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@@ -87,6 +87,7 @@ def detect(opt): |
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt |
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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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imc = im0.copy() if opt.save_crop else im0 # for opt.save_crop |
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if len(det): |
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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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@@ -109,7 +110,7 @@ def detect(opt): |
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label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}') |
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plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness) |
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if opt.save_crop: |
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save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) |
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save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) |
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# Print time (inference + NMS) |
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print(f'{s}Done. ({t2 - t1:.3f}s)') |