直方图匹配from skimage.exposure import match_histograms

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from skimage.exposure import match_histograms

match_histograms 的实现非常简洁有效。直方图匹配或者直方图规定化

import cv2
import numpy as np
from matplotlib import pyplot as pltdef match_histograms(image, reference, *, channel_axis=None):"""Adjust an image so that its cumulative histogram matches that of another.The adjustment is applied separately for each channel.Parameters----------image : ndarrayInput image. Can be gray-scale or in color.reference : ndarrayImage to match histogram of. Must have the same number of channels asimage.channel_axis : int or None, optionalIf None, the image is assumed to be a grayscale (single channel) image.Otherwise, this parameter indicates which axis of the array correspondsto channels.Returns-------matched : ndarrayTransformed input image.Raises------ValueErrorThrown when the number of channels in the input image and the referencediffer.References----------.. [1] http://paulbourke.net/miscellaneous/equalisation/"""print(image.ndim, reference.ndim)if image.ndim != reference.ndim:raise ValueError('Image and reference must have the same number ''of channels.')if channel_axis is not None:if image.shape[-1] != reference.shape[-1]:raise ValueError('Number of channels in the input image and ''reference image must match!')matched = np.empty(image.shape, dtype=image.dtype)for channel in range(image.shape[-1]):matched_channel = _match_cumulative_cdf(image[..., channel],reference[..., channel])matched[..., channel] = matched_channelelse:# _match_cumulative_cdf will always return float64 due to np.interpmatched = _match_cumulative_cdf(image, reference)# if matched.dtype.kind == 'f':#     # output a float32 result when the input is float16 or float32#     out_dtype = utils._supported_float_type(image.dtype)#     matched = matched.astype(out_dtype, copy=False)return matched
def _match_cumulative_cdf(source, template):"""Return modified source array so that the cumulative density function ofits values matches the cumulative density function of the template."""print(source.dtype.kind)if source.dtype.kind == 'u':src_lookup = source.reshape(-1)src_counts = np.bincount(src_lookup)tmpl_counts = np.bincount(template.reshape(-1))print(src_lookup.shape, src_lookup.dtype, src_counts.shape, src_counts.dtype, tmpl_counts.shape, tmpl_counts.dtype)print(tmpl_counts.shape)# omit values where the count was 0tmpl_values = np.nonzero(tmpl_counts)[0]tmpl_counts = tmpl_counts[tmpl_values]print(tmpl_values.shape, tmpl_counts.shape)else:src_values, src_lookup, src_counts = np.unique(source.reshape(-1),return_inverse=True,return_counts=True)tmpl_values, tmpl_counts = np.unique(template.reshape(-1),return_counts=True)# calculate normalized quantiles for each arraysrc_quantiles = np.cumsum(src_counts) / source.sizetmpl_quantiles = np.cumsum(tmpl_counts) / template.size# 0-255的像素值应该变为多少interp_a_values = np.interp(src_quantiles, tmpl_quantiles, tmpl_values)return interp_a_values[src_lookup].reshape(source.shape)if __name__ == "__main__":file1 = r'D:\code\3.jpg'file2 = r'D:\code\1.jpg'img1 = cv2.imread(file1, 0)img2 = cv2.imread(file2, 0)out = match_histograms(img1, img2)plt.figure()#plt.imshow(np.hstack((img1, img2, out))[...,::-1]/255)plt.imshow(np.hstack((img1, img2, out)), 'gray')plt.show()

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