer_t = typename BasicJsonType::number_integer_t; + using other_number_unsigned_t = typename BasicJsonType::number_unsigned_t; + using other_string_t = typename BasicJsonType::string_t; + using other_object_t = typename BasicJsonType::object_t; + using other_array_t = typename BasicJsonType::array_t; + using other_binary_t = typename BasicJsonType::binary_t; + + switch (val.type()) + { + case value_t::boolean: + JSONSerializer::to_json(*this, val.template get()); + break; + case value_t::number_float: + JSONSerializer::to_json(*this, val.template get()); + break; + case value_t::number_integer: + JSONSerializer::to_json(*this, val.template get()); + break; + case value_t::number_unsigned: + JSONSerializer::to_json(*this, val.template get()); + break; + case value_t::string: + JSONSerializer::to_json(*this, val.template get_ref()); + break; + case value_t::object: + JSONSerializer::to_json(*this, val.template get_ref()); + break; + case value_t::array: + JSONSerializer::to_json(*this, val.template get_ref()); + break; + case value_t::binary: + JSONSerializer::to_json(*this, val.template get_ref()); + break; + case value_t::null: + *this = nullptr; + break; + case value_t::discarded: + m_type = value_t::discarded; + break; + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + set_parents(); + assert_invariant(); + } + + /*! + @brief create a container (array or object) from an initializer list + + Creates a JSON value of type array or object from the passed initializer + list @a init. In case @a type_deduction is `true` (default), the type of + the JSON value to be created is deducted from the initializer list @a init + according to the following rules: + + 1. If the list is empty, an empty JSON object value `{}` is created. + 2. If the list consists of pairs whose first element is a string, a JSON + object value is created where the first elements of the pairs are + treated as keys and the second elements are as values. + 3. In all other cases, an array is created. + + The rules aim to create the best fit between a C++ initializer list and + JSON values. The rationale is as follows: + + 1. The empty initializer list is written as `{}` which is exactly an empty + JSON object. + 2. C++ has no way of describing mapped types other than to list a list of + pairs. As JSON requires that keys must be of type string, rule 2 is the + weakest constraint one can pose on initializer lists to interpret them + as an object. + 3. In all other cases, the initializer list could not be interpreted as + JSON object type, so interpreting it as JSON array type is safe. + + With the rules described above, the following JSON values cannot be + expressed by an initializer list: + + - the empty array (`[]`): use @ref array(initializer_list_t) + with an empty initializer list in this case + - arrays whose elements satisfy rule 2: use @ref + array(initializer_list_t) with the same initializer list + in this case + + @note When used without parentheses around an empty initializer list, @ref + basic_json() is called instead of this function, yielding the JSON null + value. + + @param[in] init initializer list with JSON values + + @param[in] type_deduction internal parameter; when set to `true`, the type + of the JSON value is deducted from the initializer list @a init; when set + to `false`, the type provided via @a manual_type is forced. This mode is + used by the functions @ref array(initializer_list_t) and + @ref object(initializer_list_t). + + @param[in] manual_type internal parameter; when @a type_deduction is set + to `false`, the created JSON value will use the provided type (only @ref + value_t::array and @ref value_t::object are valid); when @a type_deduction + is set to `true`, this parameter has no effect + + @throw type_error.301 if @a type_deduction is `false`, @a manual_type is + `value_t::object`, but @a init contains an element which is not a pair + whose first element is a string. In this case, the constructor could not + create an object. If @a type_deduction would have be `true`, an array + would have been created. See @ref object(initializer_list_t) + for an example. + + @complexity Linear in the size of the initializer list @a init. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @liveexample{The example below shows how JSON values are created from + initializer lists.,basic_json__list_init_t} + + @sa see @ref array(initializer_list_t) -- create a JSON array + value from an initializer list + @sa see @ref object(initializer_list_t) -- create a JSON object + value from an initializer list + + @since version 1.0.0 + */ + basic_json(initializer_list_t init, + bool type_deduction = true, + value_t manual_type = value_t::array) + { + // check if each element is an array with two elements whose first + // element is a string + bool is_an_object = std::all_of(init.begin(), init.end(), + [](const detail::json_ref& element_ref) + { + return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[0].is_string(); + }); + + // adjust type if type deduction is not wanted + if (!type_deduction) + { + // if array is wanted, do not create an object though possible + if (manual_type == value_t::array) + { + is_an_object = false; + } + + // if object is wanted but impossible, throw an exception + if (JSON_HEDLEY_UNLIKELY(manual_type == value_t::object && !is_an_object)) + { + JSON_THROW(type_error::create(301, "cannot create object from initializer list", basic_json())); + } + } + + if (is_an_object) + { + // the initializer list is a list of pairs -> create object + m_type = value_t::object; + m_value = value_t::object; + + for (auto& element_ref : init) + { + auto element = element_ref.moved_or_copied(); + m_value.object->emplace( + std::move(*((*element.m_value.array)[0].m_value.string)), + std::move((*element.m_value.array)[1])); + } + } + else + { + // the initializer list describes an array -> create array + m_type = value_t::array; + m_value.array = create(init.begin(), init.end()); + } + + set_parents(); + assert_invariant(); + } + + /*! + @brief explicitly create a binary array (without subtype) + + Creates a JSON binary array value from a given binary container. Binary + values are part of various binary formats, such as CBOR, MessagePack, and + BSON. This constructor is used to create a value for serialization to those + formats. + + @note Note, this function exists because of the difficulty in correctly + specifying the correct template overload in the standard value ctor, as both + JSON arrays and JSON binary arrays are backed with some form of a + `std::vector`. Because JSON binary arrays are a non-standard extension it + was decided that it would be best to prevent automatic initialization of a + binary array type, for backwards compatibility and so it does not happen on + accident. + + @param[in] init container containing bytes to use as binary type + + @return JSON binary array value + + @complexity Linear in the size of @a init. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @since version 3.8.0 + */ + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json binary(const typename binary_t::container_type& init) + { + auto res = basic_json(); + res.m_type = value_t::binary; + res.m_value = init; + return res; + } + + /*! + @brief explicitly create a binary array (with subtype) + + Creates a JSON binary array value from a given binary container. Binary + values are part of various binary formats, such as CBOR, MessagePack, and + BSON. This constructor is used to create a value for serialization to those + formats. + + @note Note, this function exists because of the difficulty in correctly + specifying the correct template overload in the standard value ctor, as both + JSON arrays and JSON binary arrays are backed with some form of a + `std::vector`. Because JSON binary arrays are a non-standard extension it + was decided that it would be best to prevent automatic initialization of a + binary array type, for backwards compatibility and so it does not happen on + accident. + + @param[in] init container containing bytes to use as binary type + @param[in] subtype subtype to use in MessagePack and BSON + + @return JSON binary array value + + @complexity Linear in the size of @a init. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @since version 3.8.0 + */ + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype) + { + auto res = basic_json(); + res.m_type = value_t::binary; + res.m_value = binary_t(init, subtype); + return res; + } + + /// @copydoc binary(const typename binary_t::container_type&) + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json binary(typename binary_t::container_type&& init) + { + auto res = basic_json(); + res.m_type = value_t::binary; + res.m_value = std::move(init); + return res; + } + + /// @copydoc binary(const typename binary_t::container_type&, typename binary_t::subtype_type) + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype) + { + auto res = basic_json(); + res.m_type = value_t::binary; + res.m_value = binary_t(std::move(init), subtype); + return res; + } + + /*! + @brief explicitly create an array from an initializer list + + Creates a JSON array value from a given initializer list. That is, given a + list of values `a, b, c`, creates the JSON value `[a, b, c]`. If the + initializer list is empty, the empty array `[]` is created. + + @note This function is only needed to express two edge cases that cannot + be realized with the initializer list constructor (@ref + basic_json(initializer_list_t, bool, value_t)). These cases + are: + 1. creating an array whose elements are all pairs whose first element is a + string -- in this case, the initializer list constructor would create an + object, taking the first elements as keys + 2. creating an empty array -- passing the empty initializer list to the + initializer list constructor yields an empty object + + @param[in] init initializer list with JSON values to create an array from + (optional) + + @return JSON array value + + @complexity Linear in the size of @a init. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @liveexample{The following code shows an example for the `array` + function.,array} + + @sa see @ref basic_json(initializer_list_t, bool, value_t) -- + create a JSON value from an initializer list + @sa see @ref object(initializer_list_t) -- create a JSON object + value from an initializer list + + @since version 1.0.0 + */ + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json array(initializer_list_t init = {}) + { + return basic_json(init, false, value_t::array); + } + + /*! + @brief explicitly create an object from an initializer list + + Creates a JSON object value from a given initializer list. The initializer + lists elements must be pairs, and their first elements must be strings. If + the initializer list is empty, the empty object `{}` is created. + + @note This function is only added for symmetry reasons. In contrast to the + related function @ref array(initializer_list_t), there are + no cases which can only be expressed by this function. That is, any + initializer list @a init can also be passed to the initializer list + constructor @ref basic_json(initializer_list_t, bool, value_t). + + @param[in] init initializer list to create an object from (optional) + + @return JSON object value + + @throw type_error.301 if @a init is not a list of pairs whose first + elements are strings. In this case, no object can be created. When such a + value is passed to @ref basic_json(initializer_list_t, bool, value_t), + an array would have been created from the passed initializer list @a init. + See example below. + + @complexity Linear in the size of @a init. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @liveexample{The following code shows an example for the `object` + function.,object} + + @sa see @ref basic_json(initializer_list_t, bool, value_t) -- + create a JSON value from an initializer list + @sa see @ref array(initializer_list_t) -- create a JSON array + value from an initializer list + + @since version 1.0.0 + */ + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json object(initializer_list_t init = {}) + { + return basic_json(init, false, value_t::object); + } + + /*! + @brief construct an array with count copies of given value + + Constructs a JSON array value by creating @a cnt copies of a passed value. + In case @a cnt is `0`, an empty array is created. + + @param[in] cnt the number of JSON copies of @a val to create + @param[in] val the JSON value to copy + + @post `std::distance(begin(),end()) == cnt` holds. + + @complexity Linear in @a cnt. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @liveexample{The following code shows examples for the @ref + basic_json(size_type\, const basic_json&) + constructor.,basic_json__size_type_basic_json} + + @since version 1.0.0 + */ + basic_json(size_type cnt, const basic_json& val) + : m_type(value_t::array) + { + m_value.array = create(cnt, val); + set_parents(); + assert_invariant(); + } + + /*! + @brief construct a JSON container given an iterator range + + Constructs the JSON value with the contents of the range `[first, last)`. + The semantics depends on the different types a JSON value can have: + - In case of a null type, invalid_iterator.206 is thrown. + - In case of other primitive types (number, boolean, or string), @a first + must be `begin()` and @a last must be `end()`. In this case, the value is + copied. Otherwise, invalid_iterator.204 is thrown. + - In case of structured types (array, object), the constructor behaves as + similar versions for `std::vector` or `std::map`; that is, a JSON array + or object is constructed from the values in the range. + + @tparam InputIT an input iterator type (@ref iterator or @ref + const_iterator) + + @param[in] first begin of the range to copy from (included) + @param[in] last end of the range to copy from (excluded) + + @pre Iterators @a first and @a last must be initialized. **This + precondition is enforced with an assertion (see warning).** If + assertions are switched off, a violation of this precondition yields + undefined behavior. + + @pre Range `[first, last)` is valid. Usually, this precondition cannot be + checked efficiently. Only certain edge cases are detected; see the + description of the exceptions below. A violation of this precondition + yields undefined behavior. + + @warning A precondition is enforced with a runtime assertion that will + result in calling `std::abort` if this precondition is not met. + Assertions can be disabled by defining `NDEBUG` at compile time. + See https://en.cppreference.com/w/cpp/error/assert for more + information. + + @throw invalid_iterator.201 if iterators @a first and @a last are not + compatible (i.e., do not belong to the same JSON value). In this case, + the range `[first, last)` is undefined. + @throw invalid_iterator.204 if iterators @a first and @a last belong to a + primitive type (number, boolean, or string), but @a first does not point + to the first element any more. In this case, the range `[first, last)` is + undefined. See example code below. + @throw invalid_iterator.206 if iterators @a first and @a last belong to a + null value. In this case, the range `[first, last)` is undefined. + + @complexity Linear in distance between @a first and @a last. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @liveexample{The example below shows several ways to create JSON values by + specifying a subrange with iterators.,basic_json__InputIt_InputIt} + + @since version 1.0.0 + */ + template < class InputIT, typename std::enable_if < + std::is_same::value || + std::is_same::value, int >::type = 0 > + basic_json(InputIT first, InputIT last) + { + JSON_ASSERT(first.m_object != nullptr); + JSON_ASSERT(last.m_object != nullptr); + + // make sure iterator fits the current value + if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object)) + { + JSON_THROW(invalid_iterator::create(201, "iterators are not compatible", basic_json())); + } + + // copy type from first iterator + m_type = first.m_object->m_type; + + // check if iterator range is complete for primitive values + switch (m_type) + { + case value_t::boolean: + case value_t::number_float: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::string: + { + if (JSON_HEDLEY_UNLIKELY(!first.m_it.primitive_iterator.is_begin() + || !last.m_it.primitive_iterator.is_end())) + { + JSON_THROW(invalid_iterator::create(204, "iterators out of range", *first.m_object)); + } + break; + } + + case value_t::null: + case value_t::object: + case value_t::array: + case value_t::binary: + case value_t::discarded: + default: + break; + } + + switch (m_type) + { + case value_t::number_integer: + { + m_value.number_integer = first.m_object->m_value.number_integer; + break; + } + + case value_t::number_unsigned: + { + m_value.number_unsigned = first.m_object->m_value.number_unsigned; + break; + } + + case value_t::number_float: + { + m_value.number_float = first.m_object->m_value.number_float; + break; + } + + case value_t::boolean: + { + m_value.boolean = first.m_object->m_value.boolean; + break; + } + + case value_t::string: + { + m_value = *first.m_object->m_value.string; + break; + } + + case value_t::object: + { + m_value.object = create(first.m_it.object_iterator, + last.m_it.object_iterator); + break; + } + + case value_t::array: + { + m_value.array = create(first.m_it.array_iterator, + last.m_it.array_iterator); + break; + } + + case value_t::binary: + { + m_value = *first.m_object->m_value.binary; + break; + } + + case value_t::null: + case value_t::discarded: + default: + JSON_THROW(invalid_iterator::create(206, "cannot construct with iterators from " + std::string(first.m_object->type_name()), *first.m_object)); + } + + set_parents(); + assert_invariant(); + } + + + /////////////////////////////////////// + // other constructors and destructor // + /////////////////////////////////////// + + template, + std::is_same>::value, int> = 0 > + basic_json(const JsonRef& ref) : basic_json(ref.moved_or_copied()) {} + + /*! + @brief copy constructor + + Creates a copy of a given JSON value. + + @param[in] other the JSON value to copy + + @post `*this == other` + + @complexity Linear in the size of @a other. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes to any JSON value. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is linear. + - As postcondition, it holds: `other == basic_json(other)`. + + @liveexample{The following code shows an example for the copy + constructor.,basic_json__basic_json} + + @since version 1.0.0 + */ + basic_json(const basic_json& other) + : m_type(other.m_type) + { + // check of passed value is valid + other.assert_invariant(); + + switch (m_type) + { + case value_t::object: + { + m_value = *other.m_value.object; + break; + } + + case value_t::array: + { + m_value = *other.m_value.array; + break; + } + + case value_t::string: + { + m_value = *other.m_value.string; + break; + } + + case value_t::boolean: + { + m_value = other.m_value.boolean; + break; + } + + case value_t::number_integer: + { + m_value = other.m_value.number_integer; + break; + } + + case value_t::number_unsigned: + { + m_value = other.m_value.number_unsigned; + break; + } + + case value_t::number_float: + { + m_value = other.m_value.number_float; + break; + } + + case value_t::binary: + { + m_value = *other.m_value.binary; + break; + } + + case value_t::null: + case value_t::discarded: + default: + break; + } + + set_parents(); + assert_invariant(); + } + + /*! + @brief move constructor + + Move constructor. Constructs a JSON value with the contents of the given + value @a other using move semantics. It "steals" the resources from @a + other and leaves it as JSON null value. + + @param[in,out] other value to move to this object + + @post `*this` has the same value as @a other before the call. + @post @a other is a JSON null value. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this constructor never throws + exceptions. + + @requirement This function helps `basic_json` satisfying the + [MoveConstructible](https://en.cppreference.com/w/cpp/named_req/MoveConstructible) + requirements. + + @liveexample{The code below shows the move constructor explicitly called + via std::move.,basic_json__moveconstructor} + + @since version 1.0.0 + */ + basic_json(basic_json&& other) noexcept + : m_type(std::move(other.m_type)), + m_value(std::move(other.m_value)) + { + // check that passed value is valid + other.assert_invariant(false); + + // invalidate payload + other.m_type = value_t::null; + other.m_value = {}; + + set_parents(); + assert_invariant(); + } + + /*! + @brief copy assignment + + Copy assignment operator. Copies a JSON value via the "copy and swap" + strategy: It is expressed in terms of the copy constructor, destructor, + and the `swap()` member function. + + @param[in] other value to copy from + + @complexity Linear. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is linear. + + @liveexample{The code below shows and example for the copy assignment. It + creates a copy of value `a` which is then swapped with `b`. Finally\, the + copy of `a` (which is the null value after the swap) is + destroyed.,basic_json__copyassignment} + + @since version 1.0.0 + */ + basic_json& operator=(basic_json other) noexcept ( + std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_assignable::value&& + std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_assignable::value + ) + { + // check that passed value is valid + other.assert_invariant(); + + using std::swap; + swap(m_type, other.m_type); + swap(m_value, other.m_value); + + set_parents(); + assert_invariant(); + return *this; + } + + /*! + @brief destructor + + Destroys the JSON value and frees all allocated memory. + + @complexity Linear. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is linear. + - All stored elements are destroyed and all memory is freed. + + @since version 1.0.0 + */ + ~basic_json() noexcept + { + assert_invariant(false); + m_value.destroy(m_type); + } + + /// @} + + public: + /////////////////////// + // object inspection // + /////////////////////// + + /// @name object inspection + /// Functions to inspect the type of a JSON value. + /// @{ + + /*! + @brief serialization + + Serialization function for JSON values. The function tries to mimic + Python's `json.dumps()` function, and currently supports its @a indent + and @a ensure_ascii parameters. + + @param[in] indent If indent is nonnegative, then array elements and object + members will be pretty-printed with that indent level. An indent level of + `0` will only insert newlines. `-1` (the default) selects the most compact + representation. + @param[in] indent_char The character to use for indentation if @a indent is + greater than `0`. The default is ` ` (space). + @param[in] ensure_ascii If @a ensure_ascii is true, all non-ASCII characters + in the output are escaped with `\uXXXX` sequences, and the result consists + of ASCII characters only. + @param[in] error_handler how to react on decoding errors; there are three + possible values: `strict` (throws and exception in case a decoding error + occurs; default), `replace` (replace invalid UTF-8 sequences with U+FFFD), + and `ignore` (ignore invalid UTF-8 sequences during serialization; all + bytes are copied to the output unchanged). + + @return string containing the serialization of the JSON value + + @throw type_error.316 if a string stored inside the JSON value is not + UTF-8 encoded and @a error_handler is set to strict + + @note Binary values are serialized as object containing two keys: + - "bytes": an array of bytes as integers + - "subtype": the subtype as integer or "null" if the binary has no subtype + + @complexity Linear. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @liveexample{The following example shows the effect of different @a indent\, + @a indent_char\, and @a ensure_ascii parameters to the result of the + serialization.,dump} + + @see https://docs.python.org/2/library/json.html#json.dump + + @since version 1.0.0; indentation character @a indent_char, option + @a ensure_ascii and exceptions added in version 3.0.0; error + handlers added in version 3.4.0; serialization of binary values added + in version 3.8.0. + */ + string_t dump(const int indent = -1, + const char indent_char = ' ', + const bool ensure_ascii = false, + const error_handler_t error_handler = error_handler_t::strict) const + { + string_t result; + serializer s(detail::output_adapter(result), indent_char, error_handler); + + if (indent >= 0) + { + s.dump(*this, true, ensure_ascii, static_cast(indent)); + } + else + { + s.dump(*this, false, ensure_ascii, 0); + } + + return result; + } + + /*! + @brief return the type of the JSON value (explicit) + + Return the type of the JSON value as a value from the @ref value_t + enumeration. + + @return the type of the JSON value + Value type | return value + ------------------------- | ------------------------- + null | value_t::null + boolean | value_t::boolean + string | value_t::string + number (integer) | value_t::number_integer + number (unsigned integer) | value_t::number_unsigned + number (floating-point) | value_t::number_float + object | value_t::object + array | value_t::array + binary | value_t::binary + discarded | value_t::discarded + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `type()` for all JSON + types.,type} + + @sa see @ref operator value_t() -- return the type of the JSON value (implicit) + @sa see @ref type_name() -- return the type as string + + @since version 1.0.0 + */ + constexpr value_t type() const noexcept + { + return m_type; + } + + /*! + @brief return whether type is primitive + + This function returns true if and only if the JSON type is primitive + (string, number, boolean, or null). + + @return `true` if type is primitive (string, number, boolean, or null), + `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_primitive()` for all JSON + types.,is_primitive} + + @sa see @ref is_structured() -- returns whether JSON value is structured + @sa see @ref is_null() -- returns whether JSON value is `null` + @sa see @ref is_string() -- returns whether JSON value is a string + @sa see @ref is_boolean() -- returns whether JSON value is a boolean + @sa see @ref is_number() -- returns whether JSON value is a number + @sa see @ref is_binary() -- returns whether JSON value is a binary array + + @since version 1.0.0 + */ + constexpr bool is_primitive() const noexcept + { + return is_null() || is_string() || is_boolean() || is_number() || is_binary(); + } + + /*! + @brief return whether type is structured + + This function returns true if and only if the JSON type is structured + (array or object). + + @return `true` if type is structured (array or object), `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_structured()` for all JSON + types.,is_structured} + + @sa see @ref is_primitive() -- returns whether value is primitive + @sa see @ref is_array() -- returns whether value is an array + @sa see @ref is_object() -- returns whether value is an object + + @since version 1.0.0 + */ + constexpr bool is_structured() const noexcept + { + return is_array() || is_object(); + } + + /*! + @brief return whether value is null + + This function returns true if and only if the JSON value is null. + + @return `true` if type is null, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_null()` for all JSON + types.,is_null} + + @since version 1.0.0 + */ + constexpr bool is_null() const noexcept + { + return m_type == value_t::null; + } + + /*! + @brief return whether value is a boolean + + This function returns true if and only if the JSON value is a boolean. + + @return `true` if type is boolean, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_boolean()` for all JSON + types.,is_boolean} + + @since version 1.0.0 + */ + constexpr bool is_boolean() const noexcept + { + return m_type == value_t::boolean; + } + + /*! + @brief return whether value is a number + + This function returns true if and only if the JSON value is a number. This + includes both integer (signed and unsigned) and floating-point values. + + @return `true` if type is number (regardless whether integer, unsigned + integer or floating-type), `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_number()` for all JSON + types.,is_number} + + @sa see @ref is_number_integer() -- check if value is an integer or unsigned + integer number + @sa see @ref is_number_unsigned() -- check if value is an unsigned integer + number + @sa see @ref is_number_float() -- check if value is a floating-point number + + @since version 1.0.0 + */ + constexpr bool is_number() const noexcept + { + return is_number_integer() || is_number_float(); + } + + /*! + @brief return whether value is an integer number + + This function returns true if and only if the JSON value is a signed or + unsigned integer number. This excludes floating-point values. + + @return `true` if type is an integer or unsigned integer number, `false` + otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_number_integer()` for all + JSON types.,is_number_integer} + + @sa see @ref is_number() -- check if value is a number + @sa see @ref is_number_unsigned() -- check if value is an unsigned integer + number + @sa see @ref is_number_float() -- check if value is a floating-point number + + @since version 1.0.0 + */ + constexpr bool is_number_integer() const noexcept + { + return m_type == value_t::number_integer || m_type == value_t::number_unsigned; + } + + /*! + @brief return whether value is an unsigned integer number + + This function returns true if and only if the JSON value is an unsigned + integer number. This excludes floating-point and signed integer values. + + @return `true` if type is an unsigned integer number, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_number_unsigned()` for all + JSON types.,is_number_unsigned} + + @sa see @ref is_number() -- check if value is a number + @sa see @ref is_number_integer() -- check if value is an integer or unsigned + integer number + @sa see @ref is_number_float() -- check if value is a floating-point number + + @since version 2.0.0 + */ + constexpr bool is_number_unsigned() const noexcept + { + return m_type == value_t::number_unsigned; + } + + /*! + @brief return whether value is a floating-point number + + This function returns true if and only if the JSON value is a + floating-point number. This excludes signed and unsigned integer values. + + @return `true` if type is a floating-point number, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_number_float()` for all + JSON types.,is_number_float} + + @sa see @ref is_number() -- check if value is number + @sa see @ref is_number_integer() -- check if value is an integer number + @sa see @ref is_number_unsigned() -- check if value is an unsigned integer + number + + @since version 1.0.0 + */ + constexpr bool is_number_float() const noexcept + { + return m_type == value_t::number_float; + } + + /*! + @brief return whether value is an object + + This function returns true if and only if the JSON value is an object. + + @return `true` if type is object, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_object()` for all JSON + types.,is_object} + + @since version 1.0.0 + */ + constexpr bool is_object() const noexcept + { + return m_type == value_t::object; + } + + /*! + @brief return whether value is an array + + This function returns true if and only if the JSON value is an array. + + @return `true` if type is array, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_array()` for all JSON + types.,is_array} + + @since version 1.0.0 + */ + constexpr bool is_array() const noexcept + { + return m_type == value_t::array; + } + + /*! + @brief return whether value is a string + + This function returns true if and only if the JSON value is a string. + + @return `true` if type is string, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_string()` for all JSON + types.,is_string} + + @since version 1.0.0 + */ + constexpr bool is_string() const noexcept + { + return m_type == value_t::string; + } + + /*! + @brief return whether value is a binary array + + This function returns true if and only if the JSON value is a binary array. + + @return `true` if type is binary array, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_binary()` for all JSON + types.,is_binary} + + @since version 3.8.0 + */ + constexpr bool is_binary() const noexcept + { + return m_type == value_t::binary; + } + + /*! + @brief return whether value is discarded + + This function returns true if and only if the JSON value was discarded + during parsing with a callback function (see @ref parser_callback_t). + + @note This function will always be `false` for JSON values after parsing. + That is, discarded values can only occur during parsing, but will be + removed when inside a structured value or replaced by null in other cases. + + @return `true` if type is discarded, `false` otherwise. + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies `is_discarded()` for all JSON + types.,is_discarded} + + @since version 1.0.0 + */ + constexpr bool is_discarded() const noexcept + { + return m_type == value_t::discarded; + } + + /*! + @brief return the type of the JSON value (implicit) + + Implicitly return the type of the JSON value as a value from the @ref + value_t enumeration. + + @return the type of the JSON value + + @complexity Constant. + + @exceptionsafety No-throw guarantee: this member function never throws + exceptions. + + @liveexample{The following code exemplifies the @ref value_t operator for + all JSON types.,operator__value_t} + + @sa see @ref type() -- return the type of the JSON value (explicit) + @sa see @ref type_name() -- return the type as string + + @since version 1.0.0 + */ + constexpr operator value_t() const noexcept + { + return m_type; + } + + /// @} + + private: + ////////////////// + // value access // + ////////////////// + + /// get a boolean (explicit) + boolean_t get_impl(boolean_t* /*unused*/) const + { + if (JSON_HEDLEY_LIKELY(is_boolean())) + { + return m_value.boolean; + } + + JSON_THROW(type_error::create(302, "type must be boolean, but is " + std::string(type_name()), *this)); + } + + /// get a pointer to the value (object) + object_t* get_impl_ptr(object_t* /*unused*/) noexcept + { + return is_object() ? m_value.object : nullptr; + } + + /// get a pointer to the value (object) + constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept + { + return is_object() ? m_value.object : nullptr; + } + + /// get a pointer to the value (array) + array_t* get_impl_ptr(array_t* /*unused*/) noexcept + { + return is_array() ? m_value.array : nullptr; + } + + /// get a pointer to the value (array) + constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept + { + return is_array() ? m_value.array : nullptr; + } + + /// get a pointer to the value (string) + string_t* get_impl_ptr(string_t* /*unused*/) noexcept + { + return is_string() ? m_value.string : nullptr; + } + + /// get a pointer to the value (string) + constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept + { + return is_string() ? m_value.string : nullptr; + } + + /// get a pointer to the value (boolean) + boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept + { + return is_boolean() ? &m_value.boolean : nullptr; + } + + /// get a pointer to the value (boolean) + constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept + { + return is_boolean() ? &m_value.boolean : nullptr; + } + + /// get a pointer to the value (integer number) + number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept + { + return is_number_integer() ? &m_value.number_integer : nullptr; + } + + /// get a pointer to the value (integer number) + constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept + { + return is_number_integer() ? &m_value.number_integer : nullptr; + } + + /// get a pointer to the value (unsigned number) + number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept + { + return is_number_unsigned() ? &m_value.number_unsigned : nullptr; + } + + /// get a pointer to the value (unsigned number) + constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept + { + return is_number_unsigned() ? &m_value.number_unsigned : nullptr; + } + + /// get a pointer to the value (floating-point number) + number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept + { + return is_number_float() ? &m_value.number_float : nullptr; + } + + /// get a pointer to the value (floating-point number) + constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept + { + return is_number_float() ? &m_value.number_float : nullptr; + } + + /// get a pointer to the value (binary) + binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept + { + return is_binary() ? m_value.binary : nullptr; + } + + /// get a pointer to the value (binary) + constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept + { + return is_binary() ? m_value.binary : nullptr; + } + + /*! + @brief helper function to implement get_ref() + + This function helps to implement get_ref() without code duplication for + const and non-const overloads + + @tparam ThisType will be deduced as `basic_json` or `const basic_json` + + @throw type_error.303 if ReferenceType does not match underlying value + type of the current JSON + */ + template + static ReferenceType get_ref_impl(ThisType& obj) + { + // delegate the call to get_ptr<>() + auto* ptr = obj.template get_ptr::type>(); + + if (JSON_HEDLEY_LIKELY(ptr != nullptr)) + { + return *ptr; + } + + JSON_THROW(type_error::create(303, "incompatible ReferenceType for get_ref, actual type is " + std::string(obj.type_name()), obj)); + } + + public: + /// @name value access + /// Direct access to the stored value of a JSON value. + /// @{ + + /*! + @brief get a pointer value (implicit) + + Implicit pointer access to the internally stored JSON value. No copies are + made. + + @warning Writing data to the pointee of the result yields an undefined + state. + + @tparam PointerType pointer type; must be a pointer to @ref array_t, @ref + object_t, @ref string_t, @ref boolean_t, @ref number_integer_t, + @ref number_unsigned_t, or @ref number_float_t. Enforced by a static + assertion. + + @return pointer to the internally stored JSON value if the requested + pointer type @a PointerType fits to the JSON value; `nullptr` otherwise + + @complexity Constant. + + @liveexample{The example below shows how pointers to internal values of a + JSON value can be requested. Note that no type conversions are made and a + `nullptr` is returned if the value and the requested pointer type does not + match.,get_ptr} + + @since version 1.0.0 + */ + template::value, int>::type = 0> + auto get_ptr() noexcept -> decltype(std::declval().get_impl_ptr(std::declval())) + { + // delegate the call to get_impl_ptr<>() + return get_impl_ptr(static_cast(nullptr)); + } + + /*! + @brief get a pointer value (implicit) + @copydoc get_ptr() + */ + template < typename PointerType, typename std::enable_if < + std::is_pointer::value&& + std::is_const::type>::value, int >::type = 0 > + constexpr auto get_ptr() const noexcept -> decltype(std::declval().get_impl_ptr(std::declval())) + { + // delegate the call to get_impl_ptr<>() const + return get_impl_ptr(static_cast(nullptr)); + } + + private: + /*! + @brief get a value (explicit) + + Explicit type conversion between the JSON value and a compatible value + which is [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible) + and [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible). + The value is converted by calling the @ref json_serializer + `from_json()` method. + + The function is equivalent to executing + @code {.cpp} + ValueType ret; + JSONSerializer::from_json(*this, ret); + return ret; + @endcode + + This overloads is chosen if: + - @a ValueType is not @ref basic_json, + - @ref json_serializer has a `from_json()` method of the form + `void from_json(const basic_json&, ValueType&)`, and + - @ref json_serializer does not have a `from_json()` method of + the form `ValueType from_json(const basic_json&)` + + @tparam ValueType the returned value type + + @return copy of the JSON value, converted to @a ValueType + + @throw what @ref json_serializer `from_json()` method throws + + @liveexample{The example below shows several conversions from JSON values + to other types. There a few things to note: (1) Floating-point numbers can + be converted to integers\, (2) A JSON array can be converted to a standard + `std::vector`\, (3) A JSON object can be converted to C++ + associative containers such as `std::unordered_map`.,get__ValueType_const} + + @since version 2.1.0 + */ + template < typename ValueType, + detail::enable_if_t < + detail::is_default_constructible::value&& + detail::has_from_json::value, + int > = 0 > + ValueType get_impl(detail::priority_tag<0> /*unused*/) const noexcept(noexcept( + JSONSerializer::from_json(std::declval(), std::declval()))) + { + auto ret = ValueType(); + JSONSerializer::from_json(*this, ret); + return ret; + } + + /*! + @brief get a value (explicit); special case + + Explicit type conversion between the JSON value and a compatible value + which is **not** [CopyConstructible](https://en.cppreference.com/w/cpp/named_req/CopyConstructible) + and **not** [DefaultConstructible](https://en.cppreference.com/w/cpp/named_req/DefaultConstructible). + The value is converted by calling the @ref json_serializer + `from_json()` method. + + The function is equivalent to executing + @code {.cpp} + return JSONSerializer::from_json(*this); + @endcode + + This overloads is chosen if: + - @a ValueType is not @ref basic_json and + - @ref json_serializer has a `from_json()` method of the form + `ValueType from_json(const basic_json&)` + + @note If @ref json_serializer has both overloads of + `from_json()`, this one is chosen. + + @tparam ValueType the returned value type + + @return copy of the JSON value, converted to @a ValueType + + @throw what @ref json_serializer `from_json()` method throws + + @since version 2.1.0 + */ + template < typename ValueType, + detail::enable_if_t < + detail::has_non_default_from_json::value, + int > = 0 > + ValueType get_impl(detail::priority_tag<1> /*unused*/) const noexcept(noexcept( + JSONSerializer::from_json(std::declval()))) + { + return JSONSerializer::from_json(*this); + } + + /*! + @brief get special-case overload + + This overloads converts the current @ref basic_json in a different + @ref basic_json type + + @tparam BasicJsonType == @ref basic_json + + @return a copy of *this, converted into @a BasicJsonType + + @complexity Depending on the implementation of the called `from_json()` + method. + + @since version 3.2.0 + */ + template < typename BasicJsonType, + detail::enable_if_t < + detail::is_basic_json::value, + int > = 0 > + BasicJsonType get_impl(detail::priority_tag<2> /*unused*/) const + { + return *this; + } + + /*! + @brief get special-case overload + + This overloads avoids a lot of template boilerplate, it can be seen as the + identity method + + @tparam BasicJsonType == @ref basic_json + + @return a copy of *this + + @complexity Constant. + + @since version 2.1.0 + */ + template::value, + int> = 0> + basic_json get_impl(detail::priority_tag<3> /*unused*/) const + { + return *this; + } + + /*! + @brief get a pointer value (explicit) + @copydoc get() + */ + template::value, + int> = 0> + constexpr auto get_impl(detail::priority_tag<4> /*unused*/) const noexcept + -> decltype(std::declval().template get_ptr()) + { + // delegate the call to get_ptr + return get_ptr(); + } + + public: + /*! + @brief get a (pointer) value (explicit) + + Performs explicit type conversion between the JSON value and a compatible value if required. + + - If the requested type is a pointer to the internally stored JSON value that pointer is returned. + No copies are made. + + - If the requested type is the current @ref basic_json, or a different @ref basic_json convertible + from the current @ref basic_json. + + - Otherwise the value is converted by calling the @ref json_serializer `from_json()` + method. + + @tparam ValueTypeCV the provided value type + @tparam ValueType the returned value type + + @return copy of the JSON value, converted to @tparam ValueType if necessary + + @throw what @ref json_serializer `from_json()` method throws if conversion is required + + @since version 2.1.0 + */ + template < typename ValueTypeCV, typename ValueType = detail::uncvref_t> +#if defined(JSON_HAS_CPP_14) + constexpr +#endif + auto get() const noexcept( + noexcept(std::declval().template get_impl(detail::priority_tag<4> {}))) + -> decltype(std::declval().template get_impl(detail::priority_tag<4> {})) + { + // we cannot static_assert on ValueTypeCV being non-const, because + // there is support for get(), which is why we + // still need the uncvref + static_assert(!std::is_reference::value, + "get() cannot be used with reference types, you might want to use get_ref()"); + return get_impl(detail::priority_tag<4> {}); + } + + /*! + @brief get a pointer value (explicit) + + Explicit pointer access to the internally stored JSON value. No copies are + made. + + @warning The pointer becomes invalid if the underlying JSON object + changes. + + @tparam PointerType pointer type; must be a pointer to @ref array_t, @ref + object_t, @ref string_t, @ref boolean_t, @ref number_integer_t, + @ref number_unsigned_t, or @ref number_float_t. + + @return pointer to the internally stored JSON value if the requested + pointer type @a PointerType fits to the JSON value; `nullptr` otherwise + + @complexity Constant. + + @liveexample{The example below shows how pointers to internal values of a + JSON value can be requested. Note that no type conversions are made and a + `nullptr` is returned if the value and the requested pointer type does not + match.,get__PointerType} + + @sa see @ref get_ptr() for explicit pointer-member access + + @since version 1.0.0 + */ + template::value, int>::type = 0> + auto get() noexcept -> decltype(std::declval().template get_ptr()) + { + // delegate the call to get_ptr + return get_ptr(); + } + + /*! + @brief get a value (explicit) + + Explicit type conversion between the JSON value and a compatible value. + The value is filled into the input parameter by calling the @ref json_serializer + `from_json()` method. + + The function is equivalent to executing + @code {.cpp} + ValueType v; + JSONSerializer::from_json(*this, v); + @endcode + + This overloads is chosen if: + - @a ValueType is not @ref basic_json, + - @ref json_serializer has a `from_json()` method of the form + `void from_json(const basic_json&, ValueType&)`, and + + @tparam ValueType the input parameter type. + + @return the input parameter, allowing chaining calls. + + @throw what @ref json_serializer `from_json()` method throws + + @liveexample{The example below shows several conversions from JSON values + to other types. There a few things to note: (1) Floating-point numbers can + be converted to integers\, (2) A JSON array can be converted to a standard + `std::vector`\, (3) A JSON object can be converted to C++ + associative containers such as `std::unordered_map`.,get_to} + + @since version 3.3.0 + */ + template < typename ValueType, + detail::enable_if_t < + !detail::is_basic_json::value&& + detail::has_from_json::value, + int > = 0 > + ValueType & get_to(ValueType& v) const noexcept(noexcept( + JSONSerializer::from_json(std::declval(), v))) + { + JSONSerializer::from_json(*this, v); + return v; + } + + // specialization to allow to call get_to with a basic_json value + // see https://github.com/nlohmann/json/issues/2175 + template::value, + int> = 0> + ValueType & get_to(ValueType& v) const + { + v = *this; + return v; + } + + template < + typename T, std::size_t N, + typename Array = T (&)[N], // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) + detail::enable_if_t < + detail::has_from_json::value, int > = 0 > + Array get_to(T (&v)[N]) const // NOLINT(cppcoreguidelines-avoid-c-arrays,hicpp-avoid-c-arrays,modernize-avoid-c-arrays) + noexcept(noexcept(JSONSerializer::from_json( + std::declval(), v))) + { + JSONSerializer::from_json(*this, v); + return v; + } + + /*! + @brief get a reference value (implicit) + + Implicit reference access to the internally stored JSON value. No copies + are made. + + @warning Writing data to the referee of the result yields an undefined + state. + + @tparam ReferenceType reference type; must be a reference to @ref array_t, + @ref object_t, @ref string_t, @ref boolean_t, @ref number_integer_t, or + @ref number_float_t. Enforced by static assertion. + + @return reference to the internally stored JSON value if the requested + reference type @a ReferenceType fits to the JSON value; throws + type_error.303 otherwise + + @throw type_error.303 in case passed type @a ReferenceType is incompatible + with the stored JSON value; see example below + + @complexity Constant. + + @liveexample{The example shows several calls to `get_ref()`.,get_ref} + + @since version 1.1.0 + */ + template::value, int>::type = 0> + ReferenceType get_ref() + { + // delegate call to get_ref_impl + return get_ref_impl(*this); + } + + /*! + @brief get a reference value (implicit) + @copydoc get_ref() + */ + template < typename ReferenceType, typename std::enable_if < + std::is_reference::value&& + std::is_const::type>::value, int >::type = 0 > + ReferenceType get_ref() const + { + // delegate call to get_ref_impl + return get_ref_impl(*this); + } + + /*! + @brief get a value (implicit) + + Implicit type conversion between the JSON value and a compatible value. + The call is realized by calling @ref get() const. + + @tparam ValueType non-pointer type compatible to the JSON value, for + instance `int` for JSON integer numbers, `bool` for JSON booleans, or + `std::vector` types for JSON arrays. The character type of @ref string_t + as well as an initializer list of this type is excluded to avoid + ambiguities as these types implicitly convert to `std::string`. + + @return copy of the JSON value, converted to type @a ValueType + + @throw type_error.302 in case passed type @a ValueType is incompatible + to the JSON value type (e.g., the JSON value is of type boolean, but a + string is requested); see example below + + @complexity Linear in the size of the JSON value. + + @liveexample{The example below shows several conversions from JSON values + to other types. There a few things to note: (1) Floating-point numbers can + be converted to integers\, (2) A JSON array can be converted to a standard + `std::vector`\, (3) A JSON object can be converted to C++ + associative containers such as `std::unordered_map`.,operator__ValueType} + + @since version 1.0.0 + */ + template < typename ValueType, typename std::enable_if < + detail::conjunction < + detail::negation>, + detail::negation>>, + detail::negation>, + detail::negation>, + detail::negation>>, + +#if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914)) + detail::negation>, +#endif + detail::is_detected_lazy + >::value, int >::type = 0 > + JSON_EXPLICIT operator ValueType() const + { + // delegate the call to get<>() const + return get(); + } + + /*! + @return reference to the binary value + + @throw type_error.302 if the value is not binary + + @sa see @ref is_binary() to check if the value is binary + + @since version 3.8.0 + */ + binary_t& get_binary() + { + if (!is_binary()) + { + JSON_THROW(type_error::create(302, "type must be binary, but is " + std::string(type_name()), *this)); + } + + return *get_ptr(); + } + + /// @copydoc get_binary() + const binary_t& get_binary() const + { + if (!is_binary()) + { + JSON_THROW(type_error::create(302, "type must be binary, but is " + std::string(type_name()), *this)); + } + + return *get_ptr(); + } + + /// @} + + + //////////////////// + // element access // + //////////////////// + + /// @name element access + /// Access to the JSON value. + /// @{ + + /*! + @brief access specified array element with bounds checking + + Returns a reference to the element at specified location @a idx, with + bounds checking. + + @param[in] idx index of the element to access + + @return reference to the element at index @a idx + + @throw type_error.304 if the JSON value is not an array; in this case, + calling `at` with an index makes no sense. See example below. + @throw out_of_range.401 if the index @a idx is out of range of the array; + that is, `idx >= size()`. See example below. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Constant. + + @since version 1.0.0 + + @liveexample{The example below shows how array elements can be read and + written using `at()`. It also demonstrates the different exceptions that + can be thrown.,at__size_type} + */ + reference at(size_type idx) + { + // at only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + JSON_TRY + { + return set_parent(m_value.array->at(idx)); + } + JSON_CATCH (std::out_of_range&) + { + // create better exception explanation + JSON_THROW(out_of_range::create(401, "array index " + std::to_string(idx) + " is out of range", *this)); + } + } + else + { + JSON_THROW(type_error::create(304, "cannot use at() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief access specified array element with bounds checking + + Returns a const reference to the element at specified location @a idx, + with bounds checking. + + @param[in] idx index of the element to access + + @return const reference to the element at index @a idx + + @throw type_error.304 if the JSON value is not an array; in this case, + calling `at` with an index makes no sense. See example below. + @throw out_of_range.401 if the index @a idx is out of range of the array; + that is, `idx >= size()`. See example below. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Constant. + + @since version 1.0.0 + + @liveexample{The example below shows how array elements can be read using + `at()`. It also demonstrates the different exceptions that can be thrown., + at__size_type_const} + */ + const_reference at(size_type idx) const + { + // at only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + JSON_TRY + { + return m_value.array->at(idx); + } + JSON_CATCH (std::out_of_range&) + { + // create better exception explanation + JSON_THROW(out_of_range::create(401, "array index " + std::to_string(idx) + " is out of range", *this)); + } + } + else + { + JSON_THROW(type_error::create(304, "cannot use at() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief access specified object element with bounds checking + + Returns a reference to the element at with specified key @a key, with + bounds checking. + + @param[in] key key of the element to access + + @return reference to the element at key @a key + + @throw type_error.304 if the JSON value is not an object; in this case, + calling `at` with a key makes no sense. See example below. + @throw out_of_range.403 if the key @a key is is not stored in the object; + that is, `find(key) == end()`. See example below. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Logarithmic in the size of the container. + + @sa see @ref operator[](const typename object_t::key_type&) for unchecked + access by reference + @sa see @ref value() for access by value with a default value + + @since version 1.0.0 + + @liveexample{The example below shows how object elements can be read and + written using `at()`. It also demonstrates the different exceptions that + can be thrown.,at__object_t_key_type} + */ + reference at(const typename object_t::key_type& key) + { + // at only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + JSON_TRY + { + return set_parent(m_value.object->at(key)); + } + JSON_CATCH (std::out_of_range&) + { + // create better exception explanation + JSON_THROW(out_of_range::create(403, "key '" + key + "' not found", *this)); + } + } + else + { + JSON_THROW(type_error::create(304, "cannot use at() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief access specified object element with bounds checking + + Returns a const reference to the element at with specified key @a key, + with bounds checking. + + @param[in] key key of the element to access + + @return const reference to the element at key @a key + + @throw type_error.304 if the JSON value is not an object; in this case, + calling `at` with a key makes no sense. See example below. + @throw out_of_range.403 if the key @a key is is not stored in the object; + that is, `find(key) == end()`. See example below. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Logarithmic in the size of the container. + + @sa see @ref operator[](const typename object_t::key_type&) for unchecked + access by reference + @sa see @ref value() for access by value with a default value + + @since version 1.0.0 + + @liveexample{The example below shows how object elements can be read using + `at()`. It also demonstrates the different exceptions that can be thrown., + at__object_t_key_type_const} + */ + const_reference at(const typename object_t::key_type& key) const + { + // at only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + JSON_TRY + { + return m_value.object->at(key); + } + JSON_CATCH (std::out_of_range&) + { + // create better exception explanation + JSON_THROW(out_of_range::create(403, "key '" + key + "' not found", *this)); + } + } + else + { + JSON_THROW(type_error::create(304, "cannot use at() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief access specified array element + + Returns a reference to the element at specified location @a idx. + + @note If @a idx is beyond the range of the array (i.e., `idx >= size()`), + then the array is silently filled up with `null` values to make `idx` a + valid reference to the last stored element. + + @param[in] idx index of the element to access + + @return reference to the element at index @a idx + + @throw type_error.305 if the JSON value is not an array or null; in that + cases, using the [] operator with an index makes no sense. + + @complexity Constant if @a idx is in the range of the array. Otherwise + linear in `idx - size()`. + + @liveexample{The example below shows how array elements can be read and + written using `[]` operator. Note the addition of `null` + values.,operatorarray__size_type} + + @since version 1.0.0 + */ + reference operator[](size_type idx) + { + // implicitly convert null value to an empty array + if (is_null()) + { + m_type = value_t::array; + m_value.array = create(); + assert_invariant(); + } + + // operator[] only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + // fill up array with null values if given idx is outside range + if (idx >= m_value.array->size()) + { +#if JSON_DIAGNOSTICS + // remember array size & capacity before resizing + const auto old_size = m_value.array->size(); + const auto old_capacity = m_value.array->capacity(); +#endif + m_value.array->resize(idx + 1); + +#if JSON_DIAGNOSTICS + if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity)) + { + // capacity has changed: update all parents + set_parents(); + } + else + { + // set parent for values added above + set_parents(begin() + static_cast(old_size), static_cast(idx + 1 - old_size)); + } +#endif + assert_invariant(); + } + + return m_value.array->operator[](idx); + } + + JSON_THROW(type_error::create(305, "cannot use operator[] with a numeric argument with " + std::string(type_name()), *this)); + } + + /*! + @brief access specified array element + + Returns a const reference to the element at specified location @a idx. + + @param[in] idx index of the element to access + + @return const reference to the element at index @a idx + + @throw type_error.305 if the JSON value is not an array; in that case, + using the [] operator with an index makes no sense. + + @complexity Constant. + + @liveexample{The example below shows how array elements can be read using + the `[]` operator.,operatorarray__size_type_const} + + @since version 1.0.0 + */ + const_reference operator[](size_type idx) const + { + // const operator[] only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + return m_value.array->operator[](idx); + } + + JSON_THROW(type_error::create(305, "cannot use operator[] with a numeric argument with " + std::string(type_name()), *this)); + } + + /*! + @brief access specified object element + + Returns a reference to the element at with specified key @a key. + + @note If @a key is not found in the object, then it is silently added to + the object and filled with a `null` value to make `key` a valid reference. + In case the value was `null` before, it is converted to an object. + + @param[in] key key of the element to access + + @return reference to the element at key @a key + + @throw type_error.305 if the JSON value is not an object or null; in that + cases, using the [] operator with a key makes no sense. + + @complexity Logarithmic in the size of the container. + + @liveexample{The example below shows how object elements can be read and + written using the `[]` operator.,operatorarray__key_type} + + @sa see @ref at(const typename object_t::key_type&) for access by reference + with range checking + @sa see @ref value() for access by value with a default value + + @since version 1.0.0 + */ + reference operator[](const typename object_t::key_type& key) + { + // implicitly convert null value to an empty object + if (is_null()) + { + m_type = value_t::object; + m_value.object = create(); + assert_invariant(); + } + + // operator[] only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + return set_parent(m_value.object->operator[](key)); + } + + JSON_THROW(type_error::create(305, "cannot use operator[] with a string argument with " + std::string(type_name()), *this)); + } + + /*! + @brief read-only access specified object element + + Returns a const reference to the element at with specified key @a key. No + bounds checking is performed. + + @warning If the element with key @a key does not exist, the behavior is + undefined. + + @param[in] key key of the element to access + + @return const reference to the element at key @a key + + @pre The element with key @a key must exist. **This precondition is + enforced with an assertion.** + + @throw type_error.305 if the JSON value is not an object; in that case, + using the [] operator with a key makes no sense. + + @complexity Logarithmic in the size of the container. + + @liveexample{The example below shows how object elements can be read using + the `[]` operator.,operatorarray__key_type_const} + + @sa see @ref at(const typename object_t::key_type&) for access by reference + with range checking + @sa see @ref value() for access by value with a default value + + @since version 1.0.0 + */ + const_reference operator[](const typename object_t::key_type& key) const + { + // const operator[] only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + JSON_ASSERT(m_value.object->find(key) != m_value.object->end()); + return m_value.object->find(key)->second; + } + + JSON_THROW(type_error::create(305, "cannot use operator[] with a string argument with " + std::string(type_name()), *this)); + } + + /*! + @brief access specified object element + + Returns a reference to the element at with specified key @a key. + + @note If @a key is not found in the object, then it is silently added to + the object and filled with a `null` value to make `key` a valid reference. + In case the value was `null` before, it is converted to an object. + + @param[in] key key of the element to access + + @return reference to the element at key @a key + + @throw type_error.305 if the JSON value is not an object or null; in that + cases, using the [] operator with a key makes no sense. + + @complexity Logarithmic in the size of the container. + + @liveexample{The example below shows how object elements can be read and + written using the `[]` operator.,operatorarray__key_type} + + @sa see @ref at(const typename object_t::key_type&) for access by reference + with range checking + @sa see @ref value() for access by value with a default value + + @since version 1.1.0 + */ + template + JSON_HEDLEY_NON_NULL(2) + reference operator[](T* key) + { + // implicitly convert null to object + if (is_null()) + { + m_type = value_t::object; + m_value = value_t::object; + assert_invariant(); + } + + // at only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + return set_parent(m_value.object->operator[](key)); + } + + JSON_THROW(type_error::create(305, "cannot use operator[] with a string argument with " + std::string(type_name()), *this)); + } + + /*! + @brief read-only access specified object element + + Returns a const reference to the element at with specified key @a key. No + bounds checking is performed. + + @warning If the element with key @a key does not exist, the behavior is + undefined. + + @param[in] key key of the element to access + + @return const reference to the element at key @a key + + @pre The element with key @a key must exist. **This precondition is + enforced with an assertion.** + + @throw type_error.305 if the JSON value is not an object; in that case, + using the [] operator with a key makes no sense. + + @complexity Logarithmic in the size of the container. + + @liveexample{The example below shows how object elements can be read using + the `[]` operator.,operatorarray__key_type_const} + + @sa see @ref at(const typename object_t::key_type&) for access by reference + with range checking + @sa see @ref value() for access by value with a default value + + @since version 1.1.0 + */ + template + JSON_HEDLEY_NON_NULL(2) + const_reference operator[](T* key) const + { + // at only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + JSON_ASSERT(m_value.object->find(key) != m_value.object->end()); + return m_value.object->find(key)->second; + } + + JSON_THROW(type_error::create(305, "cannot use operator[] with a string argument with " + std::string(type_name()), *this)); + } + + /*! + @brief access specified object element with default value + + Returns either a copy of an object's element at the specified key @a key + or a given default value if no element with key @a key exists. + + The function is basically equivalent to executing + @code {.cpp} + try { + return at(key); + } catch(out_of_range) { + return default_value; + } + @endcode + + @note Unlike @ref at(const typename object_t::key_type&), this function + does not throw if the given key @a key was not found. + + @note Unlike @ref operator[](const typename object_t::key_type& key), this + function does not implicitly add an element to the position defined by @a + key. This function is furthermore also applicable to const objects. + + @param[in] key key of the element to access + @param[in] default_value the value to return if @a key is not found + + @tparam ValueType type compatible to JSON values, for instance `int` for + JSON integer numbers, `bool` for JSON booleans, or `std::vector` types for + JSON arrays. Note the type of the expected value at @a key and the default + value @a default_value must be compatible. + + @return copy of the element at key @a key or @a default_value if @a key + is not found + + @throw type_error.302 if @a default_value does not match the type of the + value at @a key + @throw type_error.306 if the JSON value is not an object; in that case, + using `value()` with a key makes no sense. + + @complexity Logarithmic in the size of the container. + + @liveexample{The example below shows how object elements can be queried + with a default value.,basic_json__value} + + @sa see @ref at(const typename object_t::key_type&) for access by reference + with range checking + @sa see @ref operator[](const typename object_t::key_type&) for unchecked + access by reference + + @since version 1.0.0 + */ + // using std::is_convertible in a std::enable_if will fail when using explicit conversions + template < class ValueType, typename std::enable_if < + detail::is_getable::value + && !std::is_same::value, int >::type = 0 > + ValueType value(const typename object_t::key_type& key, const ValueType& default_value) const + { + // at only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + // if key is found, return value and given default value otherwise + const auto it = find(key); + if (it != end()) + { + return it->template get(); + } + + return default_value; + } + + JSON_THROW(type_error::create(306, "cannot use value() with " + std::string(type_name()), *this)); + } + + /*! + @brief overload for a default value of type const char* + @copydoc basic_json::value(const typename object_t::key_type&, const ValueType&) const + */ + string_t value(const typename object_t::key_type& key, const char* default_value) const + { + return value(key, string_t(default_value)); + } + + /*! + @brief access specified object element via JSON Pointer with default value + + Returns either a copy of an object's element at the specified key @a key + or a given default value if no element with key @a key exists. + + The function is basically equivalent to executing + @code {.cpp} + try { + return at(ptr); + } catch(out_of_range) { + return default_value; + } + @endcode + + @note Unlike @ref at(const json_pointer&), this function does not throw + if the given key @a key was not found. + + @param[in] ptr a JSON pointer to the element to access + @param[in] default_value the value to return if @a ptr found no value + + @tparam ValueType type compatible to JSON values, for instance `int` for + JSON integer numbers, `bool` for JSON booleans, or `std::vector` types for + JSON arrays. Note the type of the expected value at @a key and the default + value @a default_value must be compatible. + + @return copy of the element at key @a key or @a default_value if @a key + is not found + + @throw type_error.302 if @a default_value does not match the type of the + value at @a ptr + @throw type_error.306 if the JSON value is not an object; in that case, + using `value()` with a key makes no sense. + + @complexity Logarithmic in the size of the container. + + @liveexample{The example below shows how object elements can be queried + with a default value.,basic_json__value_ptr} + + @sa see @ref operator[](const json_pointer&) for unchecked access by reference + + @since version 2.0.2 + */ + template::value, int>::type = 0> + ValueType value(const json_pointer& ptr, const ValueType& default_value) const + { + // at only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + // if pointer resolves a value, return it or use default value + JSON_TRY + { + return ptr.get_checked(this).template get(); + } + JSON_INTERNAL_CATCH (out_of_range&) + { + return default_value; + } + } + + JSON_THROW(type_error::create(306, "cannot use value() with " + std::string(type_name()), *this)); + } + + /*! + @brief overload for a default value of type const char* + @copydoc basic_json::value(const json_pointer&, ValueType) const + */ + JSON_HEDLEY_NON_NULL(3) + string_t value(const json_pointer& ptr, const char* default_value) const + { + return value(ptr, string_t(default_value)); + } + + /*! + @brief access the first element + + Returns a reference to the first element in the container. For a JSON + container `c`, the expression `c.front()` is equivalent to `*c.begin()`. + + @return In case of a structured type (array or object), a reference to the + first element is returned. In case of number, string, boolean, or binary + values, a reference to the value is returned. + + @complexity Constant. + + @pre The JSON value must not be `null` (would throw `std::out_of_range`) + or an empty array or object (undefined behavior, **guarded by + assertions**). + @post The JSON value remains unchanged. + + @throw invalid_iterator.214 when called on `null` value + + @liveexample{The following code shows an example for `front()`.,front} + + @sa see @ref back() -- access the last element + + @since version 1.0.0 + */ + reference front() + { + return *begin(); + } + + /*! + @copydoc basic_json::front() + */ + const_reference front() const + { + return *cbegin(); + } + + /*! + @brief access the last element + + Returns a reference to the last element in the container. For a JSON + container `c`, the expression `c.back()` is equivalent to + @code {.cpp} + auto tmp = c.end(); + --tmp; + return *tmp; + @endcode + + @return In case of a structured type (array or object), a reference to the + last element is returned. In case of number, string, boolean, or binary + values, a reference to the value is returned. + + @complexity Constant. + + @pre The JSON value must not be `null` (would throw `std::out_of_range`) + or an empty array or object (undefined behavior, **guarded by + assertions**). + @post The JSON value remains unchanged. + + @throw invalid_iterator.214 when called on a `null` value. See example + below. + + @liveexample{The following code shows an example for `back()`.,back} + + @sa see @ref front() -- access the first element + + @since version 1.0.0 + */ + reference back() + { + auto tmp = end(); + --tmp; + return *tmp; + } + + /*! + @copydoc basic_json::back() + */ + const_reference back() const + { + auto tmp = cend(); + --tmp; + return *tmp; + } + + /*! + @brief remove element given an iterator + + Removes the element specified by iterator @a pos. The iterator @a pos must + be valid and dereferenceable. Thus the `end()` iterator (which is valid, + but is not dereferenceable) cannot be used as a value for @a pos. + + If called on a primitive type other than `null`, the resulting JSON value + will be `null`. + + @param[in] pos iterator to the element to remove + @return Iterator following the last removed element. If the iterator @a + pos refers to the last element, the `end()` iterator is returned. + + @tparam IteratorType an @ref iterator or @ref const_iterator + + @post Invalidates iterators and references at or after the point of the + erase, including the `end()` iterator. + + @throw type_error.307 if called on a `null` value; example: `"cannot use + erase() with null"` + @throw invalid_iterator.202 if called on an iterator which does not belong + to the current JSON value; example: `"iterator does not fit current + value"` + @throw invalid_iterator.205 if called on a primitive type with invalid + iterator (i.e., any iterator which is not `begin()`); example: `"iterator + out of range"` + + @complexity The complexity depends on the type: + - objects: amortized constant + - arrays: linear in distance between @a pos and the end of the container + - strings and binary: linear in the length of the member + - other types: constant + + @liveexample{The example shows the result of `erase()` for different JSON + types.,erase__IteratorType} + + @sa see @ref erase(IteratorType, IteratorType) -- removes the elements in + the given range + @sa see @ref erase(const typename object_t::key_type&) -- removes the element + from an object at the given key + @sa see @ref erase(const size_type) -- removes the element from an array at + the given index + + @since version 1.0.0 + */ + template < class IteratorType, typename std::enable_if < + std::is_same::value || + std::is_same::value, int >::type + = 0 > + IteratorType erase(IteratorType pos) + { + // make sure iterator fits the current value + if (JSON_HEDLEY_UNLIKELY(this != pos.m_object)) + { + JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", *this)); + } + + IteratorType result = end(); + + switch (m_type) + { + case value_t::boolean: + case value_t::number_float: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::string: + case value_t::binary: + { + if (JSON_HEDLEY_UNLIKELY(!pos.m_it.primitive_iterator.is_begin())) + { + JSON_THROW(invalid_iterator::create(205, "iterator out of range", *this)); + } + + if (is_string()) + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, m_value.string); + std::allocator_traits::deallocate(alloc, m_value.string, 1); + m_value.string = nullptr; + } + else if (is_binary()) + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, m_value.binary); + std::allocator_traits::deallocate(alloc, m_value.binary, 1); + m_value.binary = nullptr; + } + + m_type = value_t::null; + assert_invariant(); + break; + } + + case value_t::object: + { + result.m_it.object_iterator = m_value.object->erase(pos.m_it.object_iterator); + break; + } + + case value_t::array: + { + result.m_it.array_iterator = m_value.array->erase(pos.m_it.array_iterator); + break; + } + + case value_t::null: + case value_t::discarded: + default: + JSON_THROW(type_error::create(307, "cannot use erase() with " + std::string(type_name()), *this)); + } + + return result; + } + + /*! + @brief remove elements given an iterator range + + Removes the element specified by the range `[first; last)`. The iterator + @a first does not need to be dereferenceable if `first == last`: erasing + an empty range is a no-op. + + If called on a primitive type other than `null`, the resulting JSON value + will be `null`. + + @param[in] first iterator to the beginning of the range to remove + @param[in] last iterator past the end of the range to remove + @return Iterator following the last removed element. If the iterator @a + second refers to the last element, the `end()` iterator is returned. + + @tparam IteratorType an @ref iterator or @ref const_iterator + + @post Invalidates iterators and references at or after the point of the + erase, including the `end()` iterator. + + @throw type_error.307 if called on a `null` value; example: `"cannot use + erase() with null"` + @throw invalid_iterator.203 if called on iterators which does not belong + to the current JSON value; example: `"iterators do not fit current value"` + @throw invalid_iterator.204 if called on a primitive type with invalid + iterators (i.e., if `first != begin()` and `last != end()`); example: + `"iterators out of range"` + + @complexity The complexity depends on the type: + - objects: `log(size()) + std::distance(first, last)` + - arrays: linear in the distance between @a first and @a last, plus linear + in the distance between @a last and end of the container + - strings and binary: linear in the length of the member + - other types: constant + + @liveexample{The example shows the result of `erase()` for different JSON + types.,erase__IteratorType_IteratorType} + + @sa see @ref erase(IteratorType) -- removes the element at a given position + @sa see @ref erase(const typename object_t::key_type&) -- removes the element + from an object at the given key + @sa see @ref erase(const size_type) -- removes the element from an array at + the given index + + @since version 1.0.0 + */ + template < class IteratorType, typename std::enable_if < + std::is_same::value || + std::is_same::value, int >::type + = 0 > + IteratorType erase(IteratorType first, IteratorType last) + { + // make sure iterator fits the current value + if (JSON_HEDLEY_UNLIKELY(this != first.m_object || this != last.m_object)) + { + JSON_THROW(invalid_iterator::create(203, "iterators do not fit current value", *this)); + } + + IteratorType result = end(); + + switch (m_type) + { + case value_t::boolean: + case value_t::number_float: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::string: + case value_t::binary: + { + if (JSON_HEDLEY_LIKELY(!first.m_it.primitive_iterator.is_begin() + || !last.m_it.primitive_iterator.is_end())) + { + JSON_THROW(invalid_iterator::create(204, "iterators out of range", *this)); + } + + if (is_string()) + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, m_value.string); + std::allocator_traits::deallocate(alloc, m_value.string, 1); + m_value.string = nullptr; + } + else if (is_binary()) + { + AllocatorType alloc; + std::allocator_traits::destroy(alloc, m_value.binary); + std::allocator_traits::deallocate(alloc, m_value.binary, 1); + m_value.binary = nullptr; + } + + m_type = value_t::null; + assert_invariant(); + break; + } + + case value_t::object: + { + result.m_it.object_iterator = m_value.object->erase(first.m_it.object_iterator, + last.m_it.object_iterator); + break; + } + + case value_t::array: + { + result.m_it.array_iterator = m_value.array->erase(first.m_it.array_iterator, + last.m_it.array_iterator); + break; + } + + case value_t::null: + case value_t::discarded: + default: + JSON_THROW(type_error::create(307, "cannot use erase() with " + std::string(type_name()), *this)); + } + + return result; + } + + /*! + @brief remove element from a JSON object given a key + + Removes elements from a JSON object with the key value @a key. + + @param[in] key value of the elements to remove + + @return Number of elements removed. If @a ObjectType is the default + `std::map` type, the return value will always be `0` (@a key was not + found) or `1` (@a key was found). + + @post References and iterators to the erased elements are invalidated. + Other references and iterators are not affected. + + @throw type_error.307 when called on a type other than JSON object; + example: `"cannot use erase() with null"` + + @complexity `log(size()) + count(key)` + + @liveexample{The example shows the effect of `erase()`.,erase__key_type} + + @sa see @ref erase(IteratorType) -- removes the element at a given position + @sa see @ref erase(IteratorType, IteratorType) -- removes the elements in + the given range + @sa see @ref erase(const size_type) -- removes the element from an array at + the given index + + @since version 1.0.0 + */ + size_type erase(const typename object_t::key_type& key) + { + // this erase only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + return m_value.object->erase(key); + } + + JSON_THROW(type_error::create(307, "cannot use erase() with " + std::string(type_name()), *this)); + } + + /*! + @brief remove element from a JSON array given an index + + Removes element from a JSON array at the index @a idx. + + @param[in] idx index of the element to remove + + @throw type_error.307 when called on a type other than JSON object; + example: `"cannot use erase() with null"` + @throw out_of_range.401 when `idx >= size()`; example: `"array index 17 + is out of range"` + + @complexity Linear in distance between @a idx and the end of the container. + + @liveexample{The example shows the effect of `erase()`.,erase__size_type} + + @sa see @ref erase(IteratorType) -- removes the element at a given position + @sa see @ref erase(IteratorType, IteratorType) -- removes the elements in + the given range + @sa see @ref erase(const typename object_t::key_type&) -- removes the element + from an object at the given key + + @since version 1.0.0 + */ + void erase(const size_type idx) + { + // this erase only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + if (JSON_HEDLEY_UNLIKELY(idx >= size())) + { + JSON_THROW(out_of_range::create(401, "array index " + std::to_string(idx) + " is out of range", *this)); + } + + m_value.array->erase(m_value.array->begin() + static_cast(idx)); + } + else + { + JSON_THROW(type_error::create(307, "cannot use erase() with " + std::string(type_name()), *this)); + } + } + + /// @} + + + //////////// + // lookup // + //////////// + + /// @name lookup + /// @{ + + /*! + @brief find an element in a JSON object + + Finds an element in a JSON object with key equivalent to @a key. If the + element is not found or the JSON value is not an object, end() is + returned. + + @note This method always returns @ref end() when executed on a JSON type + that is not an object. + + @param[in] key key value of the element to search for. + + @return Iterator to an element with key equivalent to @a key. If no such + element is found or the JSON value is not an object, past-the-end (see + @ref end()) iterator is returned. + + @complexity Logarithmic in the size of the JSON object. + + @liveexample{The example shows how `find()` is used.,find__key_type} + + @sa see @ref contains(KeyT&&) const -- checks whether a key exists + + @since version 1.0.0 + */ + template + iterator find(KeyT&& key) + { + auto result = end(); + + if (is_object()) + { + result.m_it.object_iterator = m_value.object->find(std::forward(key)); + } + + return result; + } + + /*! + @brief find an element in a JSON object + @copydoc find(KeyT&&) + */ + template + const_iterator find(KeyT&& key) const + { + auto result = cend(); + + if (is_object()) + { + result.m_it.object_iterator = m_value.object->find(std::forward(key)); + } + + return result; + } + + /*! + @brief returns the number of occurrences of a key in a JSON object + + Returns the number of elements with key @a key. If ObjectType is the + default `std::map` type, the return value will always be `0` (@a key was + not found) or `1` (@a key was found). + + @note This method always returns `0` when executed on a JSON type that is + not an object. + + @param[in] key key value of the element to count + + @return Number of elements with key @a key. If the JSON value is not an + object, the return value will be `0`. + + @complexity Logarithmic in the size of the JSON object. + + @liveexample{The example shows how `count()` is used.,count} + + @since version 1.0.0 + */ + template + size_type count(KeyT&& key) const + { + // return 0 for all nonobject types + return is_object() ? m_value.object->count(std::forward(key)) : 0; + } + + /*! + @brief check the existence of an element in a JSON object + + Check whether an element exists in a JSON object with key equivalent to + @a key. If the element is not found or the JSON value is not an object, + false is returned. + + @note This method always returns false when executed on a JSON type + that is not an object. + + @param[in] key key value to check its existence. + + @return true if an element with specified @a key exists. If no such + element with such key is found or the JSON value is not an object, + false is returned. + + @complexity Logarithmic in the size of the JSON object. + + @liveexample{The following code shows an example for `contains()`.,contains} + + @sa see @ref find(KeyT&&) -- returns an iterator to an object element + @sa see @ref contains(const json_pointer&) const -- checks the existence for a JSON pointer + + @since version 3.6.0 + */ + template < typename KeyT, typename std::enable_if < + !std::is_same::type, json_pointer>::value, int >::type = 0 > + bool contains(KeyT && key) const + { + return is_object() && m_value.object->find(std::forward(key)) != m_value.object->end(); + } + + /*! + @brief check the existence of an element in a JSON object given a JSON pointer + + Check whether the given JSON pointer @a ptr can be resolved in the current + JSON value. + + @note This method can be executed on any JSON value type. + + @param[in] ptr JSON pointer to check its existence. + + @return true if the JSON pointer can be resolved to a stored value, false + otherwise. + + @post If `j.contains(ptr)` returns true, it is safe to call `j[ptr]`. + + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + + @complexity Logarithmic in the size of the JSON object. + + @liveexample{The following code shows an example for `contains()`.,contains_json_pointer} + + @sa see @ref contains(KeyT &&) const -- checks the existence of a key + + @since version 3.7.0 + */ + bool contains(const json_pointer& ptr) const + { + return ptr.contains(this); + } + + /// @} + + + /////////////// + // iterators // + /////////////// + + /// @name iterators + /// @{ + + /*! + @brief returns an iterator to the first element + + Returns an iterator to the first element. + + @image html range-begin-end.svg "Illustration from cppreference.com" + + @return iterator to the first element + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + + @liveexample{The following code shows an example for `begin()`.,begin} + + @sa see @ref cbegin() -- returns a const iterator to the beginning + @sa see @ref end() -- returns an iterator to the end + @sa see @ref cend() -- returns a const iterator to the end + + @since version 1.0.0 + */ + iterator begin() noexcept + { + iterator result(this); + result.set_begin(); + return result; + } + + /*! + @copydoc basic_json::cbegin() + */ + const_iterator begin() const noexcept + { + return cbegin(); + } + + /*! + @brief returns a const iterator to the first element + + Returns a const iterator to the first element. + + @image html range-begin-end.svg "Illustration from cppreference.com" + + @return const iterator to the first element + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + - Has the semantics of `const_cast(*this).begin()`. + + @liveexample{The following code shows an example for `cbegin()`.,cbegin} + + @sa see @ref begin() -- returns an iterator to the beginning + @sa see @ref end() -- returns an iterator to the end + @sa see @ref cend() -- returns a const iterator to the end + + @since version 1.0.0 + */ + const_iterator cbegin() const noexcept + { + const_iterator result(this); + result.set_begin(); + return result; + } + + /*! + @brief returns an iterator to one past the last element + + Returns an iterator to one past the last element. + + @image html range-begin-end.svg "Illustration from cppreference.com" + + @return iterator one past the last element + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + + @liveexample{The following code shows an example for `end()`.,end} + + @sa see @ref cend() -- returns a const iterator to the end + @sa see @ref begin() -- returns an iterator to the beginning + @sa see @ref cbegin() -- returns a const iterator to the beginning + + @since version 1.0.0 + */ + iterator end() noexcept + { + iterator result(this); + result.set_end(); + return result; + } + + /*! + @copydoc basic_json::cend() + */ + const_iterator end() const noexcept + { + return cend(); + } + + /*! + @brief returns a const iterator to one past the last element + + Returns a const iterator to one past the last element. + + @image html range-begin-end.svg "Illustration from cppreference.com" + + @return const iterator one past the last element + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + - Has the semantics of `const_cast(*this).end()`. + + @liveexample{The following code shows an example for `cend()`.,cend} + + @sa see @ref end() -- returns an iterator to the end + @sa see @ref begin() -- returns an iterator to the beginning + @sa see @ref cbegin() -- returns a const iterator to the beginning + + @since version 1.0.0 + */ + const_iterator cend() const noexcept + { + const_iterator result(this); + result.set_end(); + return result; + } + + /*! + @brief returns an iterator to the reverse-beginning + + Returns an iterator to the reverse-beginning; that is, the last element. + + @image html range-rbegin-rend.svg "Illustration from cppreference.com" + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [ReversibleContainer](https://en.cppreference.com/w/cpp/named_req/ReversibleContainer) + requirements: + - The complexity is constant. + - Has the semantics of `reverse_iterator(end())`. + + @liveexample{The following code shows an example for `rbegin()`.,rbegin} + + @sa see @ref crbegin() -- returns a const reverse iterator to the beginning + @sa see @ref rend() -- returns a reverse iterator to the end + @sa see @ref crend() -- returns a const reverse iterator to the end + + @since version 1.0.0 + */ + reverse_iterator rbegin() noexcept + { + return reverse_iterator(end()); + } + + /*! + @copydoc basic_json::crbegin() + */ + const_reverse_iterator rbegin() const noexcept + { + return crbegin(); + } + + /*! + @brief returns an iterator to the reverse-end + + Returns an iterator to the reverse-end; that is, one before the first + element. + + @image html range-rbegin-rend.svg "Illustration from cppreference.com" + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [ReversibleContainer](https://en.cppreference.com/w/cpp/named_req/ReversibleContainer) + requirements: + - The complexity is constant. + - Has the semantics of `reverse_iterator(begin())`. + + @liveexample{The following code shows an example for `rend()`.,rend} + + @sa see @ref crend() -- returns a const reverse iterator to the end + @sa see @ref rbegin() -- returns a reverse iterator to the beginning + @sa see @ref crbegin() -- returns a const reverse iterator to the beginning + + @since version 1.0.0 + */ + reverse_iterator rend() noexcept + { + return reverse_iterator(begin()); + } + + /*! + @copydoc basic_json::crend() + */ + const_reverse_iterator rend() const noexcept + { + return crend(); + } + + /*! + @brief returns a const reverse iterator to the last element + + Returns a const iterator to the reverse-beginning; that is, the last + element. + + @image html range-rbegin-rend.svg "Illustration from cppreference.com" + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [ReversibleContainer](https://en.cppreference.com/w/cpp/named_req/ReversibleContainer) + requirements: + - The complexity is constant. + - Has the semantics of `const_cast(*this).rbegin()`. + + @liveexample{The following code shows an example for `crbegin()`.,crbegin} + + @sa see @ref rbegin() -- returns a reverse iterator to the beginning + @sa see @ref rend() -- returns a reverse iterator to the end + @sa see @ref crend() -- returns a const reverse iterator to the end + + @since version 1.0.0 + */ + const_reverse_iterator crbegin() const noexcept + { + return const_reverse_iterator(cend()); + } + + /*! + @brief returns a const reverse iterator to one before the first + + Returns a const reverse iterator to the reverse-end; that is, one before + the first element. + + @image html range-rbegin-rend.svg "Illustration from cppreference.com" + + @complexity Constant. + + @requirement This function helps `basic_json` satisfying the + [ReversibleContainer](https://en.cppreference.com/w/cpp/named_req/ReversibleContainer) + requirements: + - The complexity is constant. + - Has the semantics of `const_cast(*this).rend()`. + + @liveexample{The following code shows an example for `crend()`.,crend} + + @sa see @ref rend() -- returns a reverse iterator to the end + @sa see @ref rbegin() -- returns a reverse iterator to the beginning + @sa see @ref crbegin() -- returns a const reverse iterator to the beginning + + @since version 1.0.0 + */ + const_reverse_iterator crend() const noexcept + { + return const_reverse_iterator(cbegin()); + } + + public: + /*! + @brief wrapper to access iterator member functions in range-based for + + This function allows to access @ref iterator::key() and @ref + iterator::value() during range-based for loops. In these loops, a + reference to the JSON values is returned, so there is no access to the + underlying iterator. + + For loop without iterator_wrapper: + + @code{cpp} + for (auto it = j_object.begin(); it != j_object.end(); ++it) + { + std::cout << "key: " << it.key() << ", value:" << it.value() << '\n'; + } + @endcode + + Range-based for loop without iterator proxy: + + @code{cpp} + for (auto it : j_object) + { + // "it" is of type json::reference and has no key() member + std::cout << "value: " << it << '\n'; + } + @endcode + + Range-based for loop with iterator proxy: + + @code{cpp} + for (auto it : json::iterator_wrapper(j_object)) + { + std::cout << "key: " << it.key() << ", value:" << it.value() << '\n'; + } + @endcode + + @note When iterating over an array, `key()` will return the index of the + element as string (see example). + + @param[in] ref reference to a JSON value + @return iteration proxy object wrapping @a ref with an interface to use in + range-based for loops + + @liveexample{The following code shows how the wrapper is used,iterator_wrapper} + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Constant. + + @note The name of this function is not yet final and may change in the + future. + + @deprecated This stream operator is deprecated and will be removed in + future 4.0.0 of the library. Please use @ref items() instead; + that is, replace `json::iterator_wrapper(j)` with `j.items()`. + */ + JSON_HEDLEY_DEPRECATED_FOR(3.1.0, items()) + static iteration_proxy iterator_wrapper(reference ref) noexcept + { + return ref.items(); + } + + /*! + @copydoc iterator_wrapper(reference) + */ + JSON_HEDLEY_DEPRECATED_FOR(3.1.0, items()) + static iteration_proxy iterator_wrapper(const_reference ref) noexcept + { + return ref.items(); + } + + /*! + @brief helper to access iterator member functions in range-based for + + This function allows to access @ref iterator::key() and @ref + iterator::value() during range-based for loops. In these loops, a + reference to the JSON values is returned, so there is no access to the + underlying iterator. + + For loop without `items()` function: + + @code{cpp} + for (auto it = j_object.begin(); it != j_object.end(); ++it) + { + std::cout << "key: " << it.key() << ", value:" << it.value() << '\n'; + } + @endcode + + Range-based for loop without `items()` function: + + @code{cpp} + for (auto it : j_object) + { + // "it" is of type json::reference and has no key() member + std::cout << "value: " << it << '\n'; + } + @endcode + + Range-based for loop with `items()` function: + + @code{cpp} + for (auto& el : j_object.items()) + { + std::cout << "key: " << el.key() << ", value:" << el.value() << '\n'; + } + @endcode + + The `items()` function also allows to use + [structured bindings](https://en.cppreference.com/w/cpp/language/structured_binding) + (C++17): + + @code{cpp} + for (auto& [key, val] : j_object.items()) + { + std::cout << "key: " << key << ", value:" << val << '\n'; + } + @endcode + + @note When iterating over an array, `key()` will return the index of the + element as string (see example). For primitive types (e.g., numbers), + `key()` returns an empty string. + + @warning Using `items()` on temporary objects is dangerous. Make sure the + object's lifetime exeeds the iteration. See + for more + information. + + @return iteration proxy object wrapping @a ref with an interface to use in + range-based for loops + + @liveexample{The following code shows how the function is used.,items} + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Constant. + + @since version 3.1.0, structured bindings support since 3.5.0. + */ + iteration_proxy items() noexcept + { + return iteration_proxy(*this); + } + + /*! + @copydoc items() + */ + iteration_proxy items() const noexcept + { + return iteration_proxy(*this); + } + + /// @} + + + ////////////// + // capacity // + ////////////// + + /// @name capacity + /// @{ + + /*! + @brief checks whether the container is empty. + + Checks if a JSON value has no elements (i.e. whether its @ref size is `0`). + + @return The return value depends on the different types and is + defined as follows: + Value type | return value + ----------- | ------------- + null | `true` + boolean | `false` + string | `false` + number | `false` + binary | `false` + object | result of function `object_t::empty()` + array | result of function `array_t::empty()` + + @liveexample{The following code uses `empty()` to check if a JSON + object contains any elements.,empty} + + @complexity Constant, as long as @ref array_t and @ref object_t satisfy + the Container concept; that is, their `empty()` functions have constant + complexity. + + @iterators No changes. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @note This function does not return whether a string stored as JSON value + is empty - it returns whether the JSON container itself is empty which is + false in the case of a string. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + - Has the semantics of `begin() == end()`. + + @sa see @ref size() -- returns the number of elements + + @since version 1.0.0 + */ + bool empty() const noexcept + { + switch (m_type) + { + case value_t::null: + { + // null values are empty + return true; + } + + case value_t::array: + { + // delegate call to array_t::empty() + return m_value.array->empty(); + } + + case value_t::object: + { + // delegate call to object_t::empty() + return m_value.object->empty(); + } + + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + // all other types are nonempty + return false; + } + } + } + + /*! + @brief returns the number of elements + + Returns the number of elements in a JSON value. + + @return The return value depends on the different types and is + defined as follows: + Value type | return value + ----------- | ------------- + null | `0` + boolean | `1` + string | `1` + number | `1` + binary | `1` + object | result of function object_t::size() + array | result of function array_t::size() + + @liveexample{The following code calls `size()` on the different value + types.,size} + + @complexity Constant, as long as @ref array_t and @ref object_t satisfy + the Container concept; that is, their size() functions have constant + complexity. + + @iterators No changes. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @note This function does not return the length of a string stored as JSON + value - it returns the number of elements in the JSON value which is 1 in + the case of a string. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + - Has the semantics of `std::distance(begin(), end())`. + + @sa see @ref empty() -- checks whether the container is empty + @sa see @ref max_size() -- returns the maximal number of elements + + @since version 1.0.0 + */ + size_type size() const noexcept + { + switch (m_type) + { + case value_t::null: + { + // null values are empty + return 0; + } + + case value_t::array: + { + // delegate call to array_t::size() + return m_value.array->size(); + } + + case value_t::object: + { + // delegate call to object_t::size() + return m_value.object->size(); + } + + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + // all other types have size 1 + return 1; + } + } + } + + /*! + @brief returns the maximum possible number of elements + + Returns the maximum number of elements a JSON value is able to hold due to + system or library implementation limitations, i.e. `std::distance(begin(), + end())` for the JSON value. + + @return The return value depends on the different types and is + defined as follows: + Value type | return value + ----------- | ------------- + null | `0` (same as `size()`) + boolean | `1` (same as `size()`) + string | `1` (same as `size()`) + number | `1` (same as `size()`) + binary | `1` (same as `size()`) + object | result of function `object_t::max_size()` + array | result of function `array_t::max_size()` + + @liveexample{The following code calls `max_size()` on the different value + types. Note the output is implementation specific.,max_size} + + @complexity Constant, as long as @ref array_t and @ref object_t satisfy + the Container concept; that is, their `max_size()` functions have constant + complexity. + + @iterators No changes. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @requirement This function helps `basic_json` satisfying the + [Container](https://en.cppreference.com/w/cpp/named_req/Container) + requirements: + - The complexity is constant. + - Has the semantics of returning `b.size()` where `b` is the largest + possible JSON value. + + @sa see @ref size() -- returns the number of elements + + @since version 1.0.0 + */ + size_type max_size() const noexcept + { + switch (m_type) + { + case value_t::array: + { + // delegate call to array_t::max_size() + return m_value.array->max_size(); + } + + case value_t::object: + { + // delegate call to object_t::max_size() + return m_value.object->max_size(); + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + // all other types have max_size() == size() + return size(); + } + } + } + + /// @} + + + /////////////// + // modifiers // + /////////////// + + /// @name modifiers + /// @{ + + /*! + @brief clears the contents + + Clears the content of a JSON value and resets it to the default value as + if @ref basic_json(value_t) would have been called with the current value + type from @ref type(): + + Value type | initial value + ----------- | ------------- + null | `null` + boolean | `false` + string | `""` + number | `0` + binary | An empty byte vector + object | `{}` + array | `[]` + + @post Has the same effect as calling + @code {.cpp} + *this = basic_json(type()); + @endcode + + @liveexample{The example below shows the effect of `clear()` to different + JSON types.,clear} + + @complexity Linear in the size of the JSON value. + + @iterators All iterators, pointers and references related to this container + are invalidated. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @sa see @ref basic_json(value_t) -- constructor that creates an object with the + same value than calling `clear()` + + @since version 1.0.0 + */ + void clear() noexcept + { + switch (m_type) + { + case value_t::number_integer: + { + m_value.number_integer = 0; + break; + } + + case value_t::number_unsigned: + { + m_value.number_unsigned = 0; + break; + } + + case value_t::number_float: + { + m_value.number_float = 0.0; + break; + } + + case value_t::boolean: + { + m_value.boolean = false; + break; + } + + case value_t::string: + { + m_value.string->clear(); + break; + } + + case value_t::binary: + { + m_value.binary->clear(); + break; + } + + case value_t::array: + { + m_value.array->clear(); + break; + } + + case value_t::object: + { + m_value.object->clear(); + break; + } + + case value_t::null: + case value_t::discarded: + default: + break; + } + } + + /*! + @brief add an object to an array + + Appends the given element @a val to the end of the JSON value. If the + function is called on a JSON null value, an empty array is created before + appending @a val. + + @param[in] val the value to add to the JSON array + + @throw type_error.308 when called on a type other than JSON array or + null; example: `"cannot use push_back() with number"` + + @complexity Amortized constant. + + @liveexample{The example shows how `push_back()` and `+=` can be used to + add elements to a JSON array. Note how the `null` value was silently + converted to a JSON array.,push_back} + + @since version 1.0.0 + */ + void push_back(basic_json&& val) + { + // push_back only works for null objects or arrays + if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array()))) + { + JSON_THROW(type_error::create(308, "cannot use push_back() with " + std::string(type_name()), *this)); + } + + // transform null object into an array + if (is_null()) + { + m_type = value_t::array; + m_value = value_t::array; + assert_invariant(); + } + + // add element to array (move semantics) + const auto old_capacity = m_value.array->capacity(); + m_value.array->push_back(std::move(val)); + set_parent(m_value.array->back(), old_capacity); + // if val is moved from, basic_json move constructor marks it null so we do not call the destructor + } + + /*! + @brief add an object to an array + @copydoc push_back(basic_json&&) + */ + reference operator+=(basic_json&& val) + { + push_back(std::move(val)); + return *this; + } + + /*! + @brief add an object to an array + @copydoc push_back(basic_json&&) + */ + void push_back(const basic_json& val) + { + // push_back only works for null objects or arrays + if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array()))) + { + JSON_THROW(type_error::create(308, "cannot use push_back() with " + std::string(type_name()), *this)); + } + + // transform null object into an array + if (is_null()) + { + m_type = value_t::array; + m_value = value_t::array; + assert_invariant(); + } + + // add element to array + const auto old_capacity = m_value.array->capacity(); + m_value.array->push_back(val); + set_parent(m_value.array->back(), old_capacity); + } + + /*! + @brief add an object to an array + @copydoc push_back(basic_json&&) + */ + reference operator+=(const basic_json& val) + { + push_back(val); + return *this; + } + + /*! + @brief add an object to an object + + Inserts the given element @a val to the JSON object. If the function is + called on a JSON null value, an empty object is created before inserting + @a val. + + @param[in] val the value to add to the JSON object + + @throw type_error.308 when called on a type other than JSON object or + null; example: `"cannot use push_back() with number"` + + @complexity Logarithmic in the size of the container, O(log(`size()`)). + + @liveexample{The example shows how `push_back()` and `+=` can be used to + add elements to a JSON object. Note how the `null` value was silently + converted to a JSON object.,push_back__object_t__value} + + @since version 1.0.0 + */ + void push_back(const typename object_t::value_type& val) + { + // push_back only works for null objects or objects + if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_object()))) + { + JSON_THROW(type_error::create(308, "cannot use push_back() with " + std::string(type_name()), *this)); + } + + // transform null object into an object + if (is_null()) + { + m_type = value_t::object; + m_value = value_t::object; + assert_invariant(); + } + + // add element to object + auto res = m_value.object->insert(val); + set_parent(res.first->second); + } + + /*! + @brief add an object to an object + @copydoc push_back(const typename object_t::value_type&) + */ + reference operator+=(const typename object_t::value_type& val) + { + push_back(val); + return *this; + } + + /*! + @brief add an object to an object + + This function allows to use `push_back` with an initializer list. In case + + 1. the current value is an object, + 2. the initializer list @a init contains only two elements, and + 3. the first element of @a init is a string, + + @a init is converted into an object element and added using + @ref push_back(const typename object_t::value_type&). Otherwise, @a init + is converted to a JSON value and added using @ref push_back(basic_json&&). + + @param[in] init an initializer list + + @complexity Linear in the size of the initializer list @a init. + + @note This function is required to resolve an ambiguous overload error, + because pairs like `{"key", "value"}` can be both interpreted as + `object_t::value_type` or `std::initializer_list`, see + https://github.com/nlohmann/json/issues/235 for more information. + + @liveexample{The example shows how initializer lists are treated as + objects when possible.,push_back__initializer_list} + */ + void push_back(initializer_list_t init) + { + if (is_object() && init.size() == 2 && (*init.begin())->is_string()) + { + basic_json&& key = init.begin()->moved_or_copied(); + push_back(typename object_t::value_type( + std::move(key.get_ref()), (init.begin() + 1)->moved_or_copied())); + } + else + { + push_back(basic_json(init)); + } + } + + /*! + @brief add an object to an object + @copydoc push_back(initializer_list_t) + */ + reference operator+=(initializer_list_t init) + { + push_back(init); + return *this; + } + + /*! + @brief add an object to an array + + Creates a JSON value from the passed parameters @a args to the end of the + JSON value. If the function is called on a JSON null value, an empty array + is created before appending the value created from @a args. + + @param[in] args arguments to forward to a constructor of @ref basic_json + @tparam Args compatible types to create a @ref basic_json object + + @return reference to the inserted element + + @throw type_error.311 when called on a type other than JSON array or + null; example: `"cannot use emplace_back() with number"` + + @complexity Amortized constant. + + @liveexample{The example shows how `push_back()` can be used to add + elements to a JSON array. Note how the `null` value was silently converted + to a JSON array.,emplace_back} + + @since version 2.0.8, returns reference since 3.7.0 + */ + template + reference emplace_back(Args&& ... args) + { + // emplace_back only works for null objects or arrays + if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_array()))) + { + JSON_THROW(type_error::create(311, "cannot use emplace_back() with " + std::string(type_name()), *this)); + } + + // transform null object into an array + if (is_null()) + { + m_type = value_t::array; + m_value = value_t::array; + assert_invariant(); + } + + // add element to array (perfect forwarding) + const auto old_capacity = m_value.array->capacity(); + m_value.array->emplace_back(std::forward(args)...); + return set_parent(m_value.array->back(), old_capacity); + } + + /*! + @brief add an object to an object if key does not exist + + Inserts a new element into a JSON object constructed in-place with the + given @a args if there is no element with the key in the container. If the + function is called on a JSON null value, an empty object is created before + appending the value created from @a args. + + @param[in] args arguments to forward to a constructor of @ref basic_json + @tparam Args compatible types to create a @ref basic_json object + + @return a pair consisting of an iterator to the inserted element, or the + already-existing element if no insertion happened, and a bool + denoting whether the insertion took place. + + @throw type_error.311 when called on a type other than JSON object or + null; example: `"cannot use emplace() with number"` + + @complexity Logarithmic in the size of the container, O(log(`size()`)). + + @liveexample{The example shows how `emplace()` can be used to add elements + to a JSON object. Note how the `null` value was silently converted to a + JSON object. Further note how no value is added if there was already one + value stored with the same key.,emplace} + + @since version 2.0.8 + */ + template + std::pair emplace(Args&& ... args) + { + // emplace only works for null objects or arrays + if (JSON_HEDLEY_UNLIKELY(!(is_null() || is_object()))) + { + JSON_THROW(type_error::create(311, "cannot use emplace() with " + std::string(type_name()), *this)); + } + + // transform null object into an object + if (is_null()) + { + m_type = value_t::object; + m_value = value_t::object; + assert_invariant(); + } + + // add element to array (perfect forwarding) + auto res = m_value.object->emplace(std::forward(args)...); + set_parent(res.first->second); + + // create result iterator and set iterator to the result of emplace + auto it = begin(); + it.m_it.object_iterator = res.first; + + // return pair of iterator and boolean + return {it, res.second}; + } + + /// Helper for insertion of an iterator + /// @note: This uses std::distance to support GCC 4.8, + /// see https://github.com/nlohmann/json/pull/1257 + template + iterator insert_iterator(const_iterator pos, Args&& ... args) + { + iterator result(this); + JSON_ASSERT(m_value.array != nullptr); + + auto insert_pos = std::distance(m_value.array->begin(), pos.m_it.array_iterator); + m_value.array->insert(pos.m_it.array_iterator, std::forward(args)...); + result.m_it.array_iterator = m_value.array->begin() + insert_pos; + + // This could have been written as: + // result.m_it.array_iterator = m_value.array->insert(pos.m_it.array_iterator, cnt, val); + // but the return value of insert is missing in GCC 4.8, so it is written this way instead. + + set_parents(); + return result; + } + + /*! + @brief inserts element + + Inserts element @a val before iterator @a pos. + + @param[in] pos iterator before which the content will be inserted; may be + the end() iterator + @param[in] val element to insert + @return iterator pointing to the inserted @a val. + + @throw type_error.309 if called on JSON values other than arrays; + example: `"cannot use insert() with string"` + @throw invalid_iterator.202 if @a pos is not an iterator of *this; + example: `"iterator does not fit current value"` + + @complexity Constant plus linear in the distance between @a pos and end of + the container. + + @liveexample{The example shows how `insert()` is used.,insert} + + @since version 1.0.0 + */ + iterator insert(const_iterator pos, const basic_json& val) + { + // insert only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + // check if iterator pos fits to this JSON value + if (JSON_HEDLEY_UNLIKELY(pos.m_object != this)) + { + JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", *this)); + } + + // insert to array and return iterator + return insert_iterator(pos, val); + } + + JSON_THROW(type_error::create(309, "cannot use insert() with " + std::string(type_name()), *this)); + } + + /*! + @brief inserts element + @copydoc insert(const_iterator, const basic_json&) + */ + iterator insert(const_iterator pos, basic_json&& val) + { + return insert(pos, val); + } + + /*! + @brief inserts elements + + Inserts @a cnt copies of @a val before iterator @a pos. + + @param[in] pos iterator before which the content will be inserted; may be + the end() iterator + @param[in] cnt number of copies of @a val to insert + @param[in] val element to insert + @return iterator pointing to the first element inserted, or @a pos if + `cnt==0` + + @throw type_error.309 if called on JSON values other than arrays; example: + `"cannot use insert() with string"` + @throw invalid_iterator.202 if @a pos is not an iterator of *this; + example: `"iterator does not fit current value"` + + @complexity Linear in @a cnt plus linear in the distance between @a pos + and end of the container. + + @liveexample{The example shows how `insert()` is used.,insert__count} + + @since version 1.0.0 + */ + iterator insert(const_iterator pos, size_type cnt, const basic_json& val) + { + // insert only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + // check if iterator pos fits to this JSON value + if (JSON_HEDLEY_UNLIKELY(pos.m_object != this)) + { + JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", *this)); + } + + // insert to array and return iterator + return insert_iterator(pos, cnt, val); + } + + JSON_THROW(type_error::create(309, "cannot use insert() with " + std::string(type_name()), *this)); + } + + /*! + @brief inserts elements + + Inserts elements from range `[first, last)` before iterator @a pos. + + @param[in] pos iterator before which the content will be inserted; may be + the end() iterator + @param[in] first begin of the range of elements to insert + @param[in] last end of the range of elements to insert + + @throw type_error.309 if called on JSON values other than arrays; example: + `"cannot use insert() with string"` + @throw invalid_iterator.202 if @a pos is not an iterator of *this; + example: `"iterator does not fit current value"` + @throw invalid_iterator.210 if @a first and @a last do not belong to the + same JSON value; example: `"iterators do not fit"` + @throw invalid_iterator.211 if @a first or @a last are iterators into + container for which insert is called; example: `"passed iterators may not + belong to container"` + + @return iterator pointing to the first element inserted, or @a pos if + `first==last` + + @complexity Linear in `std::distance(first, last)` plus linear in the + distance between @a pos and end of the container. + + @liveexample{The example shows how `insert()` is used.,insert__range} + + @since version 1.0.0 + */ + iterator insert(const_iterator pos, const_iterator first, const_iterator last) + { + // insert only works for arrays + if (JSON_HEDLEY_UNLIKELY(!is_array())) + { + JSON_THROW(type_error::create(309, "cannot use insert() with " + std::string(type_name()), *this)); + } + + // check if iterator pos fits to this JSON value + if (JSON_HEDLEY_UNLIKELY(pos.m_object != this)) + { + JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", *this)); + } + + // check if range iterators belong to the same JSON object + if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object)) + { + JSON_THROW(invalid_iterator::create(210, "iterators do not fit", *this)); + } + + if (JSON_HEDLEY_UNLIKELY(first.m_object == this)) + { + JSON_THROW(invalid_iterator::create(211, "passed iterators may not belong to container", *this)); + } + + // insert to array and return iterator + return insert_iterator(pos, first.m_it.array_iterator, last.m_it.array_iterator); + } + + /*! + @brief inserts elements + + Inserts elements from initializer list @a ilist before iterator @a pos. + + @param[in] pos iterator before which the content will be inserted; may be + the end() iterator + @param[in] ilist initializer list to insert the values from + + @throw type_error.309 if called on JSON values other than arrays; example: + `"cannot use insert() with string"` + @throw invalid_iterator.202 if @a pos is not an iterator of *this; + example: `"iterator does not fit current value"` + + @return iterator pointing to the first element inserted, or @a pos if + `ilist` is empty + + @complexity Linear in `ilist.size()` plus linear in the distance between + @a pos and end of the container. + + @liveexample{The example shows how `insert()` is used.,insert__ilist} + + @since version 1.0.0 + */ + iterator insert(const_iterator pos, initializer_list_t ilist) + { + // insert only works for arrays + if (JSON_HEDLEY_UNLIKELY(!is_array())) + { + JSON_THROW(type_error::create(309, "cannot use insert() with " + std::string(type_name()), *this)); + } + + // check if iterator pos fits to this JSON value + if (JSON_HEDLEY_UNLIKELY(pos.m_object != this)) + { + JSON_THROW(invalid_iterator::create(202, "iterator does not fit current value", *this)); + } + + // insert to array and return iterator + return insert_iterator(pos, ilist.begin(), ilist.end()); + } + + /*! + @brief inserts elements + + Inserts elements from range `[first, last)`. + + @param[in] first begin of the range of elements to insert + @param[in] last end of the range of elements to insert + + @throw type_error.309 if called on JSON values other than objects; example: + `"cannot use insert() with string"` + @throw invalid_iterator.202 if iterator @a first or @a last does does not + point to an object; example: `"iterators first and last must point to + objects"` + @throw invalid_iterator.210 if @a first and @a last do not belong to the + same JSON value; example: `"iterators do not fit"` + + @complexity Logarithmic: `O(N*log(size() + N))`, where `N` is the number + of elements to insert. + + @liveexample{The example shows how `insert()` is used.,insert__range_object} + + @since version 3.0.0 + */ + void insert(const_iterator first, const_iterator last) + { + // insert only works for objects + if (JSON_HEDLEY_UNLIKELY(!is_object())) + { + JSON_THROW(type_error::create(309, "cannot use insert() with " + std::string(type_name()), *this)); + } + + // check if range iterators belong to the same JSON object + if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object)) + { + JSON_THROW(invalid_iterator::create(210, "iterators do not fit", *this)); + } + + // passed iterators must belong to objects + if (JSON_HEDLEY_UNLIKELY(!first.m_object->is_object())) + { + JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", *this)); + } + + m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator); + } + + /*! + @brief updates a JSON object from another object, overwriting existing keys + + Inserts all values from JSON object @a j and overwrites existing keys. + + @param[in] j JSON object to read values from + + @throw type_error.312 if called on JSON values other than objects; example: + `"cannot use update() with string"` + + @complexity O(N*log(size() + N)), where N is the number of elements to + insert. + + @liveexample{The example shows how `update()` is used.,update} + + @sa https://docs.python.org/3.6/library/stdtypes.html#dict.update + + @since version 3.0.0 + */ + void update(const_reference j) + { + // implicitly convert null value to an empty object + if (is_null()) + { + m_type = value_t::object; + m_value.object = create(); + assert_invariant(); + } + + if (JSON_HEDLEY_UNLIKELY(!is_object())) + { + JSON_THROW(type_error::create(312, "cannot use update() with " + std::string(type_name()), *this)); + } + if (JSON_HEDLEY_UNLIKELY(!j.is_object())) + { + JSON_THROW(type_error::create(312, "cannot use update() with " + std::string(j.type_name()), *this)); + } + + for (auto it = j.cbegin(); it != j.cend(); ++it) + { + m_value.object->operator[](it.key()) = it.value(); +#if JSON_DIAGNOSTICS + m_value.object->operator[](it.key()).m_parent = this; +#endif + } + } + + /*! + @brief updates a JSON object from another object, overwriting existing keys + + Inserts all values from from range `[first, last)` and overwrites existing + keys. + + @param[in] first begin of the range of elements to insert + @param[in] last end of the range of elements to insert + + @throw type_error.312 if called on JSON values other than objects; example: + `"cannot use update() with string"` + @throw invalid_iterator.202 if iterator @a first or @a last does does not + point to an object; example: `"iterators first and last must point to + objects"` + @throw invalid_iterator.210 if @a first and @a last do not belong to the + same JSON value; example: `"iterators do not fit"` + + @complexity O(N*log(size() + N)), where N is the number of elements to + insert. + + @liveexample{The example shows how `update()` is used__range.,update} + + @sa https://docs.python.org/3.6/library/stdtypes.html#dict.update + + @since version 3.0.0 + */ + void update(const_iterator first, const_iterator last) + { + // implicitly convert null value to an empty object + if (is_null()) + { + m_type = value_t::object; + m_value.object = create(); + assert_invariant(); + } + + if (JSON_HEDLEY_UNLIKELY(!is_object())) + { + JSON_THROW(type_error::create(312, "cannot use update() with " + std::string(type_name()), *this)); + } + + // check if range iterators belong to the same JSON object + if (JSON_HEDLEY_UNLIKELY(first.m_object != last.m_object)) + { + JSON_THROW(invalid_iterator::create(210, "iterators do not fit", *this)); + } + + // passed iterators must belong to objects + if (JSON_HEDLEY_UNLIKELY(!first.m_object->is_object() + || !last.m_object->is_object())) + { + JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", *this)); + } + + for (auto it = first; it != last; ++it) + { + m_value.object->operator[](it.key()) = it.value(); +#if JSON_DIAGNOSTICS + m_value.object->operator[](it.key()).m_parent = this; +#endif + } + } + + /*! + @brief exchanges the values + + Exchanges the contents of the JSON value with those of @a other. Does not + invoke any move, copy, or swap operations on individual elements. All + iterators and references remain valid. The past-the-end iterator is + invalidated. + + @param[in,out] other JSON value to exchange the contents with + + @complexity Constant. + + @liveexample{The example below shows how JSON values can be swapped with + `swap()`.,swap__reference} + + @since version 1.0.0 + */ + void swap(reference other) noexcept ( + std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_assignable::value&& + std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_assignable::value + ) + { + std::swap(m_type, other.m_type); + std::swap(m_value, other.m_value); + + set_parents(); + other.set_parents(); + assert_invariant(); + } + + /*! + @brief exchanges the values + + Exchanges the contents of the JSON value from @a left with those of @a right. Does not + invoke any move, copy, or swap operations on individual elements. All + iterators and references remain valid. The past-the-end iterator is + invalidated. implemented as a friend function callable via ADL. + + @param[in,out] left JSON value to exchange the contents with + @param[in,out] right JSON value to exchange the contents with + + @complexity Constant. + + @liveexample{The example below shows how JSON values can be swapped with + `swap()`.,swap__reference} + + @since version 1.0.0 + */ + friend void swap(reference left, reference right) noexcept ( + std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_assignable::value&& + std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_assignable::value + ) + { + left.swap(right); + } + + /*! + @brief exchanges the values + + Exchanges the contents of a JSON array with those of @a other. Does not + invoke any move, copy, or swap operations on individual elements. All + iterators and references remain valid. The past-the-end iterator is + invalidated. + + @param[in,out] other array to exchange the contents with + + @throw type_error.310 when JSON value is not an array; example: `"cannot + use swap() with string"` + + @complexity Constant. + + @liveexample{The example below shows how arrays can be swapped with + `swap()`.,swap__array_t} + + @since version 1.0.0 + */ + void swap(array_t& other) // NOLINT(bugprone-exception-escape) + { + // swap only works for arrays + if (JSON_HEDLEY_LIKELY(is_array())) + { + std::swap(*(m_value.array), other); + } + else + { + JSON_THROW(type_error::create(310, "cannot use swap() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief exchanges the values + + Exchanges the contents of a JSON object with those of @a other. Does not + invoke any move, copy, or swap operations on individual elements. All + iterators and references remain valid. The past-the-end iterator is + invalidated. + + @param[in,out] other object to exchange the contents with + + @throw type_error.310 when JSON value is not an object; example: + `"cannot use swap() with string"` + + @complexity Constant. + + @liveexample{The example below shows how objects can be swapped with + `swap()`.,swap__object_t} + + @since version 1.0.0 + */ + void swap(object_t& other) // NOLINT(bugprone-exception-escape) + { + // swap only works for objects + if (JSON_HEDLEY_LIKELY(is_object())) + { + std::swap(*(m_value.object), other); + } + else + { + JSON_THROW(type_error::create(310, "cannot use swap() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief exchanges the values + + Exchanges the contents of a JSON string with those of @a other. Does not + invoke any move, copy, or swap operations on individual elements. All + iterators and references remain valid. The past-the-end iterator is + invalidated. + + @param[in,out] other string to exchange the contents with + + @throw type_error.310 when JSON value is not a string; example: `"cannot + use swap() with boolean"` + + @complexity Constant. + + @liveexample{The example below shows how strings can be swapped with + `swap()`.,swap__string_t} + + @since version 1.0.0 + */ + void swap(string_t& other) // NOLINT(bugprone-exception-escape) + { + // swap only works for strings + if (JSON_HEDLEY_LIKELY(is_string())) + { + std::swap(*(m_value.string), other); + } + else + { + JSON_THROW(type_error::create(310, "cannot use swap() with " + std::string(type_name()), *this)); + } + } + + /*! + @brief exchanges the values + + Exchanges the contents of a JSON string with those of @a other. Does not + invoke any move, copy, or swap operations on individual elements. All + iterators and references remain valid. The past-the-end iterator is + invalidated. + + @param[in,out] other binary to exchange the contents with + + @throw type_error.310 when JSON value is not a string; example: `"cannot + use swap() with boolean"` + + @complexity Constant. + + @liveexample{The example below shows how strings can be swapped with + `swap()`.,swap__binary_t} + + @since version 3.8.0 + */ + void swap(binary_t& other) // NOLINT(bugprone-exception-escape) + { + // swap only works for strings + if (JSON_HEDLEY_LIKELY(is_binary())) + { + std::swap(*(m_value.binary), other); + } + else + { + JSON_THROW(type_error::create(310, "cannot use swap() with " + std::string(type_name()), *this)); + } + } + + /// @copydoc swap(binary_t&) + void swap(typename binary_t::container_type& other) // NOLINT(bugprone-exception-escape) + { + // swap only works for strings + if (JSON_HEDLEY_LIKELY(is_binary())) + { + std::swap(*(m_value.binary), other); + } + else + { + JSON_THROW(type_error::create(310, "cannot use swap() with " + std::string(type_name()), *this)); + } + } + + /// @} + + public: + ////////////////////////////////////////// + // lexicographical comparison operators // + ////////////////////////////////////////// + + /// @name lexicographical comparison operators + /// @{ + + /*! + @brief comparison: equal + + Compares two JSON values for equality according to the following rules: + - Two JSON values are equal if (1) they are from the same type and (2) + their stored values are the same according to their respective + `operator==`. + - Integer and floating-point numbers are automatically converted before + comparison. Note that two NaN values are always treated as unequal. + - Two JSON null values are equal. + + @note Floating-point inside JSON values numbers are compared with + `json::number_float_t::operator==` which is `double::operator==` by + default. To compare floating-point while respecting an epsilon, an alternative + [comparison function](https://github.com/mariokonrad/marnav/blob/master/include/marnav/math/floatingpoint.hpp#L34-#L39) + could be used, for instance + @code {.cpp} + template::value, T>::type> + inline bool is_same(T a, T b, T epsilon = std::numeric_limits::epsilon()) noexcept + { + return std::abs(a - b) <= epsilon; + } + @endcode + Or you can self-defined operator equal function like this: + @code {.cpp} + bool my_equal(const_reference lhs, const_reference rhs) { + const auto lhs_type lhs.type(); + const auto rhs_type rhs.type(); + if (lhs_type == rhs_type) { + switch(lhs_type) + // self_defined case + case value_t::number_float: + return std::abs(lhs - rhs) <= std::numeric_limits::epsilon(); + // other cases remain the same with the original + ... + } + ... + } + @endcode + + @note NaN values never compare equal to themselves or to other NaN values. + + @param[in] lhs first JSON value to consider + @param[in] rhs second JSON value to consider + @return whether the values @a lhs and @a rhs are equal + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @complexity Linear. + + @liveexample{The example demonstrates comparing several JSON + types.,operator__equal} + + @since version 1.0.0 + */ + friend bool operator==(const_reference lhs, const_reference rhs) noexcept + { +#ifdef __GNUC__ +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wfloat-equal" +#endif + const auto lhs_type = lhs.type(); + const auto rhs_type = rhs.type(); + + if (lhs_type == rhs_type) + { + switch (lhs_type) + { + case value_t::array: + return *lhs.m_value.array == *rhs.m_value.array; + + case value_t::object: + return *lhs.m_value.object == *rhs.m_value.object; + + case value_t::null: + return true; + + case value_t::string: + return *lhs.m_value.string == *rhs.m_value.string; + + case value_t::boolean: + return lhs.m_value.boolean == rhs.m_value.boolean; + + case value_t::number_integer: + return lhs.m_value.number_integer == rhs.m_value.number_integer; + + case value_t::number_unsigned: + return lhs.m_value.number_unsigned == rhs.m_value.number_unsigned; + + case value_t::number_float: + return lhs.m_value.number_float == rhs.m_value.number_float; + + case value_t::binary: + return *lhs.m_value.binary == *rhs.m_value.binary; + + case value_t::discarded: + default: + return false; + } + } + else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float) + { + return static_cast(lhs.m_value.number_integer) == rhs.m_value.number_float; + } + else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer) + { + return lhs.m_value.number_float == static_cast(rhs.m_value.number_integer); + } + else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float) + { + return static_cast(lhs.m_value.number_unsigned) == rhs.m_value.number_float; + } + else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned) + { + return lhs.m_value.number_float == static_cast(rhs.m_value.number_unsigned); + } + else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer) + { + return static_cast(lhs.m_value.number_unsigned) == rhs.m_value.number_integer; + } + else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned) + { + return lhs.m_value.number_integer == static_cast(rhs.m_value.number_unsigned); + } + + return false; +#ifdef __GNUC__ +#pragma GCC diagnostic pop +#endif + } + + /*! + @brief comparison: equal + @copydoc operator==(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator==(const_reference lhs, ScalarType rhs) noexcept + { + return lhs == basic_json(rhs); + } + + /*! + @brief comparison: equal + @copydoc operator==(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator==(ScalarType lhs, const_reference rhs) noexcept + { + return basic_json(lhs) == rhs; + } + + /*! + @brief comparison: not equal + + Compares two JSON values for inequality by calculating `not (lhs == rhs)`. + + @param[in] lhs first JSON value to consider + @param[in] rhs second JSON value to consider + @return whether the values @a lhs and @a rhs are not equal + + @complexity Linear. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @liveexample{The example demonstrates comparing several JSON + types.,operator__notequal} + + @since version 1.0.0 + */ + friend bool operator!=(const_reference lhs, const_reference rhs) noexcept + { + return !(lhs == rhs); + } + + /*! + @brief comparison: not equal + @copydoc operator!=(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator!=(const_reference lhs, ScalarType rhs) noexcept + { + return lhs != basic_json(rhs); + } + + /*! + @brief comparison: not equal + @copydoc operator!=(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator!=(ScalarType lhs, const_reference rhs) noexcept + { + return basic_json(lhs) != rhs; + } + + /*! + @brief comparison: less than + + Compares whether one JSON value @a lhs is less than another JSON value @a + rhs according to the following rules: + - If @a lhs and @a rhs have the same type, the values are compared using + the default `<` operator. + - Integer and floating-point numbers are automatically converted before + comparison + - In case @a lhs and @a rhs have different types, the values are ignored + and the order of the types is considered, see + @ref operator<(const value_t, const value_t). + + @param[in] lhs first JSON value to consider + @param[in] rhs second JSON value to consider + @return whether @a lhs is less than @a rhs + + @complexity Linear. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @liveexample{The example demonstrates comparing several JSON + types.,operator__less} + + @since version 1.0.0 + */ + friend bool operator<(const_reference lhs, const_reference rhs) noexcept + { + const auto lhs_type = lhs.type(); + const auto rhs_type = rhs.type(); + + if (lhs_type == rhs_type) + { + switch (lhs_type) + { + case value_t::array: + // note parentheses are necessary, see + // https://github.com/nlohmann/json/issues/1530 + return (*lhs.m_value.array) < (*rhs.m_value.array); + + case value_t::object: + return (*lhs.m_value.object) < (*rhs.m_value.object); + + case value_t::null: + return false; + + case value_t::string: + return (*lhs.m_value.string) < (*rhs.m_value.string); + + case value_t::boolean: + return (lhs.m_value.boolean) < (rhs.m_value.boolean); + + case value_t::number_integer: + return (lhs.m_value.number_integer) < (rhs.m_value.number_integer); + + case value_t::number_unsigned: + return (lhs.m_value.number_unsigned) < (rhs.m_value.number_unsigned); + + case value_t::number_float: + return (lhs.m_value.number_float) < (rhs.m_value.number_float); + + case value_t::binary: + return (*lhs.m_value.binary) < (*rhs.m_value.binary); + + case value_t::discarded: + default: + return false; + } + } + else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float) + { + return static_cast(lhs.m_value.number_integer) < rhs.m_value.number_float; + } + else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer) + { + return lhs.m_value.number_float < static_cast(rhs.m_value.number_integer); + } + else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float) + { + return static_cast(lhs.m_value.number_unsigned) < rhs.m_value.number_float; + } + else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned) + { + return lhs.m_value.number_float < static_cast(rhs.m_value.number_unsigned); + } + else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned) + { + return lhs.m_value.number_integer < static_cast(rhs.m_value.number_unsigned); + } + else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer) + { + return static_cast(lhs.m_value.number_unsigned) < rhs.m_value.number_integer; + } + + // We only reach this line if we cannot compare values. In that case, + // we compare types. Note we have to call the operator explicitly, + // because MSVC has problems otherwise. + return operator<(lhs_type, rhs_type); + } + + /*! + @brief comparison: less than + @copydoc operator<(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator<(const_reference lhs, ScalarType rhs) noexcept + { + return lhs < basic_json(rhs); + } + + /*! + @brief comparison: less than + @copydoc operator<(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator<(ScalarType lhs, const_reference rhs) noexcept + { + return basic_json(lhs) < rhs; + } + + /*! + @brief comparison: less than or equal + + Compares whether one JSON value @a lhs is less than or equal to another + JSON value by calculating `not (rhs < lhs)`. + + @param[in] lhs first JSON value to consider + @param[in] rhs second JSON value to consider + @return whether @a lhs is less than or equal to @a rhs + + @complexity Linear. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @liveexample{The example demonstrates comparing several JSON + types.,operator__greater} + + @since version 1.0.0 + */ + friend bool operator<=(const_reference lhs, const_reference rhs) noexcept + { + return !(rhs < lhs); + } + + /*! + @brief comparison: less than or equal + @copydoc operator<=(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator<=(const_reference lhs, ScalarType rhs) noexcept + { + return lhs <= basic_json(rhs); + } + + /*! + @brief comparison: less than or equal + @copydoc operator<=(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator<=(ScalarType lhs, const_reference rhs) noexcept + { + return basic_json(lhs) <= rhs; + } + + /*! + @brief comparison: greater than + + Compares whether one JSON value @a lhs is greater than another + JSON value by calculating `not (lhs <= rhs)`. + + @param[in] lhs first JSON value to consider + @param[in] rhs second JSON value to consider + @return whether @a lhs is greater than to @a rhs + + @complexity Linear. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @liveexample{The example demonstrates comparing several JSON + types.,operator__lessequal} + + @since version 1.0.0 + */ + friend bool operator>(const_reference lhs, const_reference rhs) noexcept + { + return !(lhs <= rhs); + } + + /*! + @brief comparison: greater than + @copydoc operator>(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator>(const_reference lhs, ScalarType rhs) noexcept + { + return lhs > basic_json(rhs); + } + + /*! + @brief comparison: greater than + @copydoc operator>(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator>(ScalarType lhs, const_reference rhs) noexcept + { + return basic_json(lhs) > rhs; + } + + /*! + @brief comparison: greater than or equal + + Compares whether one JSON value @a lhs is greater than or equal to another + JSON value by calculating `not (lhs < rhs)`. + + @param[in] lhs first JSON value to consider + @param[in] rhs second JSON value to consider + @return whether @a lhs is greater than or equal to @a rhs + + @complexity Linear. + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @liveexample{The example demonstrates comparing several JSON + types.,operator__greaterequal} + + @since version 1.0.0 + */ + friend bool operator>=(const_reference lhs, const_reference rhs) noexcept + { + return !(lhs < rhs); + } + + /*! + @brief comparison: greater than or equal + @copydoc operator>=(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator>=(const_reference lhs, ScalarType rhs) noexcept + { + return lhs >= basic_json(rhs); + } + + /*! + @brief comparison: greater than or equal + @copydoc operator>=(const_reference, const_reference) + */ + template::value, int>::type = 0> + friend bool operator>=(ScalarType lhs, const_reference rhs) noexcept + { + return basic_json(lhs) >= rhs; + } + + /// @} + + /////////////////// + // serialization // + /////////////////// + + /// @name serialization + /// @{ +#ifndef JSON_NO_IO + /*! + @brief serialize to stream + + Serialize the given JSON value @a j to the output stream @a o. The JSON + value will be serialized using the @ref dump member function. + + - The indentation of the output can be controlled with the member variable + `width` of the output stream @a o. For instance, using the manipulator + `std::setw(4)` on @a o sets the indentation level to `4` and the + serialization result is the same as calling `dump(4)`. + + - The indentation character can be controlled with the member variable + `fill` of the output stream @a o. For instance, the manipulator + `std::setfill('\\t')` sets indentation to use a tab character rather than + the default space character. + + @param[in,out] o stream to serialize to + @param[in] j JSON value to serialize + + @return the stream @a o + + @throw type_error.316 if a string stored inside the JSON value is not + UTF-8 encoded + + @complexity Linear. + + @liveexample{The example below shows the serialization with different + parameters to `width` to adjust the indentation level.,operator_serialize} + + @since version 1.0.0; indentation character added in version 3.0.0 + */ + friend std::ostream& operator<<(std::ostream& o, const basic_json& j) + { + // read width member and use it as indentation parameter if nonzero + const bool pretty_print = o.width() > 0; + const auto indentation = pretty_print ? o.width() : 0; + + // reset width to 0 for subsequent calls to this stream + o.width(0); + + // do the actual serialization + serializer s(detail::output_adapter(o), o.fill()); + s.dump(j, pretty_print, false, static_cast(indentation)); + return o; + } + + /*! + @brief serialize to stream + @deprecated This stream operator is deprecated and will be removed in + future 4.0.0 of the library. Please use + @ref operator<<(std::ostream&, const basic_json&) + instead; that is, replace calls like `j >> o;` with `o << j;`. + @since version 1.0.0; deprecated since version 3.0.0 + */ + JSON_HEDLEY_DEPRECATED_FOR(3.0.0, operator<<(std::ostream&, const basic_json&)) + friend std::ostream& operator>>(const basic_json& j, std::ostream& o) + { + return o << j; + } +#endif // JSON_NO_IO + /// @} + + + ///////////////////// + // deserialization // + ///////////////////// + + /// @name deserialization + /// @{ + + /*! + @brief deserialize from a compatible input + + @tparam InputType A compatible input, for instance + - an std::istream object + - a FILE pointer + - a C-style array of characters + - a pointer to a null-terminated string of single byte characters + - an object obj for which begin(obj) and end(obj) produces a valid pair of + iterators. + + @param[in] i input to read from + @param[in] cb a parser callback function of type @ref parser_callback_t + which is used to control the deserialization by filtering unwanted values + (optional) + @param[in] allow_exceptions whether to throw exceptions in case of a + parse error (optional, true by default) + @param[in] ignore_comments whether comments should be ignored and treated + like whitespace (true) or yield a parse error (true); (optional, false by + default) + + @return deserialized JSON value; in case of a parse error and + @a allow_exceptions set to `false`, the return value will be + value_t::discarded. + + @throw parse_error.101 if a parse error occurs; example: `""unexpected end + of input; expected string literal""` + @throw parse_error.102 if to_unicode fails or surrogate error + @throw parse_error.103 if to_unicode fails + + @complexity Linear in the length of the input. The parser is a predictive + LL(1) parser. The complexity can be higher if the parser callback function + @a cb or reading from the input @a i has a super-linear complexity. + + @note A UTF-8 byte order mark is silently ignored. + + @liveexample{The example below demonstrates the `parse()` function reading + from an array.,parse__array__parser_callback_t} + + @liveexample{The example below demonstrates the `parse()` function with + and without callback function.,parse__string__parser_callback_t} + + @liveexample{The example below demonstrates the `parse()` function with + and without callback function.,parse__istream__parser_callback_t} + + @liveexample{The example below demonstrates the `parse()` function reading + from a contiguous container.,parse__contiguouscontainer__parser_callback_t} + + @since version 2.0.3 (contiguous containers); version 3.9.0 allowed to + ignore comments. + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json parse(InputType&& i, + const parser_callback_t cb = nullptr, + const bool allow_exceptions = true, + const bool ignore_comments = false) + { + basic_json result; + parser(detail::input_adapter(std::forward(i)), cb, allow_exceptions, ignore_comments).parse(true, result); + return result; + } + + /*! + @brief deserialize from a pair of character iterators + + The value_type of the iterator must be a integral type with size of 1, 2 or + 4 bytes, which will be interpreted respectively as UTF-8, UTF-16 and UTF-32. + + @param[in] first iterator to start of character range + @param[in] last iterator to end of character range + @param[in] cb a parser callback function of type @ref parser_callback_t + which is used to control the deserialization by filtering unwanted values + (optional) + @param[in] allow_exceptions whether to throw exceptions in case of a + parse error (optional, true by default) + @param[in] ignore_comments whether comments should be ignored and treated + like whitespace (true) or yield a parse error (true); (optional, false by + default) + + @return deserialized JSON value; in case of a parse error and + @a allow_exceptions set to `false`, the return value will be + value_t::discarded. + + @throw parse_error.101 if a parse error occurs; example: `""unexpected end + of input; expected string literal""` + @throw parse_error.102 if to_unicode fails or surrogate error + @throw parse_error.103 if to_unicode fails + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json parse(IteratorType first, + IteratorType last, + const parser_callback_t cb = nullptr, + const bool allow_exceptions = true, + const bool ignore_comments = false) + { + basic_json result; + parser(detail::input_adapter(std::move(first), std::move(last)), cb, allow_exceptions, ignore_comments).parse(true, result); + return result; + } + + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, parse(ptr, ptr + len)) + static basic_json parse(detail::span_input_adapter&& i, + const parser_callback_t cb = nullptr, + const bool allow_exceptions = true, + const bool ignore_comments = false) + { + basic_json result; + parser(i.get(), cb, allow_exceptions, ignore_comments).parse(true, result); + return result; + } + + /*! + @brief check if the input is valid JSON + + Unlike the @ref parse(InputType&&, const parser_callback_t,const bool) + function, this function neither throws an exception in case of invalid JSON + input (i.e., a parse error) nor creates diagnostic information. + + @tparam InputType A compatible input, for instance + - an std::istream object + - a FILE pointer + - a C-style array of characters + - a pointer to a null-terminated string of single byte characters + - an object obj for which begin(obj) and end(obj) produces a valid pair of + iterators. + + @param[in] i input to read from + @param[in] ignore_comments whether comments should be ignored and treated + like whitespace (true) or yield a parse error (true); (optional, false by + default) + + @return Whether the input read from @a i is valid JSON. + + @complexity Linear in the length of the input. The parser is a predictive + LL(1) parser. + + @note A UTF-8 byte order mark is silently ignored. + + @liveexample{The example below demonstrates the `accept()` function reading + from a string.,accept__string} + */ + template + static bool accept(InputType&& i, + const bool ignore_comments = false) + { + return parser(detail::input_adapter(std::forward(i)), nullptr, false, ignore_comments).accept(true); + } + + template + static bool accept(IteratorType first, IteratorType last, + const bool ignore_comments = false) + { + return parser(detail::input_adapter(std::move(first), std::move(last)), nullptr, false, ignore_comments).accept(true); + } + + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, accept(ptr, ptr + len)) + static bool accept(detail::span_input_adapter&& i, + const bool ignore_comments = false) + { + return parser(i.get(), nullptr, false, ignore_comments).accept(true); + } + + /*! + @brief generate SAX events + + The SAX event lister must follow the interface of @ref json_sax. + + This function reads from a compatible input. Examples are: + - an std::istream object + - a FILE pointer + - a C-style array of characters + - a pointer to a null-terminated string of single byte characters + - an object obj for which begin(obj) and end(obj) produces a valid pair of + iterators. + + @param[in] i input to read from + @param[in,out] sax SAX event listener + @param[in] format the format to parse (JSON, CBOR, MessagePack, or UBJSON) + @param[in] strict whether the input has to be consumed completely + @param[in] ignore_comments whether comments should be ignored and treated + like whitespace (true) or yield a parse error (true); (optional, false by + default); only applies to the JSON file format. + + @return return value of the last processed SAX event + + @throw parse_error.101 if a parse error occurs; example: `""unexpected end + of input; expected string literal""` + @throw parse_error.102 if to_unicode fails or surrogate error + @throw parse_error.103 if to_unicode fails + + @complexity Linear in the length of the input. The parser is a predictive + LL(1) parser. The complexity can be higher if the SAX consumer @a sax has + a super-linear complexity. + + @note A UTF-8 byte order mark is silently ignored. + + @liveexample{The example below demonstrates the `sax_parse()` function + reading from string and processing the events with a user-defined SAX + event consumer.,sax_parse} + + @since version 3.2.0 + */ + template + JSON_HEDLEY_NON_NULL(2) + static bool sax_parse(InputType&& i, SAX* sax, + input_format_t format = input_format_t::json, + const bool strict = true, + const bool ignore_comments = false) + { + auto ia = detail::input_adapter(std::forward(i)); + return format == input_format_t::json + ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict) + : detail::binary_reader(std::move(ia)).sax_parse(format, sax, strict); + } + + template + JSON_HEDLEY_NON_NULL(3) + static bool sax_parse(IteratorType first, IteratorType last, SAX* sax, + input_format_t format = input_format_t::json, + const bool strict = true, + const bool ignore_comments = false) + { + auto ia = detail::input_adapter(std::move(first), std::move(last)); + return format == input_format_t::json + ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict) + : detail::binary_reader(std::move(ia)).sax_parse(format, sax, strict); + } + + template + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, sax_parse(ptr, ptr + len, ...)) + JSON_HEDLEY_NON_NULL(2) + static bool sax_parse(detail::span_input_adapter&& i, SAX* sax, + input_format_t format = input_format_t::json, + const bool strict = true, + const bool ignore_comments = false) + { + auto ia = i.get(); + return format == input_format_t::json + // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg) + ? parser(std::move(ia), nullptr, true, ignore_comments).sax_parse(sax, strict) + // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg) + : detail::binary_reader(std::move(ia)).sax_parse(format, sax, strict); + } +#ifndef JSON_NO_IO + /*! + @brief deserialize from stream + @deprecated This stream operator is deprecated and will be removed in + version 4.0.0 of the library. Please use + @ref operator>>(std::istream&, basic_json&) + instead; that is, replace calls like `j << i;` with `i >> j;`. + @since version 1.0.0; deprecated since version 3.0.0 + */ + JSON_HEDLEY_DEPRECATED_FOR(3.0.0, operator>>(std::istream&, basic_json&)) + friend std::istream& operator<<(basic_json& j, std::istream& i) + { + return operator>>(i, j); + } + + /*! + @brief deserialize from stream + + Deserializes an input stream to a JSON value. + + @param[in,out] i input stream to read a serialized JSON value from + @param[in,out] j JSON value to write the deserialized input to + + @throw parse_error.101 in case of an unexpected token + @throw parse_error.102 if to_unicode fails or surrogate error + @throw parse_error.103 if to_unicode fails + + @complexity Linear in the length of the input. The parser is a predictive + LL(1) parser. + + @note A UTF-8 byte order mark is silently ignored. + + @liveexample{The example below shows how a JSON value is constructed by + reading a serialization from a stream.,operator_deserialize} + + @sa parse(std::istream&, const parser_callback_t) for a variant with a + parser callback function to filter values while parsing + + @since version 1.0.0 + */ + friend std::istream& operator>>(std::istream& i, basic_json& j) + { + parser(detail::input_adapter(i)).parse(false, j); + return i; + } +#endif // JSON_NO_IO + /// @} + + /////////////////////////// + // convenience functions // + /////////////////////////// + + /*! + @brief return the type as string + + Returns the type name as string to be used in error messages - usually to + indicate that a function was called on a wrong JSON type. + + @return a string representation of a the @a m_type member: + Value type | return value + ----------- | ------------- + null | `"null"` + boolean | `"boolean"` + string | `"string"` + number | `"number"` (for all number types) + object | `"object"` + array | `"array"` + binary | `"binary"` + discarded | `"discarded"` + + @exceptionsafety No-throw guarantee: this function never throws exceptions. + + @complexity Constant. + + @liveexample{The following code exemplifies `type_name()` for all JSON + types.,type_name} + + @sa see @ref type() -- return the type of the JSON value + @sa see @ref operator value_t() -- return the type of the JSON value (implicit) + + @since version 1.0.0, public since 2.1.0, `const char*` and `noexcept` + since 3.0.0 + */ + JSON_HEDLEY_RETURNS_NON_NULL + const char* type_name() const noexcept + { + { + switch (m_type) + { + case value_t::null: + return "null"; + case value_t::object: + return "object"; + case value_t::array: + return "array"; + case value_t::string: + return "string"; + case value_t::boolean: + return "boolean"; + case value_t::binary: + return "binary"; + case value_t::discarded: + return "discarded"; + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + default: + return "number"; + } + } + } + + + JSON_PRIVATE_UNLESS_TESTED: + ////////////////////// + // member variables // + ////////////////////// + + /// the type of the current element + value_t m_type = value_t::null; + + /// the value of the current element + json_value m_value = {}; + +#if JSON_DIAGNOSTICS + /// a pointer to a parent value (for debugging purposes) + basic_json* m_parent = nullptr; +#endif + + ////////////////////////////////////////// + // binary serialization/deserialization // + ////////////////////////////////////////// + + /// @name binary serialization/deserialization support + /// @{ + + public: + /*! + @brief create a CBOR serialization of a given JSON value + + Serializes a given JSON value @a j to a byte vector using the CBOR (Concise + Binary Object Representation) serialization format. CBOR is a binary + serialization format which aims to be more compact than JSON itself, yet + more efficient to parse. + + The library uses the following mapping from JSON values types to + CBOR types according to the CBOR specification (RFC 7049): + + JSON value type | value/range | CBOR type | first byte + --------------- | ------------------------------------------ | ---------------------------------- | --------------- + null | `null` | Null | 0xF6 + boolean | `true` | True | 0xF5 + boolean | `false` | False | 0xF4 + number_integer | -9223372036854775808..-2147483649 | Negative integer (8 bytes follow) | 0x3B + number_integer | -2147483648..-32769 | Negative integer (4 bytes follow) | 0x3A + number_integer | -32768..-129 | Negative integer (2 bytes follow) | 0x39 + number_integer | -128..-25 | Negative integer (1 byte follow) | 0x38 + number_integer | -24..-1 | Negative integer | 0x20..0x37 + number_integer | 0..23 | Integer | 0x00..0x17 + number_integer | 24..255 | Unsigned integer (1 byte follow) | 0x18 + number_integer | 256..65535 | Unsigned integer (2 bytes follow) | 0x19 + number_integer | 65536..4294967295 | Unsigned integer (4 bytes follow) | 0x1A + number_integer | 4294967296..18446744073709551615 | Unsigned integer (8 bytes follow) | 0x1B + number_unsigned | 0..23 | Integer | 0x00..0x17 + number_unsigned | 24..255 | Unsigned integer (1 byte follow) | 0x18 + number_unsigned | 256..65535 | Unsigned integer (2 bytes follow) | 0x19 + number_unsigned | 65536..4294967295 | Unsigned integer (4 bytes follow) | 0x1A + number_unsigned | 4294967296..18446744073709551615 | Unsigned integer (8 bytes follow) | 0x1B + number_float | *any value representable by a float* | Single-Precision Float | 0xFA + number_float | *any value NOT representable by a float* | Double-Precision Float | 0xFB + string | *length*: 0..23 | UTF-8 string | 0x60..0x77 + string | *length*: 23..255 | UTF-8 string (1 byte follow) | 0x78 + string | *length*: 256..65535 | UTF-8 string (2 bytes follow) | 0x79 + string | *length*: 65536..4294967295 | UTF-8 string (4 bytes follow) | 0x7A + string | *length*: 4294967296..18446744073709551615 | UTF-8 string (8 bytes follow) | 0x7B + array | *size*: 0..23 | array | 0x80..0x97 + array | *size*: 23..255 | array (1 byte follow) | 0x98 + array | *size*: 256..65535 | array (2 bytes follow) | 0x99 + array | *size*: 65536..4294967295 | array (4 bytes follow) | 0x9A + array | *size*: 4294967296..18446744073709551615 | array (8 bytes follow) | 0x9B + object | *size*: 0..23 | map | 0xA0..0xB7 + object | *size*: 23..255 | map (1 byte follow) | 0xB8 + object | *size*: 256..65535 | map (2 bytes follow) | 0xB9 + object | *size*: 65536..4294967295 | map (4 bytes follow) | 0xBA + object | *size*: 4294967296..18446744073709551615 | map (8 bytes follow) | 0xBB + binary | *size*: 0..23 | byte string | 0x40..0x57 + binary | *size*: 23..255 | byte string (1 byte follow) | 0x58 + binary | *size*: 256..65535 | byte string (2 bytes follow) | 0x59 + binary | *size*: 65536..4294967295 | byte string (4 bytes follow) | 0x5A + binary | *size*: 4294967296..18446744073709551615 | byte string (8 bytes follow) | 0x5B + + Binary values with subtype are mapped to tagged values (0xD8..0xDB) + depending on the subtype, followed by a byte string, see "binary" cells + in the table above. + + @note The mapping is **complete** in the sense that any JSON value type + can be converted to a CBOR value. + + @note If NaN or Infinity are stored inside a JSON number, they are + serialized properly. This behavior differs from the @ref dump() + function which serializes NaN or Infinity to `null`. + + @note The following CBOR types are not used in the conversion: + - UTF-8 strings terminated by "break" (0x7F) + - arrays terminated by "break" (0x9F) + - maps terminated by "break" (0xBF) + - byte strings terminated by "break" (0x5F) + - date/time (0xC0..0xC1) + - bignum (0xC2..0xC3) + - decimal fraction (0xC4) + - bigfloat (0xC5) + - expected conversions (0xD5..0xD7) + - simple values (0xE0..0xF3, 0xF8) + - undefined (0xF7) + - half-precision floats (0xF9) + - break (0xFF) + + @param[in] j JSON value to serialize + @return CBOR serialization as byte vector + + @complexity Linear in the size of the JSON value @a j. + + @liveexample{The example shows the serialization of a JSON value to a byte + vector in CBOR format.,to_cbor} + + @sa http://cbor.io + @sa see @ref from_cbor(InputType&&, const bool, const bool, const cbor_tag_handler_t) for the + analogous deserialization + @sa see @ref to_msgpack(const basic_json&) for the related MessagePack format + @sa see @ref to_ubjson(const basic_json&, const bool, const bool) for the + related UBJSON format + + @since version 2.0.9; compact representation of floating-point numbers + since version 3.8.0 + */ + static std::vector to_cbor(const basic_json& j) + { + std::vector result; + to_cbor(j, result); + return result; + } + + static void to_cbor(const basic_json& j, detail::output_adapter o) + { + binary_writer(o).write_cbor(j); + } + + static void to_cbor(const basic_json& j, detail::output_adapter o) + { + binary_writer(o).write_cbor(j); + } + + /*! + @brief create a MessagePack serialization of a given JSON value + + Serializes a given JSON value @a j to a byte vector using the MessagePack + serialization format. MessagePack is a binary serialization format which + aims to be more compact than JSON itself, yet more efficient to parse. + + The library uses the following mapping from JSON values types to + MessagePack types according to the MessagePack specification: + + JSON value type | value/range | MessagePack type | first byte + --------------- | --------------------------------- | ---------------- | ---------- + null | `null` | nil | 0xC0 + boolean | `true` | true | 0xC3 + boolean | `false` | false | 0xC2 + number_integer | -9223372036854775808..-2147483649 | int64 | 0xD3 + number_integer | -2147483648..-32769 | int32 | 0xD2 + number_integer | -32768..-129 | int16 | 0xD1 + number_integer | -128..-33 | int8 | 0xD0 + number_integer | -32..-1 | negative fixint | 0xE0..0xFF + number_integer | 0..127 | positive fixint | 0x00..0x7F + number_integer | 128..255 | uint 8 | 0xCC + number_integer | 256..65535 | uint 16 | 0xCD + number_integer | 65536..4294967295 | uint 32 | 0xCE + number_integer | 4294967296..18446744073709551615 | uint 64 | 0xCF + number_unsigned | 0..127 | positive fixint | 0x00..0x7F + number_unsigned | 128..255 | uint 8 | 0xCC + number_unsigned | 256..65535 | uint 16 | 0xCD + number_unsigned | 65536..4294967295 | uint 32 | 0xCE + number_unsigned | 4294967296..18446744073709551615 | uint 64 | 0xCF + number_float | *any value representable by a float* | float 32 | 0xCA + number_float | *any value NOT representable by a float* | float 64 | 0xCB + string | *length*: 0..31 | fixstr | 0xA0..0xBF + string | *length*: 32..255 | str 8 | 0xD9 + string | *length*: 256..65535 | str 16 | 0xDA + string | *length*: 65536..4294967295 | str 32 | 0xDB + array | *size*: 0..15 | fixarray | 0x90..0x9F + array | *size*: 16..65535 | array 16 | 0xDC + array | *size*: 65536..4294967295 | array 32 | 0xDD + object | *size*: 0..15 | fix map | 0x80..0x8F + object | *size*: 16..65535 | map 16 | 0xDE + object | *size*: 65536..4294967295 | map 32 | 0xDF + binary | *size*: 0..255 | bin 8 | 0xC4 + binary | *size*: 256..65535 | bin 16 | 0xC5 + binary | *size*: 65536..4294967295 | bin 32 | 0xC6 + + @note The mapping is **complete** in the sense that any JSON value type + can be converted to a MessagePack value. + + @note The following values can **not** be converted to a MessagePack value: + - strings with more than 4294967295 bytes + - byte strings with more than 4294967295 bytes + - arrays with more than 4294967295 elements + - objects with more than 4294967295 elements + + @note Any MessagePack output created @ref to_msgpack can be successfully + parsed by @ref from_msgpack. + + @note If NaN or Infinity are stored inside a JSON number, they are + serialized properly. This behavior differs from the @ref dump() + function which serializes NaN or Infinity to `null`. + + @param[in] j JSON value to serialize + @return MessagePack serialization as byte vector + + @complexity Linear in the size of the JSON value @a j. + + @liveexample{The example shows the serialization of a JSON value to a byte + vector in MessagePack format.,to_msgpack} + + @sa http://msgpack.org + @sa see @ref from_msgpack for the analogous deserialization + @sa see @ref to_cbor(const basic_json& for the related CBOR format + @sa see @ref to_ubjson(const basic_json&, const bool, const bool) for the + related UBJSON format + + @since version 2.0.9 + */ + static std::vector to_msgpack(const basic_json& j) + { + std::vector result; + to_msgpack(j, result); + return result; + } + + static void to_msgpack(const basic_json& j, detail::output_adapter o) + { + binary_writer(o).write_msgpack(j); + } + + static void to_msgpack(const basic_json& j, detail::output_adapter o) + { + binary_writer(o).write_msgpack(j); + } + + /*! + @brief create a UBJSON serialization of a given JSON value + + Serializes a given JSON value @a j to a byte vector using the UBJSON + (Universal Binary JSON) serialization format. UBJSON aims to be more compact + than JSON itself, yet more efficient to parse. + + The library uses the following mapping from JSON values types to + UBJSON types according to the UBJSON specification: + + JSON value type | value/range | UBJSON type | marker + --------------- | --------------------------------- | ----------- | ------ + null | `null` | null | `Z` + boolean | `true` | true | `T` + boolean | `false` | false | `F` + number_integer | -9223372036854775808..-2147483649 | int64 | `L` + number_integer | -2147483648..-32769 | int32 | `l` + number_integer | -32768..-129 | int16 | `I` + number_integer | -128..127 | int8 | `i` + number_integer | 128..255 | uint8 | `U` + number_integer | 256..32767 | int16 | `I` + number_integer | 32768..2147483647 | int32 | `l` + number_integer | 2147483648..9223372036854775807 | int64 | `L` + number_unsigned | 0..127 | int8 | `i` + number_unsigned | 128..255 | uint8 | `U` + number_unsigned | 256..32767 | int16 | `I` + number_unsigned | 32768..2147483647 | int32 | `l` + number_unsigned | 2147483648..9223372036854775807 | int64 | `L` + number_unsigned | 2147483649..18446744073709551615 | high-precision | `H` + number_float | *any value* | float64 | `D` + string | *with shortest length indicator* | string | `S` + array | *see notes on optimized format* | array | `[` + object | *see notes on optimized format* | map | `{` + + @note The mapping is **complete** in the sense that any JSON value type + can be converted to a UBJSON value. + + @note The following values can **not** be converted to a UBJSON value: + - strings with more than 9223372036854775807 bytes (theoretical) + + @note The following markers are not used in the conversion: + - `Z`: no-op values are not created. + - `C`: single-byte strings are serialized with `S` markers. + + @note Any UBJSON output created @ref to_ubjson can be successfully parsed + by @ref from_ubjson. + + @note If NaN or Infinity are stored inside a JSON number, they are + serialized properly. This behavior differs from the @ref dump() + function which serializes NaN or Infinity to `null`. + + @note The optimized formats for containers are supported: Parameter + @a use_size adds size information to the beginning of a container and + removes the closing marker. Parameter @a use_type further checks + whether all elements of a container have the same type and adds the + type marker to the beginning of the container. The @a use_type + parameter must only be used together with @a use_size = true. Note + that @a use_size = true alone may result in larger representations - + the benefit of this parameter is that the receiving side is + immediately informed on the number of elements of the container. + + @note If the JSON data contains the binary type, the value stored is a list + of integers, as suggested by the UBJSON documentation. In particular, + this means that serialization and the deserialization of a JSON + containing binary values into UBJSON and back will result in a + different JSON object. + + @param[in] j JSON value to serialize + @param[in] use_size whether to add size annotations to container types + @param[in] use_type whether to add type annotations to container types + (must be combined with @a use_size = true) + @return UBJSON serialization as byte vector + + @complexity Linear in the size of the JSON value @a j. + + @liveexample{The example shows the serialization of a JSON value to a byte + vector in UBJSON format.,to_ubjson} + + @sa http://ubjson.org + @sa see @ref from_ubjson(InputType&&, const bool, const bool) for the + analogous deserialization + @sa see @ref to_cbor(const basic_json& for the related CBOR format + @sa see @ref to_msgpack(const basic_json&) for the related MessagePack format + + @since version 3.1.0 + */ + static std::vector to_ubjson(const basic_json& j, + const bool use_size = false, + const bool use_type = false) + { + std::vector result; + to_ubjson(j, result, use_size, use_type); + return result; + } + + static void to_ubjson(const basic_json& j, detail::output_adapter o, + const bool use_size = false, const bool use_type = false) + { + binary_writer(o).write_ubjson(j, use_size, use_type); + } + + static void to_ubjson(const basic_json& j, detail::output_adapter o, + const bool use_size = false, const bool use_type = false) + { + binary_writer(o).write_ubjson(j, use_size, use_type); + } + + + /*! + @brief Serializes the given JSON object `j` to BSON and returns a vector + containing the corresponding BSON-representation. + + BSON (Binary JSON) is a binary format in which zero or more ordered key/value pairs are + stored as a single entity (a so-called document). + + The library uses the following mapping from JSON values types to BSON types: + + JSON value type | value/range | BSON type | marker + --------------- | --------------------------------- | ----------- | ------ + null | `null` | null | 0x0A + boolean | `true`, `false` | boolean | 0x08 + number_integer | -9223372036854775808..-2147483649 | int64 | 0x12 + number_integer | -2147483648..2147483647 | int32 | 0x10 + number_integer | 2147483648..9223372036854775807 | int64 | 0x12 + number_unsigned | 0..2147483647 | int32 | 0x10 + number_unsigned | 2147483648..9223372036854775807 | int64 | 0x12 + number_unsigned | 9223372036854775808..18446744073709551615| -- | -- + number_float | *any value* | double | 0x01 + string | *any value* | string | 0x02 + array | *any value* | document | 0x04 + object | *any value* | document | 0x03 + binary | *any value* | binary | 0x05 + + @warning The mapping is **incomplete**, since only JSON-objects (and things + contained therein) can be serialized to BSON. + Also, integers larger than 9223372036854775807 cannot be serialized to BSON, + and the keys may not contain U+0000, since they are serialized a + zero-terminated c-strings. + + @throw out_of_range.407 if `j.is_number_unsigned() && j.get() > 9223372036854775807` + @throw out_of_range.409 if a key in `j` contains a NULL (U+0000) + @throw type_error.317 if `!j.is_object()` + + @pre The input `j` is required to be an object: `j.is_object() == true`. + + @note Any BSON output created via @ref to_bson can be successfully parsed + by @ref from_bson. + + @param[in] j JSON value to serialize + @return BSON serialization as byte vector + + @complexity Linear in the size of the JSON value @a j. + + @liveexample{The example shows the serialization of a JSON value to a byte + vector in BSON format.,to_bson} + + @sa http://bsonspec.org/spec.html + @sa see @ref from_bson(detail::input_adapter&&, const bool strict) for the + analogous deserialization + @sa see @ref to_ubjson(const basic_json&, const bool, const bool) for the + related UBJSON format + @sa see @ref to_cbor(const basic_json&) for the related CBOR format + @sa see @ref to_msgpack(const basic_json&) for the related MessagePack format + */ + static std::vector to_bson(const basic_json& j) + { + std::vector result; + to_bson(j, result); + return result; + } + + /*! + @brief Serializes the given JSON object `j` to BSON and forwards the + corresponding BSON-representation to the given output_adapter `o`. + @param j The JSON object to convert to BSON. + @param o The output adapter that receives the binary BSON representation. + @pre The input `j` shall be an object: `j.is_object() == true` + @sa see @ref to_bson(const basic_json&) + */ + static void to_bson(const basic_json& j, detail::output_adapter o) + { + binary_writer(o).write_bson(j); + } + + /*! + @copydoc to_bson(const basic_json&, detail::output_adapter) + */ + static void to_bson(const basic_json& j, detail::output_adapter o) + { + binary_writer(o).write_bson(j); + } + + + /*! + @brief create a JSON value from an input in CBOR format + + Deserializes a given input @a i to a JSON value using the CBOR (Concise + Binary Object Representation) serialization format. + + The library maps CBOR types to JSON value types as follows: + + CBOR type | JSON value type | first byte + ---------------------- | --------------- | ---------- + Integer | number_unsigned | 0x00..0x17 + Unsigned integer | number_unsigned | 0x18 + Unsigned integer | number_unsigned | 0x19 + Unsigned integer | number_unsigned | 0x1A + Unsigned integer | number_unsigned | 0x1B + Negative integer | number_integer | 0x20..0x37 + Negative integer | number_integer | 0x38 + Negative integer | number_integer | 0x39 + Negative integer | number_integer | 0x3A + Negative integer | number_integer | 0x3B + Byte string | binary | 0x40..0x57 + Byte string | binary | 0x58 + Byte string | binary | 0x59 + Byte string | binary | 0x5A + Byte string | binary | 0x5B + UTF-8 string | string | 0x60..0x77 + UTF-8 string | string | 0x78 + UTF-8 string | string | 0x79 + UTF-8 string | string | 0x7A + UTF-8 string | string | 0x7B + UTF-8 string | string | 0x7F + array | array | 0x80..0x97 + array | array | 0x98 + array | array | 0x99 + array | array | 0x9A + array | array | 0x9B + array | array | 0x9F + map | object | 0xA0..0xB7 + map | object | 0xB8 + map | object | 0xB9 + map | object | 0xBA + map | object | 0xBB + map | object | 0xBF + False | `false` | 0xF4 + True | `true` | 0xF5 + Null | `null` | 0xF6 + Half-Precision Float | number_float | 0xF9 + Single-Precision Float | number_float | 0xFA + Double-Precision Float | number_float | 0xFB + + @warning The mapping is **incomplete** in the sense that not all CBOR + types can be converted to a JSON value. The following CBOR types + are not supported and will yield parse errors (parse_error.112): + - date/time (0xC0..0xC1) + - bignum (0xC2..0xC3) + - decimal fraction (0xC4) + - bigfloat (0xC5) + - expected conversions (0xD5..0xD7) + - simple values (0xE0..0xF3, 0xF8) + - undefined (0xF7) + + @warning CBOR allows map keys of any type, whereas JSON only allows + strings as keys in object values. Therefore, CBOR maps with keys + other than UTF-8 strings are rejected (parse_error.113). + + @note Any CBOR output created @ref to_cbor can be successfully parsed by + @ref from_cbor. + + @param[in] i an input in CBOR format convertible to an input adapter + @param[in] strict whether to expect the input to be consumed until EOF + (true by default) + @param[in] allow_exceptions whether to throw exceptions in case of a + parse error (optional, true by default) + @param[in] tag_handler how to treat CBOR tags (optional, error by default) + + @return deserialized JSON value; in case of a parse error and + @a allow_exceptions set to `false`, the return value will be + value_t::discarded. + + @throw parse_error.110 if the given input ends prematurely or the end of + file was not reached when @a strict was set to true + @throw parse_error.112 if unsupported features from CBOR were + used in the given input @a v or if the input is not valid CBOR + @throw parse_error.113 if a string was expected as map key, but not found + + @complexity Linear in the size of the input @a i. + + @liveexample{The example shows the deserialization of a byte vector in CBOR + format to a JSON value.,from_cbor} + + @sa http://cbor.io + @sa see @ref to_cbor(const basic_json&) for the analogous serialization + @sa see @ref from_msgpack(InputType&&, const bool, const bool) for the + related MessagePack format + @sa see @ref from_ubjson(InputType&&, const bool, const bool) for the + related UBJSON format + + @since version 2.0.9; parameter @a start_index since 2.1.1; changed to + consume input adapters, removed start_index parameter, and added + @a strict parameter since 3.0.0; added @a allow_exceptions parameter + since 3.2.0; added @a tag_handler parameter since 3.9.0. + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_cbor(InputType&& i, + const bool strict = true, + const bool allow_exceptions = true, + const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::forward(i)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler); + return res ? result : basic_json(value_t::discarded); + } + + /*! + @copydoc from_cbor(InputType&&, const bool, const bool, const cbor_tag_handler_t) + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_cbor(IteratorType first, IteratorType last, + const bool strict = true, + const bool allow_exceptions = true, + const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::move(first), std::move(last)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler); + return res ? result : basic_json(value_t::discarded); + } + + template + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len)) + static basic_json from_cbor(const T* ptr, std::size_t len, + const bool strict = true, + const bool allow_exceptions = true, + const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error) + { + return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler); + } + + + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len)) + static basic_json from_cbor(detail::span_input_adapter&& i, + const bool strict = true, + const bool allow_exceptions = true, + const cbor_tag_handler_t tag_handler = cbor_tag_handler_t::error) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = i.get(); + // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg) + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::cbor, &sdp, strict, tag_handler); + return res ? result : basic_json(value_t::discarded); + } + + /*! + @brief create a JSON value from an input in MessagePack format + + Deserializes a given input @a i to a JSON value using the MessagePack + serialization format. + + The library maps MessagePack types to JSON value types as follows: + + MessagePack type | JSON value type | first byte + ---------------- | --------------- | ---------- + positive fixint | number_unsigned | 0x00..0x7F + fixmap | object | 0x80..0x8F + fixarray | array | 0x90..0x9F + fixstr | string | 0xA0..0xBF + nil | `null` | 0xC0 + false | `false` | 0xC2 + true | `true` | 0xC3 + float 32 | number_float | 0xCA + float 64 | number_float | 0xCB + uint 8 | number_unsigned | 0xCC + uint 16 | number_unsigned | 0xCD + uint 32 | number_unsigned | 0xCE + uint 64 | number_unsigned | 0xCF + int 8 | number_integer | 0xD0 + int 16 | number_integer | 0xD1 + int 32 | number_integer | 0xD2 + int 64 | number_integer | 0xD3 + str 8 | string | 0xD9 + str 16 | string | 0xDA + str 32 | string | 0xDB + array 16 | array | 0xDC + array 32 | array | 0xDD + map 16 | object | 0xDE + map 32 | object | 0xDF + bin 8 | binary | 0xC4 + bin 16 | binary | 0xC5 + bin 32 | binary | 0xC6 + ext 8 | binary | 0xC7 + ext 16 | binary | 0xC8 + ext 32 | binary | 0xC9 + fixext 1 | binary | 0xD4 + fixext 2 | binary | 0xD5 + fixext 4 | binary | 0xD6 + fixext 8 | binary | 0xD7 + fixext 16 | binary | 0xD8 + negative fixint | number_integer | 0xE0-0xFF + + @note Any MessagePack output created @ref to_msgpack can be successfully + parsed by @ref from_msgpack. + + @param[in] i an input in MessagePack format convertible to an input + adapter + @param[in] strict whether to expect the input to be consumed until EOF + (true by default) + @param[in] allow_exceptions whether to throw exceptions in case of a + parse error (optional, true by default) + + @return deserialized JSON value; in case of a parse error and + @a allow_exceptions set to `false`, the return value will be + value_t::discarded. + + @throw parse_error.110 if the given input ends prematurely or the end of + file was not reached when @a strict was set to true + @throw parse_error.112 if unsupported features from MessagePack were + used in the given input @a i or if the input is not valid MessagePack + @throw parse_error.113 if a string was expected as map key, but not found + + @complexity Linear in the size of the input @a i. + + @liveexample{The example shows the deserialization of a byte vector in + MessagePack format to a JSON value.,from_msgpack} + + @sa http://msgpack.org + @sa see @ref to_msgpack(const basic_json&) for the analogous serialization + @sa see @ref from_cbor(InputType&&, const bool, const bool, const cbor_tag_handler_t) for the + related CBOR format + @sa see @ref from_ubjson(InputType&&, const bool, const bool) for + the related UBJSON format + @sa see @ref from_bson(InputType&&, const bool, const bool) for + the related BSON format + + @since version 2.0.9; parameter @a start_index since 2.1.1; changed to + consume input adapters, removed start_index parameter, and added + @a strict parameter since 3.0.0; added @a allow_exceptions parameter + since 3.2.0 + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_msgpack(InputType&& i, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::forward(i)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::msgpack, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + /*! + @copydoc from_msgpack(InputType&&, const bool, const bool) + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_msgpack(IteratorType first, IteratorType last, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::move(first), std::move(last)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::msgpack, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + + template + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_msgpack(ptr, ptr + len)) + static basic_json from_msgpack(const T* ptr, std::size_t len, + const bool strict = true, + const bool allow_exceptions = true) + { + return from_msgpack(ptr, ptr + len, strict, allow_exceptions); + } + + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_msgpack(ptr, ptr + len)) + static basic_json from_msgpack(detail::span_input_adapter&& i, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = i.get(); + // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg) + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::msgpack, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + + /*! + @brief create a JSON value from an input in UBJSON format + + Deserializes a given input @a i to a JSON value using the UBJSON (Universal + Binary JSON) serialization format. + + The library maps UBJSON types to JSON value types as follows: + + UBJSON type | JSON value type | marker + ----------- | --------------------------------------- | ------ + no-op | *no value, next value is read* | `N` + null | `null` | `Z` + false | `false` | `F` + true | `true` | `T` + float32 | number_float | `d` + float64 | number_float | `D` + uint8 | number_unsigned | `U` + int8 | number_integer | `i` + int16 | number_integer | `I` + int32 | number_integer | `l` + int64 | number_integer | `L` + high-precision number | number_integer, number_unsigned, or number_float - depends on number string | 'H' + string | string | `S` + char | string | `C` + array | array (optimized values are supported) | `[` + object | object (optimized values are supported) | `{` + + @note The mapping is **complete** in the sense that any UBJSON value can + be converted to a JSON value. + + @param[in] i an input in UBJSON format convertible to an input adapter + @param[in] strict whether to expect the input to be consumed until EOF + (true by default) + @param[in] allow_exceptions whether to throw exceptions in case of a + parse error (optional, true by default) + + @return deserialized JSON value; in case of a parse error and + @a allow_exceptions set to `false`, the return value will be + value_t::discarded. + + @throw parse_error.110 if the given input ends prematurely or the end of + file was not reached when @a strict was set to true + @throw parse_error.112 if a parse error occurs + @throw parse_error.113 if a string could not be parsed successfully + + @complexity Linear in the size of the input @a i. + + @liveexample{The example shows the deserialization of a byte vector in + UBJSON format to a JSON value.,from_ubjson} + + @sa http://ubjson.org + @sa see @ref to_ubjson(const basic_json&, const bool, const bool) for the + analogous serialization + @sa see @ref from_cbor(InputType&&, const bool, const bool, const cbor_tag_handler_t) for the + related CBOR format + @sa see @ref from_msgpack(InputType&&, const bool, const bool) for + the related MessagePack format + @sa see @ref from_bson(InputType&&, const bool, const bool) for + the related BSON format + + @since version 3.1.0; added @a allow_exceptions parameter since 3.2.0 + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_ubjson(InputType&& i, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::forward(i)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::ubjson, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + /*! + @copydoc from_ubjson(InputType&&, const bool, const bool) + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_ubjson(IteratorType first, IteratorType last, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::move(first), std::move(last)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::ubjson, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + template + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_ubjson(ptr, ptr + len)) + static basic_json from_ubjson(const T* ptr, std::size_t len, + const bool strict = true, + const bool allow_exceptions = true) + { + return from_ubjson(ptr, ptr + len, strict, allow_exceptions); + } + + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_ubjson(ptr, ptr + len)) + static basic_json from_ubjson(detail::span_input_adapter&& i, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = i.get(); + // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg) + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::ubjson, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + + /*! + @brief Create a JSON value from an input in BSON format + + Deserializes a given input @a i to a JSON value using the BSON (Binary JSON) + serialization format. + + The library maps BSON record types to JSON value types as follows: + + BSON type | BSON marker byte | JSON value type + --------------- | ---------------- | --------------------------- + double | 0x01 | number_float + string | 0x02 | string + document | 0x03 | object + array | 0x04 | array + binary | 0x05 | binary + undefined | 0x06 | still unsupported + ObjectId | 0x07 | still unsupported + boolean | 0x08 | boolean + UTC Date-Time | 0x09 | still unsupported + null | 0x0A | null + Regular Expr. | 0x0B | still unsupported + DB Pointer | 0x0C | still unsupported + JavaScript Code | 0x0D | still unsupported + Symbol | 0x0E | still unsupported + JavaScript Code | 0x0F | still unsupported + int32 | 0x10 | number_integer + Timestamp | 0x11 | still unsupported + 128-bit decimal float | 0x13 | still unsupported + Max Key | 0x7F | still unsupported + Min Key | 0xFF | still unsupported + + @warning The mapping is **incomplete**. The unsupported mappings + are indicated in the table above. + + @param[in] i an input in BSON format convertible to an input adapter + @param[in] strict whether to expect the input to be consumed until EOF + (true by default) + @param[in] allow_exceptions whether to throw exceptions in case of a + parse error (optional, true by default) + + @return deserialized JSON value; in case of a parse error and + @a allow_exceptions set to `false`, the return value will be + value_t::discarded. + + @throw parse_error.114 if an unsupported BSON record type is encountered + + @complexity Linear in the size of the input @a i. + + @liveexample{The example shows the deserialization of a byte vector in + BSON format to a JSON value.,from_bson} + + @sa http://bsonspec.org/spec.html + @sa see @ref to_bson(const basic_json&) for the analogous serialization + @sa see @ref from_cbor(InputType&&, const bool, const bool, const cbor_tag_handler_t) for the + related CBOR format + @sa see @ref from_msgpack(InputType&&, const bool, const bool) for + the related MessagePack format + @sa see @ref from_ubjson(InputType&&, const bool, const bool) for the + related UBJSON format + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_bson(InputType&& i, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::forward(i)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::bson, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + /*! + @copydoc from_bson(InputType&&, const bool, const bool) + */ + template + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json from_bson(IteratorType first, IteratorType last, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = detail::input_adapter(std::move(first), std::move(last)); + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::bson, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + + template + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_bson(ptr, ptr + len)) + static basic_json from_bson(const T* ptr, std::size_t len, + const bool strict = true, + const bool allow_exceptions = true) + { + return from_bson(ptr, ptr + len, strict, allow_exceptions); + } + + JSON_HEDLEY_WARN_UNUSED_RESULT + JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_bson(ptr, ptr + len)) + static basic_json from_bson(detail::span_input_adapter&& i, + const bool strict = true, + const bool allow_exceptions = true) + { + basic_json result; + detail::json_sax_dom_parser sdp(result, allow_exceptions); + auto ia = i.get(); + // NOLINTNEXTLINE(hicpp-move-const-arg,performance-move-const-arg) + const bool res = binary_reader(std::move(ia)).sax_parse(input_format_t::bson, &sdp, strict); + return res ? result : basic_json(value_t::discarded); + } + /// @} + + ////////////////////////// + // JSON Pointer support // + ////////////////////////// + + /// @name JSON Pointer functions + /// @{ + + /*! + @brief access specified element via JSON Pointer + + Uses a JSON pointer to retrieve a reference to the respective JSON value. + No bound checking is performed. Similar to @ref operator[](const typename + object_t::key_type&), `null` values are created in arrays and objects if + necessary. + + In particular: + - If the JSON pointer points to an object key that does not exist, it + is created an filled with a `null` value before a reference to it + is returned. + - If the JSON pointer points to an array index that does not exist, it + is created an filled with a `null` value before a reference to it + is returned. All indices between the current maximum and the given + index are also filled with `null`. + - The special value `-` is treated as a synonym for the index past the + end. + + @param[in] ptr a JSON pointer + + @return reference to the element pointed to by @a ptr + + @complexity Constant. + + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + @throw out_of_range.404 if the JSON pointer can not be resolved + + @liveexample{The behavior is shown in the example.,operatorjson_pointer} + + @since version 2.0.0 + */ + reference operator[](const json_pointer& ptr) + { + return ptr.get_unchecked(this); + } + + /*! + @brief access specified element via JSON Pointer + + Uses a JSON pointer to retrieve a reference to the respective JSON value. + No bound checking is performed. The function does not change the JSON + value; no `null` values are created. In particular, the special value + `-` yields an exception. + + @param[in] ptr JSON pointer to the desired element + + @return const reference to the element pointed to by @a ptr + + @complexity Constant. + + @throw parse_error.106 if an array index begins with '0' + @throw parse_error.109 if an array index was not a number + @throw out_of_range.402 if the array index '-' is used + @throw out_of_range.404 if the JSON pointer can not be resolved + + @liveexample{The behavior is shown in the example.,operatorjson_pointer_const} + + @since version 2.0.0 + */ + const_reference operator[](const json_pointer& ptr) const + { + return ptr.get_unchecked(this); + } + + /*! + @brief access specified element via JSON Pointer + + Returns a reference to the element at with specified JSON pointer @a ptr, + with bounds checking. + + @param[in] ptr JSON pointer to the desired element + + @return reference to the element pointed to by @a ptr + + @throw parse_error.106 if an array index in the passed JSON pointer @a ptr + begins with '0'. See example below. + + @throw parse_error.109 if an array index in the passed JSON pointer @a ptr + is not a number. See example below. + + @throw out_of_range.401 if an array index in the passed JSON pointer @a ptr + is out of range. See example below. + + @throw out_of_range.402 if the array index '-' is used in the passed JSON + pointer @a ptr. As `at` provides checked access (and no elements are + implicitly inserted), the index '-' is always invalid. See example below. + + @throw out_of_range.403 if the JSON pointer describes a key of an object + which cannot be found. See example below. + + @throw out_of_range.404 if the JSON pointer @a ptr can not be resolved. + See example below. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Constant. + + @since version 2.0.0 + + @liveexample{The behavior is shown in the example.,at_json_pointer} + */ + reference at(const json_pointer& ptr) + { + return ptr.get_checked(this); + } + + /*! + @brief access specified element via JSON Pointer + + Returns a const reference to the element at with specified JSON pointer @a + ptr, with bounds checking. + + @param[in] ptr JSON pointer to the desired element + + @return reference to the element pointed to by @a ptr + + @throw parse_error.106 if an array index in the passed JSON pointer @a ptr + begins with '0'. See example below. + + @throw parse_error.109 if an array index in the passed JSON pointer @a ptr + is not a number. See example below. + + @throw out_of_range.401 if an array index in the passed JSON pointer @a ptr + is out of range. See example below. + + @throw out_of_range.402 if the array index '-' is used in the passed JSON + pointer @a ptr. As `at` provides checked access (and no elements are + implicitly inserted), the index '-' is always invalid. See example below. + + @throw out_of_range.403 if the JSON pointer describes a key of an object + which cannot be found. See example below. + + @throw out_of_range.404 if the JSON pointer @a ptr can not be resolved. + See example below. + + @exceptionsafety Strong guarantee: if an exception is thrown, there are no + changes in the JSON value. + + @complexity Constant. + + @since version 2.0.0 + + @liveexample{The behavior is shown in the example.,at_json_pointer_const} + */ + const_reference at(const json_pointer& ptr) const + { + return ptr.get_checked(this); + } + + /*! + @brief return flattened JSON value + + The function creates a JSON object whose keys are JSON pointers (see [RFC + 6901](https://tools.ietf.org/html/rfc6901)) and whose values are all + primitive. The original JSON value can be restored using the @ref + unflatten() function. + + @return an object that maps JSON pointers to primitive values + + @note Empty objects and arrays are flattened to `null` and will not be + reconstructed correctly by the @ref unflatten() function. + + @complexity Linear in the size the JSON value. + + @liveexample{The following code shows how a JSON object is flattened to an + object whose keys consist of JSON pointers.,flatten} + + @sa see @ref unflatten() for the reverse function + + @since version 2.0.0 + */ + basic_json flatten() const + { + basic_json result(value_t::object); + json_pointer::flatten("", *this, result); + return result; + } + + /*! + @brief unflatten a previously flattened JSON value + + The function restores the arbitrary nesting of a JSON value that has been + flattened before using the @ref flatten() function. The JSON value must + meet certain constraints: + 1. The value must be an object. + 2. The keys must be JSON pointers (see + [RFC 6901](https://tools.ietf.org/html/rfc6901)) + 3. The mapped values must be primitive JSON types. + + @return the original JSON from a flattened version + + @note Empty objects and arrays are flattened by @ref flatten() to `null` + values and can not unflattened to their original type. Apart from + this example, for a JSON value `j`, the following is always true: + `j == j.flatten().unflatten()`. + + @complexity Linear in the size the JSON value. + + @throw type_error.314 if value is not an object + @throw type_error.315 if object values are not primitive + + @liveexample{The following code shows how a flattened JSON object is + unflattened into the original nested JSON object.,unflatten} + + @sa see @ref flatten() for the reverse function + + @since version 2.0.0 + */ + basic_json unflatten() const + { + return json_pointer::unflatten(*this); + } + + /// @} + + ////////////////////////// + // JSON Patch functions // + ////////////////////////// + + /// @name JSON Patch functions + /// @{ + + /*! + @brief applies a JSON patch + + [JSON Patch](http://jsonpatch.com) defines a JSON document structure for + expressing a sequence of operations to apply to a JSON) document. With + this function, a JSON Patch is applied to the current JSON value by + executing all operations from the patch. + + @param[in] json_patch JSON patch document + @return patched document + + @note The application of a patch is atomic: Either all operations succeed + and the patched document is returned or an exception is thrown. In + any case, the original value is not changed: the patch is applied + to a copy of the value. + + @throw parse_error.104 if the JSON patch does not consist of an array of + objects + + @throw parse_error.105 if the JSON patch is malformed (e.g., mandatory + attributes are missing); example: `"operation add must have member path"` + + @throw out_of_range.401 if an array index is out of range. + + @throw out_of_range.403 if a JSON pointer inside the patch could not be + resolved successfully in the current JSON value; example: `"key baz not + found"` + + @throw out_of_range.405 if JSON pointer has no parent ("add", "remove", + "move") + + @throw other_error.501 if "test" operation was unsuccessful + + @complexity Linear in the size of the JSON value and the length of the + JSON patch. As usually only a fraction of the JSON value is affected by + the patch, the complexity can usually be neglected. + + @liveexample{The following code shows how a JSON patch is applied to a + value.,patch} + + @sa see @ref diff -- create a JSON patch by comparing two JSON values + + @sa [RFC 6902 (JSON Patch)](https://tools.ietf.org/html/rfc6902) + @sa [RFC 6901 (JSON Pointer)](https://tools.ietf.org/html/rfc6901) + + @since version 2.0.0 + */ + basic_json patch(const basic_json& json_patch) const + { + // make a working copy to apply the patch to + basic_json result = *this; + + // the valid JSON Patch operations + enum class patch_operations {add, remove, replace, move, copy, test, invalid}; + + const auto get_op = [](const std::string & op) + { + if (op == "add") + { + return patch_operations::add; + } + if (op == "remove") + { + return patch_operations::remove; + } + if (op == "replace") + { + return patch_operations::replace; + } + if (op == "move") + { + return patch_operations::move; + } + if (op == "copy") + { + return patch_operations::copy; + } + if (op == "test") + { + return patch_operations::test; + } + + return patch_operations::invalid; + }; + + // wrapper for "add" operation; add value at ptr + const auto operation_add = [&result](json_pointer & ptr, basic_json val) + { + // adding to the root of the target document means replacing it + if (ptr.empty()) + { + result = val; + return; + } + + // make sure the top element of the pointer exists + json_pointer top_pointer = ptr.top(); + if (top_pointer != ptr) + { + result.at(top_pointer); + } + + // get reference to parent of JSON pointer ptr + const auto last_path = ptr.back(); + ptr.pop_back(); + basic_json& parent = result[ptr]; + + switch (parent.m_type) + { + case value_t::null: + case value_t::object: + { + // use operator[] to add value + parent[last_path] = val; + break; + } + + case value_t::array: + { + if (last_path == "-") + { + // special case: append to back + parent.push_back(val); + } + else + { + const auto idx = json_pointer::array_index(last_path); + if (JSON_HEDLEY_UNLIKELY(idx > parent.size())) + { + // avoid undefined behavior + JSON_THROW(out_of_range::create(401, "array index " + std::to_string(idx) + " is out of range", parent)); + } + + // default case: insert add offset + parent.insert(parent.begin() + static_cast(idx), val); + } + break; + } + + // if there exists a parent it cannot be primitive + case value_t::string: // LCOV_EXCL_LINE + case value_t::boolean: // LCOV_EXCL_LINE + case value_t::number_integer: // LCOV_EXCL_LINE + case value_t::number_unsigned: // LCOV_EXCL_LINE + case value_t::number_float: // LCOV_EXCL_LINE + case value_t::binary: // LCOV_EXCL_LINE + case value_t::discarded: // LCOV_EXCL_LINE + default: // LCOV_EXCL_LINE + JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE + } + }; + + // wrapper for "remove" operation; remove value at ptr + const auto operation_remove = [this, &result](json_pointer & ptr) + { + // get reference to parent of JSON pointer ptr + const auto last_path = ptr.back(); + ptr.pop_back(); + basic_json& parent = result.at(ptr); + + // remove child + if (parent.is_object()) + { + // perform range check + auto it = parent.find(last_path); + if (JSON_HEDLEY_LIKELY(it != parent.end())) + { + parent.erase(it); + } + else + { + JSON_THROW(out_of_range::create(403, "key '" + last_path + "' not found", *this)); + } + } + else if (parent.is_array()) + { + // note erase performs range check + parent.erase(json_pointer::array_index(last_path)); + } + }; + + // type check: top level value must be an array + if (JSON_HEDLEY_UNLIKELY(!json_patch.is_array())) + { + JSON_THROW(parse_error::create(104, 0, "JSON patch must be an array of objects", json_patch)); + } + + // iterate and apply the operations + for (const auto& val : json_patch) + { + // wrapper to get a value for an operation + const auto get_value = [&val](const std::string & op, + const std::string & member, + bool string_type) -> basic_json & + { + // find value + auto it = val.m_value.object->find(member); + + // context-sensitive error message + const auto error_msg = (op == "op") ? "operation" : "operation '" + op + "'"; + + // check if desired value is present + if (JSON_HEDLEY_UNLIKELY(it == val.m_value.object->end())) + { + // NOLINTNEXTLINE(performance-inefficient-string-concatenation) + JSON_THROW(parse_error::create(105, 0, error_msg + " must have member '" + member + "'", val)); + } + + // check if result is of type string + if (JSON_HEDLEY_UNLIKELY(string_type && !it->second.is_string())) + { + // NOLINTNEXTLINE(performance-inefficient-string-concatenation) + JSON_THROW(parse_error::create(105, 0, error_msg + " must have string member '" + member + "'", val)); + } + + // no error: return value + return it->second; + }; + + // type check: every element of the array must be an object + if (JSON_HEDLEY_UNLIKELY(!val.is_object())) + { + JSON_THROW(parse_error::create(104, 0, "JSON patch must be an array of objects", val)); + } + + // collect mandatory members + const auto op = get_value("op", "op", true).template get(); + const auto path = get_value(op, "path", true).template get(); + json_pointer ptr(path); + + switch (get_op(op)) + { + case patch_operations::add: + { + operation_add(ptr, get_value("add", "value", false)); + break; + } + + case patch_operations::remove: + { + operation_remove(ptr); + break; + } + + case patch_operations::replace: + { + // the "path" location must exist - use at() + result.at(ptr) = get_value("replace", "value", false); + break; + } + + case patch_operations::move: + { + const auto from_path = get_value("move", "from", true).template get(); + json_pointer from_ptr(from_path); + + // the "from" location must exist - use at() + basic_json v = result.at(from_ptr); + + // The move operation is functionally identical to a + // "remove" operation on the "from" location, followed + // immediately by an "add" operation at the target + // location with the value that was just removed. + operation_remove(from_ptr); + operation_add(ptr, v); + break; + } + + case patch_operations::copy: + { + const auto from_path = get_value("copy", "from", true).template get(); + const json_pointer from_ptr(from_path); + + // the "from" location must exist - use at() + basic_json v = result.at(from_ptr); + + // The copy is functionally identical to an "add" + // operation at the target location using the value + // specified in the "from" member. + operation_add(ptr, v); + break; + } + + case patch_operations::test: + { + bool success = false; + JSON_TRY + { + // check if "value" matches the one at "path" + // the "path" location must exist - use at() + success = (result.at(ptr) == get_value("test", "value", false)); + } + JSON_INTERNAL_CATCH (out_of_range&) + { + // ignore out of range errors: success remains false + } + + // throw an exception if test fails + if (JSON_HEDLEY_UNLIKELY(!success)) + { + JSON_THROW(other_error::create(501, "unsuccessful: " + val.dump(), val)); + } + + break; + } + + case patch_operations::invalid: + default: + { + // op must be "add", "remove", "replace", "move", "copy", or + // "test" + JSON_THROW(parse_error::create(105, 0, "operation value '" + op + "' is invalid", val)); + } + } + } + + return result; + } + + /*! + @brief creates a diff as a JSON patch + + Creates a [JSON Patch](http://jsonpatch.com) so that value @a source can + be changed into the value @a target by calling @ref patch function. + + @invariant For two JSON values @a source and @a target, the following code + yields always `true`: + @code {.cpp} + source.patch(diff(source, target)) == target; + @endcode + + @note Currently, only `remove`, `add`, and `replace` operations are + generated. + + @param[in] source JSON value to compare from + @param[in] target JSON value to compare against + @param[in] path helper value to create JSON pointers + + @return a JSON patch to convert the @a source to @a target + + @complexity Linear in the lengths of @a source and @a target. + + @liveexample{The following code shows how a JSON patch is created as a + diff for two JSON values.,diff} + + @sa see @ref patch -- apply a JSON patch + @sa see @ref merge_patch -- apply a JSON Merge Patch + + @sa [RFC 6902 (JSON Patch)](https://tools.ietf.org/html/rfc6902) + + @since version 2.0.0 + */ + JSON_HEDLEY_WARN_UNUSED_RESULT + static basic_json diff(const basic_json& source, const basic_json& target, + const std::string& path = "") + { + // the patch + basic_json result(value_t::array); + + // if the values are the same, return empty patch + if (source == target) + { + return result; + } + + if (source.type() != target.type()) + { + // different types: replace value + result.push_back( + { + {"op", "replace"}, {"path", path}, {"value", target} + }); + return result; + } + + switch (source.type()) + { + case value_t::array: + { + // first pass: traverse common elements + std::size_t i = 0; + while (i < source.size() && i < target.size()) + { + // recursive call to compare array values at index i + auto temp_diff = diff(source[i], target[i], path + "/" + std::to_string(i)); + result.insert(result.end(), temp_diff.begin(), temp_diff.end()); + ++i; + } + + // i now reached the end of at least one array + // in a second pass, traverse the remaining elements + + // remove my remaining elements + const auto end_index = static_cast(result.size()); + while (i < source.size()) + { + // add operations in reverse order to avoid invalid + // indices + result.insert(result.begin() + end_index, object( + { + {"op", "remove"}, + {"path", path + "/" + std::to_string(i)} + })); + ++i; + } + + // add other remaining elements + while (i < target.size()) + { + result.push_back( + { + {"op", "add"}, + {"path", path + "/-"}, + {"value", target[i]} + }); + ++i; + } + + break; + } + + case value_t::object: + { + // first pass: traverse this object's elements + for (auto it = source.cbegin(); it != source.cend(); ++it) + { + // escape the key name to be used in a JSON patch + const auto path_key = path + "/" + detail::escape(it.key()); + + if (target.find(it.key()) != target.end()) + { + // recursive call to compare object values at key it + auto temp_diff = diff(it.value(), target[it.key()], path_key); + result.insert(result.end(), temp_diff.begin(), temp_diff.end()); + } + else + { + // found a key that is not in o -> remove it + result.push_back(object( + { + {"op", "remove"}, {"path", path_key} + })); + } + } + + // second pass: traverse other object's elements + for (auto it = target.cbegin(); it != target.cend(); ++it) + { + if (source.find(it.key()) == source.end()) + { + // found a key that is not in this -> add it + const auto path_key = path + "/" + detail::escape(it.key()); + result.push_back( + { + {"op", "add"}, {"path", path_key}, + {"value", it.value()} + }); + } + } + + break; + } + + case value_t::null: + case value_t::string: + case value_t::boolean: + case value_t::number_integer: + case value_t::number_unsigned: + case value_t::number_float: + case value_t::binary: + case value_t::discarded: + default: + { + // both primitive type: replace value + result.push_back( + { + {"op", "replace"}, {"path", path}, {"value", target} + }); + break; + } + } + + return result; + } + + /// @} + + //////////////////////////////// + // JSON Merge Patch functions // + //////////////////////////////// + + /// @name JSON Merge Patch functions + /// @{ + + /*! + @brief applies a JSON Merge Patch + + The merge patch format is primarily intended for use with the HTTP PATCH + method as a means of describing a set of modifications to a target + resource's content. This function applies a merge patch to the current + JSON value. + + The function implements the following algorithm from Section 2 of + [RFC 7396 (JSON Merge Patch)](https://tools.ietf.org/html/rfc7396): + + ``` + define MergePatch(Target, Patch): + if Patch is an Object: + if Target is not an Object: + Target = {} // Ignore the contents and set it to an empty Object + for each Name/Value pair in Patch: + if Value is null: + if Name exists in Target: + remove the Name/Value pair from Target + else: + Target[Name] = MergePatch(Target[Name], Value) + return Target + else: + return Patch + ``` + + Thereby, `Target` is the current object; that is, the patch is applied to + the current value. + + @param[in] apply_patch the patch to apply + + @complexity Linear in the lengths of @a patch. + + @liveexample{The following code shows how a JSON Merge Patch is applied to + a JSON document.,merge_patch} + + @sa see @ref patch -- apply a JSON patch + @sa [RFC 7396 (JSON Merge Patch)](https://tools.ietf.org/html/rfc7396) + + @since version 3.0.0 + */ + void merge_patch(const basic_json& apply_patch) + { + if (apply_patch.is_object()) + { + if (!is_object()) + { + *this = object(); + } + for (auto it = apply_patch.begin(); it != apply_patch.end(); ++it) + { + if (it.value().is_null()) + { + erase(it.key()); + } + else + { + operator[](it.key()).merge_patch(it.value()); + } + } + } + else + { + *this = apply_patch; + } + } + + /// @} +}; + +/*! +@brief user-defined to_string function for JSON values + +This function implements a user-defined to_string for JSON objects. + +@param[in] j a JSON object +@return a std::string object +*/ + +NLOHMANN_BASIC_JSON_TPL_DECLARATION +std::string to_string(const NLOHMANN_BASIC_JSON_TPL& j) +{ + return j.dump(); +} +} // namespace nlohmann + +/////////////////////// +// nonmember support // +/////////////////////// + +// specialization of std::swap, and std::hash +namespace std +{ + +/// hash value for JSON objects +template<> +struct hash +{ + /*! + @brief return a hash value for a JSON object + + @since version 1.0.0 + */ + std::size_t operator()(const nlohmann::json& j) const + { + return nlohmann::detail::hash(j); + } +}; + +/// specialization for std::less +/// @note: do not remove the space after '<', +/// see https://github.com/nlohmann/json/pull/679 +template<> +struct less<::nlohmann::detail::value_t> +{ + /*! + @brief compare two value_t enum values + @since version 3.0.0 + */ + bool operator()(nlohmann::detail::value_t lhs, + nlohmann::detail::value_t rhs) const noexcept + { + return nlohmann::detail::operator<(lhs, rhs); + } +}; + +// C++20 prohibit function specialization in the std namespace. +#ifndef JSON_HAS_CPP_20 + +/*! +@brief exchanges the values of two JSON objects + +@since version 1.0.0 +*/ +template<> +inline void swap(nlohmann::json& j1, nlohmann::json& j2) noexcept( // NOLINT(readability-inconsistent-declaration-parameter-name) + is_nothrow_move_constructible::value&& // NOLINT(misc-redundant-expression) + is_nothrow_move_assignable::value + ) +{ + j1.swap(j2); +} + +#endif + +} // namespace std + +/*! +@brief user-defined string literal for JSON values + +This operator implements a user-defined string literal for JSON objects. It +can be used by adding `"_json"` to a string literal and returns a JSON object +if no parse error occurred. + +@param[in] s a string representation of a JSON object +@param[in] n the length of string @a s +@return a JSON object + +@since version 1.0.0 +*/ +JSON_HEDLEY_NON_NULL(1) +inline nlohmann::json operator "" _json(const char* s, std::size_t n) +{ + return nlohmann::json::parse(s, s + n); +} + +/*! +@brief user-defined string literal for JSON pointer + +This operator implements a user-defined string literal for JSON Pointers. It +can be used by adding `"_json_pointer"` to a string literal and returns a JSON pointer +object if no parse error occurred. + +@param[in] s a string representation of a JSON Pointer +@param[in] n the length of string @a s +@return a JSON pointer object + +@since version 2.0.0 +*/ +JSON_HEDLEY_NON_NULL(1) +inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n) +{ + return nlohmann::json::json_pointer(std::string(s, n)); +} + +// #include + + +// restore clang diagnostic settings +#if defined(__clang__) + #pragma clang diagnostic pop +#endif + +// clean up +#undef JSON_ASSERT +#undef JSON_INTERNAL_CATCH +#undef JSON_CATCH +#undef JSON_THROW +#undef JSON_TRY +#undef JSON_PRIVATE_UNLESS_TESTED +#undef JSON_HAS_CPP_11 +#undef JSON_HAS_CPP_14 +#undef JSON_HAS_CPP_17 +#undef JSON_HAS_CPP_20 +#undef NLOHMANN_BASIC_JSON_TPL_DECLARATION +#undef NLOHMANN_BASIC_JSON_TPL +#undef JSON_EXPLICIT +#undef NLOHMANN_CAN_CALL_STD_FUNC_IMPL + +// #include + + +#undef JSON_HEDLEY_ALWAYS_INLINE +#undef JSON_HEDLEY_ARM_VERSION +#undef JSON_HEDLEY_ARM_VERSION_CHECK +#undef JSON_HEDLEY_ARRAY_PARAM +#undef JSON_HEDLEY_ASSUME +#undef JSON_HEDLEY_BEGIN_C_DECLS +#undef JSON_HEDLEY_CLANG_HAS_ATTRIBUTE +#undef JSON_HEDLEY_CLANG_HAS_BUILTIN +#undef JSON_HEDLEY_CLANG_HAS_CPP_ATTRIBUTE +#undef JSON_HEDLEY_CLANG_HAS_DECLSPEC_DECLSPEC_ATTRIBUTE +#undef JSON_HEDLEY_CLANG_HAS_EXTENSION +#undef JSON_HEDLEY_CLANG_HAS_FEATURE +#undef JSON_HEDLEY_CLANG_HAS_WARNING +#undef JSON_HEDLEY_COMPCERT_VERSION +#undef JSON_HEDLEY_COMPCERT_VERSION_CHECK +#undef JSON_HEDLEY_CONCAT +#undef JSON_HEDLEY_CONCAT3 +#undef JSON_HEDLEY_CONCAT3_EX +#undef JSON_HEDLEY_CONCAT_EX +#undef JSON_HEDLEY_CONST +#undef JSON_HEDLEY_CONSTEXPR +#undef JSON_HEDLEY_CONST_CAST +#undef JSON_HEDLEY_CPP_CAST +#undef JSON_HEDLEY_CRAY_VERSION +#undef JSON_HEDLEY_CRAY_VERSION_CHECK +#undef JSON_HEDLEY_C_DECL +#undef JSON_HEDLEY_DEPRECATED +#undef JSON_HEDLEY_DEPRECATED_FOR +#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CAST_QUAL +#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_CPP98_COMPAT_WRAP_ +#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_DEPRECATED +#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_CPP_ATTRIBUTES +#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNKNOWN_PRAGMAS +#undef JSON_HEDLEY_DIAGNOSTIC_DISABLE_UNUSED_FUNCTION +#undef JSON_HEDLEY_DIAGNOSTIC_POP +#undef JSON_HEDLEY_DIAGNOSTIC_PUSH +#undef JSON_HEDLEY_DMC_VERSION +#undef JSON_HEDLEY_DMC_VERSION_CHECK +#undef JSON_HEDLEY_EMPTY_BASES +#undef JSON_HEDLEY_EMSCRIPTEN_VERSION +#undef JSON_HEDLEY_EMSCRIPTEN_VERSION_CHECK +#undef JSON_HEDLEY_END_C_DECLS +#undef JSON_HEDLEY_FLAGS +#undef JSON_HEDLEY_FLAGS_CAST +#undef JSON_HEDLEY_GCC_HAS_ATTRIBUTE +#undef JSON_HEDLEY_GCC_HAS_BUILTIN +#undef JSON_HEDLEY_GCC_HAS_CPP_ATTRIBUTE +#undef JSON_HEDLEY_GCC_HAS_DECLSPEC_ATTRIBUTE +#undef JSON_HEDLEY_GCC_HAS_EXTENSION +#undef JSON_HEDLEY_GCC_HAS_FEATURE +#undef JSON_HEDLEY_GCC_HAS_WARNING +#undef JSON_HEDLEY_GCC_NOT_CLANG_VERSION_CHECK +#undef JSON_HEDLEY_GCC_VERSION +#undef JSON_HEDLEY_GCC_VERSION_CHECK +#undef JSON_HEDLEY_GNUC_HAS_ATTRIBUTE +#undef JSON_HEDLEY_GNUC_HAS_BUILTIN +#undef JSON_HEDLEY_GNUC_HAS_CPP_ATTRIBUTE +#undef JSON_HEDLEY_GNUC_HAS_DECLSPEC_ATTRIBUTE +#undef JSON_HEDLEY_GNUC_HAS_EXTENSION +#undef JSON_HEDLEY_GNUC_HAS_FEATURE +#undef JSON_HEDLEY_GNUC_HAS_WARNING +#undef JSON_HEDLEY_GNUC_VERSION +#undef JSON_HEDLEY_GNUC_VERSION_CHECK +#undef JSON_HEDLEY_HAS_ATTRIBUTE +#undef JSON_HEDLEY_HAS_BUILTIN +#undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE +#undef JSON_HEDLEY_HAS_CPP_ATTRIBUTE_NS +#undef JSON_HEDLEY_HAS_DECLSPEC_ATTRIBUTE +#undef JSON_HEDLEY_HAS_EXTENSION +#undef JSON_HEDLEY_HAS_FEATURE +#undef JSON_HEDLEY_HAS_WARNING +#undef JSON_HEDLEY_IAR_VERSION +#undef JSON_HEDLEY_IAR_VERSION_CHECK +#undef JSON_HEDLEY_IBM_VERSION +#undef JSON_HEDLEY_IBM_VERSION_CHECK +#undef JSON_HEDLEY_IMPORT +#undef JSON_HEDLEY_INLINE +#undef JSON_HEDLEY_INTEL_CL_VERSION +#undef JSON_HEDLEY_INTEL_CL_VERSION_CHECK +#undef JSON_HEDLEY_INTEL_VERSION +#undef JSON_HEDLEY_INTEL_VERSION_CHECK +#undef JSON_HEDLEY_IS_CONSTANT +#undef JSON_HEDLEY_IS_CONSTEXPR_ +#undef JSON_HEDLEY_LIKELY +#undef JSON_HEDLEY_MALLOC +#undef JSON_HEDLEY_MCST_LCC_VERSION +#undef JSON_HEDLEY_MCST_LCC_VERSION_CHECK +#undef JSON_HEDLEY_MESSAGE +#undef JSON_HEDLEY_MSVC_VERSION +#undef JSON_HEDLEY_MSVC_VERSION_CHECK +#undef JSON_HEDLEY_NEVER_INLINE +#undef JSON_HEDLEY_NON_NULL +#undef JSON_HEDLEY_NO_ESCAPE +#undef JSON_HEDLEY_NO_RETURN +#undef JSON_HEDLEY_NO_THROW +#undef JSON_HEDLEY_NULL +#undef JSON_HEDLEY_PELLES_VERSION +#undef JSON_HEDLEY_PELLES_VERSION_CHECK +#undef JSON_HEDLEY_PGI_VERSION +#undef JSON_HEDLEY_PGI_VERSION_CHECK +#undef JSON_HEDLEY_PREDICT +#undef JSON_HEDLEY_PRINTF_FORMAT +#undef JSON_HEDLEY_PRIVATE +#undef JSON_HEDLEY_PUBLIC +#undef JSON_HEDLEY_PURE +#undef JSON_HEDLEY_REINTERPRET_CAST +#undef JSON_HEDLEY_REQUIRE +#undef JSON_HEDLEY_REQUIRE_CONSTEXPR +#undef JSON_HEDLEY_REQUIRE_MSG +#undef JSON_HEDLEY_RESTRICT +#undef JSON_HEDLEY_RETURNS_NON_NULL +#undef JSON_HEDLEY_SENTINEL +#undef JSON_HEDLEY_STATIC_ASSERT +#undef JSON_HEDLEY_STATIC_CAST +#undef JSON_HEDLEY_STRINGIFY +#undef JSON_HEDLEY_STRINGIFY_EX +#undef JSON_HEDLEY_SUNPRO_VERSION +#undef JSON_HEDLEY_SUNPRO_VERSION_CHECK +#undef JSON_HEDLEY_TINYC_VERSION +#undef JSON_HEDLEY_TINYC_VERSION_CHECK +#undef JSON_HEDLEY_TI_ARMCL_VERSION +#undef JSON_HEDLEY_TI_ARMCL_VERSION_CHECK +#undef JSON_HEDLEY_TI_CL2000_VERSION +#undef JSON_HEDLEY_TI_CL2000_VERSION_CHECK +#undef JSON_HEDLEY_TI_CL430_VERSION +#undef JSON_HEDLEY_TI_CL430_VERSION_CHECK +#undef JSON_HEDLEY_TI_CL6X_VERSION +#undef JSON_HEDLEY_TI_CL6X_VERSION_CHECK +#undef JSON_HEDLEY_TI_CL7X_VERSION +#undef JSON_HEDLEY_TI_CL7X_VERSION_CHECK +#undef JSON_HEDLEY_TI_CLPRU_VERSION +#undef JSON_HEDLEY_TI_CLPRU_VERSION_CHECK +#undef JSON_HEDLEY_TI_VERSION +#undef JSON_HEDLEY_TI_VERSION_CHECK +#undef JSON_HEDLEY_UNAVAILABLE +#undef JSON_HEDLEY_UNLIKELY +#undef JSON_HEDLEY_UNPREDICTABLE +#undef JSON_HEDLEY_UNREACHABLE +#undef JSON_HEDLEY_UNREACHABLE_RETURN +#undef JSON_HEDLEY_VERSION +#undef JSON_HEDLEY_VERSION_DECODE_MAJOR +#undef JSON_HEDLEY_VERSION_DECODE_MINOR +#undef JSON_HEDLEY_VERSION_DECODE_REVISION +#undef JSON_HEDLEY_VERSION_ENCODE +#undef JSON_HEDLEY_WARNING +#undef JSON_HEDLEY_WARN_UNUSED_RESULT +#undef JSON_HEDLEY_WARN_UNUSED_RESULT_MSG +#undef JSON_HEDLEY_FALL_THROUGH + + + +#endif // INCLUDE_NLOHMANN_JSON_HPP_ diff --git a/src/tinygltf/tiny_gltf.h b/src/tinygltf/tiny_gltf.h new file mode 100644 index 00000000..2480c835 --- /dev/null +++ b/src/tinygltf/tiny_gltf.h @@ -0,0 +1,8483 @@ +// +// Header-only tiny glTF 2.0 loader and serializer. +// +// +// The MIT License (MIT) +// +// Copyright (c) 2015 - Present Syoyo Fujita, Aurélien Chatelain and many +// contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +// THE SOFTWARE. + +// Version: - v2.8.10 +// See https://github.com/syoyo/tinygltf/releases for release history. +// +// Tiny glTF loader is using following third party libraries: +// +// - jsonhpp: C++ JSON library. +// - base64: base64 decode/encode library. +// - stb_image: Image loading library. +// +#ifndef TINY_GLTF_H_ +#define TINY_GLTF_H_ + +#include +#include +#include // std::fabs +#include +#include +#include +#include +#include +#include +#include + +// Auto-detect C++14 standard version +#if !defined(TINYGLTF_USE_CPP14) && defined(__cplusplus) && \ + (__cplusplus >= 201402L) +#define TINYGLTF_USE_CPP14 +#endif + +#ifdef __ANDROID__ +#ifdef TINYGLTF_ANDROID_LOAD_FROM_ASSETS +#include +#endif +#endif + +#ifdef __GNUC__ +#if (__GNUC__ < 4) || ((__GNUC__ == 4) && (__GNUC_MINOR__ <= 8)) +#define TINYGLTF_NOEXCEPT +#else +#define TINYGLTF_NOEXCEPT noexcept +#endif +#else +#define TINYGLTF_NOEXCEPT noexcept +#endif + +#define DEFAULT_METHODS(x) \ + ~x() = default; \ + x(const x &) = default; \ + x(x &&) TINYGLTF_NOEXCEPT = default; \ + x &operator=(const x &) = default; \ + x &operator=(x &&) TINYGLTF_NOEXCEPT = default; + +namespace tinygltf { + +#define TINYGLTF_MODE_POINTS (0) +#define TINYGLTF_MODE_LINE (1) +#define TINYGLTF_MODE_LINE_LOOP (2) +#define TINYGLTF_MODE_LINE_STRIP (3) +#define TINYGLTF_MODE_TRIANGLES (4) +#define TINYGLTF_MODE_TRIANGLE_STRIP (5) +#define TINYGLTF_MODE_TRIANGLE_FAN (6) + +#define TINYGLTF_COMPONENT_TYPE_BYTE (5120) +#define TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE (5121) +#define TINYGLTF_COMPONENT_TYPE_SHORT (5122) +#define TINYGLTF_COMPONENT_TYPE_UNSIGNED_SHORT (5123) +#define TINYGLTF_COMPONENT_TYPE_INT (5124) +#define TINYGLTF_COMPONENT_TYPE_UNSIGNED_INT (5125) +#define TINYGLTF_COMPONENT_TYPE_FLOAT (5126) +#define TINYGLTF_COMPONENT_TYPE_DOUBLE \ + (5130) // OpenGL double type. Note that some of glTF 2.0 validator does not + // support double type even the schema seems allow any value of + // integer: + // https://github.com/KhronosGroup/glTF/blob/b9884a2fd45130b4d673dd6c8a706ee21ee5c5f7/specification/2.0/schema/accessor.schema.json#L22 + +#define TINYGLTF_TEXTURE_FILTER_NEAREST (9728) +#define TINYGLTF_TEXTURE_FILTER_LINEAR (9729) +#define TINYGLTF_TEXTURE_FILTER_NEAREST_MIPMAP_NEAREST (9984) +#define TINYGLTF_TEXTURE_FILTER_LINEAR_MIPMAP_NEAREST (9985) +#define TINYGLTF_TEXTURE_FILTER_NEAREST_MIPMAP_LINEAR (9986) +#define TINYGLTF_TEXTURE_FILTER_LINEAR_MIPMAP_LINEAR (9987) + +#define TINYGLTF_TEXTURE_WRAP_REPEAT (10497) +#define TINYGLTF_TEXTURE_WRAP_CLAMP_TO_EDGE (33071) +#define TINYGLTF_TEXTURE_WRAP_MIRRORED_REPEAT (33648) + +// Redeclarations of the above for technique.parameters. +#define TINYGLTF_PARAMETER_TYPE_BYTE (5120) +#define TINYGLTF_PARAMETER_TYPE_UNSIGNED_BYTE (5121) +#define TINYGLTF_PARAMETER_TYPE_SHORT (5122) +#define TINYGLTF_PARAMETER_TYPE_UNSIGNED_SHORT (5123) +#define TINYGLTF_PARAMETER_TYPE_INT (5124) +#define TINYGLTF_PARAMETER_TYPE_UNSIGNED_INT (5125) +#define TINYGLTF_PARAMETER_TYPE_FLOAT (5126) + +#define TINYGLTF_PARAMETER_TYPE_FLOAT_VEC2 (35664) +#define TINYGLTF_PARAMETER_TYPE_FLOAT_VEC3 (35665) +#define TINYGLTF_PARAMETER_TYPE_FLOAT_VEC4 (35666) + +#define TINYGLTF_PARAMETER_TYPE_INT_VEC2 (35667) +#define TINYGLTF_PARAMETER_TYPE_INT_VEC3 (35668) +#define TINYGLTF_PARAMETER_TYPE_INT_VEC4 (35669) + +#define TINYGLTF_PARAMETER_TYPE_BOOL (35670) +#define TINYGLTF_PARAMETER_TYPE_BOOL_VEC2 (35671) +#define TINYGLTF_PARAMETER_TYPE_BOOL_VEC3 (35672) +#define TINYGLTF_PARAMETER_TYPE_BOOL_VEC4 (35673) + +#define TINYGLTF_PARAMETER_TYPE_FLOAT_MAT2 (35674) +#define TINYGLTF_PARAMETER_TYPE_FLOAT_MAT3 (35675) +#define TINYGLTF_PARAMETER_TYPE_FLOAT_MAT4 (35676) + +#define TINYGLTF_PARAMETER_TYPE_SAMPLER_2D (35678) + +// End parameter types + +#define TINYGLTF_TYPE_VEC2 (2) +#define TINYGLTF_TYPE_VEC3 (3) +#define TINYGLTF_TYPE_VEC4 (4) +#define TINYGLTF_TYPE_MAT2 (32 + 2) +#define TINYGLTF_TYPE_MAT3 (32 + 3) +#define TINYGLTF_TYPE_MAT4 (32 + 4) +#define TINYGLTF_TYPE_SCALAR (64 + 1) +#define TINYGLTF_TYPE_VECTOR (64 + 4) +#define TINYGLTF_TYPE_MATRIX (64 + 16) + +#define TINYGLTF_IMAGE_FORMAT_JPEG (0) +#define TINYGLTF_IMAGE_FORMAT_PNG (1) +#define TINYGLTF_IMAGE_FORMAT_BMP (2) +#define TINYGLTF_IMAGE_FORMAT_GIF (3) + +#define TINYGLTF_TEXTURE_FORMAT_ALPHA (6406) +#define TINYGLTF_TEXTURE_FORMAT_RGB (6407) +#define TINYGLTF_TEXTURE_FORMAT_RGBA (6408) +#define TINYGLTF_TEXTURE_FORMAT_LUMINANCE (6409) +#define TINYGLTF_TEXTURE_FORMAT_LUMINANCE_ALPHA (6410) + +#define TINYGLTF_TEXTURE_TARGET_TEXTURE2D (3553) +#define TINYGLTF_TEXTURE_TYPE_UNSIGNED_BYTE (5121) + +#define TINYGLTF_TARGET_ARRAY_BUFFER (34962) +#define TINYGLTF_TARGET_ELEMENT_ARRAY_BUFFER (34963) + +#define TINYGLTF_SHADER_TYPE_VERTEX_SHADER (35633) +#define TINYGLTF_SHADER_TYPE_FRAGMENT_SHADER (35632) + +#define TINYGLTF_DOUBLE_EPS (1.e-12) +#define TINYGLTF_DOUBLE_EQUAL(a, b) (std::fabs((b) - (a)) < TINYGLTF_DOUBLE_EPS) + +#ifdef __ANDROID__ +#ifdef TINYGLTF_ANDROID_LOAD_FROM_ASSETS +#ifdef TINYGLTF_IMPLEMENTATION +AAssetManager *asset_manager = nullptr; +#else +extern AAssetManager *asset_manager; +#endif +#endif +#endif + +typedef enum { + NULL_TYPE, + REAL_TYPE, + INT_TYPE, + BOOL_TYPE, + STRING_TYPE, + ARRAY_TYPE, + BINARY_TYPE, + OBJECT_TYPE +} Type; + +typedef enum { + Permissive, + Strict +} ParseStrictness; + +static inline int32_t GetComponentSizeInBytes(uint32_t componentType) { + if (componentType == TINYGLTF_COMPONENT_TYPE_BYTE) { + return 1; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE) { + return 1; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_SHORT) { + return 2; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_UNSIGNED_SHORT) { + return 2; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_INT) { + return 4; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_UNSIGNED_INT) { + return 4; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_FLOAT) { + return 4; + } else if (componentType == TINYGLTF_COMPONENT_TYPE_DOUBLE) { + return 8; + } else { + // Unknown component type + return -1; + } +} + +static inline int32_t GetNumComponentsInType(uint32_t ty) { + if (ty == TINYGLTF_TYPE_SCALAR) { + return 1; + } else if (ty == TINYGLTF_TYPE_VEC2) { + return 2; + } else if (ty == TINYGLTF_TYPE_VEC3) { + return 3; + } else if (ty == TINYGLTF_TYPE_VEC4) { + return 4; + } else if (ty == TINYGLTF_TYPE_MAT2) { + return 4; + } else if (ty == TINYGLTF_TYPE_MAT3) { + return 9; + } else if (ty == TINYGLTF_TYPE_MAT4) { + return 16; + } else { + // Unknown component type + return -1; + } +} + +// TODO(syoyo): Move these functions to TinyGLTF class +bool IsDataURI(const std::string &in); +bool DecodeDataURI(std::vector *out, std::string &mime_type, + const std::string &in, size_t reqBytes, bool checkSize); + +#ifdef __clang__ +#pragma clang diagnostic push +// Suppress warning for : static Value null_value +#pragma clang diagnostic ignored "-Wexit-time-destructors" +#pragma clang diagnostic ignored "-Wpadded" +#endif + +// Simple class to represent JSON object +class Value { + public: + typedef std::vector Array; + typedef std::map Object; + + Value() = default; + + explicit Value(bool b) : type_(BOOL_TYPE) { boolean_value_ = b; } + explicit Value(int i) : type_(INT_TYPE) { + int_value_ = i; + real_value_ = i; + } + explicit Value(double n) : type_(REAL_TYPE) { real_value_ = n; } + explicit Value(const std::string &s) : type_(STRING_TYPE) { + string_value_ = s; + } + explicit Value(std::string &&s) + : type_(STRING_TYPE), string_value_(std::move(s)) {} + explicit Value(const char *s) : type_(STRING_TYPE) { string_value_ = s; } + explicit Value(const unsigned char *p, size_t n) : type_(BINARY_TYPE) { + binary_value_.resize(n); + memcpy(binary_value_.data(), p, n); + } + explicit Value(std::vector &&v) noexcept + : type_(BINARY_TYPE), + binary_value_(std::move(v)) {} + explicit Value(const Array &a) : type_(ARRAY_TYPE) { array_value_ = a; } + explicit Value(Array &&a) noexcept : type_(ARRAY_TYPE), + array_value_(std::move(a)) {} + + explicit Value(const Object &o) : type_(OBJECT_TYPE) { object_value_ = o; } + explicit Value(Object &&o) noexcept : type_(OBJECT_TYPE), + object_value_(std::move(o)) {} + + DEFAULT_METHODS(Value) + + char Type() const { return static_cast(type_); } + + bool IsBool() const { return (type_ == BOOL_TYPE); } + + bool IsInt() const { return (type_ == INT_TYPE); } + + bool IsNumber() const { return (type_ == REAL_TYPE) || (type_ == INT_TYPE); } + + bool IsReal() const { return (type_ == REAL_TYPE); } + + bool IsString() const { return (type_ == STRING_TYPE); } + + bool IsBinary() const { return (type_ == BINARY_TYPE); } + + bool IsArray() const { return (type_ == ARRAY_TYPE); } + + bool IsObject() const { return (type_ == OBJECT_TYPE); } + + // Use this function if you want to have number value as double. + double GetNumberAsDouble() const { + if (type_ == INT_TYPE) { + return double(int_value_); + } else { + return real_value_; + } + } + + // Use this function if you want to have number value as int. + // TODO(syoyo): Support int value larger than 32 bits + int GetNumberAsInt() const { + if (type_ == REAL_TYPE) { + return int(real_value_); + } else { + return int_value_; + } + } + + // Accessor + template + const T &Get() const; + template + T &Get(); + + // Lookup value from an array + const Value &Get(int idx) const { + static Value null_value; + assert(IsArray()); + assert(idx >= 0); + return (static_cast(idx) < array_value_.size()) + ? array_value_[static_cast(idx)] + : null_value; + } + + // Lookup value from a key-value pair + const Value &Get(const std::string &key) const { + static Value null_value; + assert(IsObject()); + Object::const_iterator it = object_value_.find(key); + return (it != object_value_.end()) ? it->second : null_value; + } + + size_t ArrayLen() const { + if (!IsArray()) return 0; + return array_value_.size(); + } + + // Valid only for object type. + bool Has(const std::string &key) const { + if (!IsObject()) return false; + Object::const_iterator it = object_value_.find(key); + return (it != object_value_.end()) ? true : false; + } + + // List keys + std::vector Keys() const { + std::vector keys; + if (!IsObject()) return keys; // empty + + for (Object::const_iterator it = object_value_.begin(); + it != object_value_.end(); ++it) { + keys.push_back(it->first); + } + + return keys; + } + + size_t Size() const { return (IsArray() ? ArrayLen() : Keys().size()); } + + bool operator==(const tinygltf::Value &other) const; + + protected: + int type_ = NULL_TYPE; + + int int_value_ = 0; + double real_value_ = 0.0; + std::string string_value_; + std::vector binary_value_; + Array array_value_; + Object object_value_; + bool boolean_value_ = false; +}; + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +#define TINYGLTF_VALUE_GET(ctype, var) \ + template <> \ + inline const ctype &Value::Get() const { \ + return var; \ + } \ + template <> \ + inline ctype &Value::Get() { \ + return var; \ + } +TINYGLTF_VALUE_GET(bool, boolean_value_) +TINYGLTF_VALUE_GET(double, real_value_) +TINYGLTF_VALUE_GET(int, int_value_) +TINYGLTF_VALUE_GET(std::string, string_value_) +TINYGLTF_VALUE_GET(std::vector, binary_value_) +TINYGLTF_VALUE_GET(Value::Array, array_value_) +TINYGLTF_VALUE_GET(Value::Object, object_value_) +#undef TINYGLTF_VALUE_GET + +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wc++98-compat" +#pragma clang diagnostic ignored "-Wpadded" +#endif + +/// Aggregate object for representing a color +using ColorValue = std::array; + +// === legacy interface ==== +// TODO(syoyo): Deprecate `Parameter` class. +struct Parameter { + bool bool_value = false; + bool has_number_value = false; + std::string string_value; + std::vector number_array; + std::map json_double_value; + double number_value = 0.0; + + // context sensitive methods. depending the type of the Parameter you are + // accessing, these are either valid or not + // If this parameter represent a texture map in a material, will return the + // texture index + + /// Return the index of a texture if this Parameter is a texture map. + /// Returned value is only valid if the parameter represent a texture from a + /// material + int TextureIndex() const { + const auto it = json_double_value.find("index"); + if (it != std::end(json_double_value)) { + return int(it->second); + } + return -1; + } + + /// Return the index of a texture coordinate set if this Parameter is a + /// texture map. Returned value is only valid if the parameter represent a + /// texture from a material + int TextureTexCoord() const { + const auto it = json_double_value.find("texCoord"); + if (it != std::end(json_double_value)) { + return int(it->second); + } + // As per the spec, if texCoord is omitted, this parameter is 0 + return 0; + } + + /// Return the scale of a texture if this Parameter is a normal texture map. + /// Returned value is only valid if the parameter represent a normal texture + /// from a material + double TextureScale() const { + const auto it = json_double_value.find("scale"); + if (it != std::end(json_double_value)) { + return it->second; + } + // As per the spec, if scale is omitted, this parameter is 1 + return 1; + } + + /// Return the strength of a texture if this Parameter is a an occlusion map. + /// Returned value is only valid if the parameter represent an occlusion map + /// from a material + double TextureStrength() const { + const auto it = json_double_value.find("strength"); + if (it != std::end(json_double_value)) { + return it->second; + } + // As per the spec, if strength is omitted, this parameter is 1 + return 1; + } + + /// Material factor, like the roughness or metalness of a material + /// Returned value is only valid if the parameter represent a texture from a + /// material + double Factor() const { return number_value; } + + /// Return the color of a material + /// Returned value is only valid if the parameter represent a texture from a + /// material + ColorValue ColorFactor() const { + return { + {// this aggregate initialize the std::array object, and uses C++11 RVO. + number_array[0], number_array[1], number_array[2], + (number_array.size() > 3 ? number_array[3] : 1.0)}}; + } + + Parameter() = default; + DEFAULT_METHODS(Parameter) + bool operator==(const Parameter &) const; +}; + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wpadded" +#endif + +typedef std::map ParameterMap; +typedef std::map ExtensionMap; + +struct AnimationChannel { + int sampler{-1}; // required + int target_node{-1}; // optional index of the node to target (alternative + // target should be provided by extension) + std::string target_path; // required with standard values of ["translation", + // "rotation", "scale", "weights"] + Value extras; + ExtensionMap extensions; + Value target_extras; + ExtensionMap target_extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + std::string target_extras_json_string; + std::string target_extensions_json_string; + + AnimationChannel() = default; + DEFAULT_METHODS(AnimationChannel) + bool operator==(const AnimationChannel &) const; +}; + +struct AnimationSampler { + int input{-1}; // required + int output{-1}; // required + std::string interpolation; // "LINEAR", "STEP","CUBICSPLINE" or user defined + // string. default "LINEAR" + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + AnimationSampler() : interpolation("LINEAR") {} + DEFAULT_METHODS(AnimationSampler) + bool operator==(const AnimationSampler &) const; +}; + +struct Animation { + std::string name; + std::vector channels; + std::vector samplers; + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Animation() = default; + DEFAULT_METHODS(Animation) + bool operator==(const Animation &) const; +}; + +struct Skin { + std::string name; + int inverseBindMatrices{-1}; // required here but not in the spec + int skeleton{-1}; // The index of the node used as a skeleton root + std::vector joints; // Indices of skeleton nodes + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Skin() = default; + DEFAULT_METHODS(Skin) + bool operator==(const Skin &) const; +}; + +struct Sampler { + std::string name; + // glTF 2.0 spec does not define default value for `minFilter` and + // `magFilter`. Set -1 in TinyGLTF(issue #186) + int minFilter = + -1; // optional. -1 = no filter defined. ["NEAREST", "LINEAR", + // "NEAREST_MIPMAP_NEAREST", "LINEAR_MIPMAP_NEAREST", + // "NEAREST_MIPMAP_LINEAR", "LINEAR_MIPMAP_LINEAR"] + int magFilter = + -1; // optional. -1 = no filter defined. ["NEAREST", "LINEAR"] + int wrapS = + TINYGLTF_TEXTURE_WRAP_REPEAT; // ["CLAMP_TO_EDGE", "MIRRORED_REPEAT", + // "REPEAT"], default "REPEAT" + int wrapT = + TINYGLTF_TEXTURE_WRAP_REPEAT; // ["CLAMP_TO_EDGE", "MIRRORED_REPEAT", + // "REPEAT"], default "REPEAT" + // int wrapR = TINYGLTF_TEXTURE_WRAP_REPEAT; // TinyGLTF extension. currently + // not used. + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Sampler() = default; + DEFAULT_METHODS(Sampler) + bool operator==(const Sampler &) const; +}; + +struct Image { + std::string name; + int width{-1}; + int height{-1}; + int component{-1}; + int bits{-1}; // bit depth per channel. 8(byte), 16 or 32. + int pixel_type{-1}; // pixel type(TINYGLTF_COMPONENT_TYPE_***). usually + // UBYTE(bits = 8) or USHORT(bits = 16) + std::vector image; + int bufferView{-1}; // (required if no uri) + std::string mimeType; // (required if no uri) ["image/jpeg", "image/png", + // "image/bmp", "image/gif"] + std::string uri; // (required if no mimeType) uri is not decoded(e.g. + // whitespace may be represented as %20) + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + // When this flag is true, data is stored to `image` in as-is format(e.g. jpeg + // compressed for "image/jpeg" mime) This feature is good if you use custom + // image loader function. (e.g. delayed decoding of images for faster glTF + // parsing) Default parser for Image does not provide as-is loading feature at + // the moment. (You can manipulate this by providing your own LoadImageData + // function) + bool as_is{false}; + + Image() = default; + DEFAULT_METHODS(Image) + + bool operator==(const Image &) const; +}; + +struct Texture { + std::string name; + + int sampler{-1}; + int source{-1}; + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Texture() = default; + DEFAULT_METHODS(Texture) + + bool operator==(const Texture &) const; +}; + +struct TextureInfo { + int index{-1}; // required. + int texCoord{0}; // The set index of texture's TEXCOORD attribute used for + // texture coordinate mapping. + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + TextureInfo() = default; + DEFAULT_METHODS(TextureInfo) + bool operator==(const TextureInfo &) const; +}; + +struct NormalTextureInfo { + int index{-1}; // required + int texCoord{0}; // The set index of texture's TEXCOORD attribute used for + // texture coordinate mapping. + double scale{ + 1.0}; // scaledNormal = normalize(( + // * 2.0 - 1.0) * vec3(, , 1.0)) + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + NormalTextureInfo() = default; + DEFAULT_METHODS(NormalTextureInfo) + bool operator==(const NormalTextureInfo &) const; +}; + +struct OcclusionTextureInfo { + int index{-1}; // required + int texCoord{0}; // The set index of texture's TEXCOORD attribute used for + // texture coordinate mapping. + double strength{1.0}; // occludedColor = lerp(color, color * , ) + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + OcclusionTextureInfo() = default; + DEFAULT_METHODS(OcclusionTextureInfo) + bool operator==(const OcclusionTextureInfo &) const; +}; + +// pbrMetallicRoughness class defined in glTF 2.0 spec. +struct PbrMetallicRoughness { + std::vector baseColorFactor; // len = 4. default [1,1,1,1] + TextureInfo baseColorTexture; + double metallicFactor{1.0}; // default 1 + double roughnessFactor{1.0}; // default 1 + TextureInfo metallicRoughnessTexture; + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + PbrMetallicRoughness() + : baseColorFactor(std::vector{1.0, 1.0, 1.0, 1.0}) {} + DEFAULT_METHODS(PbrMetallicRoughness) + bool operator==(const PbrMetallicRoughness &) const; +}; + +// Each extension should be stored in a ParameterMap. +// members not in the values could be included in the ParameterMap +// to keep a single material model +struct Material { + std::string name; + + std::vector emissiveFactor; // length 3. default [0, 0, 0] + std::string alphaMode; // default "OPAQUE" + double alphaCutoff{0.5}; // default 0.5 + bool doubleSided{false}; // default false; + + PbrMetallicRoughness pbrMetallicRoughness; + + NormalTextureInfo normalTexture; + OcclusionTextureInfo occlusionTexture; + TextureInfo emissiveTexture; + + // For backward compatibility + // TODO(syoyo): Remove `values` and `additionalValues` in the next release. + ParameterMap values; + ParameterMap additionalValues; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Material() : alphaMode("OPAQUE") {} + DEFAULT_METHODS(Material) + + bool operator==(const Material &) const; +}; + +struct BufferView { + std::string name; + int buffer{-1}; // Required + size_t byteOffset{0}; // minimum 0, default 0 + size_t byteLength{0}; // required, minimum 1. 0 = invalid + size_t byteStride{0}; // minimum 4, maximum 252 (multiple of 4), default 0 = + // understood to be tightly packed + int target{0}; // ["ARRAY_BUFFER", "ELEMENT_ARRAY_BUFFER"] for vertex indices + // or attribs. Could be 0 for other data + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + bool dracoDecoded{false}; // Flag indicating this has been draco decoded + + BufferView() = default; + DEFAULT_METHODS(BufferView) + bool operator==(const BufferView &) const; +}; + +struct Accessor { + int bufferView{-1}; // optional in spec but required here since sparse + // accessor are not supported + std::string name; + size_t byteOffset{0}; + bool normalized{false}; // optional. + int componentType{-1}; // (required) One of TINYGLTF_COMPONENT_TYPE_*** + size_t count{0}; // required + int type{-1}; // (required) One of TINYGLTF_TYPE_*** .. + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + std::vector + minValues; // optional. integer value is promoted to double + std::vector + maxValues; // optional. integer value is promoted to double + + struct Sparse { + int count; + bool isSparse; + struct { + size_t byteOffset; + int bufferView; + int componentType; // a TINYGLTF_COMPONENT_TYPE_ value + Value extras; + ExtensionMap extensions; + std::string extras_json_string; + std::string extensions_json_string; + } indices; + struct { + int bufferView; + size_t byteOffset; + Value extras; + ExtensionMap extensions; + std::string extras_json_string; + std::string extensions_json_string; + } values; + Value extras; + ExtensionMap extensions; + std::string extras_json_string; + std::string extensions_json_string; + }; + + Sparse sparse; + + /// + /// Utility function to compute byteStride for a given bufferView object. + /// Returns -1 upon invalid glTF value or parameter configuration. + /// + int ByteStride(const BufferView &bufferViewObject) const { + if (bufferViewObject.byteStride == 0) { + // Assume data is tightly packed. + int componentSizeInBytes = + GetComponentSizeInBytes(static_cast(componentType)); + if (componentSizeInBytes <= 0) { + return -1; + } + + int numComponents = GetNumComponentsInType(static_cast(type)); + if (numComponents <= 0) { + return -1; + } + + return componentSizeInBytes * numComponents; + } else { + // Check if byteStride is a multiple of the size of the accessor's + // component type. + int componentSizeInBytes = + GetComponentSizeInBytes(static_cast(componentType)); + if (componentSizeInBytes <= 0) { + return -1; + } + + if ((bufferViewObject.byteStride % uint32_t(componentSizeInBytes)) != 0) { + return -1; + } + return static_cast(bufferViewObject.byteStride); + } + + // unreachable return 0; + } + + Accessor() + + { + sparse.isSparse = false; + } + DEFAULT_METHODS(Accessor) + bool operator==(const tinygltf::Accessor &) const; +}; + +struct PerspectiveCamera { + double aspectRatio{0.0}; // min > 0 + double yfov{0.0}; // required. min > 0 + double zfar{0.0}; // min > 0 + double znear{0.0}; // required. min > 0 + + PerspectiveCamera() = default; + DEFAULT_METHODS(PerspectiveCamera) + bool operator==(const PerspectiveCamera &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct OrthographicCamera { + double xmag{0.0}; // required. must not be zero. + double ymag{0.0}; // required. must not be zero. + double zfar{0.0}; // required. `zfar` must be greater than `znear`. + double znear{0.0}; // required + + OrthographicCamera() = default; + DEFAULT_METHODS(OrthographicCamera) + bool operator==(const OrthographicCamera &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct Camera { + std::string type; // required. "perspective" or "orthographic" + std::string name; + + PerspectiveCamera perspective; + OrthographicCamera orthographic; + + Camera() = default; + DEFAULT_METHODS(Camera) + bool operator==(const Camera &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct Primitive { + std::map attributes; // (required) A dictionary object of + // integer, where each integer + // is the index of the accessor + // containing an attribute. + int material{-1}; // The index of the material to apply to this primitive + // when rendering. + int indices{-1}; // The index of the accessor that contains the indices. + int mode{-1}; // one of TINYGLTF_MODE_*** + std::vector > targets; // array of morph targets, + // where each target is a dict with attributes in ["POSITION, "NORMAL", + // "TANGENT"] pointing + // to their corresponding accessors + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Primitive() = default; + DEFAULT_METHODS(Primitive) + bool operator==(const Primitive &) const; +}; + +struct Mesh { + std::string name; + std::vector primitives; + std::vector weights; // weights to be applied to the Morph Targets + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Mesh() = default; + DEFAULT_METHODS(Mesh) + bool operator==(const Mesh &) const; +}; + +class Node { + public: + Node() = default; + + DEFAULT_METHODS(Node) + + bool operator==(const Node &) const; + + int camera{-1}; // the index of the camera referenced by this node + + std::string name; + int skin{-1}; + int mesh{-1}; + int light{-1}; // light source index (KHR_lights_punctual) + int emitter{-1}; // audio emitter index (KHR_audio) + std::vector children; + std::vector rotation; // length must be 0 or 4 + std::vector scale; // length must be 0 or 3 + std::vector translation; // length must be 0 or 3 + std::vector matrix; // length must be 0 or 16 + std::vector weights; // The weights of the instantiated Morph Target + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct Buffer { + std::string name; + std::vector data; + std::string + uri; // considered as required here but not in the spec (need to clarify) + // uri is not decoded(e.g. whitespace may be represented as %20) + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Buffer() = default; + DEFAULT_METHODS(Buffer) + bool operator==(const Buffer &) const; +}; + +struct Asset { + std::string version = "2.0"; // required + std::string generator; + std::string minVersion; + std::string copyright; + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Asset() = default; + DEFAULT_METHODS(Asset) + bool operator==(const Asset &) const; +}; + +struct Scene { + std::string name; + std::vector nodes; + std::vector audioEmitters; // KHR_audio global emitters + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; + + Scene() = default; + DEFAULT_METHODS(Scene) + bool operator==(const Scene &) const; +}; + +struct SpotLight { + double innerConeAngle{0.0}; + double outerConeAngle{0.7853981634}; + + SpotLight() = default; + DEFAULT_METHODS(SpotLight) + bool operator==(const SpotLight &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct Light { + std::string name; + std::vector color; + double intensity{1.0}; + std::string type; + double range{0.0}; // 0.0 = infinite + SpotLight spot; + + Light() = default; + DEFAULT_METHODS(Light) + + bool operator==(const Light &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct PositionalEmitter { + double coneInnerAngle{6.283185307179586}; + double coneOuterAngle{6.283185307179586}; + double coneOuterGain{0.0}; + double maxDistance{100.0}; + double refDistance{1.0}; + double rolloffFactor{1.0}; + + PositionalEmitter() = default; + DEFAULT_METHODS(PositionalEmitter) + bool operator==(const PositionalEmitter &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct AudioEmitter { + std::string name; + double gain{1.0}; + bool loop{false}; + bool playing{false}; + std::string + type; // positional - Positional audio emitters. Using sound cones, the + // orientation is +Z having the same front side for a glTF asset. + // global - Global audio emitters are not affected by the position + // of audio listeners. coneInnerAngle, coneOuterAngle, + // coneOuterGain, distanceModel, maxDistance, refDistance, and + // rolloffFactor should all be ignored when set. + std::string + distanceModel; // linear - A linear distance model calculating the + // gain induced by the distance according to: 1.0 + // - rolloffFactor * (distance - refDistance) / + // (maxDistance - refDistance) + // inverse - (default) An inverse distance model + // calculating the gain induced by the distance according + // to: refDistance / (refDistance + rolloffFactor * + // (Math.max(distance, refDistance) - refDistance)) + // exponential - An exponential distance model calculating + // the gain induced by the distance according to: + // pow((Math.max(distance, refDistance) / refDistance, + // -rolloffFactor)) + PositionalEmitter positional; + int source{-1}; + + AudioEmitter() : type("global"), distanceModel("inverse") {} + DEFAULT_METHODS(AudioEmitter) + + bool operator==(const AudioEmitter &) const; + + ExtensionMap extensions; + Value extras; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +struct AudioSource { + std::string name; + std::string uri; + int bufferView{-1}; // (required if no uri) + std::string + mimeType; // (required if no uri) The audio's MIME type. Required if + // bufferView is defined. Unless specified by another + // extension, the only supported mimeType is audio/mpeg. + + AudioSource() = default; + DEFAULT_METHODS(AudioSource) + + bool operator==(const AudioSource &) const; + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +class Model { + public: + Model() = default; + DEFAULT_METHODS(Model) + + bool operator==(const Model &) const; + + std::vector accessors; + std::vector animations; + std::vector buffers; + std::vector bufferViews; + std::vector materials; + std::vector meshes; + std::vector nodes; + std::vector textures; + std::vector images; + std::vector skins; + std::vector samplers; + std::vector cameras; + std::vector scenes; + std::vector lights; + std::vector audioEmitters; + std::vector audioSources; + + int defaultScene{-1}; + std::vector extensionsUsed; + std::vector extensionsRequired; + + Asset asset; + + Value extras; + ExtensionMap extensions; + + // Filled when SetStoreOriginalJSONForExtrasAndExtensions is enabled. + std::string extras_json_string; + std::string extensions_json_string; +}; + +enum SectionCheck { + NO_REQUIRE = 0x00, + REQUIRE_VERSION = 0x01, + REQUIRE_SCENE = 0x02, + REQUIRE_SCENES = 0x04, + REQUIRE_NODES = 0x08, + REQUIRE_ACCESSORS = 0x10, + REQUIRE_BUFFERS = 0x20, + REQUIRE_BUFFER_VIEWS = 0x40, + REQUIRE_ALL = 0x7f +}; + +/// +/// URIEncodeFunction type. Signature for custom URI encoding of external +/// resources such as .bin and image files. Used by tinygltf to re-encode the +/// final location of saved files. object_type may be used to encode buffer and +/// image URIs differently, for example. See +/// https://registry.khronos.org/glTF/specs/2.0/glTF-2.0.html#uris +/// +typedef bool (*URIEncodeFunction)(const std::string &in_uri, + const std::string &object_type, + std::string *out_uri, void *user_data); + +/// +/// URIDecodeFunction type. Signature for custom URI decoding of external +/// resources such as .bin and image files. Used by tinygltf when computing +/// filenames to write resources. +/// +typedef bool (*URIDecodeFunction)(const std::string &in_uri, + std::string *out_uri, void *user_data); + +// Declaration of default uri decode function +bool URIDecode(const std::string &in_uri, std::string *out_uri, + void *user_data); + +/// +/// A structure containing URI callbacks and a pointer to their user data. +/// +struct URICallbacks { + URIEncodeFunction encode; // Optional encode method + URIDecodeFunction decode; // Required decode method + + void *user_data; // An argument that is passed to all uri callbacks +}; + +/// +/// LoadImageDataFunction type. Signature for custom image loading callbacks. +/// +typedef bool (*LoadImageDataFunction)(Image *, const int, std::string *, + std::string *, int, int, + const unsigned char *, int, + void *user_pointer); + +/// +/// WriteImageDataFunction type. Signature for custom image writing callbacks. +/// The out_uri parameter becomes the URI written to the gltf and may reference +/// a file or contain a data URI. +/// +typedef bool (*WriteImageDataFunction)(const std::string *basepath, + const std::string *filename, + const Image *image, bool embedImages, + const URICallbacks *uri_cb, + std::string *out_uri, + void *user_pointer); + +#ifndef TINYGLTF_NO_STB_IMAGE +// Declaration of default image loader callback +bool LoadImageData(Image *image, const int image_idx, std::string *err, + std::string *warn, int req_width, int req_height, + const unsigned char *bytes, int size, void *); +#endif + +#ifndef TINYGLTF_NO_STB_IMAGE_WRITE +// Declaration of default image writer callback +bool WriteImageData(const std::string *basepath, const std::string *filename, + const Image *image, bool embedImages, + const URICallbacks *uri_cb, std::string *out_uri, void *); +#endif + +/// +/// FilExistsFunction type. Signature for custom filesystem callbacks. +/// +typedef bool (*FileExistsFunction)(const std::string &abs_filename, void *); + +/// +/// ExpandFilePathFunction type. Signature for custom filesystem callbacks. +/// +typedef std::string (*ExpandFilePathFunction)(const std::string &, void *); + +/// +/// ReadWholeFileFunction type. Signature for custom filesystem callbacks. +/// +typedef bool (*ReadWholeFileFunction)(std::vector *, + std::string *, const std::string &, + void *); + +/// +/// WriteWholeFileFunction type. Signature for custom filesystem callbacks. +/// +typedef bool (*WriteWholeFileFunction)(std::string *, const std::string &, + const std::vector &, + void *); + +/// +/// GetFileSizeFunction type. Signature for custom filesystem callbacks. +/// +typedef bool (*GetFileSizeFunction)(size_t *filesize_out, std::string *err, + const std::string &abs_filename, + void *userdata); + +/// +/// A structure containing all required filesystem callbacks and a pointer to +/// their user data. +/// +struct FsCallbacks { + FileExistsFunction FileExists; + ExpandFilePathFunction ExpandFilePath; + ReadWholeFileFunction ReadWholeFile; + WriteWholeFileFunction WriteWholeFile; + GetFileSizeFunction GetFileSizeInBytes; // To avoid GetFileSize Win32 API, + // add `InBytes` suffix. + + void *user_data; // An argument that is passed to all fs callbacks +}; + +#ifndef TINYGLTF_NO_FS +// Declaration of default filesystem callbacks + +bool FileExists(const std::string &abs_filename, void *); + +/// +/// Expand file path(e.g. `~` to home directory on posix, `%APPDATA%` to +/// `C:\\Users\\tinygltf\\AppData`) +/// +/// @param[in] filepath File path string. Assume UTF-8 +/// @param[in] userdata User data. Set to `nullptr` if you don't need it. +/// +std::string ExpandFilePath(const std::string &filepath, void *userdata); + +bool ReadWholeFile(std::vector *out, std::string *err, + const std::string &filepath, void *); + +bool WriteWholeFile(std::string *err, const std::string &filepath, + const std::vector &contents, void *); + +bool GetFileSizeInBytes(size_t *filesize_out, std::string *err, + const std::string &filepath, void *); +#endif + +/// +/// glTF Parser/Serializer context. +/// +class TinyGLTF { + public: +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wc++98-compat" +#endif + + TinyGLTF() = default; + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + + ~TinyGLTF() = default; + + /// + /// Loads glTF ASCII asset from a file. + /// Set warning message to `warn` for example it fails to load asserts. + /// Returns false and set error string to `err` if there's an error. + /// + bool LoadASCIIFromFile(Model *model, std::string *err, std::string *warn, + const std::string &filename, + unsigned int check_sections = REQUIRE_VERSION); + + /// + /// Loads glTF ASCII asset from string(memory). + /// `length` = strlen(str); + /// `base_dir` is a search path of glTF asset(e.g. images). Path Must be an + /// expanded path (e.g. no tilde(`~`), no environment variables). Set warning + /// message to `warn` for example it fails to load asserts. Returns false and + /// set error string to `err` if there's an error. + /// + bool LoadASCIIFromString(Model *model, std::string *err, std::string *warn, + const char *str, const unsigned int length, + const std::string &base_dir, + unsigned int check_sections = REQUIRE_VERSION); + + /// + /// Loads glTF binary asset from a file. + /// Set warning message to `warn` for example it fails to load asserts. + /// Returns false and set error string to `err` if there's an error. + /// + bool LoadBinaryFromFile(Model *model, std::string *err, std::string *warn, + const std::string &filename, + unsigned int check_sections = REQUIRE_VERSION); + + /// + /// Loads glTF binary asset from memory. + /// `length` = strlen(str); + /// `base_dir` is a search path of glTF asset(e.g. images). Path Must be an + /// expanded path (e.g. no tilde(`~`), no environment variables). + /// Set warning message to `warn` for example it fails to load asserts. + /// Returns false and set error string to `err` if there's an error. + /// + bool LoadBinaryFromMemory(Model *model, std::string *err, std::string *warn, + const unsigned char *bytes, + const unsigned int length, + const std::string &base_dir = "", + unsigned int check_sections = REQUIRE_VERSION); + + /// + /// Write glTF to stream, buffers and images will be embedded + /// + bool WriteGltfSceneToStream(const Model *model, std::ostream &stream, + bool prettyPrint, bool writeBinary); + + /// + /// Write glTF to file. + /// + bool WriteGltfSceneToFile(const Model *model, const std::string &filename, + bool embedImages, bool embedBuffers, + bool prettyPrint, bool writeBinary); + + /// + /// Sets the parsing strictness. + /// + void SetParseStrictness(ParseStrictness strictness); + + /// + /// Set callback to use for loading image data + /// + void SetImageLoader(LoadImageDataFunction LoadImageData, void *user_data); + + /// + /// Unset(remove) callback of loading image data + /// + void RemoveImageLoader(); + + /// + /// Set callback to use for writing image data + /// + void SetImageWriter(WriteImageDataFunction WriteImageData, void *user_data); + + /// + /// Set callbacks to use for URI encoding and decoding and their user data + /// + void SetURICallbacks(URICallbacks callbacks); + + /// + /// Set callbacks to use for filesystem (fs) access and their user data + /// + void SetFsCallbacks(FsCallbacks callbacks); + + /// + /// Set serializing default values(default = false). + /// When true, default values are force serialized to .glTF. + /// This may be helpful if you want to serialize a full description of glTF + /// data. + /// + /// TODO(LTE): Supply parsing option as function arguments to + /// `LoadASCIIFromFile()` and others, not by a class method + /// + void SetSerializeDefaultValues(const bool enabled) { + serialize_default_values_ = enabled; + } + + bool GetSerializeDefaultValues() const { return serialize_default_values_; } + + /// + /// Store original JSON string for `extras` and `extensions`. + /// This feature will be useful when the user want to reconstruct custom data + /// structure from JSON string. + /// + void SetStoreOriginalJSONForExtrasAndExtensions(const bool enabled) { + store_original_json_for_extras_and_extensions_ = enabled; + } + + bool GetStoreOriginalJSONForExtrasAndExtensions() const { + return store_original_json_for_extras_and_extensions_; + } + + /// + /// Specify whether preserve image channels when loading images or not. + /// (Not effective when the user supplies their own LoadImageData callbacks) + /// + void SetPreserveImageChannels(bool onoff) { + preserve_image_channels_ = onoff; + } + + /// + /// Set maximum allowed external file size in bytes. + /// Default: 2GB + /// Only effective for built-in ReadWholeFileFunction FS function. + /// + void SetMaxExternalFileSize(size_t max_bytes) { + max_external_file_size_ = max_bytes; + } + + size_t GetMaxExternalFileSize() const { return max_external_file_size_; } + + bool GetPreserveImageChannels() const { return preserve_image_channels_; } + + private: + /// + /// Loads glTF asset from string(memory). + /// `length` = strlen(str); + /// Set warning message to `warn` for example it fails to load asserts + /// Returns false and set error string to `err` if there's an error. + /// + bool LoadFromString(Model *model, std::string *err, std::string *warn, + const char *str, const unsigned int length, + const std::string &base_dir, unsigned int check_sections); + + const unsigned char *bin_data_ = nullptr; + size_t bin_size_ = 0; + bool is_binary_ = false; + + ParseStrictness strictness_ = ParseStrictness::Strict; + + bool serialize_default_values_ = false; ///< Serialize default values? + + bool store_original_json_for_extras_and_extensions_ = false; + + bool preserve_image_channels_ = false; /// Default false(expand channels to + /// RGBA) for backward compatibility. + + size_t max_external_file_size_{ + size_t((std::numeric_limits::max)())}; // Default 2GB + + // Warning & error messages + std::string warn_; + std::string err_; + + FsCallbacks fs = { +#ifndef TINYGLTF_NO_FS + &tinygltf::FileExists, + &tinygltf::ExpandFilePath, + &tinygltf::ReadWholeFile, + &tinygltf::WriteWholeFile, + &tinygltf::GetFileSizeInBytes, + + nullptr // Fs callback user data +#else + nullptr, nullptr, nullptr, nullptr, nullptr, + + nullptr // Fs callback user data +#endif + }; + + URICallbacks uri_cb = { + // Use paths as-is by default. This will use JSON string escaping. + nullptr, + // Decode all URIs before using them as paths as the application may have + // percent encoded them. + &tinygltf::URIDecode, + // URI callback user data + nullptr}; + + LoadImageDataFunction LoadImageData = +#ifndef TINYGLTF_NO_STB_IMAGE + &tinygltf::LoadImageData; +#else + nullptr; +#endif + void *load_image_user_data_{nullptr}; + bool user_image_loader_{false}; + + WriteImageDataFunction WriteImageData = +#ifndef TINYGLTF_NO_STB_IMAGE_WRITE + &tinygltf::WriteImageData; +#else + nullptr; +#endif + void *write_image_user_data_{nullptr}; +}; + +#ifdef __clang__ +#pragma clang diagnostic pop // -Wpadded +#endif + +} // namespace tinygltf + +#endif // TINY_GLTF_H_ + +#if defined(TINYGLTF_IMPLEMENTATION) || defined(__INTELLISENSE__) +#include +// #include +#ifndef TINYGLTF_NO_FS +#include // for is_directory check + +#include +#include +#endif +#include + +#ifdef __clang__ +// Disable some warnings for external files. +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wfloat-equal" +#pragma clang diagnostic ignored "-Wexit-time-destructors" +#pragma clang diagnostic ignored "-Wconversion" +#pragma clang diagnostic ignored "-Wold-style-cast" +#pragma clang diagnostic ignored "-Wglobal-constructors" +#if __has_warning("-Wreserved-id-macro") +#pragma clang diagnostic ignored "-Wreserved-id-macro" +#endif +#pragma clang diagnostic ignored "-Wdisabled-macro-expansion" +#pragma clang diagnostic ignored "-Wpadded" +#pragma clang diagnostic ignored "-Wc++98-compat" +#pragma clang diagnostic ignored "-Wc++98-compat-pedantic" +#pragma clang diagnostic ignored "-Wdocumentation-unknown-command" +#pragma clang diagnostic ignored "-Wswitch-enum" +#pragma clang diagnostic ignored "-Wimplicit-fallthrough" +#pragma clang diagnostic ignored "-Wweak-vtables" +#pragma clang diagnostic ignored "-Wcovered-switch-default" +#if __has_warning("-Wdouble-promotion") +#pragma clang diagnostic ignored "-Wdouble-promotion" +#endif +#if __has_warning("-Wcomma") +#pragma clang diagnostic ignored "-Wcomma" +#endif +#if __has_warning("-Wzero-as-null-pointer-constant") +#pragma clang diagnostic ignored "-Wzero-as-null-pointer-constant" +#endif +#if __has_warning("-Wcast-qual") +#pragma clang diagnostic ignored "-Wcast-qual" +#endif +#if __has_warning("-Wmissing-variable-declarations") +#pragma clang diagnostic ignored "-Wmissing-variable-declarations" +#endif +#if __has_warning("-Wmissing-prototypes") +#pragma clang diagnostic ignored "-Wmissing-prototypes" +#endif +#if __has_warning("-Wcast-align") +#pragma clang diagnostic ignored "-Wcast-align" +#endif +#if __has_warning("-Wnewline-eof") +#pragma clang diagnostic ignored "-Wnewline-eof" +#endif +#if __has_warning("-Wunused-parameter") +#pragma clang diagnostic ignored "-Wunused-parameter" +#endif +#if __has_warning("-Wmismatched-tags") +#pragma clang diagnostic ignored "-Wmismatched-tags" +#endif +#if __has_warning("-Wextra-semi-stmt") +#pragma clang diagnostic ignored "-Wextra-semi-stmt" +#endif +#endif + +// Disable GCC warnings +#ifdef __GNUC__ +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wtype-limits" +#endif // __GNUC__ + +#ifndef TINYGLTF_NO_INCLUDE_JSON +#ifndef TINYGLTF_USE_RAPIDJSON +#include "json.hpp" +#else +#ifndef TINYGLTF_NO_INCLUDE_RAPIDJSON +#include "document.h" +#include "prettywriter.h" +#include "rapidjson.h" +#include "stringbuffer.h" +#include "writer.h" +#endif +#endif +#endif + +#ifdef TINYGLTF_ENABLE_DRACO +#include "draco/compression/decode.h" +#include "draco/core/decoder_buffer.h" +#endif + +#ifndef TINYGLTF_NO_STB_IMAGE +#ifndef TINYGLTF_NO_INCLUDE_STB_IMAGE +#include "stb_image.h" +#endif +#endif + +#ifndef TINYGLTF_NO_STB_IMAGE_WRITE +#ifndef TINYGLTF_NO_INCLUDE_STB_IMAGE_WRITE +#include "stb_image_write.h" +#endif +#endif + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +#ifdef __GNUC__ +#pragma GCC diagnostic pop +#endif + +#ifdef _WIN32 + +// issue 143. +// Define NOMINMAX to avoid min/max defines, +// but undef it after included Windows.h +#ifndef NOMINMAX +#define TINYGLTF_INTERNAL_NOMINMAX +#define NOMINMAX +#endif + +#ifndef WIN32_LEAN_AND_MEAN +#define WIN32_LEAN_AND_MEAN +#define TINYGLTF_INTERNAL_WIN32_LEAN_AND_MEAN +#endif +#ifndef __MINGW32__ +#include // include API for expanding a file path +#else +#include +#endif + +#ifdef TINYGLTF_INTERNAL_WIN32_LEAN_AND_MEAN +#undef WIN32_LEAN_AND_MEAN +#endif + +#if defined(TINYGLTF_INTERNAL_NOMINMAX) +#undef NOMINMAX +#endif + +#if defined(__GLIBCXX__) // mingw + +#include // _O_RDONLY + +#include // fstream (all sorts of IO stuff) + stdio_filebuf (=streambuf) + +#endif + +#elif !defined(__ANDROID__) && !defined(__OpenBSD__) +// #include +#endif + +#if defined(__sparcv9) || defined(__powerpc__) +// Big endian +#else +#if (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) || MINIZ_X86_OR_X64_CPU +#define TINYGLTF_LITTLE_ENDIAN 1 +#endif +#endif + +namespace tinygltf { +namespace detail { +#ifdef TINYGLTF_USE_RAPIDJSON + +#ifdef TINYGLTF_USE_RAPIDJSON_CRTALLOCATOR +// This uses the RapidJSON CRTAllocator. It is thread safe and multiple +// documents may be active at once. +using json = + rapidjson::GenericValue, rapidjson::CrtAllocator>; +using json_iterator = json::MemberIterator; +using json_const_iterator = json::ConstMemberIterator; +using json_const_array_iterator = json const *; +using JsonDocument = + rapidjson::GenericDocument, rapidjson::CrtAllocator>; +rapidjson::CrtAllocator s_CrtAllocator; // stateless and thread safe +rapidjson::CrtAllocator &GetAllocator() { return s_CrtAllocator; } +#else +// This uses the default RapidJSON MemoryPoolAllocator. It is very fast, but +// not thread safe. Only a single JsonDocument may be active at any one time, +// meaning only a single gltf load/save can be active any one time. +using json = rapidjson::Value; +using json_iterator = json::MemberIterator; +using json_const_iterator = json::ConstMemberIterator; +using json_const_array_iterator = json const *; +rapidjson::Document *s_pActiveDocument = nullptr; +rapidjson::Document::AllocatorType &GetAllocator() { + assert(s_pActiveDocument); // Root json node must be JsonDocument type + return s_pActiveDocument->GetAllocator(); +} + +#ifdef __clang__ +#pragma clang diagnostic push +// Suppress JsonDocument(JsonDocument &&rhs) noexcept +#pragma clang diagnostic ignored "-Wunused-member-function" +#endif + +struct JsonDocument : public rapidjson::Document { + JsonDocument() { + assert(s_pActiveDocument == + nullptr); // When using default allocator, only one document can be + // active at a time, if you need multiple active at once, + // define TINYGLTF_USE_RAPIDJSON_CRTALLOCATOR + s_pActiveDocument = this; + } + JsonDocument(const JsonDocument &) = delete; + JsonDocument(JsonDocument &&rhs) noexcept + : rapidjson::Document(std::move(rhs)) { + s_pActiveDocument = this; + rhs.isNil = true; + } + ~JsonDocument() { + if (!isNil) { + s_pActiveDocument = nullptr; + } + } + + private: + bool isNil = false; +}; + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +#endif // TINYGLTF_USE_RAPIDJSON_CRTALLOCATOR + +#else +using nlohmann::json; +using json_iterator = json::iterator; +using json_const_iterator = json::const_iterator; +using json_const_array_iterator = json_const_iterator; +using JsonDocument = json; +#endif + +void JsonParse(JsonDocument &doc, const char *str, size_t length, + bool throwExc = false) { +#ifdef TINYGLTF_USE_RAPIDJSON + (void)throwExc; + doc.Parse(str, length); +#else + doc = detail::json::parse(str, str + length, nullptr, throwExc); +#endif +} +} // namespace detail +} // namespace tinygltf + +#ifdef __APPLE__ +#include "TargetConditionals.h" +#endif + +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wc++98-compat" +#endif + +namespace tinygltf { + +/// +/// Internal LoadImageDataOption struct. +/// This struct is passed through `user_pointer` in LoadImageData. +/// The struct is not passed when the user supply their own LoadImageData +/// callbacks. +/// +struct LoadImageDataOption { + // true: preserve image channels(e.g. load as RGB image if the image has RGB + // channels) default `false`(channels are expanded to RGBA for backward + // compatibility). + bool preserve_channels{false}; +}; + +// Equals function for Value, for recursivity +static bool Equals(const tinygltf::Value &one, const tinygltf::Value &other) { + if (one.Type() != other.Type()) return false; + + switch (one.Type()) { + case NULL_TYPE: + return true; + case BOOL_TYPE: + return one.Get() == other.Get(); + case REAL_TYPE: + return TINYGLTF_DOUBLE_EQUAL(one.Get(), other.Get()); + case INT_TYPE: + return one.Get() == other.Get(); + case OBJECT_TYPE: { + auto oneObj = one.Get(); + auto otherObj = other.Get(); + if (oneObj.size() != otherObj.size()) return false; + for (auto &it : oneObj) { + auto otherIt = otherObj.find(it.first); + if (otherIt == otherObj.end()) return false; + + if (!Equals(it.second, otherIt->second)) return false; + } + return true; + } + case ARRAY_TYPE: { + if (one.Size() != other.Size()) return false; + for (int i = 0; i < int(one.Size()); ++i) + if (!Equals(one.Get(i), other.Get(i))) return false; + return true; + } + case STRING_TYPE: + return one.Get() == other.Get(); + case BINARY_TYPE: + return one.Get >() == + other.Get >(); + default: { + // unhandled type + return false; + } + } +} + +// Equals function for std::vector using TINYGLTF_DOUBLE_EPSILON +static bool Equals(const std::vector &one, + const std::vector &other) { + if (one.size() != other.size()) return false; + for (int i = 0; i < int(one.size()); ++i) { + if (!TINYGLTF_DOUBLE_EQUAL(one[size_t(i)], other[size_t(i)])) return false; + } + return true; +} + +bool Accessor::operator==(const Accessor &other) const { + return this->bufferView == other.bufferView && + this->byteOffset == other.byteOffset && + this->componentType == other.componentType && + this->count == other.count && this->extensions == other.extensions && + this->extras == other.extras && + Equals(this->maxValues, other.maxValues) && + Equals(this->minValues, other.minValues) && this->name == other.name && + this->normalized == other.normalized && this->type == other.type; +} +bool Animation::operator==(const Animation &other) const { + return this->channels == other.channels && + this->extensions == other.extensions && this->extras == other.extras && + this->name == other.name && this->samplers == other.samplers; +} +bool AnimationChannel::operator==(const AnimationChannel &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->target_node == other.target_node && + this->target_path == other.target_path && + this->sampler == other.sampler; +} +bool AnimationSampler::operator==(const AnimationSampler &other) const { + return this->extras == other.extras && this->extensions == other.extensions && + this->input == other.input && + this->interpolation == other.interpolation && + this->output == other.output; +} +bool Asset::operator==(const Asset &other) const { + return this->copyright == other.copyright && + this->extensions == other.extensions && this->extras == other.extras && + this->generator == other.generator && + this->minVersion == other.minVersion && this->version == other.version; +} +bool Buffer::operator==(const Buffer &other) const { + return this->data == other.data && this->extensions == other.extensions && + this->extras == other.extras && this->name == other.name && + this->uri == other.uri; +} +bool BufferView::operator==(const BufferView &other) const { + return this->buffer == other.buffer && this->byteLength == other.byteLength && + this->byteOffset == other.byteOffset && + this->byteStride == other.byteStride && this->name == other.name && + this->target == other.target && this->extensions == other.extensions && + this->extras == other.extras && + this->dracoDecoded == other.dracoDecoded; +} +bool Camera::operator==(const Camera &other) const { + return this->name == other.name && this->extensions == other.extensions && + this->extras == other.extras && + this->orthographic == other.orthographic && + this->perspective == other.perspective && this->type == other.type; +} +bool Image::operator==(const Image &other) const { + return this->bufferView == other.bufferView && + this->component == other.component && + this->extensions == other.extensions && this->extras == other.extras && + this->height == other.height && this->image == other.image && + this->mimeType == other.mimeType && this->name == other.name && + this->uri == other.uri && this->width == other.width; +} +bool Light::operator==(const Light &other) const { + return Equals(this->color, other.color) && this->name == other.name && + this->type == other.type; +} +bool AudioEmitter::operator==(const AudioEmitter &other) const { + return this->name == other.name && + TINYGLTF_DOUBLE_EQUAL(this->gain, other.gain) && + this->loop == other.loop && this->playing == other.playing && + this->type == other.type && + this->distanceModel == other.distanceModel && + this->source == other.source; +} +bool AudioSource::operator==(const AudioSource &other) const { + return this->name == other.name && this->uri == other.uri; +} +bool Material::operator==(const Material &other) const { + return (this->pbrMetallicRoughness == other.pbrMetallicRoughness) && + (this->normalTexture == other.normalTexture) && + (this->occlusionTexture == other.occlusionTexture) && + (this->emissiveTexture == other.emissiveTexture) && + Equals(this->emissiveFactor, other.emissiveFactor) && + (this->alphaMode == other.alphaMode) && + TINYGLTF_DOUBLE_EQUAL(this->alphaCutoff, other.alphaCutoff) && + (this->doubleSided == other.doubleSided) && + (this->extensions == other.extensions) && + (this->extras == other.extras) && (this->values == other.values) && + (this->additionalValues == other.additionalValues) && + (this->name == other.name); +} +bool Mesh::operator==(const Mesh &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->name == other.name && Equals(this->weights, other.weights) && + this->primitives == other.primitives; +} +bool Model::operator==(const Model &other) const { + return this->accessors == other.accessors && + this->animations == other.animations && this->asset == other.asset && + this->buffers == other.buffers && + this->bufferViews == other.bufferViews && + this->cameras == other.cameras && + this->defaultScene == other.defaultScene && + this->extensions == other.extensions && + this->extensionsRequired == other.extensionsRequired && + this->extensionsUsed == other.extensionsUsed && + this->extras == other.extras && this->images == other.images && + this->lights == other.lights && this->materials == other.materials && + this->meshes == other.meshes && this->nodes == other.nodes && + this->samplers == other.samplers && this->scenes == other.scenes && + this->skins == other.skins && this->textures == other.textures; +} +bool Node::operator==(const Node &other) const { + return this->camera == other.camera && this->children == other.children && + this->extensions == other.extensions && this->extras == other.extras && + Equals(this->matrix, other.matrix) && this->mesh == other.mesh && + (this->light == other.light) && (this->emitter == other.emitter) && + this->name == other.name && Equals(this->rotation, other.rotation) && + Equals(this->scale, other.scale) && this->skin == other.skin && + Equals(this->translation, other.translation) && + Equals(this->weights, other.weights); +} +bool SpotLight::operator==(const SpotLight &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + TINYGLTF_DOUBLE_EQUAL(this->innerConeAngle, other.innerConeAngle) && + TINYGLTF_DOUBLE_EQUAL(this->outerConeAngle, other.outerConeAngle); +} +bool PositionalEmitter::operator==(const PositionalEmitter &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + TINYGLTF_DOUBLE_EQUAL(this->coneInnerAngle, other.coneInnerAngle) && + TINYGLTF_DOUBLE_EQUAL(this->coneOuterAngle, other.coneOuterAngle) && + TINYGLTF_DOUBLE_EQUAL(this->coneOuterGain, other.coneOuterGain) && + TINYGLTF_DOUBLE_EQUAL(this->maxDistance, other.maxDistance) && + TINYGLTF_DOUBLE_EQUAL(this->refDistance, other.refDistance) && + TINYGLTF_DOUBLE_EQUAL(this->rolloffFactor, other.rolloffFactor); +} +bool OrthographicCamera::operator==(const OrthographicCamera &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + TINYGLTF_DOUBLE_EQUAL(this->xmag, other.xmag) && + TINYGLTF_DOUBLE_EQUAL(this->ymag, other.ymag) && + TINYGLTF_DOUBLE_EQUAL(this->zfar, other.zfar) && + TINYGLTF_DOUBLE_EQUAL(this->znear, other.znear); +} +bool Parameter::operator==(const Parameter &other) const { + if (this->bool_value != other.bool_value || + this->has_number_value != other.has_number_value) + return false; + + if (!TINYGLTF_DOUBLE_EQUAL(this->number_value, other.number_value)) + return false; + + if (this->json_double_value.size() != other.json_double_value.size()) + return false; + for (auto &it : this->json_double_value) { + auto otherIt = other.json_double_value.find(it.first); + if (otherIt == other.json_double_value.end()) return false; + + if (!TINYGLTF_DOUBLE_EQUAL(it.second, otherIt->second)) return false; + } + + if (!Equals(this->number_array, other.number_array)) return false; + + if (this->string_value != other.string_value) return false; + + return true; +} +bool PerspectiveCamera::operator==(const PerspectiveCamera &other) const { + return TINYGLTF_DOUBLE_EQUAL(this->aspectRatio, other.aspectRatio) && + this->extensions == other.extensions && this->extras == other.extras && + TINYGLTF_DOUBLE_EQUAL(this->yfov, other.yfov) && + TINYGLTF_DOUBLE_EQUAL(this->zfar, other.zfar) && + TINYGLTF_DOUBLE_EQUAL(this->znear, other.znear); +} +bool Primitive::operator==(const Primitive &other) const { + return this->attributes == other.attributes && this->extras == other.extras && + this->indices == other.indices && this->material == other.material && + this->mode == other.mode && this->targets == other.targets; +} +bool Sampler::operator==(const Sampler &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->magFilter == other.magFilter && + this->minFilter == other.minFilter && this->name == other.name && + this->wrapS == other.wrapS && this->wrapT == other.wrapT; + + // this->wrapR == other.wrapR +} +bool Scene::operator==(const Scene &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->name == other.name && this->nodes == other.nodes; +} +bool Skin::operator==(const Skin &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->inverseBindMatrices == other.inverseBindMatrices && + this->joints == other.joints && this->name == other.name && + this->skeleton == other.skeleton; +} +bool Texture::operator==(const Texture &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->name == other.name && this->sampler == other.sampler && + this->source == other.source; +} +bool TextureInfo::operator==(const TextureInfo &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->index == other.index && this->texCoord == other.texCoord; +} +bool NormalTextureInfo::operator==(const NormalTextureInfo &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->index == other.index && this->texCoord == other.texCoord && + TINYGLTF_DOUBLE_EQUAL(this->scale, other.scale); +} +bool OcclusionTextureInfo::operator==(const OcclusionTextureInfo &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + this->index == other.index && this->texCoord == other.texCoord && + TINYGLTF_DOUBLE_EQUAL(this->strength, other.strength); +} +bool PbrMetallicRoughness::operator==(const PbrMetallicRoughness &other) const { + return this->extensions == other.extensions && this->extras == other.extras && + (this->baseColorTexture == other.baseColorTexture) && + (this->metallicRoughnessTexture == other.metallicRoughnessTexture) && + Equals(this->baseColorFactor, other.baseColorFactor) && + TINYGLTF_DOUBLE_EQUAL(this->metallicFactor, other.metallicFactor) && + TINYGLTF_DOUBLE_EQUAL(this->roughnessFactor, other.roughnessFactor); +} +bool Value::operator==(const Value &other) const { + return Equals(*this, other); +} + +static void swap4(unsigned int *val) { +#ifdef TINYGLTF_LITTLE_ENDIAN + (void)val; +#else + unsigned int tmp = *val; + unsigned char *dst = reinterpret_cast(val); + unsigned char *src = reinterpret_cast(&tmp); + + dst[0] = src[3]; + dst[1] = src[2]; + dst[2] = src[1]; + dst[3] = src[0]; +#endif +} + +static std::string JoinPath(const std::string &path0, + const std::string &path1) { + if (path0.empty()) { + return path1; + } else { + // check '/' + char lastChar = *path0.rbegin(); + if (lastChar != '/') { + return path0 + std::string("/") + path1; + } else { + return path0 + path1; + } + } +} + +static std::string FindFile(const std::vector &paths, + const std::string &filepath, FsCallbacks *fs) { + if (fs == nullptr || fs->ExpandFilePath == nullptr || + fs->FileExists == nullptr) { + // Error, fs callback[s] missing + return std::string(); + } + + // https://github.com/syoyo/tinygltf/issues/416 + // Use strlen() since std::string's size/length reports the number of elements + // in the buffer, not the length of string(null-terminated) strip + // null-character in the middle of string. + size_t slength = strlen(filepath.c_str()); + if (slength == 0) { + return std::string(); + } + + std::string cleaned_filepath = std::string(filepath.c_str()); + + for (size_t i = 0; i < paths.size(); i++) { + std::string absPath = + fs->ExpandFilePath(JoinPath(paths[i], cleaned_filepath), fs->user_data); + if (fs->FileExists(absPath, fs->user_data)) { + return absPath; + } + } + + return std::string(); +} + +static std::string GetFilePathExtension(const std::string &FileName) { + if (FileName.find_last_of(".") != std::string::npos) + return FileName.substr(FileName.find_last_of(".") + 1); + return ""; +} + +static std::string GetBaseDir(const std::string &filepath) { + if (filepath.find_last_of("/\\") != std::string::npos) + return filepath.substr(0, filepath.find_last_of("/\\")); + return ""; +} + +static std::string GetBaseFilename(const std::string &filepath) { + auto idx = filepath.find_last_of("/\\"); + if (idx != std::string::npos) return filepath.substr(idx + 1); + return filepath; +} + +std::string base64_encode(unsigned char const *, unsigned int len); +std::string base64_decode(std::string const &s); + +/* + base64.cpp and base64.h + + Copyright (C) 2004-2008 René Nyffenegger + + This source code is provided 'as-is', without any express or implied + warranty. In no event will the author be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this source code must not be misrepresented; you must not + claim that you wrote the original source code. If you use this source code + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original source code. + + 3. This notice may not be removed or altered from any source distribution. + + René Nyffenegger rene.nyffenegger@adp-gmbh.ch + +*/ + +#ifdef __clang__ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wsign-conversion" +#pragma clang diagnostic ignored "-Wconversion" +#endif + +static inline bool is_base64(unsigned char c) { + return (isalnum(c) || (c == '+') || (c == '/')); +} + +std::string base64_encode(unsigned char const *bytes_to_encode, + unsigned int in_len) { + std::string ret; + int i = 0; + int j = 0; + unsigned char char_array_3[3]; + unsigned char char_array_4[4]; + + const char *base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; + + while (in_len--) { + char_array_3[i++] = *(bytes_to_encode++); + if (i == 3) { + char_array_4[0] = (char_array_3[0] & 0xfc) >> 2; + char_array_4[1] = + ((char_array_3[0] & 0x03) << 4) + ((char_array_3[1] & 0xf0) >> 4); + char_array_4[2] = + ((char_array_3[1] & 0x0f) << 2) + ((char_array_3[2] & 0xc0) >> 6); + char_array_4[3] = char_array_3[2] & 0x3f; + + for (i = 0; (i < 4); i++) ret += base64_chars[char_array_4[i]]; + i = 0; + } + } + + if (i) { + for (j = i; j < 3; j++) char_array_3[j] = '\0'; + + char_array_4[0] = (char_array_3[0] & 0xfc) >> 2; + char_array_4[1] = + ((char_array_3[0] & 0x03) << 4) + ((char_array_3[1] & 0xf0) >> 4); + char_array_4[2] = + ((char_array_3[1] & 0x0f) << 2) + ((char_array_3[2] & 0xc0) >> 6); + + for (j = 0; (j < i + 1); j++) ret += base64_chars[char_array_4[j]]; + + while ((i++ < 3)) ret += '='; + } + + return ret; +} + +std::string base64_decode(std::string const &encoded_string) { + int in_len = static_cast(encoded_string.size()); + int i = 0; + int j = 0; + int in_ = 0; + unsigned char char_array_4[4], char_array_3[3]; + std::string ret; + + const std::string base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; + + while (in_len-- && (encoded_string[in_] != '=') && + is_base64(encoded_string[in_])) { + char_array_4[i++] = encoded_string[in_]; + in_++; + if (i == 4) { + for (i = 0; i < 4; i++) + char_array_4[i] = + static_cast(base64_chars.find(char_array_4[i])); + + char_array_3[0] = + (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = + ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) ret += char_array_3[i]; + i = 0; + } + } + + if (i) { + for (j = i; j < 4; j++) char_array_4[j] = 0; + + for (j = 0; j < 4; j++) + char_array_4[j] = + static_cast(base64_chars.find(char_array_4[j])); + + char_array_3[0] = (char_array_4[0] << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = + ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (j = 0; (j < i - 1); j++) ret += char_array_3[j]; + } + + return ret; +} +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +// https://github.com/syoyo/tinygltf/issues/228 +// TODO(syoyo): Use uriparser https://uriparser.github.io/ for stricter Uri +// decoding? +// +// Uri Decoding from DLIB +// http://dlib.net/dlib/server/server_http.cpp.html +// --- dlib begin ------------------------------------------------------------ +// Copyright (C) 2003 Davis E. King (davis@dlib.net) +// License: Boost Software License +// Boost Software License - Version 1.0 - August 17th, 2003 + +// Permission is hereby granted, free of charge, to any person or organization +// obtaining a copy of the software and accompanying documentation covered by +// this license (the "Software") to use, reproduce, display, distribute, +// execute, and transmit the Software, and to prepare derivative works of the +// Software, and to permit third-parties to whom the Software is furnished to +// do so, all subject to the following: +// The copyright notices in the Software and this entire statement, including +// the above license grant, this restriction and the following disclaimer, +// must be included in all copies of the Software, in whole or in part, and +// all derivative works of the Software, unless such copies or derivative +// works are solely in the form of machine-executable object code generated by +// a source language processor. +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT +// SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +// FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +// ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +// DEALINGS IN THE SOFTWARE. +// +namespace dlib { + +inline unsigned char from_hex(unsigned char ch) { + if (ch <= '9' && ch >= '0') + ch -= '0'; + else if (ch <= 'f' && ch >= 'a') + ch -= 'a' - 10; + else if (ch <= 'F' && ch >= 'A') + ch -= 'A' - 10; + else + ch = 0; + return ch; +} + +static const std::string urldecode(const std::string &str) { + using namespace std; + string result; + string::size_type i; + for (i = 0; i < str.size(); ++i) { + if (str[i] == '+') { + result += ' '; + } else if (str[i] == '%' && str.size() > i + 2) { + const unsigned char ch1 = + from_hex(static_cast(str[i + 1])); + const unsigned char ch2 = + from_hex(static_cast(str[i + 2])); + const unsigned char ch = static_cast((ch1 << 4) | ch2); + result += static_cast(ch); + i += 2; + } else { + result += str[i]; + } + } + return result; +} + +} // namespace dlib +// --- dlib end -------------------------------------------------------------- + +bool URIDecode(const std::string &in_uri, std::string *out_uri, + void *user_data) { + (void)user_data; + *out_uri = dlib::urldecode(in_uri); + return true; +} + +static bool LoadExternalFile(std::vector *out, std::string *err, + std::string *warn, const std::string &filename, + const std::string &basedir, bool required, + size_t reqBytes, bool checkSize, + size_t maxFileSize, FsCallbacks *fs) { + if (fs == nullptr || fs->FileExists == nullptr || + fs->ExpandFilePath == nullptr || fs->ReadWholeFile == nullptr) { + // This is a developer error, assert() ? + if (err) { + (*err) += "FS callback[s] not set\n"; + } + return false; + } + + std::string *failMsgOut = required ? err : warn; + + out->clear(); + + std::vector paths; + paths.push_back(basedir); + paths.push_back("."); + + std::string filepath = FindFile(paths, filename, fs); + if (filepath.empty() || filename.empty()) { + if (failMsgOut) { + (*failMsgOut) += "File not found : " + filename + "\n"; + } + return false; + } + + // Check file size + if (fs->GetFileSizeInBytes) { + size_t file_size{0}; + std::string _err; + bool ok = + fs->GetFileSizeInBytes(&file_size, &_err, filepath, fs->user_data); + if (!ok) { + if (_err.size()) { + if (failMsgOut) { + (*failMsgOut) += "Getting file size failed : " + filename + + ", err = " + _err + "\n"; + } + } + return false; + } + + if (file_size > maxFileSize) { + if (failMsgOut) { + (*failMsgOut) += "File size " + std::to_string(file_size) + + " exceeds maximum allowed file size " + + std::to_string(maxFileSize) + " : " + filepath + "\n"; + } + return false; + } + } + + std::vector buf; + std::string fileReadErr; + bool fileRead = + fs->ReadWholeFile(&buf, &fileReadErr, filepath, fs->user_data); + if (!fileRead) { + if (failMsgOut) { + (*failMsgOut) += + "File read error : " + filepath + " : " + fileReadErr + "\n"; + } + return false; + } + + size_t sz = buf.size(); + if (sz == 0) { + if (failMsgOut) { + (*failMsgOut) += "File is empty : " + filepath + "\n"; + } + return false; + } + + if (checkSize) { + if (reqBytes == sz) { + out->swap(buf); + return true; + } else { + std::stringstream ss; + ss << "File size mismatch : " << filepath << ", requestedBytes " + << reqBytes << ", but got " << sz << std::endl; + if (failMsgOut) { + (*failMsgOut) += ss.str(); + } + return false; + } + } + + out->swap(buf); + return true; +} + +void TinyGLTF::SetParseStrictness(ParseStrictness strictness) { + strictness_ = strictness; +} + +void TinyGLTF::SetImageLoader(LoadImageDataFunction func, void *user_data) { + LoadImageData = func; + load_image_user_data_ = user_data; + user_image_loader_ = true; +} + +void TinyGLTF::RemoveImageLoader() { + LoadImageData = +#ifndef TINYGLTF_NO_STB_IMAGE + &tinygltf::LoadImageData; +#else + nullptr; +#endif + + load_image_user_data_ = nullptr; + user_image_loader_ = false; +} + +#ifndef TINYGLTF_NO_STB_IMAGE +bool LoadImageData(Image *image, const int image_idx, std::string *err, + std::string *warn, int req_width, int req_height, + const unsigned char *bytes, int size, void *user_data) { + (void)warn; + + LoadImageDataOption option; + if (user_data) { + option = *reinterpret_cast(user_data); + } + + int w = 0, h = 0, comp = 0, req_comp = 0; + + unsigned char *data = nullptr; + + // preserve_channels true: Use channels stored in the image file. + // false: force 32-bit textures for common Vulkan compatibility. It appears + // that some GPU drivers do not support 24-bit images for Vulkan + req_comp = option.preserve_channels ? 0 : 4; + int bits = 8; + int pixel_type = TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE; + + // It is possible that the image we want to load is a 16bit per channel image + // We are going to attempt to load it as 16bit per channel, and if it worked, + // set the image data accordingly. We are casting the returned pointer into + // unsigned char, because we are representing "bytes". But we are updating + // the Image metadata to signal that this image uses 2 bytes (16bits) per + // channel: + if (stbi_is_16_bit_from_memory(bytes, size)) { + data = reinterpret_cast( + stbi_load_16_from_memory(bytes, size, &w, &h, &comp, req_comp)); + if (data) { + bits = 16; + pixel_type = TINYGLTF_COMPONENT_TYPE_UNSIGNED_SHORT; + } + } + + // at this point, if data is still NULL, it means that the image wasn't + // 16bit per channel, we are going to load it as a normal 8bit per channel + // image as we used to do: + // if image cannot be decoded, ignore parsing and keep it by its path + // don't break in this case + // FIXME we should only enter this function if the image is embedded. If + // image->uri references + // an image file, it should be left as it is. Image loading should not be + // mandatory (to support other formats) + if (!data) data = stbi_load_from_memory(bytes, size, &w, &h, &comp, req_comp); + if (!data) { + // NOTE: you can use `warn` instead of `err` + if (err) { + (*err) += + "Unknown image format. STB cannot decode image data for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + "\".\n"; + } + return false; + } + + if ((w < 1) || (h < 1)) { + stbi_image_free(data); + if (err) { + (*err) += "Invalid image data for image[" + std::to_string(image_idx) + + "] name = \"" + image->name + "\"\n"; + } + return false; + } + + if (req_width > 0) { + if (req_width != w) { + stbi_image_free(data); + if (err) { + (*err) += "Image width mismatch for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\"\n"; + } + return false; + } + } + + if (req_height > 0) { + if (req_height != h) { + stbi_image_free(data); + if (err) { + (*err) += "Image height mismatch. for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\"\n"; + } + return false; + } + } + + if (req_comp != 0) { + // loaded data has `req_comp` channels(components) + comp = req_comp; + } + + image->width = w; + image->height = h; + image->component = comp; + image->bits = bits; + image->pixel_type = pixel_type; + image->image.resize(static_cast(w * h * comp) * size_t(bits / 8)); + std::copy(data, data + w * h * comp * (bits / 8), image->image.begin()); + stbi_image_free(data); + + return true; +} +#endif + +void TinyGLTF::SetImageWriter(WriteImageDataFunction func, void *user_data) { + WriteImageData = func; + write_image_user_data_ = user_data; +} + +#ifndef TINYGLTF_NO_STB_IMAGE_WRITE +static void WriteToMemory_stbi(void *context, void *data, int size) { + std::vector *buffer = + reinterpret_cast *>(context); + + unsigned char *pData = reinterpret_cast(data); + + buffer->insert(buffer->end(), pData, pData + size); +} + +bool WriteImageData(const std::string *basepath, const std::string *filename, + const Image *image, bool embedImages, + const URICallbacks *uri_cb, std::string *out_uri, + void *fsPtr) { + const std::string ext = GetFilePathExtension(*filename); + + // Write image to temporary buffer + std::string header; + std::vector data; + + if (ext == "png") { + if ((image->bits != 8) || + (image->pixel_type != TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE)) { + // Unsupported pixel format + return false; + } + + if (!stbi_write_png_to_func(WriteToMemory_stbi, &data, image->width, + image->height, image->component, + &image->image[0], 0)) { + return false; + } + header = "data:image/png;base64,"; + } else if (ext == "jpg") { + if (!stbi_write_jpg_to_func(WriteToMemory_stbi, &data, image->width, + image->height, image->component, + &image->image[0], 100)) { + return false; + } + header = "data:image/jpeg;base64,"; + } else if (ext == "bmp") { + if (!stbi_write_bmp_to_func(WriteToMemory_stbi, &data, image->width, + image->height, image->component, + &image->image[0])) { + return false; + } + header = "data:image/bmp;base64,"; + } else if (!embedImages) { + // Error: can't output requested format to file + return false; + } + + if (embedImages) { + // Embed base64-encoded image into URI + if (data.size()) { + *out_uri = header + base64_encode(&data[0], + static_cast(data.size())); + } else { + // Throw error? + } + } else { + // Write image to disc + FsCallbacks *fs = reinterpret_cast(fsPtr); + if ((fs != nullptr) && (fs->WriteWholeFile != nullptr)) { + const std::string imagefilepath = JoinPath(*basepath, *filename); + std::string writeError; + if (!fs->WriteWholeFile(&writeError, imagefilepath, data, + fs->user_data)) { + // Could not write image file to disc; Throw error ? + return false; + } + } else { + // Throw error? + } + if (uri_cb->encode) { + if (!uri_cb->encode(*filename, "image", out_uri, uri_cb->user_data)) { + return false; + } + } else { + *out_uri = *filename; + } + } + + return true; +} +#endif + +void TinyGLTF::SetURICallbacks(URICallbacks callbacks) { + assert(callbacks.decode); + if (callbacks.decode) { + uri_cb = callbacks; + } +} + +void TinyGLTF::SetFsCallbacks(FsCallbacks callbacks) { fs = callbacks; } + +#ifdef _WIN32 +static inline std::wstring UTF8ToWchar(const std::string &str) { + int wstr_size = + MultiByteToWideChar(CP_UTF8, 0, str.data(), (int)str.size(), nullptr, 0); + std::wstring wstr((size_t)wstr_size, 0); + MultiByteToWideChar(CP_UTF8, 0, str.data(), (int)str.size(), &wstr[0], + (int)wstr.size()); + return wstr; +} + +static inline std::string WcharToUTF8(const std::wstring &wstr) { + int str_size = WideCharToMultiByte(CP_UTF8, 0, wstr.data(), (int)wstr.size(), + nullptr, 0, nullptr, nullptr); + std::string str((size_t)str_size, 0); + WideCharToMultiByte(CP_UTF8, 0, wstr.data(), (int)wstr.size(), &str[0], + (int)str.size(), nullptr, nullptr); + return str; +} +#endif + +#ifndef TINYGLTF_NO_FS +// Default implementations of filesystem functions + +bool FileExists(const std::string &abs_filename, void *) { + bool ret; +#ifdef TINYGLTF_ANDROID_LOAD_FROM_ASSETS + if (asset_manager) { + AAsset *asset = AAssetManager_open(asset_manager, abs_filename.c_str(), + AASSET_MODE_STREAMING); + if (!asset) { + return false; + } + AAsset_close(asset); + ret = true; + } else { + return false; + } +#else +#ifdef _WIN32 +#if defined(_MSC_VER) || defined(_LIBCPP_VERSION) + + // First check if a file is a directory. + DWORD result = GetFileAttributesW(UTF8ToWchar(abs_filename).c_str()); + if (result == INVALID_FILE_ATTRIBUTES) { + return false; + } + if (result & FILE_ATTRIBUTE_DIRECTORY) { + return false; + } + + FILE *fp = nullptr; + errno_t err = _wfopen_s(&fp, UTF8ToWchar(abs_filename).c_str(), L"rb"); + if (err != 0) { + return false; + } +#elif defined(__GLIBCXX__) + FILE *fp = fopen(abs_filename.c_str(), "rb"); + if (!fp) { + return false; + } +#else + // TODO: is_directory check + FILE *fp = nullptr; + errno_t err = fopen_s(&fp, abs_filename.c_str(), "rb"); + if (err != 0) { + return false; + } +#endif + +#else + struct stat sb; + if (stat(abs_filename.c_str(), &sb)) { + return false; + } + if (S_ISDIR(sb.st_mode)) { + return false; + } + + FILE *fp = fopen(abs_filename.c_str(), "rb"); +#endif + if (fp) { + ret = true; + fclose(fp); + } else { + ret = false; + } +#endif + + return ret; +} + +std::string ExpandFilePath(const std::string &filepath, void *) { + // https://github.com/syoyo/tinygltf/issues/368 + // + // No file path expansion in built-in FS function anymore, since glTF URI + // should not contain tilde('~') and environment variables, and for security + // reason(`wordexp`). + // + // Users need to supply `base_dir`(in `LoadASCIIFromString`, + // `LoadBinaryFromMemory`) in expanded absolute path. + + return filepath; + +#if 0 +#ifdef _WIN32 + // Assume input `filepath` is encoded in UTF-8 + std::wstring wfilepath = UTF8ToWchar(filepath); + DWORD wlen = ExpandEnvironmentStringsW(wfilepath.c_str(), nullptr, 0); + wchar_t *wstr = new wchar_t[wlen]; + ExpandEnvironmentStringsW(wfilepath.c_str(), wstr, wlen); + + std::wstring ws(wstr); + delete[] wstr; + return WcharToUTF8(ws); + +#else + +#if defined(TARGET_OS_IPHONE) || defined(TARGET_IPHONE_SIMULATOR) || \ + defined(__ANDROID__) || defined(__EMSCRIPTEN__) || defined(__OpenBSD__) + // no expansion + std::string s = filepath; +#else + std::string s; + wordexp_t p; + + if (filepath.empty()) { + return ""; + } + + // Quote the string to keep any spaces in filepath intact. + std::string quoted_path = "\"" + filepath + "\""; + // char** w; + int ret = wordexp(quoted_path.c_str(), &p, 0); + if (ret) { + // err + s = filepath; + return s; + } + + // Use first element only. + if (p.we_wordv) { + s = std::string(p.we_wordv[0]); + wordfree(&p); + } else { + s = filepath; + } + +#endif + + return s; +#endif +#endif +} + +bool GetFileSizeInBytes(size_t *filesize_out, std::string *err, + const std::string &filepath, void *userdata) { + (void)userdata; + +#ifdef TINYGLTF_ANDROID_LOAD_FROM_ASSETS + if (asset_manager) { + AAsset *asset = AAssetManager_open(asset_manager, filepath.c_str(), + AASSET_MODE_STREAMING); + if (!asset) { + if (err) { + (*err) += "File open error : " + filepath + "\n"; + } + return false; + } + size_t size = AAsset_getLength(asset); + + if (size == 0) { + if (err) { + (*err) += "Invalid file size : " + filepath + + " (does the path point to a directory?)"; + } + return false; + } + + return true; + } else { + if (err) { + (*err) += "No asset manager specified : " + filepath + "\n"; + } + return false; + } +#else +#ifdef _WIN32 +#if defined(__GLIBCXX__) // mingw + int file_descriptor = + _wopen(UTF8ToWchar(filepath).c_str(), _O_RDONLY | _O_BINARY); + __gnu_cxx::stdio_filebuf wfile_buf(file_descriptor, std::ios_base::in); + std::istream f(&wfile_buf); +#elif defined(_MSC_VER) || defined(_LIBCPP_VERSION) + // For libcxx, assume _LIBCPP_HAS_OPEN_WITH_WCHAR is defined to accept + // `wchar_t *` + std::ifstream f(UTF8ToWchar(filepath).c_str(), std::ifstream::binary); +#else + // Unknown compiler/runtime + std::ifstream f(filepath.c_str(), std::ifstream::binary); +#endif +#else + std::ifstream f(filepath.c_str(), std::ifstream::binary); +#endif + if (!f) { + if (err) { + (*err) += "File open error : " + filepath + "\n"; + } + return false; + } + + // For directory(and pipe?), peek() will fail(Posix gnustl/libc++ only) + f.peek(); + if (!f) { + if (err) { + (*err) += + "File read error. Maybe empty file or invalid file : " + filepath + + "\n"; + } + return false; + } + + f.seekg(0, f.end); + size_t sz = static_cast(f.tellg()); + + // std::cout << "sz = " << sz << "\n"; + f.seekg(0, f.beg); + + if (int64_t(sz) < 0) { + if (err) { + (*err) += "Invalid file size : " + filepath + + " (does the path point to a directory?)"; + } + return false; + } else if (sz == 0) { + if (err) { + (*err) += "File is empty : " + filepath + "\n"; + } + return false; + } else if (sz >= (std::numeric_limits::max)()) { + if (err) { + (*err) += "Invalid file size : " + filepath + "\n"; + } + return false; + } + + (*filesize_out) = sz; + return true; +#endif +} + +bool ReadWholeFile(std::vector *out, std::string *err, + const std::string &filepath, void *) { +#ifdef TINYGLTF_ANDROID_LOAD_FROM_ASSETS + if (asset_manager) { + AAsset *asset = AAssetManager_open(asset_manager, filepath.c_str(), + AASSET_MODE_STREAMING); + if (!asset) { + if (err) { + (*err) += "File open error : " + filepath + "\n"; + } + return false; + } + size_t size = AAsset_getLength(asset); + if (size == 0) { + if (err) { + (*err) += "Invalid file size : " + filepath + + " (does the path point to a directory?)"; + } + return false; + } + out->resize(size); + AAsset_read(asset, reinterpret_cast(&out->at(0)), size); + AAsset_close(asset); + return true; + } else { + if (err) { + (*err) += "No asset manager specified : " + filepath + "\n"; + } + return false; + } +#else +#ifdef _WIN32 +#if defined(__GLIBCXX__) // mingw + int file_descriptor = + _wopen(UTF8ToWchar(filepath).c_str(), _O_RDONLY | _O_BINARY); + __gnu_cxx::stdio_filebuf wfile_buf(file_descriptor, std::ios_base::in); + std::istream f(&wfile_buf); +#elif defined(_MSC_VER) || defined(_LIBCPP_VERSION) + // For libcxx, assume _LIBCPP_HAS_OPEN_WITH_WCHAR is defined to accept + // `wchar_t *` + std::ifstream f(UTF8ToWchar(filepath).c_str(), std::ifstream::binary); +#else + // Unknown compiler/runtime + std::ifstream f(filepath.c_str(), std::ifstream::binary); +#endif +#else + std::ifstream f(filepath.c_str(), std::ifstream::binary); +#endif + if (!f) { + if (err) { + (*err) += "File open error : " + filepath + "\n"; + } + return false; + } + + // For directory(and pipe?), peek() will fail(Posix gnustl/libc++ only) + f.peek(); + if (!f) { + if (err) { + (*err) += + "File read error. Maybe empty file or invalid file : " + filepath + + "\n"; + } + return false; + } + + f.seekg(0, f.end); + size_t sz = static_cast(f.tellg()); + + // std::cout << "sz = " << sz << "\n"; + f.seekg(0, f.beg); + + if (int64_t(sz) < 0) { + if (err) { + (*err) += "Invalid file size : " + filepath + + " (does the path point to a directory?)"; + } + return false; + } else if (sz == 0) { + if (err) { + (*err) += "File is empty : " + filepath + "\n"; + } + return false; + } else if (sz >= (std::numeric_limits::max)()) { + if (err) { + (*err) += "Invalid file size : " + filepath + "\n"; + } + return false; + } + + out->resize(sz); + f.read(reinterpret_cast(&out->at(0)), + static_cast(sz)); + + return true; +#endif +} + +bool WriteWholeFile(std::string *err, const std::string &filepath, + const std::vector &contents, void *) { +#ifdef _WIN32 +#if defined(__GLIBCXX__) // mingw + int file_descriptor = _wopen(UTF8ToWchar(filepath).c_str(), + _O_CREAT | _O_WRONLY | _O_TRUNC | _O_BINARY); + __gnu_cxx::stdio_filebuf wfile_buf( + file_descriptor, std::ios_base::out | std::ios_base::binary); + std::ostream f(&wfile_buf); +#elif defined(_MSC_VER) + std::ofstream f(UTF8ToWchar(filepath).c_str(), std::ofstream::binary); +#else // clang? + std::ofstream f(filepath.c_str(), std::ofstream::binary); +#endif +#else + std::ofstream f(filepath.c_str(), std::ofstream::binary); +#endif + if (!f) { + if (err) { + (*err) += "File open error for writing : " + filepath + "\n"; + } + return false; + } + + f.write(reinterpret_cast(&contents.at(0)), + static_cast(contents.size())); + if (!f) { + if (err) { + (*err) += "File write error: " + filepath + "\n"; + } + return false; + } + + return true; +} + +#endif // TINYGLTF_NO_FS + +static std::string MimeToExt(const std::string &mimeType) { + if (mimeType == "image/jpeg") { + return "jpg"; + } else if (mimeType == "image/png") { + return "png"; + } else if (mimeType == "image/bmp") { + return "bmp"; + } else if (mimeType == "image/gif") { + return "gif"; + } + + return ""; +} + +static bool UpdateImageObject(const Image &image, std::string &baseDir, + int index, bool embedImages, + const URICallbacks *uri_cb, + WriteImageDataFunction *WriteImageData, + void *user_data, std::string *out_uri) { + std::string filename; + std::string ext; + // If image has uri, use it as a filename + if (image.uri.size()) { + std::string decoded_uri; + if (!uri_cb->decode(image.uri, &decoded_uri, uri_cb->user_data)) { + // A decode failure results in a failure to write the gltf. + return false; + } + filename = GetBaseFilename(decoded_uri); + ext = GetFilePathExtension(filename); + } else if (image.bufferView != -1) { + // If there's no URI and the data exists in a buffer, + // don't change properties or write images + } else if (image.name.size()) { + ext = MimeToExt(image.mimeType); + // Otherwise use name as filename + filename = image.name + "." + ext; + } else { + ext = MimeToExt(image.mimeType); + // Fallback to index of image as filename + filename = std::to_string(index) + "." + ext; + } + + // If callback is set and image data exists, modify image data object. If + // image data does not exist, this is not considered a failure and the + // original uri should be maintained. + bool imageWritten = false; + if (*WriteImageData != nullptr && !filename.empty() && !image.image.empty()) { + imageWritten = (*WriteImageData)(&baseDir, &filename, &image, embedImages, + uri_cb, out_uri, user_data); + if (!imageWritten) { + return false; + } + } + + // Use the original uri if the image was not written. + if (!imageWritten) { + *out_uri = image.uri; + } + + return true; +} + +bool IsDataURI(const std::string &in) { + std::string header = "data:application/octet-stream;base64,"; + if (in.find(header) == 0) { + return true; + } + + header = "data:image/jpeg;base64,"; + if (in.find(header) == 0) { + return true; + } + + header = "data:image/png;base64,"; + if (in.find(header) == 0) { + return true; + } + + header = "data:image/bmp;base64,"; + if (in.find(header) == 0) { + return true; + } + + header = "data:image/gif;base64,"; + if (in.find(header) == 0) { + return true; + } + + header = "data:text/plain;base64,"; + if (in.find(header) == 0) { + return true; + } + + header = "data:application/gltf-buffer;base64,"; + if (in.find(header) == 0) { + return true; + } + + return false; +} + +bool DecodeDataURI(std::vector *out, std::string &mime_type, + const std::string &in, size_t reqBytes, bool checkSize) { + std::string header = "data:application/octet-stream;base64,"; + std::string data; + if (in.find(header) == 0) { + data = base64_decode(in.substr(header.size())); // cut mime string. + } + + if (data.empty()) { + header = "data:image/jpeg;base64,"; + if (in.find(header) == 0) { + mime_type = "image/jpeg"; + data = base64_decode(in.substr(header.size())); // cut mime string. + } + } + + if (data.empty()) { + header = "data:image/png;base64,"; + if (in.find(header) == 0) { + mime_type = "image/png"; + data = base64_decode(in.substr(header.size())); // cut mime string. + } + } + + if (data.empty()) { + header = "data:image/bmp;base64,"; + if (in.find(header) == 0) { + mime_type = "image/bmp"; + data = base64_decode(in.substr(header.size())); // cut mime string. + } + } + + if (data.empty()) { + header = "data:image/gif;base64,"; + if (in.find(header) == 0) { + mime_type = "image/gif"; + data = base64_decode(in.substr(header.size())); // cut mime string. + } + } + + if (data.empty()) { + header = "data:text/plain;base64,"; + if (in.find(header) == 0) { + mime_type = "text/plain"; + data = base64_decode(in.substr(header.size())); + } + } + + if (data.empty()) { + header = "data:application/gltf-buffer;base64,"; + if (in.find(header) == 0) { + data = base64_decode(in.substr(header.size())); + } + } + + // TODO(syoyo): Allow empty buffer? #229 + if (data.empty()) { + return false; + } + + if (checkSize) { + if (data.size() != reqBytes) { + return false; + } + out->resize(reqBytes); + } else { + out->resize(data.size()); + } + std::copy(data.begin(), data.end(), out->begin()); + return true; +} + +namespace detail { +bool GetInt(const detail::json &o, int &val) { +#ifdef TINYGLTF_USE_RAPIDJSON + if (!o.IsDouble()) { + if (o.IsInt()) { + val = o.GetInt(); + return true; + } else if (o.IsUint()) { + val = static_cast(o.GetUint()); + return true; + } else if (o.IsInt64()) { + val = static_cast(o.GetInt64()); + return true; + } else if (o.IsUint64()) { + val = static_cast(o.GetUint64()); + return true; + } + } + + return false; +#else + auto type = o.type(); + + if ((type == detail::json::value_t::number_integer) || + (type == detail::json::value_t::number_unsigned)) { + val = static_cast(o.get()); + return true; + } + + return false; +#endif +} + +#ifdef TINYGLTF_USE_RAPIDJSON +bool GetDouble(const detail::json &o, double &val) { + if (o.IsDouble()) { + val = o.GetDouble(); + return true; + } + + return false; +} +#endif + +bool GetNumber(const detail::json &o, double &val) { +#ifdef TINYGLTF_USE_RAPIDJSON + if (o.IsNumber()) { + val = o.GetDouble(); + return true; + } + + return false; +#else + if (o.is_number()) { + val = o.get(); + return true; + } + + return false; +#endif +} + +bool GetString(const detail::json &o, std::string &val) { +#ifdef TINYGLTF_USE_RAPIDJSON + if (o.IsString()) { + val = o.GetString(); + return true; + } + + return false; +#else + if (o.type() == detail::json::value_t::string) { + val = o.get(); + return true; + } + + return false; +#endif +} + +bool IsArray(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.IsArray(); +#else + return o.is_array(); +#endif +} + +detail::json_const_array_iterator ArrayBegin(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.Begin(); +#else + return o.begin(); +#endif +} + +detail::json_const_array_iterator ArrayEnd(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.End(); +#else + return o.end(); +#endif +} + +bool IsObject(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.IsObject(); +#else + return o.is_object(); +#endif +} + +detail::json_const_iterator ObjectBegin(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.MemberBegin(); +#else + return o.begin(); +#endif +} + +detail::json_const_iterator ObjectEnd(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.MemberEnd(); +#else + return o.end(); +#endif +} + +// Making this a const char* results in a pointer to a temporary when +// TINYGLTF_USE_RAPIDJSON is off. +std::string GetKey(detail::json_const_iterator &it) { +#ifdef TINYGLTF_USE_RAPIDJSON + return it->name.GetString(); +#else + return it.key().c_str(); +#endif +} + +bool FindMember(const detail::json &o, const char *member, + detail::json_const_iterator &it) { +#ifdef TINYGLTF_USE_RAPIDJSON + if (!o.IsObject()) { + return false; + } + it = o.FindMember(member); + return it != o.MemberEnd(); +#else + it = o.find(member); + return it != o.end(); +#endif +} + +bool FindMember(detail::json &o, const char *member, + detail::json_iterator &it) { +#ifdef TINYGLTF_USE_RAPIDJSON + if (!o.IsObject()) { + return false; + } + it = o.FindMember(member); + return it != o.MemberEnd(); +#else + it = o.find(member); + return it != o.end(); +#endif +} + +void Erase(detail::json &o, detail::json_iterator &it) { +#ifdef TINYGLTF_USE_RAPIDJSON + o.EraseMember(it); +#else + o.erase(it); +#endif +} + +bool IsEmpty(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.ObjectEmpty(); +#else + return o.empty(); +#endif +} + +const detail::json &GetValue(detail::json_const_iterator &it) { +#ifdef TINYGLTF_USE_RAPIDJSON + return it->value; +#else + return it.value(); +#endif +} + +detail::json &GetValue(detail::json_iterator &it) { +#ifdef TINYGLTF_USE_RAPIDJSON + return it->value; +#else + return it.value(); +#endif +} + +std::string JsonToString(const detail::json &o, int spacing = -1) { +#ifdef TINYGLTF_USE_RAPIDJSON + using namespace rapidjson; + StringBuffer buffer; + if (spacing == -1) { + Writer writer(buffer); + // TODO: Better error handling. + // https://github.com/syoyo/tinygltf/issues/332 + if (!o.Accept(writer)) { + return "tiny_gltf::JsonToString() failed rapidjson conversion"; + } + } else { + PrettyWriter writer(buffer); + writer.SetIndent(' ', uint32_t(spacing)); + if (!o.Accept(writer)) { + return "tiny_gltf::JsonToString() failed rapidjson conversion"; + } + } + return buffer.GetString(); +#else + return o.dump(spacing); +#endif +} + +} // namespace detail + +static bool ParseJsonAsValue(Value *ret, const detail::json &o) { + Value val{}; +#ifdef TINYGLTF_USE_RAPIDJSON + using rapidjson::Type; + switch (o.GetType()) { + case Type::kObjectType: { + Value::Object value_object; + for (auto it = o.MemberBegin(); it != o.MemberEnd(); ++it) { + Value entry; + ParseJsonAsValue(&entry, it->value); + if (entry.Type() != NULL_TYPE) + value_object.emplace(detail::GetKey(it), std::move(entry)); + } + if (value_object.size() > 0) val = Value(std::move(value_object)); + } break; + case Type::kArrayType: { + Value::Array value_array; + value_array.reserve(o.Size()); + for (auto it = o.Begin(); it != o.End(); ++it) { + Value entry; + ParseJsonAsValue(&entry, *it); + if (entry.Type() != NULL_TYPE) + value_array.emplace_back(std::move(entry)); + } + if (value_array.size() > 0) val = Value(std::move(value_array)); + } break; + case Type::kStringType: + val = Value(std::string(o.GetString())); + break; + case Type::kFalseType: + case Type::kTrueType: + val = Value(o.GetBool()); + break; + case Type::kNumberType: + if (!o.IsDouble()) { + int i = 0; + detail::GetInt(o, i); + val = Value(i); + } else { + double d = 0.0; + detail::GetDouble(o, d); + val = Value(d); + } + break; + case Type::kNullType: + break; + // all types are covered, so no `case default` + } +#else + switch (o.type()) { + case detail::json::value_t::object: { + Value::Object value_object; + for (auto it = o.begin(); it != o.end(); it++) { + Value entry; + ParseJsonAsValue(&entry, it.value()); + if (entry.Type() != NULL_TYPE) + value_object.emplace(it.key(), std::move(entry)); + } + if (value_object.size() > 0) val = Value(std::move(value_object)); + } break; + case detail::json::value_t::array: { + Value::Array value_array; + value_array.reserve(o.size()); + for (auto it = o.begin(); it != o.end(); it++) { + Value entry; + ParseJsonAsValue(&entry, it.value()); + if (entry.Type() != NULL_TYPE) + value_array.emplace_back(std::move(entry)); + } + if (value_array.size() > 0) val = Value(std::move(value_array)); + } break; + case detail::json::value_t::string: + val = Value(o.get()); + break; + case detail::json::value_t::boolean: + val = Value(o.get()); + break; + case detail::json::value_t::number_integer: + case detail::json::value_t::number_unsigned: + val = Value(static_cast(o.get())); + break; + case detail::json::value_t::number_float: + val = Value(o.get()); + break; + case detail::json::value_t::null: + case detail::json::value_t::discarded: + case detail::json::value_t::binary: + // default: + break; + } +#endif + const bool isNotNull = val.Type() != NULL_TYPE; + + if (ret) *ret = std::move(val); + + return isNotNull; +} + +static bool ParseExtrasProperty(Value *ret, const detail::json &o) { + detail::json_const_iterator it; + if (!detail::FindMember(o, "extras", it)) { + return false; + } + + return ParseJsonAsValue(ret, detail::GetValue(it)); +} + +static bool ParseBooleanProperty(bool *ret, std::string *err, + const detail::json &o, + const std::string &property, + const bool required, + const std::string &parent_node = "") { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + auto &value = detail::GetValue(it); + + bool isBoolean; + bool boolValue = false; +#ifdef TINYGLTF_USE_RAPIDJSON + isBoolean = value.IsBool(); + if (isBoolean) { + boolValue = value.GetBool(); + } +#else + isBoolean = value.is_boolean(); + if (isBoolean) { + boolValue = value.get(); + } +#endif + if (!isBoolean) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not a bool type.\n"; + } + } + return false; + } + + if (ret) { + (*ret) = boolValue; + } + + return true; +} + +static bool ParseIntegerProperty(int *ret, std::string *err, + const detail::json &o, + const std::string &property, + const bool required, + const std::string &parent_node = "") { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + int intValue; + bool isInt = detail::GetInt(detail::GetValue(it), intValue); + if (!isInt) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not an integer type.\n"; + } + } + return false; + } + + if (ret) { + (*ret) = intValue; + } + + return true; +} + +static bool ParseUnsignedProperty(size_t *ret, std::string *err, + const detail::json &o, + const std::string &property, + const bool required, + const std::string &parent_node = "") { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + auto &value = detail::GetValue(it); + + size_t uValue = 0; + bool isUValue; +#ifdef TINYGLTF_USE_RAPIDJSON + isUValue = false; + if (value.IsUint()) { + uValue = value.GetUint(); + isUValue = true; + } else if (value.IsUint64()) { + uValue = value.GetUint64(); + isUValue = true; + } +#else + isUValue = value.is_number_unsigned(); + if (isUValue) { + uValue = value.get(); + } +#endif + if (!isUValue) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not a positive integer.\n"; + } + } + return false; + } + + if (ret) { + (*ret) = uValue; + } + + return true; +} + +static bool ParseNumberProperty(double *ret, std::string *err, + const detail::json &o, + const std::string &property, + const bool required, + const std::string &parent_node = "") { + detail::json_const_iterator it; + + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + double numberValue; + bool isNumber = detail::GetNumber(detail::GetValue(it), numberValue); + + if (!isNumber) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not a number type.\n"; + } + } + return false; + } + + if (ret) { + (*ret) = numberValue; + } + + return true; +} + +static bool ParseNumberArrayProperty(std::vector *ret, std::string *err, + const detail::json &o, + const std::string &property, bool required, + const std::string &parent_node = "") { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + if (!detail::IsArray(detail::GetValue(it))) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not an array"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + ret->clear(); + auto end = detail::ArrayEnd(detail::GetValue(it)); + for (auto i = detail::ArrayBegin(detail::GetValue(it)); i != end; ++i) { + double numberValue; + const bool isNumber = detail::GetNumber(*i, numberValue); + if (!isNumber) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not a number.\n"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + ret->push_back(numberValue); + } + + return true; +} + +static bool ParseIntegerArrayProperty(std::vector *ret, std::string *err, + const detail::json &o, + const std::string &property, + bool required, + const std::string &parent_node = "") { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + if (!detail::IsArray(detail::GetValue(it))) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not an array"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + + ret->clear(); + auto end = detail::ArrayEnd(detail::GetValue(it)); + for (auto i = detail::ArrayBegin(detail::GetValue(it)); i != end; ++i) { + int numberValue; + bool isNumber = detail::GetInt(*i, numberValue); + if (!isNumber) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not an integer type.\n"; + if (!parent_node.empty()) { + (*err) += " in " + parent_node; + } + (*err) += ".\n"; + } + } + return false; + } + ret->push_back(numberValue); + } + + return true; +} + +static bool ParseStringProperty( + std::string *ret, std::string *err, const detail::json &o, + const std::string &property, bool required, + const std::string &parent_node = std::string()) { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing"; + if (parent_node.empty()) { + (*err) += ".\n"; + } else { + (*err) += " in `" + parent_node + "'.\n"; + } + } + } + return false; + } + + std::string strValue; + if (!detail::GetString(detail::GetValue(it), strValue)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not a string type.\n"; + } + } + return false; + } + + if (ret) { + (*ret) = std::move(strValue); + } + + return true; +} + +static bool ParseStringIntegerProperty(std::map *ret, + std::string *err, const detail::json &o, + const std::string &property, + bool required, + const std::string &parent = "") { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + if (!parent.empty()) { + (*err) += + "'" + property + "' property is missing in " + parent + ".\n"; + } else { + (*err) += "'" + property + "' property is missing.\n"; + } + } + } + return false; + } + + const detail::json &dict = detail::GetValue(it); + + // Make sure we are dealing with an object / dictionary. + if (!detail::IsObject(dict)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not an object.\n"; + } + } + return false; + } + + ret->clear(); + + detail::json_const_iterator dictIt(detail::ObjectBegin(dict)); + detail::json_const_iterator dictItEnd(detail::ObjectEnd(dict)); + + for (; dictIt != dictItEnd; ++dictIt) { + int intVal; + if (!detail::GetInt(detail::GetValue(dictIt), intVal)) { + if (required) { + if (err) { + (*err) += "'" + property + "' value is not an integer type.\n"; + } + } + return false; + } + + // Insert into the list. + (*ret)[detail::GetKey(dictIt)] = intVal; + } + return true; +} + +static bool ParseJSONProperty(std::map *ret, + std::string *err, const detail::json &o, + const std::string &property, bool required) { + detail::json_const_iterator it; + if (!detail::FindMember(o, property.c_str(), it)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is missing. \n'"; + } + } + return false; + } + + const detail::json &obj = detail::GetValue(it); + + if (!detail::IsObject(obj)) { + if (required) { + if (err) { + (*err) += "'" + property + "' property is not a JSON object.\n"; + } + } + return false; + } + + ret->clear(); + + detail::json_const_iterator it2(detail::ObjectBegin(obj)); + detail::json_const_iterator itEnd(detail::ObjectEnd(obj)); + for (; it2 != itEnd; ++it2) { + double numVal; + if (detail::GetNumber(detail::GetValue(it2), numVal)) + ret->emplace(std::string(detail::GetKey(it2)), numVal); + } + + return true; +} + +static bool ParseParameterProperty(Parameter *param, std::string *err, + const detail::json &o, + const std::string &prop, bool required) { + // A parameter value can either be a string or an array of either a boolean or + // a number. Booleans of any kind aren't supported here. Granted, it + // complicates the Parameter structure and breaks it semantically in the sense + // that the client probably works off the assumption that if the string is + // empty the vector is used, etc. Would a tagged union work? + if (ParseStringProperty(¶m->string_value, err, o, prop, false)) { + // Found string property. + return true; + } else if (ParseNumberArrayProperty(¶m->number_array, err, o, prop, + false)) { + // Found a number array. + return true; + } else if (ParseNumberProperty(¶m->number_value, err, o, prop, false)) { + param->has_number_value = true; + return true; + } else if (ParseJSONProperty(¶m->json_double_value, err, o, prop, + false)) { + return true; + } else if (ParseBooleanProperty(¶m->bool_value, err, o, prop, false)) { + return true; + } else { + if (required) { + if (err) { + (*err) += "parameter must be a string or number / number array.\n"; + } + } + return false; + } +} + +static bool ParseExtensionsProperty(ExtensionMap *ret, std::string *err, + const detail::json &o) { + (void)err; + + detail::json_const_iterator it; + if (!detail::FindMember(o, "extensions", it)) { + return false; + } + + auto &obj = detail::GetValue(it); + if (!detail::IsObject(obj)) { + return false; + } + ExtensionMap extensions; + detail::json_const_iterator extIt = + detail::ObjectBegin(obj); // it.value().begin(); + detail::json_const_iterator extEnd = detail::ObjectEnd(obj); + for (; extIt != extEnd; ++extIt) { + auto &itObj = detail::GetValue(extIt); + if (!detail::IsObject(itObj)) continue; + std::string key(detail::GetKey(extIt)); + if (!ParseJsonAsValue(&extensions[key], itObj)) { + if (!key.empty()) { + // create empty object so that an extension object is still of type + // object + extensions[key] = Value{Value::Object{}}; + } + } + } + if (ret) { + (*ret) = std::move(extensions); + } + return true; +} + +template +static bool ParseExtrasAndExtensions(GltfType *target, std::string *err, + const detail::json &o, + bool store_json_strings) { + ParseExtensionsProperty(&target->extensions, err, o); + ParseExtrasProperty(&target->extras, o); + + if (store_json_strings) { + { + detail::json_const_iterator it; + if (detail::FindMember(o, "extensions", it)) { + target->extensions_json_string = + detail::JsonToString(detail::GetValue(it)); + } + } + { + detail::json_const_iterator it; + if (detail::FindMember(o, "extras", it)) { + target->extras_json_string = detail::JsonToString(detail::GetValue(it)); + } + } + } + return true; +} + +static bool ParseAsset(Asset *asset, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseStringProperty(&asset->version, err, o, "version", true, "Asset"); + ParseStringProperty(&asset->generator, err, o, "generator", false, "Asset"); + ParseStringProperty(&asset->minVersion, err, o, "minVersion", false, "Asset"); + ParseStringProperty(&asset->copyright, err, o, "copyright", false, "Asset"); + + ParseExtrasAndExtensions(asset, err, o, + store_original_json_for_extras_and_extensions); + return true; +} + +static bool ParseImage(Image *image, const int image_idx, std::string *err, + std::string *warn, const detail::json &o, + bool store_original_json_for_extras_and_extensions, + const std::string &basedir, const size_t max_file_size, + FsCallbacks *fs, const URICallbacks *uri_cb, + LoadImageDataFunction *LoadImageData = nullptr, + void *load_image_user_data = nullptr) { + // A glTF image must either reference a bufferView or an image uri + + // schema says oneOf [`bufferView`, `uri`] + // TODO(syoyo): Check the type of each parameters. + detail::json_const_iterator it; + bool hasBufferView = detail::FindMember(o, "bufferView", it); + bool hasURI = detail::FindMember(o, "uri", it); + + ParseStringProperty(&image->name, err, o, "name", false); + + if (hasBufferView && hasURI) { + // Should not both defined. + if (err) { + (*err) += + "Only one of `bufferView` or `uri` should be defined, but both are " + "defined for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + "\"\n"; + } + return false; + } + + if (!hasBufferView && !hasURI) { + if (err) { + (*err) += "Neither required `bufferView` nor `uri` defined for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\"\n"; + } + return false; + } + + ParseExtrasAndExtensions(image, err, o, + store_original_json_for_extras_and_extensions); + + if (hasBufferView) { + int bufferView = -1; + if (!ParseIntegerProperty(&bufferView, err, o, "bufferView", true)) { + if (err) { + (*err) += "Failed to parse `bufferView` for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\"\n"; + } + return false; + } + + std::string mime_type; + ParseStringProperty(&mime_type, err, o, "mimeType", false); + + int width = 0; + ParseIntegerProperty(&width, err, o, "width", false); + + int height = 0; + ParseIntegerProperty(&height, err, o, "height", false); + + // Just only save some information here. Loading actual image data from + // bufferView is done after this `ParseImage` function. + image->bufferView = bufferView; + image->mimeType = mime_type; + image->width = width; + image->height = height; + + return true; + } + + // Parse URI & Load image data. + + std::string uri; + std::string tmp_err; + if (!ParseStringProperty(&uri, &tmp_err, o, "uri", true)) { + if (err) { + (*err) += "Failed to parse `uri` for image[" + std::to_string(image_idx) + + "] name = \"" + image->name + "\".\n"; + } + return false; + } + + std::vector img; + + if (IsDataURI(uri)) { + if (!DecodeDataURI(&img, image->mimeType, uri, 0, false)) { + if (err) { + (*err) += "Failed to decode 'uri' for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\"\n"; + } + return false; + } + } else { + // Assume external file + // Keep texture path (for textures that cannot be decoded) + image->uri = uri; +#ifdef TINYGLTF_NO_EXTERNAL_IMAGE + return true; +#else + std::string decoded_uri; + if (!uri_cb->decode(uri, &decoded_uri, uri_cb->user_data)) { + if (warn) { + (*warn) += "Failed to decode 'uri' for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\"\n"; + } + + // Image loading failure is not critical to overall gltf loading. + return true; + } + + if (!LoadExternalFile(&img, err, warn, decoded_uri, basedir, + /* required */ false, /* required bytes */ 0, + /* checksize */ false, + /* max file size */ max_file_size, fs)) { + if (warn) { + (*warn) += "Failed to load external 'uri' for image[" + + std::to_string(image_idx) + "] name = \"" + decoded_uri + + "\"\n"; + } + // If the image cannot be loaded, keep uri as image->uri. + return true; + } + + if (img.empty()) { + if (warn) { + (*warn) += "Image data is empty for image[" + + std::to_string(image_idx) + "] name = \"" + image->name + + "\" \n"; + } + return false; + } +#endif + } + + if (*LoadImageData == nullptr) { + if (err) { + (*err) += "No LoadImageData callback specified.\n"; + } + return false; + } + return (*LoadImageData)(image, image_idx, err, warn, 0, 0, &img.at(0), + static_cast(img.size()), load_image_user_data); +} + +static bool ParseTexture(Texture *texture, std::string *err, + const detail::json &o, + bool store_original_json_for_extras_and_extensions, + const std::string &basedir) { + (void)basedir; + int sampler = -1; + int source = -1; + ParseIntegerProperty(&sampler, err, o, "sampler", false); + + ParseIntegerProperty(&source, err, o, "source", false); + + texture->sampler = sampler; + texture->source = source; + + ParseExtrasAndExtensions(texture, err, o, + store_original_json_for_extras_and_extensions); + + ParseStringProperty(&texture->name, err, o, "name", false); + + return true; +} + +static bool ParseTextureInfo( + TextureInfo *texinfo, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (texinfo == nullptr) { + return false; + } + + if (!ParseIntegerProperty(&texinfo->index, err, o, "index", + /* required */ true, "TextureInfo")) { + return false; + } + + ParseIntegerProperty(&texinfo->texCoord, err, o, "texCoord", false); + + ParseExtrasAndExtensions(texinfo, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseNormalTextureInfo( + NormalTextureInfo *texinfo, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (texinfo == nullptr) { + return false; + } + + if (!ParseIntegerProperty(&texinfo->index, err, o, "index", + /* required */ true, "NormalTextureInfo")) { + return false; + } + + ParseIntegerProperty(&texinfo->texCoord, err, o, "texCoord", false); + ParseNumberProperty(&texinfo->scale, err, o, "scale", false); + + ParseExtrasAndExtensions(texinfo, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseOcclusionTextureInfo( + OcclusionTextureInfo *texinfo, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (texinfo == nullptr) { + return false; + } + + if (!ParseIntegerProperty(&texinfo->index, err, o, "index", + /* required */ true, "NormalTextureInfo")) { + return false; + } + + ParseIntegerProperty(&texinfo->texCoord, err, o, "texCoord", false); + ParseNumberProperty(&texinfo->strength, err, o, "strength", false); + + ParseExtrasAndExtensions(texinfo, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseBuffer(Buffer *buffer, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions, + FsCallbacks *fs, const URICallbacks *uri_cb, + const std::string &basedir, + const size_t max_buffer_size, bool is_binary = false, + const unsigned char *bin_data = nullptr, + size_t bin_size = 0) { + size_t byteLength; + if (!ParseUnsignedProperty(&byteLength, err, o, "byteLength", true, + "Buffer")) { + return false; + } + + // In glTF 2.0, uri is not mandatory anymore + buffer->uri.clear(); + ParseStringProperty(&buffer->uri, err, o, "uri", false, "Buffer"); + + // having an empty uri for a non embedded image should not be valid + if (!is_binary && buffer->uri.empty()) { + if (err) { + (*err) += "'uri' is missing from non binary glTF file buffer.\n"; + } + } + + detail::json_const_iterator type; + if (detail::FindMember(o, "type", type)) { + std::string typeStr; + if (detail::GetString(detail::GetValue(type), typeStr)) { + if (typeStr.compare("arraybuffer") == 0) { + // buffer.type = "arraybuffer"; + } + } + } + + if (is_binary) { + // Still binary glTF accepts external dataURI. + if (!buffer->uri.empty()) { + // First try embedded data URI. + if (IsDataURI(buffer->uri)) { + std::string mime_type; + if (!DecodeDataURI(&buffer->data, mime_type, buffer->uri, byteLength, + true)) { + if (err) { + (*err) += + "Failed to decode 'uri' : " + buffer->uri + " in Buffer\n"; + } + return false; + } + } else { + // External .bin file. + std::string decoded_uri; + if (!uri_cb->decode(buffer->uri, &decoded_uri, uri_cb->user_data)) { + return false; + } + if (!LoadExternalFile(&buffer->data, err, /* warn */ nullptr, + decoded_uri, basedir, /* required */ true, + byteLength, /* checkSize */ true, + /* max_file_size */ max_buffer_size, fs)) { + return false; + } + } + } else { + // load data from (embedded) binary data + + if ((bin_size == 0) || (bin_data == nullptr)) { + if (err) { + (*err) += + "Invalid binary data in `Buffer', or GLB with empty BIN chunk.\n"; + } + return false; + } + + if (byteLength > bin_size) { + if (err) { + std::stringstream ss; + ss << "Invalid `byteLength'. Must be equal or less than binary size: " + "`byteLength' = " + << byteLength << ", binary size = " << bin_size << std::endl; + (*err) += ss.str(); + } + return false; + } + + // Read buffer data + buffer->data.resize(static_cast(byteLength)); + memcpy(&(buffer->data.at(0)), bin_data, static_cast(byteLength)); + } + + } else { + if (IsDataURI(buffer->uri)) { + std::string mime_type; + if (!DecodeDataURI(&buffer->data, mime_type, buffer->uri, byteLength, + true)) { + if (err) { + (*err) += "Failed to decode 'uri' : " + buffer->uri + " in Buffer\n"; + } + return false; + } + } else { + // Assume external .bin file. + std::string decoded_uri; + if (!uri_cb->decode(buffer->uri, &decoded_uri, uri_cb->user_data)) { + return false; + } + if (!LoadExternalFile(&buffer->data, err, /* warn */ nullptr, decoded_uri, + basedir, /* required */ true, byteLength, + /* checkSize */ true, + /* max file size */ max_buffer_size, fs)) { + return false; + } + } + } + + ParseStringProperty(&buffer->name, err, o, "name", false); + + ParseExtrasAndExtensions(buffer, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseBufferView( + BufferView *bufferView, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + int buffer = -1; + if (!ParseIntegerProperty(&buffer, err, o, "buffer", true, "BufferView")) { + return false; + } + + size_t byteOffset = 0; + ParseUnsignedProperty(&byteOffset, err, o, "byteOffset", false); + + size_t byteLength = 1; + if (!ParseUnsignedProperty(&byteLength, err, o, "byteLength", true, + "BufferView")) { + return false; + } + + size_t byteStride = 0; + if (!ParseUnsignedProperty(&byteStride, err, o, "byteStride", false)) { + // Spec says: When byteStride of referenced bufferView is not defined, it + // means that accessor elements are tightly packed, i.e., effective stride + // equals the size of the element. + // We cannot determine the actual byteStride until Accessor are parsed, thus + // set 0(= tightly packed) here(as done in OpenGL's VertexAttribPoiner) + byteStride = 0; + } + + if ((byteStride > 252) || ((byteStride % 4) != 0)) { + if (err) { + std::stringstream ss; + ss << "Invalid `byteStride' value. `byteStride' must be the multiple of " + "4 : " + << byteStride << std::endl; + + (*err) += ss.str(); + } + return false; + } + + int target = 0; + ParseIntegerProperty(&target, err, o, "target", false); + if ((target == TINYGLTF_TARGET_ARRAY_BUFFER) || + (target == TINYGLTF_TARGET_ELEMENT_ARRAY_BUFFER)) { + // OK + } else { + target = 0; + } + bufferView->target = target; + + ParseStringProperty(&bufferView->name, err, o, "name", false); + + ParseExtrasAndExtensions(bufferView, err, o, + store_original_json_for_extras_and_extensions); + + bufferView->buffer = buffer; + bufferView->byteOffset = byteOffset; + bufferView->byteLength = byteLength; + bufferView->byteStride = byteStride; + return true; +} + +static bool ParseSparseAccessor( + Accessor::Sparse *sparse, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + sparse->isSparse = true; + + int count = 0; + if (!ParseIntegerProperty(&count, err, o, "count", true, "SparseAccessor")) { + return false; + } + + ParseExtrasAndExtensions(sparse, err, o, + store_original_json_for_extras_and_extensions); + + detail::json_const_iterator indices_iterator; + detail::json_const_iterator values_iterator; + if (!detail::FindMember(o, "indices", indices_iterator)) { + (*err) = "the sparse object of this accessor doesn't have indices"; + return false; + } + + if (!detail::FindMember(o, "values", values_iterator)) { + (*err) = "the sparse object of this accessor doesn't have values"; + return false; + } + + const detail::json &indices_obj = detail::GetValue(indices_iterator); + const detail::json &values_obj = detail::GetValue(values_iterator); + + int indices_buffer_view = 0, component_type = 0; + size_t indices_byte_offset = 0; + if (!ParseIntegerProperty(&indices_buffer_view, err, indices_obj, + "bufferView", true, "SparseAccessor")) { + return false; + } + ParseUnsignedProperty(&indices_byte_offset, err, indices_obj, "byteOffset", + false); + if (!ParseIntegerProperty(&component_type, err, indices_obj, "componentType", + true, "SparseAccessor")) { + return false; + } + + int values_buffer_view = 0; + size_t values_byte_offset = 0; + if (!ParseIntegerProperty(&values_buffer_view, err, values_obj, "bufferView", + true, "SparseAccessor")) { + return false; + } + ParseUnsignedProperty(&values_byte_offset, err, values_obj, "byteOffset", + false); + + sparse->count = count; + sparse->indices.bufferView = indices_buffer_view; + sparse->indices.byteOffset = indices_byte_offset; + sparse->indices.componentType = component_type; + ParseExtrasAndExtensions(&sparse->indices, err, indices_obj, + store_original_json_for_extras_and_extensions); + + sparse->values.bufferView = values_buffer_view; + sparse->values.byteOffset = values_byte_offset; + ParseExtrasAndExtensions(&sparse->values, err, values_obj, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseAccessor(Accessor *accessor, std::string *err, + const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + int bufferView = -1; + ParseIntegerProperty(&bufferView, err, o, "bufferView", false, "Accessor"); + + size_t byteOffset = 0; + ParseUnsignedProperty(&byteOffset, err, o, "byteOffset", false, "Accessor"); + + bool normalized = false; + ParseBooleanProperty(&normalized, err, o, "normalized", false, "Accessor"); + + size_t componentType = 0; + if (!ParseUnsignedProperty(&componentType, err, o, "componentType", true, + "Accessor")) { + return false; + } + + size_t count = 0; + if (!ParseUnsignedProperty(&count, err, o, "count", true, "Accessor")) { + return false; + } + + std::string type; + if (!ParseStringProperty(&type, err, o, "type", true, "Accessor")) { + return false; + } + + if (type.compare("SCALAR") == 0) { + accessor->type = TINYGLTF_TYPE_SCALAR; + } else if (type.compare("VEC2") == 0) { + accessor->type = TINYGLTF_TYPE_VEC2; + } else if (type.compare("VEC3") == 0) { + accessor->type = TINYGLTF_TYPE_VEC3; + } else if (type.compare("VEC4") == 0) { + accessor->type = TINYGLTF_TYPE_VEC4; + } else if (type.compare("MAT2") == 0) { + accessor->type = TINYGLTF_TYPE_MAT2; + } else if (type.compare("MAT3") == 0) { + accessor->type = TINYGLTF_TYPE_MAT3; + } else if (type.compare("MAT4") == 0) { + accessor->type = TINYGLTF_TYPE_MAT4; + } else { + std::stringstream ss; + ss << "Unsupported `type` for accessor object. Got \"" << type << "\"\n"; + if (err) { + (*err) += ss.str(); + } + return false; + } + + ParseStringProperty(&accessor->name, err, o, "name", false); + + accessor->minValues.clear(); + accessor->maxValues.clear(); + ParseNumberArrayProperty(&accessor->minValues, err, o, "min", false, + "Accessor"); + + ParseNumberArrayProperty(&accessor->maxValues, err, o, "max", false, + "Accessor"); + + accessor->count = count; + accessor->bufferView = bufferView; + accessor->byteOffset = byteOffset; + accessor->normalized = normalized; + { + if (componentType >= TINYGLTF_COMPONENT_TYPE_BYTE && + componentType <= TINYGLTF_COMPONENT_TYPE_DOUBLE) { + // OK + accessor->componentType = int(componentType); + } else { + std::stringstream ss; + ss << "Invalid `componentType` in accessor. Got " << componentType + << "\n"; + if (err) { + (*err) += ss.str(); + } + return false; + } + } + + ParseExtrasAndExtensions(accessor, err, o, + store_original_json_for_extras_and_extensions); + + // check if accessor has a "sparse" object: + detail::json_const_iterator iterator; + if (detail::FindMember(o, "sparse", iterator)) { + // here this accessor has a "sparse" subobject + return ParseSparseAccessor(&accessor->sparse, err, + detail::GetValue(iterator), + store_original_json_for_extras_and_extensions); + } + + return true; +} + +#ifdef TINYGLTF_ENABLE_DRACO + +static void DecodeIndexBuffer(draco::Mesh *mesh, size_t componentSize, + std::vector &outBuffer) { + if (componentSize == 4) { + assert(sizeof(mesh->face(draco::FaceIndex(0))[0]) == componentSize); + memcpy(outBuffer.data(), &mesh->face(draco::FaceIndex(0))[0], + outBuffer.size()); + } else { + size_t faceStride = componentSize * 3; + for (draco::FaceIndex f(0); f < mesh->num_faces(); ++f) { + const draco::Mesh::Face &face = mesh->face(f); + if (componentSize == 2) { + uint16_t indices[3] = {(uint16_t)face[0].value(), + (uint16_t)face[1].value(), + (uint16_t)face[2].value()}; + memcpy(outBuffer.data() + f.value() * faceStride, &indices[0], + faceStride); + } else { + uint8_t indices[3] = {(uint8_t)face[0].value(), + (uint8_t)face[1].value(), + (uint8_t)face[2].value()}; + memcpy(outBuffer.data() + f.value() * faceStride, &indices[0], + faceStride); + } + } + } +} + +template +static bool GetAttributeForAllPoints(draco::Mesh *mesh, + const draco::PointAttribute *pAttribute, + std::vector &outBuffer) { + size_t byteOffset = 0; + T values[4] = {0, 0, 0, 0}; + for (draco::PointIndex i(0); i < mesh->num_points(); ++i) { + const draco::AttributeValueIndex val_index = pAttribute->mapped_index(i); + if (!pAttribute->ConvertValue(val_index, pAttribute->num_components(), + values)) + return false; + + memcpy(outBuffer.data() + byteOffset, &values[0], + sizeof(T) * pAttribute->num_components()); + byteOffset += sizeof(T) * pAttribute->num_components(); + } + + return true; +} + +static bool GetAttributeForAllPoints(uint32_t componentType, draco::Mesh *mesh, + const draco::PointAttribute *pAttribute, + std::vector &outBuffer) { + bool decodeResult = false; + switch (componentType) { + case TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_BYTE: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_UNSIGNED_SHORT: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_SHORT: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_INT: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_UNSIGNED_INT: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_FLOAT: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + case TINYGLTF_COMPONENT_TYPE_DOUBLE: + decodeResult = + GetAttributeForAllPoints(mesh, pAttribute, outBuffer); + break; + default: + return false; + } + + return decodeResult; +} + +static bool ParseDracoExtension(Primitive *primitive, Model *model, + std::string *err, std::string *warn, + const Value &dracoExtensionValue, + ParseStrictness strictness) { + (void)err; + auto bufferViewValue = dracoExtensionValue.Get("bufferView"); + if (!bufferViewValue.IsInt()) return false; + auto attributesValue = dracoExtensionValue.Get("attributes"); + if (!attributesValue.IsObject()) return false; + + auto attributesObject = attributesValue.Get(); + int bufferView = bufferViewValue.Get(); + + BufferView &view = model->bufferViews[bufferView]; + Buffer &buffer = model->buffers[view.buffer]; + // BufferView has already been decoded + if (view.dracoDecoded) return true; + view.dracoDecoded = true; + + const char *bufferViewData = + reinterpret_cast(buffer.data.data() + view.byteOffset); + size_t bufferViewSize = view.byteLength; + + // decode draco + draco::DecoderBuffer decoderBuffer; + decoderBuffer.Init(bufferViewData, bufferViewSize); + draco::Decoder decoder; + auto decodeResult = decoder.DecodeMeshFromBuffer(&decoderBuffer); + if (!decodeResult.ok()) { + return false; + } + const std::unique_ptr &mesh = decodeResult.value(); + + // create new bufferView for indices + if (primitive->indices >= 0) { + if (strictness == ParseStrictness::Permissive) { + const draco::PointIndex::ValueType numPoint = mesh->num_points(); + // handle the situation where the stored component type does not match the + // required type for the actual number of stored points + int supposedComponentType = TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE; + if (numPoint < static_cast( + std::numeric_limits::max())) { + supposedComponentType = TINYGLTF_COMPONENT_TYPE_UNSIGNED_BYTE; + } else if ( + numPoint < static_cast( + std::numeric_limits::max())) { + supposedComponentType = TINYGLTF_COMPONENT_TYPE_UNSIGNED_SHORT; + } else { + supposedComponentType = TINYGLTF_COMPONENT_TYPE_UNSIGNED_INT; + } + + if (supposedComponentType > model->accessors[primitive->indices].componentType) { + if (warn) { + (*warn) += + "GLTF component type " + std::to_string(model->accessors[primitive->indices].componentType) + + " is not sufficient for number of stored points," + " treating as " + std::to_string(supposedComponentType) + "\n"; + } + model->accessors[primitive->indices].componentType = supposedComponentType; + } + } + + int32_t componentSize = GetComponentSizeInBytes( + model->accessors[primitive->indices].componentType); + Buffer decodedIndexBuffer; + decodedIndexBuffer.data.resize(mesh->num_faces() * 3 * componentSize); + + DecodeIndexBuffer(mesh.get(), componentSize, decodedIndexBuffer.data); + + model->buffers.emplace_back(std::move(decodedIndexBuffer)); + + BufferView decodedIndexBufferView; + decodedIndexBufferView.buffer = int(model->buffers.size() - 1); + decodedIndexBufferView.byteLength = + int(mesh->num_faces() * 3 * componentSize); + decodedIndexBufferView.byteOffset = 0; + decodedIndexBufferView.byteStride = 0; + decodedIndexBufferView.target = TINYGLTF_TARGET_ARRAY_BUFFER; + model->bufferViews.emplace_back(std::move(decodedIndexBufferView)); + + model->accessors[primitive->indices].bufferView = + int(model->bufferViews.size() - 1); + model->accessors[primitive->indices].count = int(mesh->num_faces() * 3); + } + + for (const auto &attribute : attributesObject) { + if (!attribute.second.IsInt()) return false; + auto primitiveAttribute = primitive->attributes.find(attribute.first); + if (primitiveAttribute == primitive->attributes.end()) return false; + + int dracoAttributeIndex = attribute.second.Get(); + const auto pAttribute = mesh->GetAttributeByUniqueId(dracoAttributeIndex); + const auto componentType = + model->accessors[primitiveAttribute->second].componentType; + + // Create a new buffer for this decoded buffer + Buffer decodedBuffer; + size_t bufferSize = mesh->num_points() * pAttribute->num_components() * + GetComponentSizeInBytes(componentType); + decodedBuffer.data.resize(bufferSize); + + if (!GetAttributeForAllPoints(componentType, mesh.get(), pAttribute, + decodedBuffer.data)) + return false; + + model->buffers.emplace_back(std::move(decodedBuffer)); + + BufferView decodedBufferView; + decodedBufferView.buffer = int(model->buffers.size() - 1); + decodedBufferView.byteLength = bufferSize; + decodedBufferView.byteOffset = pAttribute->byte_offset(); + decodedBufferView.byteStride = pAttribute->byte_stride(); + decodedBufferView.target = primitive->indices >= 0 + ? TINYGLTF_TARGET_ELEMENT_ARRAY_BUFFER + : TINYGLTF_TARGET_ARRAY_BUFFER; + model->bufferViews.emplace_back(std::move(decodedBufferView)); + + model->accessors[primitiveAttribute->second].bufferView = + int(model->bufferViews.size() - 1); + model->accessors[primitiveAttribute->second].count = + int(mesh->num_points()); + } + + return true; +} +#endif + +static bool ParsePrimitive(Primitive *primitive, Model *model, + std::string *err, std::string *warn, + const detail::json &o, + bool store_original_json_for_extras_and_extensions, + ParseStrictness strictness) { + int material = -1; + ParseIntegerProperty(&material, err, o, "material", false); + primitive->material = material; + + int mode = TINYGLTF_MODE_TRIANGLES; + ParseIntegerProperty(&mode, err, o, "mode", false); + primitive->mode = mode; // Why only triangles were supported ? + + int indices = -1; + ParseIntegerProperty(&indices, err, o, "indices", false); + primitive->indices = indices; + if (!ParseStringIntegerProperty(&primitive->attributes, err, o, "attributes", + true, "Primitive")) { + return false; + } + + // Look for morph targets + detail::json_const_iterator targetsObject; + if (detail::FindMember(o, "targets", targetsObject) && + detail::IsArray(detail::GetValue(targetsObject))) { + auto targetsObjectEnd = detail::ArrayEnd(detail::GetValue(targetsObject)); + for (detail::json_const_array_iterator i = + detail::ArrayBegin(detail::GetValue(targetsObject)); + i != targetsObjectEnd; ++i) { + std::map targetAttribues; + + const detail::json &dict = *i; + if (detail::IsObject(dict)) { + detail::json_const_iterator dictIt(detail::ObjectBegin(dict)); + detail::json_const_iterator dictItEnd(detail::ObjectEnd(dict)); + + for (; dictIt != dictItEnd; ++dictIt) { + int iVal; + if (detail::GetInt(detail::GetValue(dictIt), iVal)) + targetAttribues[detail::GetKey(dictIt)] = iVal; + } + primitive->targets.emplace_back(std::move(targetAttribues)); + } + } + } + + ParseExtrasAndExtensions(primitive, err, o, + store_original_json_for_extras_and_extensions); + +#ifdef TINYGLTF_ENABLE_DRACO + auto dracoExtension = + primitive->extensions.find("KHR_draco_mesh_compression"); + if (dracoExtension != primitive->extensions.end()) { + ParseDracoExtension(primitive, model, err, warn, dracoExtension->second, strictness); + } +#else + (void)model; + (void)warn; + (void)strictness; +#endif + + return true; +} + +static bool ParseMesh(Mesh *mesh, Model *model, + std::string *err, std::string *warn, + const detail::json &o, + bool store_original_json_for_extras_and_extensions, + ParseStrictness strictness) { + ParseStringProperty(&mesh->name, err, o, "name", false); + + mesh->primitives.clear(); + detail::json_const_iterator primObject; + if (detail::FindMember(o, "primitives", primObject) && + detail::IsArray(detail::GetValue(primObject))) { + detail::json_const_array_iterator primEnd = + detail::ArrayEnd(detail::GetValue(primObject)); + for (detail::json_const_array_iterator i = + detail::ArrayBegin(detail::GetValue(primObject)); + i != primEnd; ++i) { + Primitive primitive; + if (ParsePrimitive(&primitive, model, err, warn, *i, + store_original_json_for_extras_and_extensions, + strictness)) { + // Only add the primitive if the parsing succeeds. + mesh->primitives.emplace_back(std::move(primitive)); + } + } + } + + // Should probably check if has targets and if dimensions fit + ParseNumberArrayProperty(&mesh->weights, err, o, "weights", false); + + ParseExtrasAndExtensions(mesh, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseNode(Node *node, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseStringProperty(&node->name, err, o, "name", false); + + int skin = -1; + ParseIntegerProperty(&skin, err, o, "skin", false); + node->skin = skin; + + // Matrix and T/R/S are exclusive + if (!ParseNumberArrayProperty(&node->matrix, err, o, "matrix", false)) { + ParseNumberArrayProperty(&node->rotation, err, o, "rotation", false); + ParseNumberArrayProperty(&node->scale, err, o, "scale", false); + ParseNumberArrayProperty(&node->translation, err, o, "translation", false); + } + + int camera = -1; + ParseIntegerProperty(&camera, err, o, "camera", false); + node->camera = camera; + + int mesh = -1; + ParseIntegerProperty(&mesh, err, o, "mesh", false); + node->mesh = mesh; + + node->children.clear(); + ParseIntegerArrayProperty(&node->children, err, o, "children", false); + + ParseNumberArrayProperty(&node->weights, err, o, "weights", false); + + ParseExtrasAndExtensions(node, err, o, + store_original_json_for_extras_and_extensions); + + // KHR_lights_punctual: parse light source reference + int light = -1; + if (node->extensions.count("KHR_lights_punctual") != 0) { + auto const &light_ext = node->extensions["KHR_lights_punctual"]; + if (light_ext.Has("light")) { + light = light_ext.Get("light").GetNumberAsInt(); + } else { + if (err) { + *err += + "Node has extension KHR_lights_punctual, but does not reference " + "a light source.\n"; + } + return false; + } + } + node->light = light; + + // KHR_audio: parse audio source reference + int emitter = -1; + if (node->extensions.count("KHR_audio") != 0) { + auto const &audio_ext = node->extensions["KHR_audio"]; + if (audio_ext.Has("emitter")) { + emitter = audio_ext.Get("emitter").GetNumberAsInt(); + } else { + if (err) { + *err += + "Node has extension KHR_audio, but does not reference " + "a audio emitter.\n"; + } + return false; + } + } + node->emitter = emitter; + + return true; +} + +static bool ParseScene(Scene *scene, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseStringProperty(&scene->name, err, o, "name", false); + ParseIntegerArrayProperty(&scene->nodes, err, o, "nodes", false); + + ParseExtrasAndExtensions(scene, err, o, + store_original_json_for_extras_and_extensions); + + // Parse KHR_audio global emitters + if (scene->extensions.count("KHR_audio") != 0) { + auto const &audio_ext = scene->extensions["KHR_audio"]; + if (audio_ext.Has("emitters")) { + auto emittersArr = audio_ext.Get("emitters"); + for (size_t i = 0; i < emittersArr.ArrayLen(); ++i) { + scene->audioEmitters.emplace_back(emittersArr.Get(i).GetNumberAsInt()); + } + } else { + if (err) { + *err += + "Node has extension KHR_audio, but does not reference " + "a audio emitter.\n"; + } + return false; + } + } + + return true; +} + +static bool ParsePbrMetallicRoughness( + PbrMetallicRoughness *pbr, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (pbr == nullptr) { + return false; + } + + std::vector baseColorFactor; + if (ParseNumberArrayProperty(&baseColorFactor, err, o, "baseColorFactor", + /* required */ false)) { + if (baseColorFactor.size() != 4) { + if (err) { + (*err) += + "Array length of `baseColorFactor` parameter in " + "pbrMetallicRoughness must be 4, but got " + + std::to_string(baseColorFactor.size()) + "\n"; + } + return false; + } + pbr->baseColorFactor = baseColorFactor; + } + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "baseColorTexture", it)) { + ParseTextureInfo(&pbr->baseColorTexture, err, detail::GetValue(it), + store_original_json_for_extras_and_extensions); + } + } + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "metallicRoughnessTexture", it)) { + ParseTextureInfo(&pbr->metallicRoughnessTexture, err, + detail::GetValue(it), + store_original_json_for_extras_and_extensions); + } + } + + ParseNumberProperty(&pbr->metallicFactor, err, o, "metallicFactor", false); + ParseNumberProperty(&pbr->roughnessFactor, err, o, "roughnessFactor", false); + + ParseExtrasAndExtensions(pbr, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseMaterial(Material *material, std::string *err, std::string *warn, + const detail::json &o, + bool store_original_json_for_extras_and_extensions, + ParseStrictness strictness) { + ParseStringProperty(&material->name, err, o, "name", /* required */ false); + + if (ParseNumberArrayProperty(&material->emissiveFactor, err, o, + "emissiveFactor", + /* required */ false)) { + if (strictness==ParseStrictness::Permissive && material->emissiveFactor.size() == 4) { + if (warn) { + (*warn) += + "Array length of `emissiveFactor` parameter in " + "material must be 3, but got 4\n"; + } + material->emissiveFactor.resize(3); + } + else if (material->emissiveFactor.size() != 3) { + if (err) { + (*err) += + "Array length of `emissiveFactor` parameter in " + "material must be 3, but got " + + std::to_string(material->emissiveFactor.size()) + "\n"; + } + return false; + } + } else { + // fill with default values + material->emissiveFactor = {0.0, 0.0, 0.0}; + } + + ParseStringProperty(&material->alphaMode, err, o, "alphaMode", + /* required */ false); + ParseNumberProperty(&material->alphaCutoff, err, o, "alphaCutoff", + /* required */ false); + ParseBooleanProperty(&material->doubleSided, err, o, "doubleSided", + /* required */ false); + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "pbrMetallicRoughness", it)) { + ParsePbrMetallicRoughness(&material->pbrMetallicRoughness, err, + detail::GetValue(it), + store_original_json_for_extras_and_extensions); + } + } + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "normalTexture", it)) { + ParseNormalTextureInfo(&material->normalTexture, err, + detail::GetValue(it), + store_original_json_for_extras_and_extensions); + } + } + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "occlusionTexture", it)) { + ParseOcclusionTextureInfo(&material->occlusionTexture, err, + detail::GetValue(it), + store_original_json_for_extras_and_extensions); + } + } + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "emissiveTexture", it)) { + ParseTextureInfo(&material->emissiveTexture, err, detail::GetValue(it), + store_original_json_for_extras_and_extensions); + } + } + + // Old code path. For backward compatibility, we still store material values + // as Parameter. This will create duplicated information for + // example(pbrMetallicRoughness), but should be negligible in terms of memory + // consumption. + // TODO(syoyo): Remove in the next major release. + material->values.clear(); + material->additionalValues.clear(); + + detail::json_const_iterator it(detail::ObjectBegin(o)); + detail::json_const_iterator itEnd(detail::ObjectEnd(o)); + + for (; it != itEnd; ++it) { + std::string key(detail::GetKey(it)); + if (key == "pbrMetallicRoughness") { + if (detail::IsObject(detail::GetValue(it))) { + const detail::json &values_object = detail::GetValue(it); + + detail::json_const_iterator itVal(detail::ObjectBegin(values_object)); + detail::json_const_iterator itValEnd(detail::ObjectEnd(values_object)); + + for (; itVal != itValEnd; ++itVal) { + Parameter param; + if (ParseParameterProperty(¶m, err, values_object, + detail::GetKey(itVal), false)) { + material->values.emplace(detail::GetKey(itVal), std::move(param)); + } + } + } + } else if (key == "extensions" || key == "extras") { + // done later, skip, otherwise poorly parsed contents will be saved in the + // parametermap and serialized again later + } else { + Parameter param; + if (ParseParameterProperty(¶m, err, o, key, false)) { + // names of materials have already been parsed. Putting it in this map + // doesn't correctly reflect the glTF specification + if (key != "name") + material->additionalValues.emplace(std::move(key), std::move(param)); + } + } + } + + material->extensions.clear(); // Note(agnat): Why? + ParseExtrasAndExtensions(material, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseAnimationChannel( + AnimationChannel *channel, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + int samplerIndex = -1; + int targetIndex = -1; + if (!ParseIntegerProperty(&samplerIndex, err, o, "sampler", true, + "AnimationChannel")) { + if (err) { + (*err) += "`sampler` field is missing in animation channels\n"; + } + return false; + } + + detail::json_const_iterator targetIt; + if (detail::FindMember(o, "target", targetIt) && + detail::IsObject(detail::GetValue(targetIt))) { + const detail::json &target_object = detail::GetValue(targetIt); + + ParseIntegerProperty(&targetIndex, err, target_object, "node", false); + + if (!ParseStringProperty(&channel->target_path, err, target_object, "path", + true)) { + if (err) { + (*err) += "`path` field is missing in animation.channels.target\n"; + } + return false; + } + ParseExtensionsProperty(&channel->target_extensions, err, target_object); + ParseExtrasProperty(&channel->target_extras, target_object); + if (store_original_json_for_extras_and_extensions) { + { + detail::json_const_iterator it; + if (detail::FindMember(target_object, "extensions", it)) { + channel->target_extensions_json_string = + detail::JsonToString(detail::GetValue(it)); + } + } + { + detail::json_const_iterator it; + if (detail::FindMember(target_object, "extras", it)) { + channel->target_extras_json_string = + detail::JsonToString(detail::GetValue(it)); + } + } + } + } + + channel->sampler = samplerIndex; + channel->target_node = targetIndex; + + ParseExtrasAndExtensions(channel, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseAnimation(Animation *animation, std::string *err, + const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + { + detail::json_const_iterator channelsIt; + if (detail::FindMember(o, "channels", channelsIt) && + detail::IsArray(detail::GetValue(channelsIt))) { + detail::json_const_array_iterator channelEnd = + detail::ArrayEnd(detail::GetValue(channelsIt)); + for (detail::json_const_array_iterator i = + detail::ArrayBegin(detail::GetValue(channelsIt)); + i != channelEnd; ++i) { + AnimationChannel channel; + if (ParseAnimationChannel( + &channel, err, *i, + store_original_json_for_extras_and_extensions)) { + // Only add the channel if the parsing succeeds. + animation->channels.emplace_back(std::move(channel)); + } + } + } + } + + { + detail::json_const_iterator samplerIt; + if (detail::FindMember(o, "samplers", samplerIt) && + detail::IsArray(detail::GetValue(samplerIt))) { + const detail::json &sampler_array = detail::GetValue(samplerIt); + + detail::json_const_array_iterator it = detail::ArrayBegin(sampler_array); + detail::json_const_array_iterator itEnd = detail::ArrayEnd(sampler_array); + + for (; it != itEnd; ++it) { + const detail::json &s = *it; + + AnimationSampler sampler; + int inputIndex = -1; + int outputIndex = -1; + if (!ParseIntegerProperty(&inputIndex, err, s, "input", true)) { + if (err) { + (*err) += "`input` field is missing in animation.sampler\n"; + } + return false; + } + ParseStringProperty(&sampler.interpolation, err, s, "interpolation", + false); + if (!ParseIntegerProperty(&outputIndex, err, s, "output", true)) { + if (err) { + (*err) += "`output` field is missing in animation.sampler\n"; + } + return false; + } + sampler.input = inputIndex; + sampler.output = outputIndex; + ParseExtrasAndExtensions(&sampler, err, o, + store_original_json_for_extras_and_extensions); + + animation->samplers.emplace_back(std::move(sampler)); + } + } + } + + ParseStringProperty(&animation->name, err, o, "name", false); + + ParseExtrasAndExtensions(animation, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseSampler(Sampler *sampler, std::string *err, + const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseStringProperty(&sampler->name, err, o, "name", false); + + int minFilter = -1; + int magFilter = -1; + int wrapS = TINYGLTF_TEXTURE_WRAP_REPEAT; + int wrapT = TINYGLTF_TEXTURE_WRAP_REPEAT; + // int wrapR = TINYGLTF_TEXTURE_WRAP_REPEAT; + ParseIntegerProperty(&minFilter, err, o, "minFilter", false); + ParseIntegerProperty(&magFilter, err, o, "magFilter", false); + ParseIntegerProperty(&wrapS, err, o, "wrapS", false); + ParseIntegerProperty(&wrapT, err, o, "wrapT", false); + // ParseIntegerProperty(&wrapR, err, o, "wrapR", false); // tinygltf + // extension + + // TODO(syoyo): Check the value is allowed one. + // (e.g. we allow 9728(NEAREST), but don't allow 9727) + + sampler->minFilter = minFilter; + sampler->magFilter = magFilter; + sampler->wrapS = wrapS; + sampler->wrapT = wrapT; + // sampler->wrapR = wrapR; + + ParseExtrasAndExtensions(sampler, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseSkin(Skin *skin, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseStringProperty(&skin->name, err, o, "name", false, "Skin"); + + std::vector joints; + if (!ParseIntegerArrayProperty(&joints, err, o, "joints", false, "Skin")) { + return false; + } + skin->joints = std::move(joints); + + int skeleton = -1; + ParseIntegerProperty(&skeleton, err, o, "skeleton", false, "Skin"); + skin->skeleton = skeleton; + + int invBind = -1; + ParseIntegerProperty(&invBind, err, o, "inverseBindMatrices", true, "Skin"); + skin->inverseBindMatrices = invBind; + + ParseExtrasAndExtensions(skin, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParsePerspectiveCamera( + PerspectiveCamera *camera, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + double yfov = 0.0; + if (!ParseNumberProperty(&yfov, err, o, "yfov", true, "OrthographicCamera")) { + return false; + } + + double znear = 0.0; + if (!ParseNumberProperty(&znear, err, o, "znear", true, + "PerspectiveCamera")) { + return false; + } + + double aspectRatio = 0.0; // = invalid + ParseNumberProperty(&aspectRatio, err, o, "aspectRatio", false, + "PerspectiveCamera"); + + double zfar = 0.0; // = invalid + ParseNumberProperty(&zfar, err, o, "zfar", false, "PerspectiveCamera"); + + camera->aspectRatio = aspectRatio; + camera->zfar = zfar; + camera->yfov = yfov; + camera->znear = znear; + + ParseExtrasAndExtensions(camera, err, o, + store_original_json_for_extras_and_extensions); + + // TODO(syoyo): Validate parameter values. + + return true; +} + +static bool ParseSpotLight(SpotLight *light, std::string *err, + const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseNumberProperty(&light->innerConeAngle, err, o, "innerConeAngle", false); + ParseNumberProperty(&light->outerConeAngle, err, o, "outerConeAngle", false); + + ParseExtrasAndExtensions(light, err, o, + store_original_json_for_extras_and_extensions); + + // TODO(syoyo): Validate parameter values. + + return true; +} + +static bool ParseOrthographicCamera( + OrthographicCamera *camera, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + double xmag = 0.0; + if (!ParseNumberProperty(&xmag, err, o, "xmag", true, "OrthographicCamera")) { + return false; + } + + double ymag = 0.0; + if (!ParseNumberProperty(&ymag, err, o, "ymag", true, "OrthographicCamera")) { + return false; + } + + double zfar = 0.0; + if (!ParseNumberProperty(&zfar, err, o, "zfar", true, "OrthographicCamera")) { + return false; + } + + double znear = 0.0; + if (!ParseNumberProperty(&znear, err, o, "znear", true, + "OrthographicCamera")) { + return false; + } + + ParseExtrasAndExtensions(camera, err, o, + store_original_json_for_extras_and_extensions); + + camera->xmag = xmag; + camera->ymag = ymag; + camera->zfar = zfar; + camera->znear = znear; + + // TODO(syoyo): Validate parameter values. + + return true; +} + +static bool ParseCamera(Camera *camera, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (!ParseStringProperty(&camera->type, err, o, "type", true, "Camera")) { + return false; + } + + if (camera->type.compare("orthographic") == 0) { + detail::json_const_iterator orthoIt; + if (!detail::FindMember(o, "orthographic", orthoIt)) { + if (err) { + std::stringstream ss; + ss << "Orthographic camera description not found." << std::endl; + (*err) += ss.str(); + } + return false; + } + + const detail::json &v = detail::GetValue(orthoIt); + if (!detail::IsObject(v)) { + if (err) { + std::stringstream ss; + ss << "\"orthographic\" is not a JSON object." << std::endl; + (*err) += ss.str(); + } + return false; + } + + if (!ParseOrthographicCamera( + &camera->orthographic, err, v, + store_original_json_for_extras_and_extensions)) { + return false; + } + } else if (camera->type.compare("perspective") == 0) { + detail::json_const_iterator perspIt; + if (!detail::FindMember(o, "perspective", perspIt)) { + if (err) { + std::stringstream ss; + ss << "Perspective camera description not found." << std::endl; + (*err) += ss.str(); + } + return false; + } + + const detail::json &v = detail::GetValue(perspIt); + if (!detail::IsObject(v)) { + if (err) { + std::stringstream ss; + ss << "\"perspective\" is not a JSON object." << std::endl; + (*err) += ss.str(); + } + return false; + } + + if (!ParsePerspectiveCamera( + &camera->perspective, err, v, + store_original_json_for_extras_and_extensions)) { + return false; + } + } else { + if (err) { + std::stringstream ss; + ss << "Invalid camera type: \"" << camera->type + << "\". Must be \"perspective\" or \"orthographic\"" << std::endl; + (*err) += ss.str(); + } + return false; + } + + ParseStringProperty(&camera->name, err, o, "name", false); + + ParseExtrasAndExtensions(camera, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseLight(Light *light, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (!ParseStringProperty(&light->type, err, o, "type", true)) { + return false; + } + + if (light->type == "spot") { + detail::json_const_iterator spotIt; + if (!detail::FindMember(o, "spot", spotIt)) { + if (err) { + std::stringstream ss; + ss << "Spot light description not found." << std::endl; + (*err) += ss.str(); + } + return false; + } + + const detail::json &v = detail::GetValue(spotIt); + if (!detail::IsObject(v)) { + if (err) { + std::stringstream ss; + ss << "\"spot\" is not a JSON object." << std::endl; + (*err) += ss.str(); + } + return false; + } + + if (!ParseSpotLight(&light->spot, err, v, + store_original_json_for_extras_and_extensions)) { + return false; + } + } + + ParseStringProperty(&light->name, err, o, "name", false); + ParseNumberArrayProperty(&light->color, err, o, "color", false); + ParseNumberProperty(&light->range, err, o, "range", false); + ParseNumberProperty(&light->intensity, err, o, "intensity", false); + + ParseExtrasAndExtensions(light, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParsePositionalEmitter( + PositionalEmitter *positional, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseNumberProperty(&positional->coneInnerAngle, err, o, "coneInnerAngle", + false); + ParseNumberProperty(&positional->coneOuterAngle, err, o, "coneOuterAngle", + false); + ParseNumberProperty(&positional->coneOuterGain, err, o, "coneOuterGain", + false); + ParseNumberProperty(&positional->maxDistance, err, o, "maxDistance", false); + ParseNumberProperty(&positional->refDistance, err, o, "refDistance", false); + ParseNumberProperty(&positional->rolloffFactor, err, o, "rolloffFactor", + false); + + ParseExtrasAndExtensions(positional, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseAudioEmitter( + AudioEmitter *emitter, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + if (!ParseStringProperty(&emitter->type, err, o, "type", true)) { + return false; + } + + if (emitter->type == "positional") { + detail::json_const_iterator positionalIt; + if (!detail::FindMember(o, "positional", positionalIt)) { + if (err) { + std::stringstream ss; + ss << "Positional emitter description not found." << std::endl; + (*err) += ss.str(); + } + return false; + } + + const detail::json &v = detail::GetValue(positionalIt); + if (!detail::IsObject(v)) { + if (err) { + std::stringstream ss; + ss << "\"positional\" is not a JSON object." << std::endl; + (*err) += ss.str(); + } + return false; + } + + if (!ParsePositionalEmitter( + &emitter->positional, err, v, + store_original_json_for_extras_and_extensions)) { + return false; + } + } + + ParseStringProperty(&emitter->name, err, o, "name", false); + ParseNumberProperty(&emitter->gain, err, o, "gain", false); + ParseBooleanProperty(&emitter->loop, err, o, "loop", false); + ParseBooleanProperty(&emitter->playing, err, o, "playing", false); + ParseStringProperty(&emitter->distanceModel, err, o, "distanceModel", false); + ParseIntegerProperty(&emitter->source, err, o, "source", true); + + ParseExtrasAndExtensions(emitter, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +static bool ParseAudioSource( + AudioSource *source, std::string *err, const detail::json &o, + bool store_original_json_for_extras_and_extensions) { + ParseStringProperty(&source->name, err, o, "name", false); + ParseStringProperty(&source->uri, err, o, "uri", false); + + if (source->uri.empty()) { + ParseIntegerProperty(&source->bufferView, err, o, "bufferView", true); + ParseStringProperty(&source->mimeType, err, o, "mimeType", true); + } + + ParseExtrasAndExtensions(source, err, o, + store_original_json_for_extras_and_extensions); + + return true; +} + +namespace detail { + +template +bool ForEachInArray(const detail::json &_v, const char *member, Callback &&cb) { + detail::json_const_iterator itm; + if (detail::FindMember(_v, member, itm) && + detail::IsArray(detail::GetValue(itm))) { + const detail::json &root = detail::GetValue(itm); + auto it = detail::ArrayBegin(root); + auto end = detail::ArrayEnd(root); + for (; it != end; ++it) { + if (!cb(*it)) return false; + } + } + return true; +}; + +} // end of namespace detail + +bool TinyGLTF::LoadFromString(Model *model, std::string *err, std::string *warn, + const char *json_str, + unsigned int json_str_length, + const std::string &base_dir, + unsigned int check_sections) { + if (json_str_length < 4) { + if (err) { + (*err) = "JSON string too short.\n"; + } + return false; + } + + detail::JsonDocument v; + +#if (defined(__cpp_exceptions) || defined(__EXCEPTIONS) || \ + defined(_CPPUNWIND)) && \ + !defined(TINYGLTF_NOEXCEPTION) + try { + detail::JsonParse(v, json_str, json_str_length, true); + + } catch (const std::exception &e) { + if (err) { + (*err) = e.what(); + } + return false; + } +#else + { + detail::JsonParse(v, json_str, json_str_length); + + if (!detail::IsObject(v)) { + // Assume parsing was failed. + if (err) { + (*err) = "Failed to parse JSON object\n"; + } + return false; + } + } +#endif + + if (!detail::IsObject(v)) { + // root is not an object. + if (err) { + (*err) = "Root element is not a JSON object\n"; + } + return false; + } + + { + bool version_found = false; + detail::json_const_iterator it; + if (detail::FindMember(v, "asset", it) && + detail::IsObject(detail::GetValue(it))) { + auto &itObj = detail::GetValue(it); + detail::json_const_iterator version_it; + std::string versionStr; + if (detail::FindMember(itObj, "version", version_it) && + detail::GetString(detail::GetValue(version_it), versionStr)) { + version_found = true; + } + } + if (version_found) { + // OK + } else if (check_sections & REQUIRE_VERSION) { + if (err) { + (*err) += "\"asset\" object not found in .gltf or not an object type\n"; + } + return false; + } + } + + // scene is not mandatory. + // FIXME Maybe a better way to handle it than removing the code + + auto IsArrayMemberPresent = [](const detail::json &_v, + const char *name) -> bool { + detail::json_const_iterator it; + return detail::FindMember(_v, name, it) && + detail::IsArray(detail::GetValue(it)); + }; + + { + if ((check_sections & REQUIRE_SCENES) && + !IsArrayMemberPresent(v, "scenes")) { + if (err) { + (*err) += "\"scenes\" object not found in .gltf or not an array type\n"; + } + return false; + } + } + + { + if ((check_sections & REQUIRE_NODES) && !IsArrayMemberPresent(v, "nodes")) { + if (err) { + (*err) += "\"nodes\" object not found in .gltf\n"; + } + return false; + } + } + + { + if ((check_sections & REQUIRE_ACCESSORS) && + !IsArrayMemberPresent(v, "accessors")) { + if (err) { + (*err) += "\"accessors\" object not found in .gltf\n"; + } + return false; + } + } + + { + if ((check_sections & REQUIRE_BUFFERS) && + !IsArrayMemberPresent(v, "buffers")) { + if (err) { + (*err) += "\"buffers\" object not found in .gltf\n"; + } + return false; + } + } + + { + if ((check_sections & REQUIRE_BUFFER_VIEWS) && + !IsArrayMemberPresent(v, "bufferViews")) { + if (err) { + (*err) += "\"bufferViews\" object not found in .gltf\n"; + } + return false; + } + } + + model->buffers.clear(); + model->bufferViews.clear(); + model->accessors.clear(); + model->meshes.clear(); + model->cameras.clear(); + model->nodes.clear(); + model->extensionsUsed.clear(); + model->extensionsRequired.clear(); + model->extensions.clear(); + model->defaultScene = -1; + + // 1. Parse Asset + { + detail::json_const_iterator it; + if (detail::FindMember(v, "asset", it) && + detail::IsObject(detail::GetValue(it))) { + const detail::json &root = detail::GetValue(it); + + ParseAsset(&model->asset, err, root, + store_original_json_for_extras_and_extensions_); + } + } + + using detail::ForEachInArray; + + // 2. Parse extensionUsed + { + ForEachInArray(v, "extensionsUsed", [&](const detail::json &o) { + std::string str; + detail::GetString(o, str); + model->extensionsUsed.emplace_back(std::move(str)); + return true; + }); + } + + { + ForEachInArray(v, "extensionsRequired", [&](const detail::json &o) { + std::string str; + detail::GetString(o, str); + model->extensionsRequired.emplace_back(std::move(str)); + return true; + }); + } + + // 3. Parse Buffer + { + bool success = ForEachInArray(v, "buffers", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`buffers' does not contain an JSON object."; + } + return false; + } + Buffer buffer; + if (!ParseBuffer(&buffer, err, o, + store_original_json_for_extras_and_extensions_, &fs, + &uri_cb, base_dir, max_external_file_size_, is_binary_, + bin_data_, bin_size_)) { + return false; + } + + model->buffers.emplace_back(std::move(buffer)); + return true; + }); + + if (!success) { + return false; + } + } + // 4. Parse BufferView + { + bool success = ForEachInArray(v, "bufferViews", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`bufferViews' does not contain an JSON object."; + } + return false; + } + BufferView bufferView; + if (!ParseBufferView(&bufferView, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->bufferViews.emplace_back(std::move(bufferView)); + return true; + }); + + if (!success) { + return false; + } + } + + // 5. Parse Accessor + { + bool success = ForEachInArray(v, "accessors", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`accessors' does not contain an JSON object."; + } + return false; + } + Accessor accessor; + if (!ParseAccessor(&accessor, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->accessors.emplace_back(std::move(accessor)); + return true; + }); + + if (!success) { + return false; + } + } + + // 6. Parse Mesh + { + bool success = ForEachInArray(v, "meshes", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`meshes' does not contain an JSON object."; + } + return false; + } + Mesh mesh; + if (!ParseMesh(&mesh, model, err, warn, o, + store_original_json_for_extras_and_extensions_, + strictness_)) { + return false; + } + + model->meshes.emplace_back(std::move(mesh)); + return true; + }); + + if (!success) { + return false; + } + } + + // Assign missing bufferView target types + // - Look for missing Mesh indices + // - Look for missing Mesh attributes + for (auto &mesh : model->meshes) { + for (auto &primitive : mesh.primitives) { + if (primitive.indices > + -1) // has indices from parsing step, must be Element Array Buffer + { + if (size_t(primitive.indices) >= model->accessors.size()) { + if (err) { + (*err) += "primitive indices accessor out of bounds"; + } + return false; + } + + auto bufferView = + model->accessors[size_t(primitive.indices)].bufferView; + if (bufferView < 0 || size_t(bufferView) >= model->bufferViews.size()) { + if (err) { + (*err) += "accessor[" + std::to_string(primitive.indices) + + "] invalid bufferView"; + } + return false; + } + + model->bufferViews[size_t(bufferView)].target = + TINYGLTF_TARGET_ELEMENT_ARRAY_BUFFER; + // we could optionally check if accessors' bufferView type is Scalar, as + // it should be + } + + for (auto &attribute : primitive.attributes) { + const auto accessorsIndex = size_t(attribute.second); + if (accessorsIndex < model->accessors.size()) { + const auto bufferView = model->accessors[accessorsIndex].bufferView; + // bufferView could be null(-1) for sparse morph target + if (bufferView >= 0 && bufferView < (int)model->bufferViews.size()) { + model->bufferViews[size_t(bufferView)].target = + TINYGLTF_TARGET_ARRAY_BUFFER; + } + } + } + + for (auto &target : primitive.targets) { + for (auto &attribute : target) { + const auto accessorsIndex = size_t(attribute.second); + if (accessorsIndex < model->accessors.size()) { + const auto bufferView = model->accessors[accessorsIndex].bufferView; + // bufferView could be null(-1) for sparse morph target + if (bufferView >= 0 && + bufferView < (int)model->bufferViews.size()) { + model->bufferViews[size_t(bufferView)].target = + TINYGLTF_TARGET_ARRAY_BUFFER; + } + } + } + } + } + } + + // 7. Parse Node + { + bool success = ForEachInArray(v, "nodes", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`nodes' does not contain an JSON object."; + } + return false; + } + Node node; + if (!ParseNode(&node, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->nodes.emplace_back(std::move(node)); + return true; + }); + + if (!success) { + return false; + } + } + + // 8. Parse scenes. + { + bool success = ForEachInArray(v, "scenes", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`scenes' does not contain an JSON object."; + } + return false; + } + + Scene scene; + if (!ParseScene(&scene, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->scenes.emplace_back(std::move(scene)); + return true; + }); + + if (!success) { + return false; + } + } + + // 9. Parse default scenes. + { + detail::json_const_iterator rootIt; + int iVal; + if (detail::FindMember(v, "scene", rootIt) && + detail::GetInt(detail::GetValue(rootIt), iVal)) { + model->defaultScene = iVal; + } + } + + // 10. Parse Material + { + bool success = ForEachInArray(v, "materials", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`materials' does not contain an JSON object."; + } + return false; + } + Material material; + ParseStringProperty(&material.name, err, o, "name", false); + + if (!ParseMaterial(&material, err, warn, o, + store_original_json_for_extras_and_extensions_, + strictness_)) { + return false; + } + + model->materials.emplace_back(std::move(material)); + return true; + }); + + if (!success) { + return false; + } + } + + // 11. Parse Image + void *load_image_user_data{nullptr}; + + LoadImageDataOption load_image_option; + + if (user_image_loader_) { + // Use user supplied pointer + load_image_user_data = load_image_user_data_; + } else { + load_image_option.preserve_channels = preserve_image_channels_; + load_image_user_data = reinterpret_cast(&load_image_option); + } + + { + int idx = 0; + bool success = ForEachInArray(v, "images", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "image[" + std::to_string(idx) + "] is not a JSON object."; + } + return false; + } + Image image; + if (!ParseImage(&image, idx, err, warn, o, + store_original_json_for_extras_and_extensions_, base_dir, + max_external_file_size_, &fs, &uri_cb, + &this->LoadImageData, load_image_user_data)) { + return false; + } + + if (image.bufferView != -1) { + // Load image from the buffer view. + if (size_t(image.bufferView) >= model->bufferViews.size()) { + if (err) { + std::stringstream ss; + ss << "image[" << idx << "] bufferView \"" << image.bufferView + << "\" not found in the scene." << std::endl; + (*err) += ss.str(); + } + return false; + } + + const BufferView &bufferView = + model->bufferViews[size_t(image.bufferView)]; + if (size_t(bufferView.buffer) >= model->buffers.size()) { + if (err) { + std::stringstream ss; + ss << "image[" << idx << "] buffer \"" << bufferView.buffer + << "\" not found in the scene." << std::endl; + (*err) += ss.str(); + } + return false; + } + const Buffer &buffer = model->buffers[size_t(bufferView.buffer)]; + + if (*LoadImageData == nullptr) { + if (err) { + (*err) += "No LoadImageData callback specified.\n"; + } + return false; + } + bool ret = LoadImageData( + &image, idx, err, warn, image.width, image.height, + &buffer.data[bufferView.byteOffset], + static_cast(bufferView.byteLength), load_image_user_data); + if (!ret) { + return false; + } + } + + model->images.emplace_back(std::move(image)); + ++idx; + return true; + }); + + if (!success) { + return false; + } + } + + // 12. Parse Texture + { + bool success = ForEachInArray(v, "textures", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`textures' does not contain an JSON object."; + } + return false; + } + Texture texture; + if (!ParseTexture(&texture, err, o, + store_original_json_for_extras_and_extensions_, + base_dir)) { + return false; + } + + model->textures.emplace_back(std::move(texture)); + return true; + }); + + if (!success) { + return false; + } + } + + // 13. Parse Animation + { + bool success = ForEachInArray(v, "animations", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`animations' does not contain an JSON object."; + } + return false; + } + Animation animation; + if (!ParseAnimation(&animation, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->animations.emplace_back(std::move(animation)); + return true; + }); + + if (!success) { + return false; + } + } + + // 14. Parse Skin + { + bool success = ForEachInArray(v, "skins", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`skins' does not contain an JSON object."; + } + return false; + } + Skin skin; + if (!ParseSkin(&skin, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->skins.emplace_back(std::move(skin)); + return true; + }); + + if (!success) { + return false; + } + } + + // 15. Parse Sampler + { + bool success = ForEachInArray(v, "samplers", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`samplers' does not contain an JSON object."; + } + return false; + } + Sampler sampler; + if (!ParseSampler(&sampler, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->samplers.emplace_back(std::move(sampler)); + return true; + }); + + if (!success) { + return false; + } + } + + // 16. Parse Camera + { + bool success = ForEachInArray(v, "cameras", [&](const detail::json &o) { + if (!detail::IsObject(o)) { + if (err) { + (*err) += "`cameras' does not contain an JSON object."; + } + return false; + } + Camera camera; + if (!ParseCamera(&camera, err, o, + store_original_json_for_extras_and_extensions_)) { + return false; + } + + model->cameras.emplace_back(std::move(camera)); + return true; + }); + + if (!success) { + return false; + } + } + + // 17. Parse Extras & Extensions + ParseExtrasAndExtensions(model, err, v, + store_original_json_for_extras_and_extensions_); + + // 18. Specific extension implementations + { + detail::json_const_iterator rootIt; + if (detail::FindMember(v, "extensions", rootIt) && + detail::IsObject(detail::GetValue(rootIt))) { + const detail::json &root = detail::GetValue(rootIt); + + detail::json_const_iterator it(detail::ObjectBegin(root)); + detail::json_const_iterator itEnd(detail::ObjectEnd(root)); + for (; it != itEnd; ++it) { + // parse KHR_lights_punctual extension + std::string key(detail::GetKey(it)); + if ((key == "KHR_lights_punctual") && + detail::IsObject(detail::GetValue(it))) { + const detail::json &object = detail::GetValue(it); + detail::json_const_iterator itLight; + if (detail::FindMember(object, "lights", itLight)) { + const detail::json &lights = detail::GetValue(itLight); + if (!detail::IsArray(lights)) { + continue; + } + + auto arrayIt(detail::ArrayBegin(lights)); + auto arrayItEnd(detail::ArrayEnd(lights)); + for (; arrayIt != arrayItEnd; ++arrayIt) { + Light light; + if (!ParseLight(&light, err, *arrayIt, + store_original_json_for_extras_and_extensions_)) { + return false; + } + model->lights.emplace_back(std::move(light)); + } + } + } + // parse KHR_audio extension + if ((key == "KHR_audio") && detail::IsObject(detail::GetValue(it))) { + const detail::json &object = detail::GetValue(it); + detail::json_const_iterator itKhrAudio; + if (detail::FindMember(object, "emitters", itKhrAudio)) { + const detail::json &emitters = detail::GetValue(itKhrAudio); + if (!detail::IsArray(emitters)) { + continue; + } + + auto arrayIt(detail::ArrayBegin(emitters)); + auto arrayItEnd(detail::ArrayEnd(emitters)); + for (; arrayIt != arrayItEnd; ++arrayIt) { + AudioEmitter emitter; + if (!ParseAudioEmitter( + &emitter, err, *arrayIt, + store_original_json_for_extras_and_extensions_)) { + return false; + } + model->audioEmitters.emplace_back(std::move(emitter)); + } + } + + if (detail::FindMember(object, "sources", itKhrAudio)) { + const detail::json &sources = detail::GetValue(itKhrAudio); + if (!detail::IsArray(sources)) { + continue; + } + + auto arrayIt(detail::ArrayBegin(sources)); + auto arrayItEnd(detail::ArrayEnd(sources)); + for (; arrayIt != arrayItEnd; ++arrayIt) { + AudioSource source; + if (!ParseAudioSource( + &source, err, *arrayIt, + store_original_json_for_extras_and_extensions_)) { + return false; + } + model->audioSources.emplace_back(std::move(source)); + } + } + } + } + } + } + + return true; +} + +bool TinyGLTF::LoadASCIIFromString(Model *model, std::string *err, + std::string *warn, const char *str, + unsigned int length, + const std::string &base_dir, + unsigned int check_sections) { + is_binary_ = false; + bin_data_ = nullptr; + bin_size_ = 0; + + return LoadFromString(model, err, warn, str, length, base_dir, + check_sections); +} + +bool TinyGLTF::LoadASCIIFromFile(Model *model, std::string *err, + std::string *warn, const std::string &filename, + unsigned int check_sections) { + std::stringstream ss; + + if (fs.ReadWholeFile == nullptr) { + // Programmer error, assert() ? + ss << "Failed to read file: " << filename + << ": one or more FS callback not set" << std::endl; + if (err) { + (*err) = ss.str(); + } + return false; + } + + std::vector data; + std::string fileerr; + bool fileread = fs.ReadWholeFile(&data, &fileerr, filename, fs.user_data); + if (!fileread) { + ss << "Failed to read file: " << filename << ": " << fileerr << std::endl; + if (err) { + (*err) = ss.str(); + } + return false; + } + + size_t sz = data.size(); + if (sz == 0) { + if (err) { + (*err) = "Empty file."; + } + return false; + } + + std::string basedir = GetBaseDir(filename); + + bool ret = LoadASCIIFromString( + model, err, warn, reinterpret_cast(&data.at(0)), + static_cast(data.size()), basedir, check_sections); + + return ret; +} + +bool TinyGLTF::LoadBinaryFromMemory(Model *model, std::string *err, + std::string *warn, + const unsigned char *bytes, + unsigned int size, + const std::string &base_dir, + unsigned int check_sections) { + if (size < 20) { + if (err) { + (*err) = "Too short data size for glTF Binary."; + } + return false; + } + + if (bytes[0] == 'g' && bytes[1] == 'l' && bytes[2] == 'T' && + bytes[3] == 'F') { + // ok + } else { + if (err) { + (*err) = "Invalid magic."; + } + return false; + } + + unsigned int version; // 4 bytes + unsigned int length; // 4 bytes + unsigned int chunk0_length; // 4 bytes + unsigned int chunk0_format; // 4 bytes; + + memcpy(&version, bytes + 4, 4); + swap4(&version); + memcpy(&length, bytes + 8, 4); + swap4(&length); + memcpy(&chunk0_length, bytes + 12, 4); // JSON data length + swap4(&chunk0_length); + memcpy(&chunk0_format, bytes + 16, 4); + swap4(&chunk0_format); + + // https://registry.khronos.org/glTF/specs/2.0/glTF-2.0.html#binary-gltf-layout + // + // In case the Bin buffer is not present, the size is exactly 20 + size of + // JSON contents, + // so use "greater than" operator. + // + // https://github.com/syoyo/tinygltf/issues/372 + // Use 64bit uint to avoid integer overflow. + uint64_t header_and_json_size = 20ull + uint64_t(chunk0_length); + + if (header_and_json_size > std::numeric_limits::max()) { + // Do not allow 4GB or more GLB data. + (*err) = "Invalid glTF binary. GLB data exceeds 4GB."; + } + + if ((header_and_json_size > uint64_t(size)) || (chunk0_length < 1) || + (length > size) || (header_and_json_size > uint64_t(length)) || + (chunk0_format != 0x4E4F534A)) { // 0x4E4F534A = JSON format. + if (err) { + (*err) = "Invalid glTF binary."; + } + return false; + } + + // Padding check + // The start and the end of each chunk must be aligned to a 4-byte boundary. + // No padding check for chunk0 start since its 4byte-boundary is ensured. + if ((header_and_json_size % 4) != 0) { + if (err) { + (*err) = "JSON Chunk end does not aligned to a 4-byte boundary."; + } + } + + // std::cout << "header_and_json_size = " << header_and_json_size << "\n"; + // std::cout << "length = " << length << "\n"; + + // Chunk1(BIN) data + // The spec says: When the binary buffer is empty or when it is stored by + // other means, this chunk SHOULD be omitted. So when header + JSON data == + // binary size, Chunk1 is omitted. + if (header_and_json_size == uint64_t(length)) { + bin_data_ = nullptr; + bin_size_ = 0; + } else { + // Read Chunk1 info(BIN data) + // At least Chunk1 should have 12 bytes(8 bytes(header) + 4 bytes(bin + // payload could be 1~3 bytes, but need to be aligned to 4 bytes) + if ((header_and_json_size + 12ull) > uint64_t(length)) { + if (err) { + (*err) = + "Insufficient storage space for Chunk1(BIN data). At least Chunk1 " + "Must have 4 or more bytes, but got " + + std::to_string((header_and_json_size + 8ull) - uint64_t(length)) + + ".\n"; + } + return false; + } + + unsigned int chunk1_length; // 4 bytes + unsigned int chunk1_format; // 4 bytes; + memcpy(&chunk1_length, bytes + header_and_json_size, + 4); // JSON data length + swap4(&chunk1_length); + memcpy(&chunk1_format, bytes + header_and_json_size + 4, 4); + swap4(&chunk1_format); + + // std::cout << "chunk1_length = " << chunk1_length << "\n"; + + if (chunk1_length < 4) { + if (err) { + (*err) = "Insufficient Chunk1(BIN) data size."; + } + return false; + } + + if ((chunk1_length % 4) != 0) { + if (strictness_==ParseStrictness::Permissive) { + if (warn) { + (*warn) += "BIN Chunk end is not aligned to a 4-byte boundary.\n"; + } + } + else { + if (err) { + (*err) = "BIN Chunk end is not aligned to a 4-byte boundary."; + } + return false; + } + } + + if (uint64_t(chunk1_length) + header_and_json_size > uint64_t(length)) { + if (err) { + (*err) = "BIN Chunk data length exceeds the GLB size."; + } + return false; + } + + if (chunk1_format != 0x004e4942) { + if (err) { + (*err) = "Invalid type for chunk1 data."; + } + return false; + } + + // std::cout << "chunk1_length = " << chunk1_length << "\n"; + + bin_data_ = bytes + header_and_json_size + + 8; // 4 bytes (bin_buffer_length) + 4 bytes(bin_buffer_format) + + bin_size_ = size_t(chunk1_length); + } + + is_binary_ = true; + + bool ret = LoadFromString(model, err, warn, + reinterpret_cast(&bytes[20]), + chunk0_length, base_dir, check_sections); + if (!ret) { + return ret; + } + + return true; +} + +bool TinyGLTF::LoadBinaryFromFile(Model *model, std::string *err, + std::string *warn, + const std::string &filename, + unsigned int check_sections) { + std::stringstream ss; + + if (fs.ReadWholeFile == nullptr) { + // Programmer error, assert() ? + ss << "Failed to read file: " << filename + << ": one or more FS callback not set" << std::endl; + if (err) { + (*err) = ss.str(); + } + return false; + } + + std::vector data; + std::string fileerr; + bool fileread = fs.ReadWholeFile(&data, &fileerr, filename, fs.user_data); + if (!fileread) { + ss << "Failed to read file: " << filename << ": " << fileerr << std::endl; + if (err) { + (*err) = ss.str(); + } + return false; + } + + std::string basedir = GetBaseDir(filename); + + bool ret = LoadBinaryFromMemory(model, err, warn, &data.at(0), + static_cast(data.size()), + basedir, check_sections); + + return ret; +} + +/////////////////////// +// GLTF Serialization +/////////////////////// +namespace detail { +detail::json JsonFromString(const char *s) { +#ifdef TINYGLTF_USE_RAPIDJSON + return detail::json(s, detail::GetAllocator()); +#else + return detail::json(s); +#endif +} + +void JsonAssign(detail::json &dest, const detail::json &src) { +#ifdef TINYGLTF_USE_RAPIDJSON + dest.CopyFrom(src, detail::GetAllocator()); +#else + dest = src; +#endif +} + +void JsonAddMember(detail::json &o, const char *key, detail::json &&value) { +#ifdef TINYGLTF_USE_RAPIDJSON + if (!o.IsObject()) { + o.SetObject(); + } + + // Issue 420. + // AddMember may create duplicated key, so use [] API when a key already + // exists. + // https://github.com/Tencent/rapidjson/issues/771#issuecomment-254386863 + detail::json_const_iterator it; + if (detail::FindMember(o, key, it)) { + o[key] = std::move(value); // replace + } else { + o.AddMember(detail::json(key, detail::GetAllocator()), std::move(value), + detail::GetAllocator()); + } +#else + o[key] = std::move(value); +#endif +} + +void JsonPushBack(detail::json &o, detail::json &&value) { +#ifdef TINYGLTF_USE_RAPIDJSON + o.PushBack(std::move(value), detail::GetAllocator()); +#else + o.push_back(std::move(value)); +#endif +} + +bool JsonIsNull(const detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + return o.IsNull(); +#else + return o.is_null(); +#endif +} + +void JsonSetObject(detail::json &o) { +#ifdef TINYGLTF_USE_RAPIDJSON + o.SetObject(); +#else + o = o.object({}); +#endif +} + +void JsonReserveArray(detail::json &o, size_t s) { +#ifdef TINYGLTF_USE_RAPIDJSON + o.SetArray(); + o.Reserve(static_cast(s), detail::GetAllocator()); +#endif + (void)(o); + (void)(s); +} +} // namespace detail + +// typedef std::pair json_object_pair; + +template +static void SerializeNumberProperty(const std::string &key, T number, + detail::json &obj) { + // obj.insert( + // json_object_pair(key, detail::json(static_cast(number)))); + // obj[key] = static_cast(number); + detail::JsonAddMember(obj, key.c_str(), detail::json(number)); +} + +#ifdef TINYGLTF_USE_RAPIDJSON +template <> +void SerializeNumberProperty(const std::string &key, size_t number, + detail::json &obj) { + detail::JsonAddMember(obj, key.c_str(), + detail::json(static_cast(number))); +} +#endif + +template +static void SerializeNumberArrayProperty(const std::string &key, + const std::vector &value, + detail::json &obj) { + if (value.empty()) return; + + detail::json ary; + detail::JsonReserveArray(ary, value.size()); + for (const auto &s : value) { + detail::JsonPushBack(ary, detail::json(s)); + } + detail::JsonAddMember(obj, key.c_str(), std::move(ary)); +} + +static void SerializeStringProperty(const std::string &key, + const std::string &value, + detail::json &obj) { + detail::JsonAddMember(obj, key.c_str(), + detail::JsonFromString(value.c_str())); +} + +static void SerializeStringArrayProperty(const std::string &key, + const std::vector &value, + detail::json &obj) { + detail::json ary; + detail::JsonReserveArray(ary, value.size()); + for (auto &s : value) { + detail::JsonPushBack(ary, detail::JsonFromString(s.c_str())); + } + detail::JsonAddMember(obj, key.c_str(), std::move(ary)); +} + +static bool ValueToJson(const Value &value, detail::json *ret) { + detail::json obj; +#ifdef TINYGLTF_USE_RAPIDJSON + switch (value.Type()) { + case REAL_TYPE: + obj.SetDouble(value.Get()); + break; + case INT_TYPE: + obj.SetInt(value.Get()); + break; + case BOOL_TYPE: + obj.SetBool(value.Get()); + break; + case STRING_TYPE: + obj.SetString(value.Get().c_str(), detail::GetAllocator()); + break; + case ARRAY_TYPE: { + obj.SetArray(); + obj.Reserve(static_cast(value.ArrayLen()), + detail::GetAllocator()); + for (unsigned int i = 0; i < value.ArrayLen(); ++i) { + Value elementValue = value.Get(int(i)); + detail::json elementJson; + if (ValueToJson(value.Get(int(i)), &elementJson)) + obj.PushBack(std::move(elementJson), detail::GetAllocator()); + } + break; + } + case BINARY_TYPE: + // TODO + // obj = detail::json(value.Get>()); + return false; + break; + case OBJECT_TYPE: { + obj.SetObject(); + Value::Object objMap = value.Get(); + for (auto &it : objMap) { + detail::json elementJson; + if (ValueToJson(it.second, &elementJson)) { + obj.AddMember(detail::json(it.first.c_str(), detail::GetAllocator()), + std::move(elementJson), detail::GetAllocator()); + } + } + break; + } + case NULL_TYPE: + default: + return false; + } +#else + switch (value.Type()) { + case REAL_TYPE: + obj = detail::json(value.Get()); + break; + case INT_TYPE: + obj = detail::json(value.Get()); + break; + case BOOL_TYPE: + obj = detail::json(value.Get()); + break; + case STRING_TYPE: + obj = detail::json(value.Get()); + break; + case ARRAY_TYPE: { + for (unsigned int i = 0; i < value.ArrayLen(); ++i) { + Value elementValue = value.Get(int(i)); + detail::json elementJson; + if (ValueToJson(value.Get(int(i)), &elementJson)) + obj.push_back(elementJson); + } + break; + } + case BINARY_TYPE: + // TODO + // obj = json(value.Get>()); + return false; + break; + case OBJECT_TYPE: { + Value::Object objMap = value.Get(); + for (auto &it : objMap) { + detail::json elementJson; + if (ValueToJson(it.second, &elementJson)) obj[it.first] = elementJson; + } + break; + } + case NULL_TYPE: + default: + return false; + } +#endif + if (ret) *ret = std::move(obj); + return true; +} + +static void SerializeValue(const std::string &key, const Value &value, + detail::json &obj) { + detail::json ret; + if (ValueToJson(value, &ret)) { + detail::JsonAddMember(obj, key.c_str(), std::move(ret)); + } +} + +static void SerializeGltfBufferData(const std::vector &data, + detail::json &o) { + std::string header = "data:application/octet-stream;base64,"; + if (data.size() > 0) { + std::string encodedData = + base64_encode(&data[0], static_cast(data.size())); + SerializeStringProperty("uri", header + encodedData, o); + } else { + // Issue #229 + // size 0 is allowed. Just emit mime header. + SerializeStringProperty("uri", header, o); + } +} + +static bool SerializeGltfBufferData(const std::vector &data, + const std::string &binFilename) { +#ifdef _WIN32 +#if defined(__GLIBCXX__) // mingw + int file_descriptor = _wopen(UTF8ToWchar(binFilename).c_str(), + _O_CREAT | _O_WRONLY | _O_TRUNC | _O_BINARY); + __gnu_cxx::stdio_filebuf wfile_buf( + file_descriptor, std::ios_base::out | std::ios_base::binary); + std::ostream output(&wfile_buf); + if (!wfile_buf.is_open()) return false; +#elif defined(_MSC_VER) + std::ofstream output(UTF8ToWchar(binFilename).c_str(), std::ofstream::binary); + if (!output.is_open()) return false; +#else + std::ofstream output(binFilename.c_str(), std::ofstream::binary); + if (!output.is_open()) return false; +#endif +#else + std::ofstream output(binFilename.c_str(), std::ofstream::binary); + if (!output.is_open()) return false; +#endif + if (data.size() > 0) { + output.write(reinterpret_cast(&data[0]), + std::streamsize(data.size())); + } else { + // Issue #229 + // size 0 will be still valid buffer data. + // write empty file. + } + return true; +} + +#if 0 // FIXME(syoyo): not used. will be removed in the future release. +static void SerializeParameterMap(ParameterMap ¶m, detail::json &o) { + for (ParameterMap::iterator paramIt = param.begin(); paramIt != param.end(); + ++paramIt) { + if (paramIt->second.number_array.size()) { + SerializeNumberArrayProperty(paramIt->first, + paramIt->second.number_array, o); + } else if (paramIt->second.json_double_value.size()) { + detail::json json_double_value; + for (std::map::iterator it = + paramIt->second.json_double_value.begin(); + it != paramIt->second.json_double_value.end(); ++it) { + if (it->first == "index") { + json_double_value[it->first] = paramIt->second.TextureIndex(); + } else { + json_double_value[it->first] = it->second; + } + } + + o[paramIt->first] = json_double_value; + } else if (!paramIt->second.string_value.empty()) { + SerializeStringProperty(paramIt->first, paramIt->second.string_value, o); + } else if (paramIt->second.has_number_value) { + o[paramIt->first] = paramIt->second.number_value; + } else { + o[paramIt->first] = paramIt->second.bool_value; + } + } +} +#endif + +static void SerializeExtensionMap(const ExtensionMap &extensions, + detail::json &o) { + if (!extensions.size()) return; + + detail::json extMap; + for (ExtensionMap::const_iterator extIt = extensions.begin(); + extIt != extensions.end(); ++extIt) { + // Allow an empty object for extension(#97) + detail::json ret; + bool isNull = true; + if (ValueToJson(extIt->second, &ret)) { + isNull = detail::JsonIsNull(ret); + detail::JsonAddMember(extMap, extIt->first.c_str(), std::move(ret)); + } + if (isNull) { + if (!(extIt->first.empty())) { // name should not be empty, but for sure + // create empty object so that an extension name is still included in + // json. + detail::json empty; + detail::JsonSetObject(empty); + detail::JsonAddMember(extMap, extIt->first.c_str(), std::move(empty)); + } + } + } + detail::JsonAddMember(o, "extensions", std::move(extMap)); +} + +static void SerializeExtras(const Value &extras, detail::json &o) { + if (extras.Type() != NULL_TYPE) SerializeValue("extras", extras, o); +} + +template +void SerializeExtrasAndExtensions(const GltfType &obj, detail::json &o) { + SerializeExtensionMap(obj.extensions, o); + SerializeExtras(obj.extras, o); +} + +static void SerializeGltfAccessor(const Accessor &accessor, detail::json &o) { + if (accessor.bufferView >= 0) + SerializeNumberProperty("bufferView", accessor.bufferView, o); + + if (accessor.byteOffset != 0) + SerializeNumberProperty("byteOffset", accessor.byteOffset, o); + + SerializeNumberProperty("componentType", accessor.componentType, o); + SerializeNumberProperty("count", accessor.count, o); + + if ((accessor.componentType == TINYGLTF_COMPONENT_TYPE_FLOAT) || + (accessor.componentType == TINYGLTF_COMPONENT_TYPE_DOUBLE)) { + SerializeNumberArrayProperty("min", accessor.minValues, o); + SerializeNumberArrayProperty("max", accessor.maxValues, o); + } else { + // Issue #301. Serialize as integer. + // Assume int value is within [-2**31-1, 2**31-1] + { + std::vector values; + std::transform(accessor.minValues.begin(), accessor.minValues.end(), + std::back_inserter(values), + [](double v) { return static_cast(v); }); + + SerializeNumberArrayProperty("min", values, o); + } + + { + std::vector values; + std::transform(accessor.maxValues.begin(), accessor.maxValues.end(), + std::back_inserter(values), + [](double v) { return static_cast(v); }); + + SerializeNumberArrayProperty("max", values, o); + } + } + + if (accessor.normalized) + SerializeValue("normalized", Value(accessor.normalized), o); + std::string type; + switch (accessor.type) { + case TINYGLTF_TYPE_SCALAR: + type = "SCALAR"; + break; + case TINYGLTF_TYPE_VEC2: + type = "VEC2"; + break; + case TINYGLTF_TYPE_VEC3: + type = "VEC3"; + break; + case TINYGLTF_TYPE_VEC4: + type = "VEC4"; + break; + case TINYGLTF_TYPE_MAT2: + type = "MAT2"; + break; + case TINYGLTF_TYPE_MAT3: + type = "MAT3"; + break; + case TINYGLTF_TYPE_MAT4: + type = "MAT4"; + break; + } + + SerializeStringProperty("type", type, o); + if (!accessor.name.empty()) SerializeStringProperty("name", accessor.name, o); + + SerializeExtrasAndExtensions(accessor, o); + + // sparse + if (accessor.sparse.isSparse) { + detail::json sparse; + SerializeNumberProperty("count", accessor.sparse.count, sparse); + { + detail::json indices; + SerializeNumberProperty("bufferView", + accessor.sparse.indices.bufferView, indices); + SerializeNumberProperty("byteOffset", + accessor.sparse.indices.byteOffset, indices); + SerializeNumberProperty( + "componentType", accessor.sparse.indices.componentType, indices); + SerializeExtrasAndExtensions(accessor.sparse.indices, indices); + detail::JsonAddMember(sparse, "indices", std::move(indices)); + } + { + detail::json values; + SerializeNumberProperty("bufferView", + accessor.sparse.values.bufferView, values); + SerializeNumberProperty("byteOffset", + accessor.sparse.values.byteOffset, values); + SerializeExtrasAndExtensions(accessor.sparse.values, values); + detail::JsonAddMember(sparse, "values", std::move(values)); + } + SerializeExtrasAndExtensions(accessor.sparse, sparse); + detail::JsonAddMember(o, "sparse", std::move(sparse)); + } +} + +static void SerializeGltfAnimationChannel(const AnimationChannel &channel, + detail::json &o) { + SerializeNumberProperty("sampler", channel.sampler, o); + { + detail::json target; + + if (channel.target_node >= 0) { + SerializeNumberProperty("node", channel.target_node, target); + } + + SerializeStringProperty("path", channel.target_path, target); + + SerializeExtensionMap(channel.target_extensions, target); + SerializeExtras(channel.target_extras, target); + + detail::JsonAddMember(o, "target", std::move(target)); + } + + SerializeExtrasAndExtensions(channel, o); +} + +static void SerializeGltfAnimationSampler(const AnimationSampler &sampler, + detail::json &o) { + SerializeNumberProperty("input", sampler.input, o); + SerializeNumberProperty("output", sampler.output, o); + SerializeStringProperty("interpolation", sampler.interpolation, o); + + SerializeExtrasAndExtensions(sampler, o); +} + +static void SerializeGltfAnimation(const Animation &animation, + detail::json &o) { + if (!animation.name.empty()) + SerializeStringProperty("name", animation.name, o); + + { + detail::json channels; + detail::JsonReserveArray(channels, animation.channels.size()); + for (unsigned int i = 0; i < animation.channels.size(); ++i) { + detail::json channel; + AnimationChannel gltfChannel = animation.channels[i]; + SerializeGltfAnimationChannel(gltfChannel, channel); + detail::JsonPushBack(channels, std::move(channel)); + } + + detail::JsonAddMember(o, "channels", std::move(channels)); + } + + { + detail::json samplers; + detail::JsonReserveArray(samplers, animation.samplers.size()); + for (unsigned int i = 0; i < animation.samplers.size(); ++i) { + detail::json sampler; + AnimationSampler gltfSampler = animation.samplers[i]; + SerializeGltfAnimationSampler(gltfSampler, sampler); + detail::JsonPushBack(samplers, std::move(sampler)); + } + detail::JsonAddMember(o, "samplers", std::move(samplers)); + } + + SerializeExtrasAndExtensions(animation, o); +} + +static void SerializeGltfAsset(const Asset &asset, detail::json &o) { + if (!asset.generator.empty()) { + SerializeStringProperty("generator", asset.generator, o); + } + + if (!asset.copyright.empty()) { + SerializeStringProperty("copyright", asset.copyright, o); + } + + auto version = asset.version; + if (version.empty()) { + // Just in case + // `version` must be defined + version = "2.0"; + } + + // TODO(syoyo): Do we need to check if `version` is greater or equal to 2.0? + SerializeStringProperty("version", version, o); + + SerializeExtrasAndExtensions(asset, o); +} + +static void SerializeGltfBufferBin(const Buffer &buffer, detail::json &o, + std::vector &binBuffer) { + SerializeNumberProperty("byteLength", buffer.data.size(), o); + binBuffer = buffer.data; + + if (buffer.name.size()) SerializeStringProperty("name", buffer.name, o); + + SerializeExtrasAndExtensions(buffer, o); +} + +static void SerializeGltfBuffer(const Buffer &buffer, detail::json &o) { + SerializeNumberProperty("byteLength", buffer.data.size(), o); + SerializeGltfBufferData(buffer.data, o); + + if (buffer.name.size()) SerializeStringProperty("name", buffer.name, o); + + SerializeExtrasAndExtensions(buffer, o); +} + +static bool SerializeGltfBuffer(const Buffer &buffer, detail::json &o, + const std::string &binFilename, + const std::string &binUri) { + if (!SerializeGltfBufferData(buffer.data, binFilename)) return false; + SerializeNumberProperty("byteLength", buffer.data.size(), o); + SerializeStringProperty("uri", binUri, o); + + if (buffer.name.size()) SerializeStringProperty("name", buffer.name, o); + + SerializeExtrasAndExtensions(buffer, o); + return true; +} + +static void SerializeGltfBufferView(const BufferView &bufferView, + detail::json &o) { + SerializeNumberProperty("buffer", bufferView.buffer, o); + SerializeNumberProperty("byteLength", bufferView.byteLength, o); + + // byteStride is optional, minimum allowed is 4 + if (bufferView.byteStride >= 4) { + SerializeNumberProperty("byteStride", bufferView.byteStride, o); + } + // byteOffset is optional, default is 0 + if (bufferView.byteOffset > 0) { + SerializeNumberProperty("byteOffset", bufferView.byteOffset, o); + } + // Target is optional, check if it contains a valid value + if (bufferView.target == TINYGLTF_TARGET_ARRAY_BUFFER || + bufferView.target == TINYGLTF_TARGET_ELEMENT_ARRAY_BUFFER) { + SerializeNumberProperty("target", bufferView.target, o); + } + if (bufferView.name.size()) { + SerializeStringProperty("name", bufferView.name, o); + } + + SerializeExtrasAndExtensions(bufferView, o); +} + +static void SerializeGltfImage(const Image &image, const std::string &uri, + detail::json &o) { + // From 2.7.0, we look for `uri` parameter, not `Image.uri` + // if uri is empty, the mimeType and bufferview should be set + if (uri.empty()) { + SerializeStringProperty("mimeType", image.mimeType, o); + SerializeNumberProperty("bufferView", image.bufferView, o); + } else { + SerializeStringProperty("uri", uri, o); + } + + if (image.name.size()) { + SerializeStringProperty("name", image.name, o); + } + + SerializeExtrasAndExtensions(image, o); +} + +static void SerializeGltfTextureInfo(const TextureInfo &texinfo, + detail::json &o) { + SerializeNumberProperty("index", texinfo.index, o); + + if (texinfo.texCoord != 0) { + SerializeNumberProperty("texCoord", texinfo.texCoord, o); + } + + SerializeExtrasAndExtensions(texinfo, o); +} + +static void SerializeGltfNormalTextureInfo(const NormalTextureInfo &texinfo, + detail::json &o) { + SerializeNumberProperty("index", texinfo.index, o); + + if (texinfo.texCoord != 0) { + SerializeNumberProperty("texCoord", texinfo.texCoord, o); + } + + if (!TINYGLTF_DOUBLE_EQUAL(texinfo.scale, 1.0)) { + SerializeNumberProperty("scale", texinfo.scale, o); + } + + SerializeExtrasAndExtensions(texinfo, o); +} + +static void SerializeGltfOcclusionTextureInfo( + const OcclusionTextureInfo &texinfo, detail::json &o) { + SerializeNumberProperty("index", texinfo.index, o); + + if (texinfo.texCoord != 0) { + SerializeNumberProperty("texCoord", texinfo.texCoord, o); + } + + if (!TINYGLTF_DOUBLE_EQUAL(texinfo.strength, 1.0)) { + SerializeNumberProperty("strength", texinfo.strength, o); + } + + SerializeExtrasAndExtensions(texinfo, o); +} + +static void SerializeGltfPbrMetallicRoughness(const PbrMetallicRoughness &pbr, + detail::json &o) { + std::vector default_baseColorFactor = {1.0, 1.0, 1.0, 1.0}; + if (!Equals(pbr.baseColorFactor, default_baseColorFactor)) { + SerializeNumberArrayProperty("baseColorFactor", pbr.baseColorFactor, + o); + } + + if (!TINYGLTF_DOUBLE_EQUAL(pbr.metallicFactor, 1.0)) { + SerializeNumberProperty("metallicFactor", pbr.metallicFactor, o); + } + + if (!TINYGLTF_DOUBLE_EQUAL(pbr.roughnessFactor, 1.0)) { + SerializeNumberProperty("roughnessFactor", pbr.roughnessFactor, o); + } + + if (pbr.baseColorTexture.index > -1) { + detail::json texinfo; + SerializeGltfTextureInfo(pbr.baseColorTexture, texinfo); + detail::JsonAddMember(o, "baseColorTexture", std::move(texinfo)); + } + + if (pbr.metallicRoughnessTexture.index > -1) { + detail::json texinfo; + SerializeGltfTextureInfo(pbr.metallicRoughnessTexture, texinfo); + detail::JsonAddMember(o, "metallicRoughnessTexture", std::move(texinfo)); + } + + SerializeExtrasAndExtensions(pbr, o); +} + +static void SerializeGltfMaterial(const Material &material, detail::json &o) { + if (material.name.size()) { + SerializeStringProperty("name", material.name, o); + } + + // QUESTION(syoyo): Write material parameters regardless of its default value? + + if (!TINYGLTF_DOUBLE_EQUAL(material.alphaCutoff, 0.5)) { + SerializeNumberProperty("alphaCutoff", material.alphaCutoff, o); + } + + if (material.alphaMode.compare("OPAQUE") != 0) { + SerializeStringProperty("alphaMode", material.alphaMode, o); + } + + if (material.doubleSided != false) + detail::JsonAddMember(o, "doubleSided", detail::json(material.doubleSided)); + + if (material.normalTexture.index > -1) { + detail::json texinfo; + SerializeGltfNormalTextureInfo(material.normalTexture, texinfo); + detail::JsonAddMember(o, "normalTexture", std::move(texinfo)); + } + + if (material.occlusionTexture.index > -1) { + detail::json texinfo; + SerializeGltfOcclusionTextureInfo(material.occlusionTexture, texinfo); + detail::JsonAddMember(o, "occlusionTexture", std::move(texinfo)); + } + + if (material.emissiveTexture.index > -1) { + detail::json texinfo; + SerializeGltfTextureInfo(material.emissiveTexture, texinfo); + detail::JsonAddMember(o, "emissiveTexture", std::move(texinfo)); + } + + std::vector default_emissiveFactor = {0.0, 0.0, 0.0}; + if (!Equals(material.emissiveFactor, default_emissiveFactor)) { + SerializeNumberArrayProperty("emissiveFactor", + material.emissiveFactor, o); + } + + { + detail::json pbrMetallicRoughness; + SerializeGltfPbrMetallicRoughness(material.pbrMetallicRoughness, + pbrMetallicRoughness); + // Issue 204 + // Do not serialize `pbrMetallicRoughness` if pbrMetallicRoughness has all + // default values(json is null). Otherwise it will serialize to + // `pbrMetallicRoughness : null`, which cannot be read by other glTF + // importers (and validators). + // + if (!detail::JsonIsNull(pbrMetallicRoughness)) { + detail::JsonAddMember(o, "pbrMetallicRoughness", + std::move(pbrMetallicRoughness)); + } + } + +#if 0 // legacy way. just for the record. + if (material.values.size()) { + detail::json pbrMetallicRoughness; + SerializeParameterMap(material.values, pbrMetallicRoughness); + detail::JsonAddMember(o, "pbrMetallicRoughness", std::move(pbrMetallicRoughness)); + } + + SerializeParameterMap(material.additionalValues, o); +#else + +#endif + + SerializeExtrasAndExtensions(material, o); +} + +static void SerializeGltfMesh(const Mesh &mesh, detail::json &o) { + detail::json primitives; + detail::JsonReserveArray(primitives, mesh.primitives.size()); + for (unsigned int i = 0; i < mesh.primitives.size(); ++i) { + detail::json primitive; + const Primitive &gltfPrimitive = mesh.primitives[i]; // don't make a copy + { + detail::json attributes; + for (auto attrIt = gltfPrimitive.attributes.begin(); + attrIt != gltfPrimitive.attributes.end(); ++attrIt) { + SerializeNumberProperty(attrIt->first, attrIt->second, attributes); + } + + detail::JsonAddMember(primitive, "attributes", std::move(attributes)); + } + + // Indices is optional + if (gltfPrimitive.indices > -1) { + SerializeNumberProperty("indices", gltfPrimitive.indices, primitive); + } + // Material is optional + if (gltfPrimitive.material > -1) { + SerializeNumberProperty("material", gltfPrimitive.material, + primitive); + } + SerializeNumberProperty("mode", gltfPrimitive.mode, primitive); + + // Morph targets + if (gltfPrimitive.targets.size()) { + detail::json targets; + detail::JsonReserveArray(targets, gltfPrimitive.targets.size()); + for (unsigned int k = 0; k < gltfPrimitive.targets.size(); ++k) { + detail::json targetAttributes; + std::map targetData = gltfPrimitive.targets[k]; + for (std::map::iterator attrIt = targetData.begin(); + attrIt != targetData.end(); ++attrIt) { + SerializeNumberProperty(attrIt->first, attrIt->second, + targetAttributes); + } + detail::JsonPushBack(targets, std::move(targetAttributes)); + } + detail::JsonAddMember(primitive, "targets", std::move(targets)); + } + + SerializeExtrasAndExtensions(gltfPrimitive, primitive); + + detail::JsonPushBack(primitives, std::move(primitive)); + } + + detail::JsonAddMember(o, "primitives", std::move(primitives)); + + if (mesh.weights.size()) { + SerializeNumberArrayProperty("weights", mesh.weights, o); + } + + if (mesh.name.size()) { + SerializeStringProperty("name", mesh.name, o); + } + + SerializeExtrasAndExtensions(mesh, o); +} + +static void SerializeSpotLight(const SpotLight &spot, detail::json &o) { + SerializeNumberProperty("innerConeAngle", spot.innerConeAngle, o); + SerializeNumberProperty("outerConeAngle", spot.outerConeAngle, o); + SerializeExtrasAndExtensions(spot, o); +} + +static void SerializeGltfLight(const Light &light, detail::json &o) { + if (!light.name.empty()) SerializeStringProperty("name", light.name, o); + SerializeNumberProperty("intensity", light.intensity, o); + if (light.range > 0.0) { + SerializeNumberProperty("range", light.range, o); + } + SerializeNumberArrayProperty("color", light.color, o); + SerializeStringProperty("type", light.type, o); + if (light.type == "spot") { + detail::json spot; + SerializeSpotLight(light.spot, spot); + detail::JsonAddMember(o, "spot", std::move(spot)); + } + SerializeExtrasAndExtensions(light, o); +} + +static void SerializeGltfPositionalEmitter(const PositionalEmitter &positional, + detail::json &o) { + if (!TINYGLTF_DOUBLE_EQUAL(positional.coneInnerAngle, 6.283185307179586)) + SerializeNumberProperty("coneInnerAngle", positional.coneInnerAngle, o); + if (!TINYGLTF_DOUBLE_EQUAL(positional.coneOuterAngle, 6.283185307179586)) + SerializeNumberProperty("coneOuterAngle", positional.coneOuterAngle, o); + if (positional.coneOuterGain > 0.0) + SerializeNumberProperty("coneOuterGain", positional.coneOuterGain, o); + if (!TINYGLTF_DOUBLE_EQUAL(positional.maxDistance, 100.0)) + SerializeNumberProperty("maxDistance", positional.maxDistance, o); + if (!TINYGLTF_DOUBLE_EQUAL(positional.refDistance, 1.0)) + SerializeNumberProperty("refDistance", positional.refDistance, o); + if (!TINYGLTF_DOUBLE_EQUAL(positional.rolloffFactor, 1.0)) + SerializeNumberProperty("rolloffFactor", positional.rolloffFactor, o); + + SerializeExtrasAndExtensions(positional, o); +} + +static void SerializeGltfAudioEmitter(const AudioEmitter &emitter, + detail::json &o) { + if (!emitter.name.empty()) SerializeStringProperty("name", emitter.name, o); + if (!TINYGLTF_DOUBLE_EQUAL(emitter.gain, 1.0)) + SerializeNumberProperty("gain", emitter.gain, o); + if (emitter.loop) SerializeNumberProperty("loop", emitter.loop, o); + if (emitter.playing) SerializeNumberProperty("playing", emitter.playing, o); + if (!emitter.type.empty()) SerializeStringProperty("type", emitter.type, o); + if (!emitter.distanceModel.empty()) + SerializeStringProperty("distanceModel", emitter.distanceModel, o); + if (emitter.type == "positional") { + detail::json positional; + SerializeGltfPositionalEmitter(emitter.positional, positional); + detail::JsonAddMember(o, "positional", std::move(positional)); + } + SerializeNumberProperty("source", emitter.source, o); + SerializeExtrasAndExtensions(emitter, o); +} + +static void SerializeGltfAudioSource(const AudioSource &source, + detail::json &o) { + std::string name; + std::string uri; + std::string mimeType; // (required if no uri) ["audio/mp3", "audio/ogg", + // "audio/wav", "audio/m4a"] + + if (!source.name.empty()) SerializeStringProperty("name", source.name, o); + if (source.uri.empty()) { + SerializeStringProperty("mimeType", source.mimeType, o); + SerializeNumberProperty("bufferView", source.bufferView, o); + } else { + SerializeStringProperty("uri", source.uri, o); + } + SerializeExtrasAndExtensions(source, o); +} + +static void SerializeGltfNode(const Node &node, detail::json &o) { + if (node.translation.size() > 0) { + SerializeNumberArrayProperty("translation", node.translation, o); + } + if (node.rotation.size() > 0) { + SerializeNumberArrayProperty("rotation", node.rotation, o); + } + if (node.scale.size() > 0) { + SerializeNumberArrayProperty("scale", node.scale, o); + } + if (node.matrix.size() > 0) { + SerializeNumberArrayProperty("matrix", node.matrix, o); + } + if (node.mesh != -1) { + SerializeNumberProperty("mesh", node.mesh, o); + } + + if (node.skin != -1) { + SerializeNumberProperty("skin", node.skin, o); + } + + if (node.camera != -1) { + SerializeNumberProperty("camera", node.camera, o); + } + + if (node.weights.size() > 0) { + SerializeNumberArrayProperty("weights", node.weights, o); + } + + SerializeExtrasAndExtensions(node, o); + + // Note(agnat): If the asset was loaded from disk, the node may already + // contain the KHR_lights_punctual extension. If it was constructed in + // memory it does not. In any case we update the JSON property using + // the value from the struct. Last, if the node does not have a light + // reference but the extension is still present, we remove it. + if (node.light != -1) { + detail::json_iterator it; + if (!detail::FindMember(o, "extensions", it)) { + detail::json extensions; + detail::JsonSetObject(extensions); + detail::JsonAddMember(o, "extensions", std::move(extensions)); + detail::FindMember(o, "extensions", it); + } + auto &extensions = detail::GetValue(it); + if (!detail::FindMember(extensions, "KHR_lights_punctual", it)) { + detail::json lights_punctual; + detail::JsonSetObject(lights_punctual); + detail::JsonAddMember(extensions, "KHR_lights_punctual", + std::move(lights_punctual)); + detail::FindMember(o, "KHR_lights_punctual", it); + } + SerializeNumberProperty("light", node.light, detail::GetValue(it)); + } else { + // node has no light ref (any longer)... so we clean up + detail::json_iterator ext_it; + if (detail::FindMember(o, "extensions", ext_it)) { + auto &extensions = detail::GetValue(ext_it); + detail::json_iterator lp_it; + if (detail::FindMember(extensions, "KHR_lights_punctual", lp_it)) { + detail::Erase(extensions, lp_it); + } + if (detail::IsEmpty(extensions)) { + detail::Erase(o, ext_it); + } + } + } + + // KHR_audio + if (node.emitter != -1) { + detail::json_iterator it; + if (!detail::FindMember(o, "extensions", it)) { + detail::json extensions; + detail::JsonSetObject(extensions); + detail::JsonAddMember(o, "extensions", std::move(extensions)); + detail::FindMember(o, "extensions", it); + } + auto &extensions = detail::GetValue(it); + if (!detail::FindMember(extensions, "KHR_audio", it)) { + detail::json audio; + detail::JsonSetObject(audio); + detail::JsonAddMember(extensions, "KHR_audio", std::move(audio)); + detail::FindMember(o, "KHR_audio", it); + } + SerializeNumberProperty("emitter", node.emitter, detail::GetValue(it)); + } else { + detail::json_iterator ext_it; + if (detail::FindMember(o, "extensions", ext_it)) { + auto &extensions = detail::GetValue(ext_it); + detail::json_iterator lp_it; + if (detail::FindMember(extensions, "KHR_audio", lp_it)) { + detail::Erase(extensions, lp_it); + } + if (detail::IsEmpty(extensions)) { + detail::Erase(o, ext_it); + } + } + } + + if (!node.name.empty()) SerializeStringProperty("name", node.name, o); + SerializeNumberArrayProperty("children", node.children, o); +} + +static void SerializeGltfSampler(const Sampler &sampler, detail::json &o) { + if (!sampler.name.empty()) { + SerializeStringProperty("name", sampler.name, o); + } + if (sampler.magFilter != -1) { + SerializeNumberProperty("magFilter", sampler.magFilter, o); + } + if (sampler.minFilter != -1) { + SerializeNumberProperty("minFilter", sampler.minFilter, o); + } + // SerializeNumberProperty("wrapR", sampler.wrapR, o); + SerializeNumberProperty("wrapS", sampler.wrapS, o); + SerializeNumberProperty("wrapT", sampler.wrapT, o); + + SerializeExtrasAndExtensions(sampler, o); +} + +static void SerializeGltfOrthographicCamera(const OrthographicCamera &camera, + detail::json &o) { + SerializeNumberProperty("zfar", camera.zfar, o); + SerializeNumberProperty("znear", camera.znear, o); + SerializeNumberProperty("xmag", camera.xmag, o); + SerializeNumberProperty("ymag", camera.ymag, o); + + SerializeExtrasAndExtensions(camera, o); +} + +static void SerializeGltfPerspectiveCamera(const PerspectiveCamera &camera, + detail::json &o) { + SerializeNumberProperty("zfar", camera.zfar, o); + SerializeNumberProperty("znear", camera.znear, o); + if (camera.aspectRatio > 0) { + SerializeNumberProperty("aspectRatio", camera.aspectRatio, o); + } + + if (camera.yfov > 0) { + SerializeNumberProperty("yfov", camera.yfov, o); + } + + SerializeExtrasAndExtensions(camera, o); +} + +static void SerializeGltfCamera(const Camera &camera, detail::json &o) { + SerializeStringProperty("type", camera.type, o); + if (!camera.name.empty()) { + SerializeStringProperty("name", camera.name, o); + } + + if (camera.type.compare("orthographic") == 0) { + detail::json orthographic; + SerializeGltfOrthographicCamera(camera.orthographic, orthographic); + detail::JsonAddMember(o, "orthographic", std::move(orthographic)); + } else if (camera.type.compare("perspective") == 0) { + detail::json perspective; + SerializeGltfPerspectiveCamera(camera.perspective, perspective); + detail::JsonAddMember(o, "perspective", std::move(perspective)); + } else { + // ??? + } + + SerializeExtrasAndExtensions(camera, o); +} + +static void SerializeGltfScene(const Scene &scene, detail::json &o) { + SerializeNumberArrayProperty("nodes", scene.nodes, o); + + if (scene.name.size()) { + SerializeStringProperty("name", scene.name, o); + } + SerializeExtrasAndExtensions(scene, o); + + // KHR_audio + if (!scene.audioEmitters.empty()) { + detail::json_iterator it; + if (!detail::FindMember(o, "extensions", it)) { + detail::json extensions; + detail::JsonSetObject(extensions); + detail::JsonAddMember(o, "extensions", std::move(extensions)); + detail::FindMember(o, "extensions", it); + } + auto &extensions = detail::GetValue(it); + if (!detail::FindMember(extensions, "KHR_audio", it)) { + detail::json audio; + detail::JsonSetObject(audio); + detail::JsonAddMember(extensions, "KHR_audio", std::move(audio)); + detail::FindMember(o, "KHR_audio", it); + } + SerializeNumberArrayProperty("emitters", scene.audioEmitters, + detail::GetValue(it)); + } else { + detail::json_iterator ext_it; + if (detail::FindMember(o, "extensions", ext_it)) { + auto &extensions = detail::GetValue(ext_it); + detail::json_iterator lp_it; + if (detail::FindMember(extensions, "KHR_audio", lp_it)) { + detail::Erase(extensions, lp_it); + } + if (detail::IsEmpty(extensions)) { + detail::Erase(o, ext_it); + } + } + } +} + +static void SerializeGltfSkin(const Skin &skin, detail::json &o) { + // required + SerializeNumberArrayProperty("joints", skin.joints, o); + + if (skin.inverseBindMatrices >= 0) { + SerializeNumberProperty("inverseBindMatrices", skin.inverseBindMatrices, o); + } + + if (skin.skeleton >= 0) { + SerializeNumberProperty("skeleton", skin.skeleton, o); + } + + if (skin.name.size()) { + SerializeStringProperty("name", skin.name, o); + } + + SerializeExtrasAndExtensions(skin, o); +} + +static void SerializeGltfTexture(const Texture &texture, detail::json &o) { + if (texture.sampler > -1) { + SerializeNumberProperty("sampler", texture.sampler, o); + } + if (texture.source > -1) { + SerializeNumberProperty("source", texture.source, o); + } + if (texture.name.size()) { + SerializeStringProperty("name", texture.name, o); + } + SerializeExtrasAndExtensions(texture, o); +} + +/// +/// Serialize all properties except buffers and images. +/// +static void SerializeGltfModel(const Model *model, detail::json &o) { + // ACCESSORS + if (model->accessors.size()) { + detail::json accessors; + detail::JsonReserveArray(accessors, model->accessors.size()); + for (unsigned int i = 0; i < model->accessors.size(); ++i) { + detail::json accessor; + SerializeGltfAccessor(model->accessors[i], accessor); + detail::JsonPushBack(accessors, std::move(accessor)); + } + detail::JsonAddMember(o, "accessors", std::move(accessors)); + } + + // ANIMATIONS + if (model->animations.size()) { + detail::json animations; + detail::JsonReserveArray(animations, model->animations.size()); + for (unsigned int i = 0; i < model->animations.size(); ++i) { + if (model->animations[i].channels.size()) { + detail::json animation; + SerializeGltfAnimation(model->animations[i], animation); + detail::JsonPushBack(animations, std::move(animation)); + } + } + + detail::JsonAddMember(o, "animations", std::move(animations)); + } + + // ASSET + detail::json asset; + SerializeGltfAsset(model->asset, asset); + detail::JsonAddMember(o, "asset", std::move(asset)); + + // BUFFERVIEWS + if (model->bufferViews.size()) { + detail::json bufferViews; + detail::JsonReserveArray(bufferViews, model->bufferViews.size()); + for (unsigned int i = 0; i < model->bufferViews.size(); ++i) { + detail::json bufferView; + SerializeGltfBufferView(model->bufferViews[i], bufferView); + detail::JsonPushBack(bufferViews, std::move(bufferView)); + } + detail::JsonAddMember(o, "bufferViews", std::move(bufferViews)); + } + + // Extensions required + if (model->extensionsRequired.size()) { + SerializeStringArrayProperty("extensionsRequired", + model->extensionsRequired, o); + } + + // MATERIALS + if (model->materials.size()) { + detail::json materials; + detail::JsonReserveArray(materials, model->materials.size()); + for (unsigned int i = 0; i < model->materials.size(); ++i) { + detail::json material; + SerializeGltfMaterial(model->materials[i], material); + + if (detail::JsonIsNull(material)) { + // Issue 294. + // `material` does not have any required parameters + // so the result may be null(unmodified) when all material parameters + // have default value. + // + // null is not allowed thus we create an empty JSON object. + detail::JsonSetObject(material); + } + detail::JsonPushBack(materials, std::move(material)); + } + detail::JsonAddMember(o, "materials", std::move(materials)); + } + + // MESHES + if (model->meshes.size()) { + detail::json meshes; + detail::JsonReserveArray(meshes, model->meshes.size()); + for (unsigned int i = 0; i < model->meshes.size(); ++i) { + detail::json mesh; + SerializeGltfMesh(model->meshes[i], mesh); + detail::JsonPushBack(meshes, std::move(mesh)); + } + detail::JsonAddMember(o, "meshes", std::move(meshes)); + } + + // NODES + if (model->nodes.size()) { + detail::json nodes; + detail::JsonReserveArray(nodes, model->nodes.size()); + for (unsigned int i = 0; i < model->nodes.size(); ++i) { + detail::json node; + SerializeGltfNode(model->nodes[i], node); + detail::JsonPushBack(nodes, std::move(node)); + } + detail::JsonAddMember(o, "nodes", std::move(nodes)); + } + + // SCENE + if (model->defaultScene > -1) { + SerializeNumberProperty("scene", model->defaultScene, o); + } + + // SCENES + if (model->scenes.size()) { + detail::json scenes; + detail::JsonReserveArray(scenes, model->scenes.size()); + for (unsigned int i = 0; i < model->scenes.size(); ++i) { + detail::json currentScene; + SerializeGltfScene(model->scenes[i], currentScene); + detail::JsonPushBack(scenes, std::move(currentScene)); + } + detail::JsonAddMember(o, "scenes", std::move(scenes)); + } + + // SKINS + if (model->skins.size()) { + detail::json skins; + detail::JsonReserveArray(skins, model->skins.size()); + for (unsigned int i = 0; i < model->skins.size(); ++i) { + detail::json skin; + SerializeGltfSkin(model->skins[i], skin); + detail::JsonPushBack(skins, std::move(skin)); + } + detail::JsonAddMember(o, "skins", std::move(skins)); + } + + // TEXTURES + if (model->textures.size()) { + detail::json textures; + detail::JsonReserveArray(textures, model->textures.size()); + for (unsigned int i = 0; i < model->textures.size(); ++i) { + detail::json texture; + SerializeGltfTexture(model->textures[i], texture); + detail::JsonPushBack(textures, std::move(texture)); + } + detail::JsonAddMember(o, "textures", std::move(textures)); + } + + // SAMPLERS + if (model->samplers.size()) { + detail::json samplers; + detail::JsonReserveArray(samplers, model->samplers.size()); + for (unsigned int i = 0; i < model->samplers.size(); ++i) { + detail::json sampler; + SerializeGltfSampler(model->samplers[i], sampler); + detail::JsonPushBack(samplers, std::move(sampler)); + } + detail::JsonAddMember(o, "samplers", std::move(samplers)); + } + + // CAMERAS + if (model->cameras.size()) { + detail::json cameras; + detail::JsonReserveArray(cameras, model->cameras.size()); + for (unsigned int i = 0; i < model->cameras.size(); ++i) { + detail::json camera; + SerializeGltfCamera(model->cameras[i], camera); + detail::JsonPushBack(cameras, std::move(camera)); + } + detail::JsonAddMember(o, "cameras", std::move(cameras)); + } + + // EXTRAS & EXTENSIONS + SerializeExtrasAndExtensions(*model, o); + + auto extensionsUsed = model->extensionsUsed; + + // LIGHTS as KHR_lights_punctual + if (model->lights.size()) { + detail::json lights; + detail::JsonReserveArray(lights, model->lights.size()); + for (unsigned int i = 0; i < model->lights.size(); ++i) { + detail::json light; + SerializeGltfLight(model->lights[i], light); + detail::JsonPushBack(lights, std::move(light)); + } + detail::json khr_lights_cmn; + detail::JsonAddMember(khr_lights_cmn, "lights", std::move(lights)); + detail::json ext_j; + + { + detail::json_const_iterator it; + if (detail::FindMember(o, "extensions", it)) { + detail::JsonAssign(ext_j, detail::GetValue(it)); + } + } + + detail::JsonAddMember(ext_j, "KHR_lights_punctual", + std::move(khr_lights_cmn)); + + detail::JsonAddMember(o, "extensions", std::move(ext_j)); + + // Also add "KHR_lights_punctual" to `extensionsUsed` + { + auto has_khr_lights_punctual = + std::find_if(extensionsUsed.begin(), extensionsUsed.end(), + [](const std::string &s) { + return (s.compare("KHR_lights_punctual") == 0); + }); + + if (has_khr_lights_punctual == extensionsUsed.end()) { + extensionsUsed.push_back("KHR_lights_punctual"); + } + } + } + + // KHR_audio + if (!model->audioEmitters.empty() || !model->audioSources.empty()) { + detail::json emitters; + detail::JsonReserveArray(emitters, model->audioEmitters.size()); + for (unsigned int i = 0; i < model->audioEmitters.size(); ++i) { + detail::json emitter; + SerializeGltfAudioEmitter(model->audioEmitters[i], emitter); + detail::JsonPushBack(emitters, std::move(emitter)); + } + detail::json khr_audio_cmn; + detail::JsonAddMember(khr_audio_cmn, "emitters", std::move(emitters)); + + detail::json sources; + detail::JsonReserveArray(sources, model->audioSources.size()); + for (unsigned int i = 0; i < model->audioSources.size(); ++i) { + detail::json source; + SerializeGltfAudioSource(model->audioSources[i], source); + detail::JsonPushBack(sources, std::move(source)); + } + detail::JsonAddMember(khr_audio_cmn, "sources", std::move(sources)); + + detail::json ext_j; + { + detail::json_const_iterator it; + if (detail::FindMember(o, "extensions", it)) { + detail::JsonAssign(ext_j, detail::GetValue(it)); + } + } + + detail::JsonAddMember(ext_j, "KHR_audio", std::move(khr_audio_cmn)); + + detail::JsonAddMember(o, "extensions", std::move(ext_j)); + + // Also add "KHR_audio" to `extensionsUsed` + { + auto has_khr_audio = std::find_if( + extensionsUsed.begin(), extensionsUsed.end(), + [](const std::string &s) { return (s.compare("KHR_audio") == 0); }); + + if (has_khr_audio == extensionsUsed.end()) { + extensionsUsed.push_back("KHR_audio"); + } + } + } + + // Extensions used + if (extensionsUsed.size()) { + SerializeStringArrayProperty("extensionsUsed", extensionsUsed, o); + } +} + +static bool WriteGltfStream(std::ostream &stream, const std::string &content) { + stream << content << std::endl; + return stream.good(); +} + +static bool WriteGltfFile(const std::string &output, + const std::string &content) { +#ifdef _WIN32 +#if defined(_MSC_VER) + std::ofstream gltfFile(UTF8ToWchar(output).c_str()); +#elif defined(__GLIBCXX__) + int file_descriptor = _wopen(UTF8ToWchar(output).c_str(), + _O_CREAT | _O_WRONLY | _O_TRUNC | _O_BINARY); + __gnu_cxx::stdio_filebuf wfile_buf( + file_descriptor, std::ios_base::out | std::ios_base::binary); + std::ostream gltfFile(&wfile_buf); + if (!wfile_buf.is_open()) return false; +#else + std::ofstream gltfFile(output.c_str()); + if (!gltfFile.is_open()) return false; +#endif +#else + std::ofstream gltfFile(output.c_str()); + if (!gltfFile.is_open()) return false; +#endif + return WriteGltfStream(gltfFile, content); +} + +static bool WriteBinaryGltfStream(std::ostream &stream, + const std::string &content, + const std::vector &binBuffer) { + const std::string header = "glTF"; + const int version = 2; + + const uint32_t content_size = uint32_t(content.size()); + const uint32_t binBuffer_size = uint32_t(binBuffer.size()); + // determine number of padding bytes required to ensure 4 byte alignment + const uint32_t content_padding_size = + content_size % 4 == 0 ? 0 : 4 - content_size % 4; + const uint32_t bin_padding_size = + binBuffer_size % 4 == 0 ? 0 : 4 - binBuffer_size % 4; + + // 12 bytes for header, JSON content length, 8 bytes for JSON chunk info. + // Chunk data must be located at 4-byte boundary, which may require padding + const uint32_t length = + 12 + 8 + content_size + content_padding_size + + (binBuffer_size ? (8 + binBuffer_size + bin_padding_size) : 0); + + stream.write(header.c_str(), std::streamsize(header.size())); + stream.write(reinterpret_cast(&version), sizeof(version)); + stream.write(reinterpret_cast(&length), sizeof(length)); + + // JSON chunk info, then JSON data + const uint32_t model_length = uint32_t(content.size()) + content_padding_size; + const uint32_t model_format = 0x4E4F534A; + stream.write(reinterpret_cast(&model_length), + sizeof(model_length)); + stream.write(reinterpret_cast(&model_format), + sizeof(model_format)); + stream.write(content.c_str(), std::streamsize(content.size())); + + // Chunk must be multiplies of 4, so pad with spaces + if (content_padding_size > 0) { + const std::string padding = std::string(size_t(content_padding_size), ' '); + stream.write(padding.c_str(), std::streamsize(padding.size())); + } + if (binBuffer.size() > 0) { + // BIN chunk info, then BIN data + const uint32_t bin_length = uint32_t(binBuffer.size()) + bin_padding_size; + const uint32_t bin_format = 0x004e4942; + stream.write(reinterpret_cast(&bin_length), + sizeof(bin_length)); + stream.write(reinterpret_cast(&bin_format), + sizeof(bin_format)); + stream.write(reinterpret_cast(binBuffer.data()), + std::streamsize(binBuffer.size())); + // Chunksize must be multiplies of 4, so pad with zeroes + if (bin_padding_size > 0) { + const std::vector padding = + std::vector(size_t(bin_padding_size), 0); + stream.write(reinterpret_cast(padding.data()), + std::streamsize(padding.size())); + } + } + + stream.flush(); + return stream.good(); +} + +static bool WriteBinaryGltfFile(const std::string &output, + const std::string &content, + const std::vector &binBuffer) { +#ifdef _WIN32 +#if defined(_MSC_VER) + std::ofstream gltfFile(UTF8ToWchar(output).c_str(), std::ios::binary); +#elif defined(__GLIBCXX__) + int file_descriptor = _wopen(UTF8ToWchar(output).c_str(), + _O_CREAT | _O_WRONLY | _O_TRUNC | _O_BINARY); + __gnu_cxx::stdio_filebuf wfile_buf( + file_descriptor, std::ios_base::out | std::ios_base::binary); + std::ostream gltfFile(&wfile_buf); +#else + std::ofstream gltfFile(output.c_str(), std::ios::binary); +#endif +#else + std::ofstream gltfFile(output.c_str(), std::ios::binary); +#endif + return WriteBinaryGltfStream(gltfFile, content, binBuffer); +} + +bool TinyGLTF::WriteGltfSceneToStream(const Model *model, std::ostream &stream, + bool prettyPrint = true, + bool writeBinary = false) { + detail::JsonDocument output; + + /// Serialize all properties except buffers and images. + SerializeGltfModel(model, output); + + // BUFFERS + std::vector binBuffer; + if (model->buffers.size()) { + detail::json buffers; + detail::JsonReserveArray(buffers, model->buffers.size()); + for (unsigned int i = 0; i < model->buffers.size(); ++i) { + detail::json buffer; + if (writeBinary && i == 0 && model->buffers[i].uri.empty()) { + SerializeGltfBufferBin(model->buffers[i], buffer, binBuffer); + } else { + SerializeGltfBuffer(model->buffers[i], buffer); + } + detail::JsonPushBack(buffers, std::move(buffer)); + } + detail::JsonAddMember(output, "buffers", std::move(buffers)); + } + + // IMAGES + if (model->images.size()) { + detail::json images; + detail::JsonReserveArray(images, model->images.size()); + for (unsigned int i = 0; i < model->images.size(); ++i) { + detail::json image; + + std::string dummystring; + // UpdateImageObject need baseDir but only uses it if embeddedImages is + // enabled, since we won't write separate images when writing to a stream + // we + std::string uri; + if (!UpdateImageObject(model->images[i], dummystring, int(i), true, + &uri_cb, &this->WriteImageData, + this->write_image_user_data_, &uri)) { + return false; + } + SerializeGltfImage(model->images[i], uri, image); + detail::JsonPushBack(images, std::move(image)); + } + detail::JsonAddMember(output, "images", std::move(images)); + } + + if (writeBinary) { + return WriteBinaryGltfStream(stream, detail::JsonToString(output), + binBuffer); + } else { + return WriteGltfStream(stream, + detail::JsonToString(output, prettyPrint ? 2 : -1)); + } +} + +bool TinyGLTF::WriteGltfSceneToFile(const Model *model, + const std::string &filename, + bool embedImages = false, + bool embedBuffers = false, + bool prettyPrint = true, + bool writeBinary = false) { + detail::JsonDocument output; + std::string defaultBinFilename = GetBaseFilename(filename); + std::string defaultBinFileExt = ".bin"; + std::string::size_type pos = + defaultBinFilename.rfind('.', defaultBinFilename.length()); + + if (pos != std::string::npos) { + defaultBinFilename = defaultBinFilename.substr(0, pos); + } + std::string baseDir = GetBaseDir(filename); + if (baseDir.empty()) { + baseDir = "./"; + } + /// Serialize all properties except buffers and images. + SerializeGltfModel(model, output); + + // BUFFERS + std::vector usedFilenames; + std::vector binBuffer; + if (model->buffers.size()) { + detail::json buffers; + detail::JsonReserveArray(buffers, model->buffers.size()); + for (unsigned int i = 0; i < model->buffers.size(); ++i) { + detail::json buffer; + if (writeBinary && i == 0 && model->buffers[i].uri.empty()) { + SerializeGltfBufferBin(model->buffers[i], buffer, binBuffer); + } else if (embedBuffers) { + SerializeGltfBuffer(model->buffers[i], buffer); + } else { + std::string binSavePath; + std::string binFilename; + std::string binUri; + if (!model->buffers[i].uri.empty() && + !IsDataURI(model->buffers[i].uri)) { + binUri = model->buffers[i].uri; + if (!uri_cb.decode(binUri, &binFilename, uri_cb.user_data)) { + return false; + } + } else { + binFilename = defaultBinFilename + defaultBinFileExt; + bool inUse = true; + int numUsed = 0; + while (inUse) { + inUse = false; + for (const std::string &usedName : usedFilenames) { + if (binFilename.compare(usedName) != 0) continue; + inUse = true; + binFilename = defaultBinFilename + std::to_string(numUsed++) + + defaultBinFileExt; + break; + } + } + + if (uri_cb.encode) { + if (!uri_cb.encode(binFilename, "buffer", &binUri, + uri_cb.user_data)) { + return false; + } + } else { + binUri = binFilename; + } + } + usedFilenames.push_back(binFilename); + binSavePath = JoinPath(baseDir, binFilename); + if (!SerializeGltfBuffer(model->buffers[i], buffer, binSavePath, + binUri)) { + return false; + } + } + detail::JsonPushBack(buffers, std::move(buffer)); + } + detail::JsonAddMember(output, "buffers", std::move(buffers)); + } + + // IMAGES + if (model->images.size()) { + detail::json images; + detail::JsonReserveArray(images, model->images.size()); + for (unsigned int i = 0; i < model->images.size(); ++i) { + detail::json image; + + std::string uri; + if (!UpdateImageObject(model->images[i], baseDir, int(i), embedImages, + &uri_cb, &this->WriteImageData, + this->write_image_user_data_, &uri)) { + return false; + } + SerializeGltfImage(model->images[i], uri, image); + detail::JsonPushBack(images, std::move(image)); + } + detail::JsonAddMember(output, "images", std::move(images)); + } + + if (writeBinary) { + return WriteBinaryGltfFile(filename, detail::JsonToString(output), + binBuffer); + } else { + return WriteGltfFile(filename, + detail::JsonToString(output, (prettyPrint ? 2 : -1))); + } +} + +} // namespace tinygltf + +#ifdef __clang__ +#pragma clang diagnostic pop +#endif + +#endif // TINYGLTF_IMPLEMENTATION diff --git a/src/utilities.cpp b/src/utilities.cpp index 9c06c686..859319e1 100644 --- a/src/utilities.cpp +++ b/src/utilities.cpp @@ -110,3 +110,20 @@ std::istream& utilityCore::safeGetline(std::istream& is, std::string& t) { } } } + +std::string utilityCore::extractFilename(const std::string& filePath) { + size_t lastSlash = filePath.find_last_of("/\\"); // Find the last directory separator + + if (lastSlash != std::string::npos) { + return filePath.substr(lastSlash + 1); + } + return filePath; +} + +bool utilityCore::matchFileExtension(const std::string& filePath, std::string extension) { + size_t lastDot = filePath.find_last_of("."); + if (lastDot != std::string::npos) { + return filePath.substr(lastDot + 1) == extension; + } + return false; +} \ No newline at end of file diff --git a/src/utilities.h b/src/utilities.h index d459e335..4c1de800 100644 --- a/src/utilities.h +++ b/src/utilities.h @@ -8,17 +8,32 @@ #include #include #include +#include + +#define USE_BVH 1 +#define NUM_BVHBINS 8 #define PI 3.1415926535897932384626422832795028841971f #define TWO_PI 6.2831853071795864769252867665590057683943f +#define INV_PI 0.3183098861837906715377675267450287240689f +#define HALF_PI 1.5707963267948966192313216916397514420985f +#define QUARTER_PI 0.7853981633974483096156608458198757210493f #define SQRT_OF_ONE_THIRD 0.5773502691896257645091487805019574556476f #define EPSILON 0.00001f +#define LUMINANCE(color) (0.2126 * (color).r + 0.7152 * (color).g + 0.0722 * (color).b) +#define FLOATMAX(a, b) ((a) < (b) ? (b) : (a)) + class GuiDataContainer { public: - GuiDataContainer() : TracedDepth(0) {} + GuiDataContainer() : TracedDepth(0), sortMaterial(false), cacheFirstBounce(false), fbCached(false), lensRadius(0.f), focalDistance(0.f) {} int TracedDepth; + bool sortMaterial; + bool cacheFirstBounce; + bool fbCached; + float lensRadius; + float focalDistance; }; namespace utilityCore { @@ -30,4 +45,6 @@ namespace utilityCore { extern glm::mat4 buildTransformationMatrix(glm::vec3 translation, glm::vec3 rotation, glm::vec3 scale); extern std::string convertIntToString(int number); extern std::istream& safeGetline(std::istream& is, std::string& t); //Thanks to http://stackoverflow.com/a/6089413 + extern std::string extractFilename(const std::string& filePath); + extern bool matchFileExtension(const std::string& filePath, std::string extension); }