updates
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@ -43,7 +43,7 @@ def detect(
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# Classes and colors
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classes = load_classes(parse_data_cfg('cfg/coco.data')['names']) # Extracts class labels from file
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color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
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colors = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
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for i, (path, img, img0) in enumerate(dataloader):
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print("%g/%g '%s': " % (i + 1, len(dataloader), path), end='')
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@ -73,7 +73,6 @@ def detect(
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unpad_w = img_size - pad_x
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unique_classes = detections[:, -1].cpu().unique()
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bbox_colors = random.sample(color_list, len(unique_classes))
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# write results to .txt file
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results_img_path = os.path.join(output, path.split('/')[-1])
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@ -101,10 +100,9 @@ def detect(
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file.write(('%g %g %g %g %g %g\n') % (x1, y1, x2, y2, cls_pred, cls_conf * conf))
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if save_images:
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# Add the bbox to the plot
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# Add bbox to the image
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label = '%s %.2f' % (classes[int(cls_pred)], conf)
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color = bbox_colors[list(unique_classes).index(cls_pred)]
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plot_one_box([x1, y1, x2, y2], img, label=label, color=color)
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plot_one_box([x1, y1, x2, y2], img, label=label, color=colors[int(cls_pred)])
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if save_images:
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# Save generated image with detections
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