CLI interface

README.md

@@ -1,5 +1,8 @@
 # Deep3D: Automatic 2D-to-3D Video Conversion with CNNs
 
+## Model to try out online
+http://www.somatic.io/models/oEG0wMkR
+
 ## How To Run
 To run this code. Please install MXNet following the official document.
 Deep3D requires MXNet to be built with Cuda 7.0 and Cudnn 4 or above. Please open mxnet/config.mk and set USE_CUDA and USE_CUDNN to 1. Then, append EXTRA\_OPERATORS=path/to/deep3d/operators to path/to/mxnet/config.mk and recompile MXNet.

main.py

@@ -0,0 +1,64 @@
+import argparse
+import mxnet as mx
+import numpy as np
+import os
+import json
+import urllib
+import cv2
+import subprocess
+import matplotlib.pyplot as plt
+from PIL import Image
+from images2gif import writeGif
+import logging
+import time
+logging.basicConfig(level=logging.DEBUG)
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required=True, help="path to base image")
+ap.add_argument("-j", "--json",default=False,  help="upload images")
+ap.add_argument("-o", "--output_folder", default="./", help="output folder")
+args = ap.parse_args()
+param_path = '/tmp/deep3d-0050.params'
+junk = subprocess.check_output(["cp", 'deep3d-symbol.json', '/tmp/'])
+if not os.path.exists(param_path):
+      urllib.urlretrieve('http://homes.cs.washington.edu/~jxie/download/deep3d-0050.params', param_path)
+      model = mx.model.FeedForward.load('/tmp/deep3d', 50, mx.gpu(0))
+
+model = mx.model.FeedForward.load('/tmp/deep3d', 50, mx.gpu(0))
+
+shape = (384, 160)
+img = cv2.imread(args.image)
+raw_shape = (img.shape[1], img.shape[0])
+img = cv2.resize(img, shape)
+#plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+#plt.axis('off')
+#plt.show()
+
+X = img.astype(np.float32).transpose((2,0,1))
+X = X.reshape((1,)+X.shape)
+test_iter = mx.io.NDArrayIter({'left': X, 'left0':X})
+Y = model.predict(test_iter)
+
+right = np.clip(Y.squeeze().transpose((1,2,0)), 0, 255).astype(np.uint8)
+right = Image.fromarray(cv2.cvtColor(right, cv2.COLOR_BGR2RGB))
+left = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+if args.json != False:
+    right_name = str(int(time.time())) + ".jpg"
+    right_path = args.output_folder + "/" + right_name
+    left_name = str(int(time.time())) + ".jpg"
+    left_path = args.output_folder + "/" + left_name
+    right.save(right_path,"JPEG") 
+    left.save(left_path,"JPEG") 
+
+    right_output = subprocess.check_output(["curl","-s", "--upload-file", right_path,("https://transfer.sh/"+right_name)]).strip()
+    left_output = subprocess.check_output(["curl", "-s","--upload-file", left_path,("https://transfer.sh/"+left_name)]).strip()
+    data = {'right':right_output,'left':left_output}
+    output_file = args.output_folder + "/"+ str(int(time.time())) + ".json"
+    print(data)
+    with open(output_file, 'w') as outfile:
+        json.dump(data, outfile)
+else:
+    put_file = args.output_folder + "/"+ str(int(time.time())) + ".gif"
+    writeGif(output_file, [left, right], duration=0.08)
+print(output_file)