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save_path = str(Path(out) / Path(p).name) |
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save_path = str(Path(out) / Path(p).name) |
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txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') |
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txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') |
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s += '%gx%g ' % img.shape[2:] # print string |
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s += '%gx%g ' % img.shape[2:] # print string |
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh |
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh |
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if det is not None and len(det): |
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if det is not None and len(det): |
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# Rescale boxes from img_size to im0 size |
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# Rescale boxes from img_size to im0 size |
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() |
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() |