updates
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@ -75,6 +75,7 @@ def detect(
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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# Print results to screen
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print('%gx%g ' % img.shape[2:], end='') # print image size
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum()
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print('%g %ss' % (n, classes[int(c)]), end=', ')
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@ -75,7 +75,6 @@ class LoadImages: # for inference
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# Padded resize
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img, _, _, _ = letterbox(img0, new_shape=self.height)
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print('%gx%g ' % img.shape[:2], end='') # print image size
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# Normalize RGB
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img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
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@ -114,6 +113,7 @@ class LoadWebcam: # for inference
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assert ret_val, 'Webcam Error'
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img_path = 'webcam_%g.jpg' % self.count
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img0 = cv2.flip(img0, 1) # flip left-right
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print('webcam %g: ' % self.count, end='')
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# Padded resize
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img, _, _, _ = letterbox(img0, new_shape=self.height)
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@ -144,7 +144,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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for x in self.img_files]
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# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
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self.train_rectangular = False
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self.train_rectangular = True
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if self.train_rectangular:
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bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
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nb = bi[-1] + 1 # number of batches
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