我以前写过一个答案
这里解释如何做
图像直方图的分段线性插值
高光/中音/阴影的特定比率。
相同的基本原则是[直方图]的基础
匹配两人之间
图像。基本上，你要计算出你的源和目标的累积直方图
模板图像，然后线性插值，以找到唯一的像素值
与唯一像素的分位数最接近的模板图像
源图像中的值：
import numpy as npdef hist_match(source, template): """ Adjust the pixel values of a grayscale image such that its histogram matches that of a target image Arguments: ----------- source: np.ndarray Image to transform; the histogram is computed over the flattened array template: np.ndarray Template image; can have different dimensions to source Returns: ----------- matched: np.ndarray The transformed output image """ oldshape = source.shape source = source.ravel() template = template.ravel() # get the set of unique pixel values and their corresponding indices and # counts s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,return_counts=True) t_values, t_counts = np.unique(template, return_counts=True) # take the cumsum of the counts and normalize by the number of pixels to # get the empirical cumulative distribution functions for the source and # template images (maps pixel value --> quantile) s_quantiles = np.cumsum(s_counts).astype(np.float64) s_quantiles /= s_quantiles[-1] t_quantiles = np.cumsum(t_counts).astype(np.float64) t_quantiles /= t_quantiles[-1] # interpolate linearly to find the pixel values in the template image # that correspond most closely to the quantiles in the source image interp_t_values = np.interp(s_quantiles, t_quantiles, t_values) return interp_t_values[bin_idx].reshape(oldshape)
For example:
from matplotlib import pyplot as pltfrom scipy.misc import lena, ascentsource = lena()template = ascent()matched = hist_match(source, template)def ecdf(x): """convenience function for computing the empirical CDF""" vals, counts = np.unique(x, return_counts=True) ecdf = np.cumsum(counts).astype(np.float64) ecdf /= ecdf[-1] return vals, ecdfx1, y1 = ecdf(source.ravel())x2, y2 = ecdf(template.ravel())x3, y3 = ecdf(matched.ravel())fig = plt.figure()gs = plt.GridSpec(2, 3)ax1 = fig.add_subplot(gs[0, 0])ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1)ax3 = fig.add_subplot(gs[0, 2], sharex=ax1, sharey=ax1)ax4 = fig.add_subplot(gs[1, :])for aa in (ax1, ax2, ax3): aa.set_axis_off()ax1.imshow(source, cmap=plt.cm.gray)ax1.set_title('Source')ax2.imshow(template, cmap=plt.cm.gray)ax2.set_title('template')ax3.imshow(matched, cmap=plt.cm.gray)ax3.set_title('Matched')ax4.plot(x1, y1 * 100, '-r', lw=3, label='Source')ax4.plot(x2, y2 * 100, '-k', lw=3, label='Template')ax4.plot(x3, y3 * 100, '--r', lw=3, label='Matched')ax4.set_xlim(x1[0], x1[-1])ax4.set_xlabel('Pixel value')ax4.set_ylabel('Cumulative %')ax4.legend(loc=5)对于一对RGB图像，您可以将此函数分别应用于每个图像频道。根据你想要达到的效果，你可能想要
首先将图像转换为不同的颜色空间。例如，你可以转换成HSV空间然后呢如果你想匹配亮度，就在V频道上进行匹配，但不是色调或饱和度。
