updates
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@ -77,7 +77,6 @@ def detect(save_img=False):
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# Run inference
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# Run inference
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t0 = time.time()
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t0 = time.time()
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for path, img, im0s, vid_cap in dataset:
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for path, img, im0s, vid_cap in dataset:
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t = time.time()
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img = torch.from_numpy(img).to(device)
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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@ -85,7 +84,9 @@ def detect(save_img=False):
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img = img.unsqueeze(0)
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img = img.unsqueeze(0)
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# Inference
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# Inference
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t1 = torch_utils.time_synchronized()
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pred = model(img)[0].float() if half else model(img)[0]
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pred = model(img)[0].float() if half else model(img)[0]
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t2 = torch_utils.time_synchronized()
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# Apply NMS
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# Apply NMS
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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@ -123,7 +124,7 @@ def detect(save_img=False):
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
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# Print time (inference + NMS)
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# Print time (inference + NMS)
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print('%sDone. (%.3fs)' % (s, time.time() - t))
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print('%sDone. (%.3fs)' % (s, t2 - t1))
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# Stream results
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# Stream results
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if view_img:
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if view_img:
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