Update datasets.py
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@ -137,20 +137,20 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# Load labels
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# Load labels
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if os.path.isfile(label_path):
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if os.path.isfile(label_path):
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# labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 5) # SLOWER
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with open(label_path, 'r') as file:
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with open(label_path, 'r') as file:
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lines = file.read().splitlines()
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lines = file.read().splitlines()
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labels0 = np.array([x.split() for x in lines], dtype=np.float32)
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# If label file is empty
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x = np.array([x.split() for x in lines], dtype=np.float32)
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if labels0.size is 0:
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if x.size is 0:
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# Empty labels file
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labels = np.array([])
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labels = np.array([])
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else:
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else:
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# Normalized xywh to pixel xyxy format
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# Normalized xywh to pixel xyxy format
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labels = labels0.copy()
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labels = x.copy()
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labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw
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labels[:, 1] = ratio * w * (x[:, 1] - x[:, 3] / 2) + padw
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labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh
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labels[:, 2] = ratio * h * (x[:, 2] - x[:, 4] / 2) + padh
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labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw
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labels[:, 3] = ratio * w * (x[:, 1] + x[:, 3] / 2) + padw
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labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 2) + padh
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labels[:, 4] = ratio * h * (x[:, 2] + x[:, 4] / 2) + padh
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else:
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else:
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labels = np.array([])
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labels = np.array([])
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