dataloader default color to imagenet mean 114
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@ -536,7 +536,7 @@ def load_mosaic(self, index):
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labels4 = []
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s = self.img_size
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xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)] # mosaic center x, y
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img4 = np.zeros((s * 2, s * 2, 3), dtype=np.uint8) + 128 # base image with 4 tiles
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img4 = np.full((s * 2, s * 2, 3), 114, dtype=np.uint8) # base image with 4 tiles
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indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices
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for i, index in enumerate(indices):
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# Load image
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@ -597,7 +597,7 @@ def load_mosaic(self, index):
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return img4, labels4
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def letterbox(img, new_shape=(416, 416), color=(128, 128, 128),
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def letterbox(img, new_shape=(416, 416), color=(114, 114, 114),
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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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shape = img.shape[:2] # current shape [height, width]
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@ -661,7 +661,7 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
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M = S @ T @ R # ORDER IS IMPORTANT HERE!!
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changed = (border != 0) or (M != np.eye(3)).any()
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if changed:
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img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(128, 128, 128))
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img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(114, 114, 114))
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# Transform label coordinates
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n = len(targets)
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