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bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
img_center = img_size / 2     

offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1], (det[:, 1] + det[:, 3]) / 2 - img_center[0]])

offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) 
index = np.argmax(bounding_box_size - offset_dist_squared * 2.0) 
det = det[index, :]
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
#print(det[0], det[1], det[2], det[3])
bb[0] = np.maximum(det[0] - args.margin / 2, 0)     # ??????
bb[1] = np.maximum(det[1] - args.margin / 2, 0)     # ????
bb[2] = np.minimum(det[2] + args.margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + args.margin / 2, img_size[0])


index = np.argmax(bounding_box_size - offset_dist_squared * 2.0)     what means?
Stephen Rauch
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csu
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1 Answers1

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Pad the bounding box with a margin?

user1538798
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