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hub / github.com/drinkingcoder/NeuralMarker / demo

Function demo

demo_video.py:155–198  ·  view source on GitHub ↗
()

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153 return blend
154
155def demo():
156 args = get_demo_video_args()
157 scene_path = os.path.join(args.demo_root, args.scene_name)
158 marker_path = os.path.join(args.demo_root, args.marker_name)
159 movie_path = os.path.join(args.demo_root, args.movie_name)
160 save_path = os.path.join(args.demo_root, args.save_name)
161 print('===> Path Config')
162 print(f"marker: {marker_path}")
163 print(f"scene video: {scene_path}")
164 print(f"movie: {movie_path}")
165 print(f"save to: {save_path}")
166
167 print('\n===> Loading marker image')
168 marker = cv2.imread(marker_path)
169
170 print('===> Loading scene video')
171 scenes, _ = read_video(scene_path)
172 scenes = scenes[args.scene_start_idx:]
173
174 print('===> Loading movie')
175 frames, fps = read_video(movie_path)
176 frames = frames[args.movie_start_idx:]
177
178 print('===> Loading Model\n')
179 model_args = get_life_args()
180 estimator = Flow_estimator(model_args)
181
182 if args.test:
183 print('> Test mode')
184 if args.draw:
185 plt.figure()
186 plt.imshow(scenes[args.scene_id][:,:,::-1])
187 blend(estimator, marker, scenes[args.scene_id], frames[args.frame_id], args)
188 else:
189 print('> Video saving mode')
190 imgs = []
191 for i, scene in tqdm(enumerate(scenes), total = len(scenes)):
192 frame = frames[i%len(frames)]
193 if i == 0:
194 args.save = True
195 else:
196 args.save = False
197 imgs.append(blend(estimator, marker, scene, frame, args))
198 save_video(imgs, fps=fps, size=(imgs[0].shape[1], imgs[0].shape[0]), video_path=save_path)
199
200if __name__=='__main__':
201 demo()

Callers 1

demo_video.pyFile · 0.85

Calls 6

get_demo_video_argsFunction · 0.90
get_life_argsFunction · 0.90
Flow_estimatorClass · 0.90
read_videoFunction · 0.85
save_videoFunction · 0.85
blendFunction · 0.70

Tested by

no test coverage detected