32 lines
921 B
Python
32 lines
921 B
Python
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import cv2
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import numpy as np
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import sys
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weights = 'models/detection.weights'
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cfg = 'models/detection.cfg'
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img = sys.argv[1]
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net = cv2.dnn.readNet(weights, cfg)
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blob = cv2.dnn.blobFromImage(cv2.imread(img), 0.00392, (416,416), (0,0,0), True, crop=False)
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net.setInput(blob)
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layer_names = net.getLayerNames()
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unconnected = net.getUnconnectedOutLayers()
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indices = []
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if isinstance(unconnected, np.ndarray):
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indices = unconnected.flatten()
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else:
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try:
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indices = [u[0] if hasattr(u, '__iter__') else u for u in unconnected]
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except Exception:
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indices = list(unconnected)
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output_layers = [layer_names[int(i) - 1] for i in indices]
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print('output_layers=', output_layers)
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outs = net.forward(output_layers)
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print('outs len:', len(outs))
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for i, om in enumerate(outs):
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print(i, 'rows', om.shape[0], 'cols', om.shape[1])
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for r in range(min(3, om.shape[0])):
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print(' row', r, om[r,:10])
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