updates
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@ -425,7 +425,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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# Letterbox
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# Letterbox
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h, w = img.shape[:2]
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h, w = img.shape[:2]
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shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
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shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
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img, ratio, pad = letterbox(img, shape, auto=False)
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img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
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# Load labels
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# Load labels
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labels = []
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labels = []
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@ -609,16 +609,16 @@ def load_mosaic(self, index):
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return img4, labels4
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return img4, labels4
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def letterbox(img, new_shape=(416, 416), color=(128, 128, 128), auto=True, scaleFill=False, interp=cv2.INTER_AREA):
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def letterbox(img, new_shape=(416, 416), color=(128, 128, 128),
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auto=True, scaleFill=False, scaleup=True, interp=cv2.INTER_AREA):
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# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
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# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
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shape = img.shape[:2] # current shape [height, width]
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shape = img.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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# Scale ratio (new / old)
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scaleup_ok = True
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r = max(new_shape) / max(shape)
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r = max(new_shape) / max(shape)
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if not scaleup_ok: # only scale down
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if not scaleup: # only scale down, do not scale up (for better test mAP)
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r = min(r, 1.0)
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r = min(r, 1.0)
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# Compute padding
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# Compute padding
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