效果如下：
色彩源 目标图 转移结果图代码如下：
# 色彩转移 # import the necessary packages import numpy as np import cv2 def color_transfer(source, target, clip=True, preserve_paper=True): """ Transfers the color distribution from the source to the target image using the mean and standard deviations of the L*a*b* color space. This implementation is (loosely) based on to the "Color Transfer between Images" paper by Reinhard et al., 2001. Parameters: ------- source: NumPy array OpenCV image in BGR color space (the source image) target: NumPy array OpenCV image in BGR color space (the target image) clip: Should components of L*a*b* image be scaled by np.clip before converting back to BGR color space? If False then components will be min-max scaled appropriately. Clipping will keep target image brightness truer to the input. Scaling will adjust image brightness to avoid washed out portions in the resulting color transfer that can be caused by clipping. preserve_paper: Should color transfer strictly follow methodology layed out in original paper? The method does not always produce aesthetically pleasing results. If False then L*a*b* components will scaled using the reciprocal of the scaling factor proposed in the paper. This method seems to produce more consistently aesthetically pleasing results Returns: ------- transfer: NumPy array OpenCV image (w, h, 3) NumPy array (uint8) """ # convert the images from the RGB to L*ab* color space, being # sure to utilizing the floating point data type (note: OpenCV # expects floats to be 32-bit, so use that instead of 64-bit) source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32") target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32") # compute color statistics for the source and target images (lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = image_stats(source) (lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = image_stats(target) # subtract the means from the target image (l, a, b) = cv2.split(target) l -= lMeanTar a -= aMeanTar b -= bMeanTar if preserve_paper: # scale by the standard deviations using paper proposed factor l = (lStdTar / lStdSrc) * l a = (aStdTar / aStdSrc) * a b = (bStdTar / bStdSrc) * b else: # scale by the standard deviations using reciprocal of paper proposed factor l = (lStdSrc / lStdTar) * l a = (aStdSrc / aStdTar) * a b = (bStdSrc / bStdTar) * b # add in the source mean l += lMeanSrc a += aMeanSrc b += bMeanSrc # clip/scale the pixel intensities to [0, 255] if they fall # outside this range l = _scale_array(l, clip=clip) a = _scale_array(a, clip=clip) b = _scale_array(b, clip=clip) # merge the channels together and convert back to the RGB color # space, being sure to utilize the 8-bit unsigned integer data # type transfer = cv2.merge([l, a, b]) transfer = cv2.cvtColor(transfer.astype("uint8"), cv2.COLOR_LAB2BGR) # return the color transferred image return transfer def image_stats(image): """ Parameters: ------- image: NumPy array OpenCV image in L*a*b* color space Returns: ------- Tuple of mean and standard deviations for the L*, a*, and b* channels, respectively """ # compute the mean and standard deviation of each channel (l, a, b) = cv2.split(image) (lMean, lStd) = (l.mean(), l.std()) (aMean, aStd) = (a.mean(), a.std()) (bMean, bStd) = (b.mean(), b.std()) # return the color statistics return (lMean, lStd, aMean, aStd, bMean, bStd) def _min_max_scale(arr, new_range=(0, 255)): """ Perform min-max scaling to a NumPy array Parameters: ------- arr: NumPy array to be scaled to [new_min, new_max] range new_range: tuple of form (min, max) specifying range of transformed array Returns: ------- NumPy array that has been scaled to be in [new_range[0], new_range[1]] range """ # get array's current min and max mn = arr.min() mx = arr.max() # check if scaling needs to be done to be in new_range if mn < new_range[0] or mx > new_range[1]: # perform min-max scaling scaled = (new_range[1] - new_range[0]) * (arr - mn) / (mx - mn) + new_range[0] else: # return array if already in range scaled = arr return scaled def _scale_array(arr, clip=True): """ Trim NumPy array values to be in [0, 255] range with option of clipping or scaling. Parameters: ------- arr: array to be trimmed to [0, 255] range clip: should array be scaled by np.clip? if False then input array will be min-max scaled to range [max([arr.min(), 0]), min([arr.max(), 255])] Returns: ------- NumPy array that has been scaled to be in [0, 255] range """ if clip: scaled = np.clip(arr, 0, 255) else: scale_range = (max([arr.min(), 0]), min([arr.max(), 255])) scaled = _min_max_scale(arr, new_range=scale_range) return scaled cv2.imshow("target", color_transfer(cv2.imread("C:/Users/Desktop/autumn.jpg"), cv2.imread("C:/Users/Desktop/123.png"))) cv2.waitKey(0)步骤说明：
1、选择源色彩图和目标图，打开，转Lab 颜色空间。
2、计算源图像和目标图像的颜色统计信息。
3、我们将目标图像分为L，a和b分量，并减去它们各自的均值。
4、按目标标准偏差的比率除以源图像的标准偏差的比例进行缩放。
5、添加源通道的平均值
6、裁剪超出[0，255]范围的值，将缩放的Lab通道合并，最后转换回原始RGB颜色空间。
