绘制直方图

from skimage import exposure

# 绘制彩色图像的c通道的直方图

img_hist, bins = exposure.histogram(img[..., c], source_range=‘dtype‘)

# 以第c行第i列的形式绘制归一化直方图

axes[c, i].plot(bins, img_hist / img_hist.max())

绘制累积直方图

from skimage import exposure

img_cdf, bins = exposure.cumulative_distribution(img[..., c])

axes[c, i].plot(bins, img_cdf)

直方图匹配(histogram matching)

含义：使源图像的累积直方图和目标图像一致

from skimage.exposure import match_histograms

# 参数1：源图像；参数2：目标图像；参数3：多通道匹配

matched = match_histograms(image, reference, multichannel=True)

实验：直方图匹配效果

"""

==================

Histogram matching

==================

This example demonstrates the feature of histogram matching. It manipulates the

pixels of an input image so that its histogram matches the histogram of the

reference image. If the images have multiple channels, the matching is done

independently for each channel, as long as the number of channels is equal in

the input image and the reference.2

Histogram matching can be used as a lightweight normalisation for image

processing, such as feature matching, especially in circumstances where the

images have been taken from different sources or in different conditions (i.e.

lighting).

"""

import matplotlib.pyplot as plt

from skimage import data

from skimage import exposure

from skimage.exposure import match_histograms

reference = data.coffee()

image = data.chelsea()

matched = match_histograms(image, reference, multichannel=True)

fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),

sharex=True, sharey=True)

for aa in (ax1, ax2, ax3):

aa.set_axis_off()

ax1.imshow(image)

ax1.set_title(‘Source‘)

ax2.imshow(reference)

ax2.set_title(‘Reference‘)

ax3.imshow(matched)

ax3.set_title(‘Matched‘)

plt.tight_layout()

plt.show()

######################################################################

# To illustrate the effect of the histogram matching, we plot for each

# RGB channel, the histogram and the cumulative histogram. Clearly,

# the matched image has the same cumulative histogram as the reference

# image for each channel.

fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(8, 8))

for i, img in enumerate((image, reference, matched)):

for c, c_color in enumerate((‘red‘, ‘green‘, ‘blue‘)):

img_hist, bins = exposure.histogram(img[..., c], source_range=‘dtype‘)

axes[c, i].plot(bins, img_hist / img_hist.max())

img_cdf, bins = exposure.cumulative_distribution(img[..., c])

axes[c, i].plot(bins, img_cdf)

axes[c, 0].set_ylabel(c_color)

axes[0, 0].set_title(‘Source‘)

axes[0, 1].set_title(‘Reference‘)

axes[0, 2].set_title(‘Matched‘)

plt.tight_layout()

plt.show()

实验输出

原文：https://www.cnblogs.com/wojianxin/p/12641405.html