| | |
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| | | # Dlib face detection model |
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| | | cnn_face_detector = dlib.cnn_face_detection_model_v1(args.face_model) |
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| | | |
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| | | print 'Loading snapshot.' |
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| | | print('Loading snapshot.') |
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| | | # Load snapshot |
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| | | saved_state_dict = torch.load(snapshot_path) |
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| | | model.load_state_dict(saved_state_dict) |
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| | | |
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| | | print 'Loading data.' |
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| | | print ('Loading data.') |
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| | | |
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| | | transformations = transforms.Compose([transforms.Scale(224), |
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| | | transforms.CenterCrop(224), transforms.ToTensor(), |
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| | |
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| | | |
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| | | model.cuda(gpu) |
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| | | |
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| | | print 'Ready to test network.' |
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| | | print('Ready to test network.') |
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| | | |
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| | | # Test the Model |
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| | | model.eval() # Change model to 'eval' mode (BN uses moving mean/var). |
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| | | total = 0 |
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| | | |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = [idx for idx in range(66)] |
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| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
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| | | |
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| | | video = cv2.VideoCapture(video_path) |
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| | |
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| | | frame_num = 1 |
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| | | |
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| | | while frame_num <= args.n_frames: |
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| | | print frame_num |
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| | | print(frame_num) |
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| | | |
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| | | ret,frame = video.read() |
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| | | if ret == False: |
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| | |
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| | | x_min = max(x_min, 0); y_min = max(y_min, 0) |
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| | | x_max = min(frame.shape[1], x_max); y_max = min(frame.shape[0], y_max) |
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| | | # Crop image |
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| | | img = cv2_frame[y_min:y_max,x_min:x_max] |
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| | | img = cv2_frame[int(y_min):int(y_max),int(x_min):int(x_max)] |
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| | | img = Image.fromarray(img) |
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| | | |
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| | | # Transform |
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