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41 lines
1.8 KiB
41 lines
1.8 KiB
2 years ago
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import random
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import numpy as np
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random.seed(0)
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# detection model classes
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CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
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'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
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'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
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'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
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'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
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'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
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'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
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'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
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'scissors', 'teddy bear', 'hair drier', 'toothbrush')
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# colors for per classes
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COLORS = {
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cls: [random.randint(0, 255) for _ in range(3)]
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for i, cls in enumerate(CLASSES)
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}
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# colors for segment masks
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MASK_COLORS = np.array([(255, 56, 56), (255, 157, 151), (255, 112, 31),
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(255, 178, 29), (207, 210, 49), (72, 249, 10),
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(146, 204, 23), (61, 219, 134), (26, 147, 52),
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(0, 212, 187), (44, 153, 168), (0, 194, 255),
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(52, 69, 147), (100, 115, 255), (0, 24, 236),
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(132, 56, 255), (82, 0, 133), (203, 56, 255),
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(255, 149, 200), (255, 55, 199)],
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dtype=np.float32) / 255.
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# alpha for segment masks
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ALPHA = 0.5
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