updates
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@ -93,7 +93,7 @@ class LoadImages: # for inference
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# Read image
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self.count += 1
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img0 = cv2.imread(path) # BGR
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assert img0 is not None, 'File Not Found ' + path
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assert img0 is not None, 'Image Not Found ' + path
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print('image %g/%g %s: ' % (self.count, self.nF, path), end='')
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# Padded resize
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@ -279,13 +279,13 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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img = self.imgs[index]
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if img is None:
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img = cv2.imread(img_path) # BGR
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assert img is not None, 'File Not Found ' + img_path
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assert img is not None, 'Image Not Found ' + img_path
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r = self.img_size / max(img.shape) # size ratio
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if self.augment and r < 1: # if training (NOT testing), downsize to inference shape
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h, w, _ = img.shape
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img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # INTER_LINEAR fastest
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if self.n < 5000: # cache into memory if image count < 5000
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if index < 5000: # cache first 5000 images into memory (~5GB)
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self.imgs[index] = img
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# Augment colorspace
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