繪制直方圖

from skimage import exposure
# 繪制彩色圖像的c通道的直方圖
img_hist, bins = exposure.histogram(img[..., c], source_range='dtype')
# 以第c行第i列的形式繪制歸一化直方圖
axes[c, i].plot(bins, img_hist / img_hist.max())

繪制累積直方圖

from skimage import exposure
img_cdf, bins = exposure.cumulative_distribution(img[..., c])
axes[c, i].plot(bins, img_cdf)

直方圖匹配(histogram matching)

含義：使源圖像的累積直方圖和目標(biāo)圖像一致

from skimage.exposure import match_histograms
# 參數(shù)1：源圖像；參數(shù)2：目標(biāo)圖像肃廓；參數(shù)3：多通道匹配
matched = match_histograms(image, reference, multichannel=True)

實(shí)驗(yàn)：直方圖匹配效果

"""
==================
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()

實(shí)驗(yàn)輸出

左圖：源圖像智厌；中圖：目標(biāo)圖像(參考圖像)；右圖：源圖直方圖匹配后圖像

直方圖匹配操作含義展示：可以看到匹配后盲赊，源圖像和目標(biāo)圖像的累積直方圖趨于一致