updates
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@ -18,8 +18,8 @@ parser.add_argument('-txt_out', type=bool, default=False)
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parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
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parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
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parser.add_argument('-conf_thres', type=float, default=0.8, help='object confidence threshold')
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parser.add_argument('-nms_thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
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parser.add_argument('-conf_thres', type=float, default=0.99, help='object confidence threshold')
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parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
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parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
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parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
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opt = parser.parse_args()
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@ -33,7 +33,8 @@ def detect(opt):
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# Load model
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model = Darknet(opt.cfg, opt.img_size)
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weights_path = 'checkpoints/yolov3.weights'
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#weights_path = 'checkpoints/yolov3.weights'
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weights_path = 'checkpoints/latest.pt'
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if weights_path.endswith('.weights'): # saved in darknet format
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load_weights(model, weights_path)
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else: # endswith('.pt'), saved in pytorch format
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@ -130,7 +131,7 @@ def detect(opt):
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if opt.plot_flag:
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# Add the bbox to the plot
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label = '%s %.2f' % (classes[int(cls_pred)], cls_conf) if cls_conf > 0.05 else None
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label = '%s %.2f' % (classes[int(cls_pred)], conf)
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color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
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plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3)
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