diff --git a/bayes3d/genjax/model.py b/bayes3d/genjax/model.py index 6f667197..d49b7d3f 100644 --- a/bayes3d/genjax/model.py +++ b/bayes3d/genjax/model.py @@ -7,6 +7,7 @@ from genjax.incremental import Diff, NoChange, UnknownChange import bayes3d as b +import bayes3d.scene_graph from .genjax_distributions import ( contact_params_uniform, @@ -127,14 +128,14 @@ def get_far_plane(trace): def add_object(trace, key, obj_id, parent, face_parent, face_child): - N = b.get_indices(trace).shape[0] + 1 + N = get_indices(trace).shape[0] + 1 choices = trace.get_choices() choices[f"parent_{N-1}"] = parent choices[f"id_{N-1}"] = obj_id choices[f"face_parent_{N-1}"] = face_parent choices[f"face_child_{N-1}"] = face_child choices[f"contact_params_{N-1}"] = jnp.zeros(3) - return model.importance(key, choices, (jnp.arange(N), *trace.get_args()[1:]))[1] + return model.importance(key, choices, (jnp.arange(N), *trace.get_args()[1:]))[0] add_object_jit = jax.jit(add_object) @@ -151,7 +152,7 @@ def print_trace(trace): def viz_trace_meshcat(trace, colors=None): - b.clear() + b.clear_visualizer() b.show_cloud( "1", b.apply_transform_jit(trace["image"].reshape(-1, 3), trace["camera_pose"]) ) @@ -223,14 +224,14 @@ def enumerator(trace, key, *args): key, chm_builder(addresses, args, chm_args), argdiff_f(trace), - )[2] + )[0] def enumerator_with_weight(trace, key, *args): return trace.update( key, chm_builder(addresses, args, chm_args), argdiff_f(trace), - )[1:3] + )[0:2] def enumerator_score(trace, key, *args): return enumerator(trace, key, *args).get_score() @@ -301,4 +302,4 @@ def update_address(trace, key, address, value): key, genjax.choice_map({address: value}), tuple(map(lambda v: Diff(v, UnknownChange), trace.args)), - )[2] + )[0] \ No newline at end of file diff --git a/bayes3d/viser.py b/bayes3d/viser.py new file mode 100644 index 00000000..ca9e8a33 --- /dev/null +++ b/bayes3d/viser.py @@ -0,0 +1,58 @@ +import viser +import random +import time + +import imageio.v3 as iio +import numpy as onp + +server.add_frame( + "/tree", + wxyz=(1.0, 0.0, 0.0, 0.0), + position=(random.random() * 2.0, 2.0, 0.2), +) +server.add_frame( + "/tree/branch", + wxyz=(1.0, 0.0, 0.0, 0.0), + position=(random.random() * 2.0, 2.0, 0.2), +) + +client_handle = list(server.get_clients().values())[0] + +p,q = client_handle.camera.position, client_handle.camera.wxyz + +client_handle.camera.position = p +client_handle.camera.wxyz = q + +img = client_handle.camera.get_render(100,100) + + + +server = viser.ViserServer() + +import os +import trimesh +i = 9 +model_dir = os.path.join(b.utils.get_assets_dir(), "ycb_video_models/models") +mesh_path = os.path.join(model_dir, b.utils.ycb_loader.MODEL_NAMES[i],"textured.obj") +mesh = trimesh.load(mesh_path) + +server.add_mesh_trimesh( + name="/trimesh", + mesh=mesh, +) + +server.reset_scene() + + +server.add_mesh( + name="/trimesh", + vertices=mesh.vertices, + faces=mesh.faces, +) + +sphere = trimesh.creation.uv_sphere(0.1, (10,10,)) +server.add_mesh( + name="/trimesh2", + vertices=sphere.vertices * np.array([1.0, 2.0, 3.0]), + faces=sphere.faces, +) \ No newline at end of file diff --git a/bayes3d/viz/viz.py b/bayes3d/viz/viz.py index c611825f..7fc3ba2b 100644 --- a/bayes3d/viz/viz.py +++ b/bayes3d/viz/viz.py @@ -7,6 +7,7 @@ import matplotlib import matplotlib.pyplot as plt import numpy as np +import plotly.graph_objects as go from PIL import Image, ImageDraw, ImageFont import bayes3d.utils @@ -45,13 +46,12 @@ def preprocess_for_viz(img): return depth_np -cmap = copy.copy(plt.get_cmap("turbo")) +cmap = copy.copy(plt.get_cmap('turbo')) cmap.set_bad(color=(1.0, 1.0, 1.0, 1.0)) - -def get_depth_image(image, min_val=None, max_val=None, remove_max=True): +def get_depth_image(image, max=None): """Convert a depth image to a PIL image. - + Args: image (np.ndarray): Depth image. Shape (H, W). min (float): Minimum depth value for colormap. @@ -60,28 +60,22 @@ def get_depth_image(image, min_val=None, max_val=None, remove_max=True): Returns: PIL.Image: Depth image visualized as a PIL image. """ - if len(image.shape) > 2: - depth = np.array(image[:, :, -1]) + depth = np.array(image) + if max is None: + maxim = depth.max() else: - depth = np.array(image) - - if max_val is None: - max_val = depth.max() - if not remove_max: - max_val += 1 - if min_val is None: - min_val = depth.min() - - mask = (depth < max_val) * (depth > min_val) + maxim = max + mask = depth < maxim depth[np.logical_not(mask)] = np.nan - depth = (depth - min_val) / (max_val - min_val + 1e-10) + vmin = depth[mask].min() + vmax = depth[mask].max() + depth = (depth - vmin) / (vmax - vmin) img = Image.fromarray( np.rint(cmap(depth) * 255.0).astype(np.int8), mode="RGBA" ).convert("RGB") return img - def get_rgb_image(image, max=255.0): """Convert an RGB image to a PIL image. @@ -465,3 +459,59 @@ def viz_graph(num_nodes, edges, filename, node_names=None): ) filename_prefix, filetype = filename.split(".") g_out.render(filename_prefix, format=filetype) + + + + +def visualize_rotation_headings(rot_matrices, init_vector = None): + """ + rot_matrices: rotation matrics of shape (N,3,3) + init_vector: rotation about init vector of shape (3,) + if not specified, it will use the first rotation matrix about the vector [1,0,0] + """ + + def plot_vector(fig, start, end, name, color, width = 5): + fig.add_trace(go.Scatter3d(x=[start[0], end[0]], y=[start[1], end[1]], z=[start[2], end[2]], + mode='lines+text', + line=dict(width=width, color=color))) + + # Sphere + u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j] + x = np.cos(u) * np.sin(v) + y = np.sin(u) * np.sin(v) + z = np.cos(v) + + # Initialize figure + fig = go.Figure(data=[go.Surface(z=z, x=x, y=y, colorscale='Greys', opacity=0.3, showscale = False)]) + + # Original unit vector + if init_vector is None: + unit_vector = np.array([1, 0, 0]) + origin_vector = rot_matrices[0] @ unit_vector + + else: + unit_vector = init_vector/np.linalg.norm(init_vector) + origin_vector = unit_vector + + plot_vector(fig, [0, 0, 0], origin_vector, "Original", "blue", width = 5) + + # Apply rotation matrices to the unit vector + for i, R in enumerate(rot_matrices): + if i == 0 and init_vector is None: + continue + transformed_vector = R @ unit_vector + plot_vector(fig, [0, 0, 0], transformed_vector, f"Transformed {i+1}", "red") + + # Update layout for a better view + fig.update_layout(scene=dict(xaxis_title='X axis', + yaxis_title='Y axis', + zaxis_title='Z axis', + xaxis=dict(range=[-1,1], autorange=False), + yaxis=dict(range=[-1,1], autorange=False), + zaxis=dict(range=[-1,1], autorange=False), + aspectratio=dict(x=1, y=1, z=1)), + showlegend = False, + + margin=dict(l=0, r=0, b=0, t=0)) + + fig.show() \ No newline at end of file diff --git a/demo_c2f.ipynb b/demo_c2f.ipynb new file mode 100644 index 00000000..22b06f30 --- /dev/null +++ b/demo_c2f.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5300d4b8-7b89-492c-950f-3e56fa9d46f2", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import jax\n", + "import bayes3d as b\n", + "import time\n", + "from PIL import Image\n", + "from scipy.spatial.transform import Rotation as R\n", + "import matplotlib.pyplot as plt\n", + "import cv2\n", + "import trimesh\n", + "import os\n", + "import glob\n", + "import bayes3d.neural\n", + "import pickle\n", + "# Can be helpful for debugging:\n", + "# jax.config.update('jax_enable_checks', True) \n", + "# from bayes3d.neural.segmentation import carvekit_get_foreground_mask\n", + "import genjax\n", + "import bayes3d.genjax" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2448882a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
╭─────────────── viser ───────────────╮\n",
+       "│             ╷                       │\n",
+       "│   HTTP      │ http://0.0.0.0:8081   │\n",
+       "│   Websocket │ ws://0.0.0.0:8081     │\n",
+       "│             ╵                       │\n",
+       "╰─────────────────────────────────────╯\n",
+       "
\n" + ], + "text/plain": [ + "╭─────────────── \u001b[1mviser\u001b[0m ───────────────╮\n", + "│ ╷ │\n", + "│ HTTP │ http://0.0.0.0:8081 │\n", + "│ Websocket │ ws://0.0.0.0:8081 │\n", + "│ ╵ │\n", + "╰─────────────────────────────────────╯\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import viser\n", + "server = viser.ViserServer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "57e1fa42-9f39-437f-b408-7c9760a86413", + "metadata": {}, + "outputs": [], + "source": [ + "importance_jit = jax.jit(b.genjax.model.importance)\n", + "key = jax.random.PRNGKey(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "ed42e5c3-be5d-420b-9a21-759247e5d7b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['rgbPixels', 'depthPixels', 'segmentationMaskBuffer', 'camera_pose', 'camera_matrix'])\n" + ] + }, + { + "data": { + "image/jpeg": 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", + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file = os.path.join(b.utils.get_assets_dir(),\"tutorial_mug_image.pkl\")\n", + "all_data = pickle.load(open(file, \"rb\"))\n", + "IDX = 0\n", + "data = all_data[IDX]\n", + "print(data[\"camera_image\"].keys())\n", + "K = data[\"camera_image\"]['camera_matrix'][0]\n", + "rgb = data[\"camera_image\"]['rgbPixels']\n", + "depth = data[\"camera_image\"]['depthPixels']\n", + "camera_pose = data[\"camera_image\"]['camera_pose']\n", + "camera_pose = b.t3d.pybullet_pose_to_transform(camera_pose)\n", + "fx, fy, cx, cy = K[0,0],K[1,1],K[0,2],K[1,2]\n", + "h,w = depth.shape\n", + "near = 0.001\n", + "rgbd_original = b.RGBD(rgb, depth, camera_pose, b.Intrinsics(h,w,fx,fy,cx,cy,0.001,10.0))\n", + "scaling_factor = 0.2\n", + "rgbd_scaled_down = b.RGBD.scale_rgbd(rgbd_original, scaling_factor)\n", + "b.hstack_images([b.get_rgb_image(rgbd_scaled_down.rgb), b.get_depth_image(rgbd_scaled_down.depth,max_val=2.5)])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "432c5da8-eb91-408f-b2e4-501eef3cc221", + "metadata": {}, + "outputs": [], + "source": [ + "table_pose, plane_dims = b.utils.infer_table_plane(\n", + " b.unproject_depth(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics),\n", + " jnp.eye(4), rgbd_scaled_down.intrinsics, \n", + " ransac_threshold=0.001, inlier_threshold=0.005, segmentation_threshold=0.2\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "52b57c87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FrameHandle(_impl=_SceneNodeHandleState(name='/table', api=, wxyz=array([-0.3109156 , -0.40532318, 0.71136296, -0.48178506], dtype=float32), position=array([0.13536738, 0.06300807, 0.7492305 ], dtype=float32), visible=True, click_cb=None))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "server.add_point_cloud(\n", + " \"/cloud\",\n", + " points=np.array(b.unproject_depth(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics).reshape(-1,3)),\n", + " colors=np.array(rgbd_scaled_down.rgb.reshape(-1,3)),\n", + " point_size=0.01\n", + ")\n", + "server.add_frame(\n", + " \"/table\",\n", + " position=np.array(table_pose[:3,3]),\n", + " wxyz=b.rotation_matrix_to_quaternion(table_pose[:3,:3]),\n", + " axes_length=0.2,\n", + " axes_radius=0.005\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "89d55282-eb41-4c8e-97fe-1b9528f52481", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Increasing frame buffer size to (width, height, depth) = (192, 96, 1024)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[E rasterize_gl.cpp:121] OpenGL version reported as 4.6\n" + ] + } + ], + "source": [ + "b.setup_renderer(rgbd_scaled_down.intrinsics)\n", + "model_dir = os.path.join(b.utils.get_assets_dir(),\"bop/ycbv/models\")\n", + "mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(13+1).rjust(6, '0') + \".ply\")\n", + "b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", + "b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=jnp.array([0.5,0.5,0.001]))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "width = 0.03\n", + "ang = jnp.pi\n", + "num_position_grids = 51\n", + "num_angle_grids = 51\n", + "contact_param_deltas = b.utils.make_translation_grid_enumeration_3d(\n", + " -width, -width, -ang,\n", + " width, width, ang,\n", + " num_position_grids,num_position_grids,num_angle_grids\n", + ")\n", + "\n", + "grid_params = [\n", + " (0.5, jnp.pi, (15,15,15)), (0.2, jnp.pi, (15,15,15)), (0.1, jnp.pi, (15,15,15)),\n", + " (0.05, jnp.pi/3, (15,15,15)),\n", + " (0.02, jnp.pi, (9,9,51))\n", + " , (0.01, jnp.pi/5, (15,15,15)),\n", + " (0.01, 0.0, (31,31,1)),(0.05, 0.0, (31,31,1))\n", + "]\n", + "contact_param_gridding_schedule = [\n", + " b.utils.make_translation_grid_enumeration_3d(\n", + " -x, -x, -ang,\n", + " x, x, ang,\n", + " *nums\n", + " )\n", + " for (x,ang,nums) in grid_params\n", + "]\n", + "\n", + "OBJECT_NUMBER = 1\n", + "address = f\"contact_params_{OBJECT_NUMBER}\"\n", + "enumerators = b.genjax.make_enumerator([address])\n", + "\n", + "def c2f_(potential_trace, contact_param_gridding_schedule):\n", + " cp = potential_trace[address]\n", + " for cp_grid in contact_param_gridding_schedule:\n", + " cps = cp + cp_grid\n", + " scores = enumerators.enumerate_choices_get_scores(potential_trace, key, cps)\n", + " cp = cps[scores.argmax()]\n", + " potential_trace = enumerators.update_choices(potential_trace, key, cp)\n", + " return potential_trace, scores.argmax()\n", + "c2f = jax.jit(c2f_)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "5a628704", + "metadata": {}, + "outputs": [], + "source": [ + "def viz_trace_viser(server, trace, colors=None):\n", + " indices = b.genjax.get_indices(trace)\n", + " poses = b.genjax.get_poses(trace)\n", + " for i in range(len(poses)):\n", + " mesh = b.RENDERER.meshes[indices[i]]\n", + " server.add_mesh_trimesh(\n", + " name=f\"/trimesh/{i}\",\n", + " mesh=trimesh.Trimesh(mesh.vertices, mesh.faces),\n", + " position=np.array(poses[i][:3,3]),\n", + " wxyz=b.rotation_matrix_to_quaternion(poses[i][:3,:3]),\n", + " )\n", + " server.add_point_cloud(\n", + " \"/observed_cloud\",\n", + " points=np.array(trace[\"image\"].reshape(-1,3)),\n", + " colors=np.array([0.0, 0.0, 0.0]),\n", + " point_size=0.005\n", + " )\n", + " server.add_point_cloud(\n", + " \"/rendered_cloud\",\n", + " points=np.array(b.genjax.get_rendered_image(trace).reshape(-1,3)),\n", + " colors=np.array([1.0, 0.0, 0.0]),\n", + " point_size=0.005\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-54.53688\n" + ] + } + ], + "source": [ + "key = jax.random.split(key)[0]\n", + "obs_img = b.unproject_depth_jit(rgbd_scaled_down.depth, rgbd_scaled_down.intrinsics)\n", + "trace, weight = importance_jit(key, genjax.choice_map({\n", + " \"parent_0\": -1,\n", + " \"parent_1\": 0,\n", + " \"id_0\": jnp.int32(1),\n", + " \"id_1\": jnp.int32(0),\n", + " \"camera_pose\": jnp.eye(4),\n", + " \"root_pose_0\": table_pose,\n", + " \"face_parent_1\": 2,\n", + " \"face_child_1\": 3,\n", + " \"image\": obs_img,\n", + " \"variance\": 0.03,\n", + " \"outlier_prob\": 0.0001,\n", + "}), (\n", + " jnp.arange(2),\n", + " jnp.arange(22),\n", + " jnp.array([-jnp.ones(3)*100.0, jnp.ones(3)*100.0]),\n", + " jnp.array([jnp.array([-0.3, -0.3, -22*jnp.pi]), jnp.array([0.3, 0.3, 22*jnp.pi])]),\n", + " b.RENDERER.model_box_dims)\n", + ")\n", + "print(trace.get_score())\n", + "viz_trace_viser(server, trace)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "9170b633", + "metadata": {}, + "outputs": [], + "source": [ + "potential_trace = trace\n", + "import time\n", + "cp = potential_trace[address]\n", + "for cp_grid in contact_param_gridding_schedule:\n", + " cps = cp + cp_grid\n", + " scores = enumerators.enumerate_choices_get_scores(potential_trace, key, cps)\n", + " cp = cps[scores.argmax()]\n", + " potential_trace = enumerators.update_choices(potential_trace, key, cp)\n", + "viz_trace_viser(server, potential_trace)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dd46342", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c4398d2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/likelihood_debug.ipynb b/likelihood_debug.ipynb new file mode 100644 index 00000000..445f639f --- /dev/null +++ b/likelihood_debug.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c9a75992-9ded-4c10-bcbe-a68d4e817125", + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import bayes3d as b\n", + "import os\n", + "import jax\n", + "import functools\n", + "from jax.scipy.special import logsumexp\n", + "from functools import partial\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "import bayes3d.genjax\n", + "import genjax\n", + "import pathlib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1e9cc139-2449-4532-acf4-af71ccd6a24d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can open the visualizer by visiting the following URL:\n", + "http://127.0.0.1:7000/static/\n" + ] + } + ], + "source": [ + "b.setup_visualizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd797e9e", + "metadata": {}, + "outputs": [], + "source": [ + "intrinsics = b.Intrinsics(\n", + " height=100,\n", + " width=100,\n", + " fx=200.0, fy=200.0,\n", + " cx=50.0, cy=50.0,\n", + " near=0.0001, far=2.0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "211f5ade", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[E rasterize_gl.cpp:121] OpenGL version reported as 4.6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Increasing frame buffer size to (width, height, depth) = (128, 128, 1024)\n" + ] + } + ], + "source": [ + "\n", + "b.setup_renderer(intrinsics)\n", + "model_dir = os.path.join(b.utils.get_assets_dir(),\"bop/ycbv/models\")\n", + "meshes = []\n", + "for idx in range(1,22):\n", + " mesh_path = os.path.join(model_dir,\"obj_\" + \"{}\".format(idx).rjust(6, '0') + \".ply\")\n", + " b.RENDERER.add_mesh_from_file(mesh_path, scaling_factor=1.0/1000.0)\n", + "# b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=1.0/10.0)\n", + "b.RENDERER.add_mesh_from_file(os.path.join(b.utils.get_assets_dir(), \"sample_objs/cube.obj\"), scaling_factor=1.0/1000000000.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ceed122b", + "metadata": {}, + "outputs": [], + "source": [ + "width = 0.03\n", + "ang = jnp.pi\n", + "num_position_grids = 15\n", + "num_angle_grids = 15\n", + "contact_param_deltas = b.utils.make_translation_grid_enumeration_3d(\n", + " -width, -width, -ang,\n", + " width, width, ang,\n", + " num_position_grids,num_position_grids,num_angle_grids\n", + ")\n", + "\n", + "grid_params = [\n", + " (0.3, jnp.pi, (15,15,15)), (0.2, jnp.pi, (15,15,15)), (0.1, jnp.pi, (15,15,15)),\n", + " (0.05, jnp.pi/3, (15,15,15)), (0.02, jnp.pi, (9,9,51)), (0.01, jnp.pi/5, (15,15,15)), (0.01, 0.0, (31,31,1)),(0.05, 0.0, (31,31,1))\n", + "]\n", + "contact_param_gridding_schedule = [\n", + " b.utils.make_translation_grid_enumeration_3d(\n", + " -x, -x, -ang,\n", + " x, x, ang,\n", + " *nums\n", + " )\n", + " for (x,ang,nums) in grid_params\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f4648f31-caf1-4792-bd83-9d652a8c5e4b", + "metadata": {}, + "outputs": [], + "source": [ + "table_pose = b.t3d.inverse_pose(\n", + " b.t3d.transform_from_pos_target_up(\n", + " jnp.array([0.0, 0.8, .15]),\n", + " jnp.array([0.0, 0.0, 0.0]),\n", + " jnp.array([0.0, 0.0, 1.0]),\n", + " )\n", + ")\n", + "face_child = 3\n", + "cp_to_pose = lambda cp: table_pose@ b.scene_graph.relative_pose_from_edge(cp, face_child, b.RENDERER.model_box_dims[13])\n", + "cp_to_pose_jit = jax.jit(cp_to_pose)\n", + "cp_to_pose_parallel = jax.jit(jax.vmap(cp_to_pose, in_axes=(0,)))\n", + "\n", + "key = jax.random.PRNGKey(30)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "id": "53c182df", + "metadata": {}, + "outputs": [], + "source": [ + "def score_images(rendered, observed):\n", + " return -jnp.linalg.norm(observed - rendered, axis=-1).mean()\n", + "\n", + "def score_images(rendered, observed):\n", + " mask = observed[...,2] < intrinsics.far\n", + " return (jnp.linalg.norm(observed - rendered, axis=-1)* (1.0 * mask)).sum() / mask.sum()\n", + "\n", + "\n", + "# def score_images(rendered, observed):\n", + "# return -jnp.linalg.norm(observed - rendered, axis=-1).mean()\n", + "\n", + "\n", + "\n", + "# def score_images(rendered, observed):\n", + "# distances = jnp.linalg.norm(observed - rendered, axis=-1)\n", + "# width = 0.01\n", + "# outlier_probability = 0.001\n", + "# probabilities_per_pixel = (1.0 - outlier_probability) * (distances < width/2) / width + outlier_probability * (1/10000.0)\n", + "# return jnp.log(probabilities_per_pixel).sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "5253e2ee", + "metadata": {}, + "outputs": [], + "source": [ + "key = jax.random.split(key,2)[0]\n", + "key = jnp.array([2755247810, 1586593754], dtype=np.uint32)\n", + "low, high = jnp.array([-0.2, -0.2, -jnp.pi]), jnp.array([0.2, 0.2, jnp.pi])\n", + "gt_cp = jax.random.uniform(key, shape=(3,),minval=low, maxval=high)\n", + "gt_pose = cp_to_pose_jit(gt_cp)\n", + "obs_img = b.RENDERER.render(gt_pose[None,...], jnp.array([13]))[...,:3]\n", + "# b.viz.scale_image(b.get_depth_image(obs_img[...,2]),3.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "id": "00c00a84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[43792.79 43497.336 43450.188 43235.336 43193.297 43193.297 43164.523\n", + " 43088.688 43088.688 43032.523]\n" + ] + }, + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "def score_images(rendered, observed):\n", + " distances = jnp.linalg.norm(observed - rendered, axis=-1)\n", + " log_probabilities_per_pixel = jax.scipy.stats.norm.logpdf(\n", + " distances,\n", + " loc=0.0, \n", + " scale=0.005\n", + " )\n", + " outlier_probability = 0.0001\n", + " log_probabilities_per_pixel = jnp.logaddexp(\n", + " jnp.log(1.0 - outlier_probability) + log_probabilities_per_pixel,\n", + " (jnp.log(outlier_probability) + jnp.log(1/10000.0)) * jnp.ones_like(log_probabilities_per_pixel)\n", + " )\n", + " return log_probabilities_per_pixel.sum()\n", + "score_vmap = jax.jit(jax.vmap(score_images, in_axes=(0, None)))\n", + "\n", + "contact_param_grid = gt_cp + contact_param_deltas\n", + "scores = jnp.concatenate([\n", + " score_vmap(b.RENDERER.render_many(cp_to_pose_parallel(cps)[:,None,...], jnp.array([13]))[...,:3], obs_img)\n", + " for cps in jnp.array_split(contact_param_grid, 15)\n", + "],axis=0)\n", + "\n", + "sort_order = jnp.argsort(-scores)\n", + "sorted_scores = scores[sort_order]\n", + "k = 10\n", + "# print(\"GT CP: \", gt_cp)\n", + "# print(sorted_scores[:k])\n", + "# print(contact_param_grid[sort_order[:k]])\n", + "poses = cp_to_pose_parallel(contact_param_grid[sort_order[:k]])[:,None,...]\n", + "rendered_top_k = b.RENDERER.render_many(poses, jnp.array([13]))[...,:3]\n", + "\n", + "print(sorted_scores[:k])\n", + "b.viz.scale_image(b.hstack_images([b.get_depth_image(obs_img[...,2]), *[b.get_depth_image(i[...,2]) for i in rendered_top_k]]),3.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "566c9480", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "647aa535", + "metadata": {}, + "outputs": [], + "source": [ + "log_probabilities_per_pixel = jnp.log(jnp.ones((100,100))* 0.2)\n", + "outlier_probability = 0.001\n", + "probabilities_per_pixel = jnp.logaddexp(\n", + " (1.0 - outlier_probability) + log_probabilities_per_pixel,\n", + " (jnp.log(outlier_probability) + jnp.log(1/10000.0)) * jnp.ones_like(log_probabilities_per_pixel)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "b7604635", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([[-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " ...,\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775],\n", + " [-0.61043775, -0.61043775, -0.61043775, ..., -0.61043775,\n", + " -0.61043775, -0.61043775]], dtype=float32)" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probabilities_per_pixel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcdf5636", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index c67cc688..66cb997a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,7 @@ dependencies = [ "numpy", "open3d", "opencv-python", + "plotly", "plyfile", "pyliblzfse", "pyransac3d", diff --git a/scripts/experiments/slam/slam_with_room_obj.ipynb b/scripts/experiments/slam/slam_with_room_obj.ipynb index 5f1e5ce2..ed10b320 100644 --- a/scripts/experiments/slam/slam_with_room_obj.ipynb +++ b/scripts/experiments/slam/slam_with_room_obj.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ "output_type": "stream", "text": [ "You can open the visualizer by visiting the following URL:\n", - "http://127.0.0.1:7050/static/\n" + "http://127.0.0.1:7001/static/\n" ] } ], @@ -35,7 +35,17 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import viser\n", + "server = viser.ViserServer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -98,7 +108,30 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GlbHandle(_impl=_SceneNodeHandleState(name='/trimesh', api=, wxyz=array([1., 0., 0., 0.]), position=array([0., 0., 0.]), visible=True, click_cb=None))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "server.add_mesh_trimesh(\n", + " name=\"/trimesh\",\n", + " mesh=mesh,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -157,11 +190,11 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "b.clear()\n", + "b.clear_visualizer()\n", "b.show_pose(\"actual\", camera_poses[1])\n", "tr,q = b.pose_matrix_to_translation_and_quaternion(camera_poses[0])\n", "b.show_pose(\"inferred\", b.translation_and_quaternion_to_pose_matrix(tr,q), size=0.1)" @@ -169,21 +202,21 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "start (Array(0.48633558, dtype=float32), (Array([-0.01954 , 0.06823298, -0.4547913 ], dtype=float32), Array([ 0. , 1.0255171 , -0.06746437, -0.06139849], dtype=float32)))\n" + "start (Array(0.48633558, dtype=float32), (Array([-0.01954 , 0.06823303, -0.45479128], dtype=float32), Array([ 0. , 1.025517 , -0.06746437, -0.06139864], dtype=float32)))\n" ] }, { "name": "stderr", 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MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", - "[libx264 @ 0x555a26780740] profile High 4:4:4 Predictive, level 3.2, 4:4:4, 8-bit\n", - "[libx264 @ 0x555a26780740] 264 - core 160 r3011 cde9a93 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=3 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", + "[libx264 @ 0x564a196c9700] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n", + "[libx264 @ 0x564a196c9700] profile High 4:4:4 Predictive, level 3.2, 4:4:4, 8-bit\n", + "[libx264 @ 0x564a196c9700] 264 - core 160 r3011 cde9a93 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=3 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", "Output #0, mp4, to 'localization_with_gradients.mp4':\n", " Metadata:\n", " encoder : Lavf58.45.100\n", @@ -393,24 +433,24 @@ " encoder : Lavc58.91.100 libx264\n", " Side data:\n", " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", - "frame= 53 fps=0.0 q=-1.0 Lsize= 258kB time=00:00:16.66 bitrate= 126.7kbits/s speed=19.3x \n", - "video:257kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.479635%\n", - "[libx264 @ 0x555a26780740] frame I:1 Avg QP:12.62 size: 42569\n", - "[libx264 @ 0x555a26780740] frame P:40 Avg QP:12.86 size: 3571\n", - "[libx264 @ 0x555a26780740] frame B:12 Avg QP:15.89 size: 6382\n", - "[libx264 @ 0x555a26780740] consecutive B-frames: 60.4% 22.6% 17.0% 0.0%\n", - "[libx264 @ 0x555a26780740] mb I I16..4: 34.1% 43.3% 22.6%\n", - "[libx264 @ 0x555a26780740] mb P I16..4: 11.2% 5.2% 1.1% P16..4: 2.0% 1.4% 0.4% 0.0% 0.0% skip:78.7%\n", - "[libx264 @ 0x555a26780740] mb B I16..4: 3.8% 1.8% 1.4% B16..8: 4.0% 1.8% 1.1% direct: 4.2% skip:81.9% L0:52.4% L1:34.6% BI:13.0%\n", - "[libx264 @ 0x555a26780740] 8x8 transform intra:31.0% inter:68.5%\n", - "[libx264 @ 0x555a26780740] coded y,u,v intra: 6.6% 6.5% 6.3% inter: 1.3% 1.9% 2.3%\n", - "[libx264 @ 0x555a26780740] i16 v,h,dc,p: 85% 13% 1% 1%\n", - "[libx264 @ 0x555a26780740] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 64% 11% 23% 0% 0% 0% 0% 0% 0%\n", - "[libx264 @ 0x555a26780740] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 49% 26% 16% 2% 2% 2% 2% 1% 1%\n", - "[libx264 @ 0x555a26780740] Weighted P-Frames: Y:0.0% UV:0.0%\n", - "[libx264 @ 0x555a26780740] ref P L0: 64.5% 6.9% 18.1% 10.5%\n", - "[libx264 @ 0x555a26780740] ref B L0: 78.6% 19.1% 2.3%\n", - "[libx264 @ 0x555a26780740] kb/s:118.65\n" + "frame= 53 fps=0.0 q=-1.0 Lsize= 259kB time=00:00:16.66 bitrate= 127.1kbits/s speed=19.6x \n", + "video:257kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.484197%\n", + "[libx264 @ 0x564a196c9700] frame I:1 Avg QP:12.64 size: 42550\n", + "[libx264 @ 0x564a196c9700] frame P:38 Avg QP:12.79 size: 3663\n", + "[libx264 @ 0x564a196c9700] frame B:14 Avg QP:16.70 size: 5792\n", + "[libx264 @ 0x564a196c9700] consecutive B-frames: 54.7% 22.6% 22.6% 0.0%\n", + "[libx264 @ 0x564a196c9700] mb I I16..4: 34.1% 43.3% 22.6%\n", + "[libx264 @ 0x564a196c9700] mb P I16..4: 11.2% 5.6% 1.1% P16..4: 2.0% 1.3% 0.4% 0.0% 0.0% skip:78.4%\n", + "[libx264 @ 0x564a196c9700] mb B I16..4: 4.0% 2.0% 1.2% B16..8: 4.1% 1.8% 0.9% direct: 3.7% skip:82.3% L0:53.3% L1:34.9% BI:11.8%\n", + "[libx264 @ 0x564a196c9700] 8x8 transform intra:32.2% inter:69.4%\n", + "[libx264 @ 0x564a196c9700] coded y,u,v intra: 6.7% 6.6% 6.5% inter: 1.2% 1.9% 2.4%\n", + "[libx264 @ 0x564a196c9700] i16 v,h,dc,p: 85% 13% 1% 1%\n", + "[libx264 @ 0x564a196c9700] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 65% 10% 22% 0% 0% 0% 0% 0% 0%\n", + "[libx264 @ 0x564a196c9700] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 48% 26% 16% 2% 2% 2% 2% 1% 1%\n", + "[libx264 @ 0x564a196c9700] Weighted P-Frames: Y:0.0% UV:0.0%\n", + "[libx264 @ 0x564a196c9700] ref P L0: 64.9% 6.9% 17.6% 10.6%\n", + "[libx264 @ 0x564a196c9700] ref B L0: 77.7% 19.2% 3.2%\n", + "[libx264 @ 0x564a196c9700] kb/s:119.02\n" ] }, { @@ -419,7 +459,7 @@ "0" ] }, - "execution_count": 91, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -435,11 +475,55 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
╭─────────────── viser ───────────────╮\n",
+       "│             ╷                       │\n",
+       "│   HTTP      │ http://0.0.0.0:8081   │\n",
+       "│   Websocket │ ws://0.0.0.0:8081     │\n",
+       "│             ╵                       │\n",
+       "╰─────────────────────────────────────╯\n",
+       "
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