我以前写过一个答案here解释如何在图像直方图上进行分段线性插值，以实现高光/中音/阴影的特定比率。

两幅图像之间histogram matching的基本原理相同。基本上，计算源图像和模板图像的累积直方图，然后进行线性插值，以找到模板图像中与源图像中唯一像素值的分位数最接近的唯一像素值：import numpy as np

def 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)

例如：from matplotlib import pyplot as plt

from scipy.misc import lena, ascent

source = 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, ecdf

x1, 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，可以将此函数分别应用于每个通道。根据您试图达到的效果，您可能希望首先将图像转换为不同的颜色空间。例如，如果要匹配亮度，而不是色调或饱和度，可以转换为HSV space，然后仅在V通道上进行匹配。