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diff --git a/r_facelib/detection/yolov5face/utils/datasets.py b/r_facelib/detection/yolov5face/utils/datasets.py
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+++ b/r_facelib/detection/yolov5face/utils/datasets.py
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+import cv2

+import numpy as np

+

+

+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale_fill=False, scaleup=True):

+    # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232

+    shape = img.shape[:2]  # current shape [height, width]

+    if isinstance(new_shape, int):

+        new_shape = (new_shape, new_shape)

+

+    # Scale ratio (new / old)

+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])

+    if not scaleup:  # only scale down, do not scale up (for better test mAP)

+        r = min(r, 1.0)

+

+    # Compute padding

+    ratio = r, r  # width, height ratios

+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))

+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding

+    if auto:  # minimum rectangle

+        dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding

+    elif scale_fill:  # stretch

+        dw, dh = 0.0, 0.0

+        new_unpad = (new_shape[1], new_shape[0])

+        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

+

+    dw /= 2  # divide padding into 2 sides

+    dh /= 2

+

+    if shape[::-1] != new_unpad:  # resize

+        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)

+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))

+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))

+    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border

+    return img, ratio, (dw, dh)