直方图全局均衡化

from skimage import exposure

# Equalization

img_eq = exposure.equalize_hist(img)

直方图自适应均衡化

# Adaptive Equalization

# 参数2：Clipping limit, normalized between 0 and 1 (higher values give more contrast).

img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

直方图对比度拉伸

# Contrast stretching

p2, p98 = np.percentile(img, (2, 98))

img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))

实验：直方图全局均衡化、自适应均衡化、对比度拉伸效果对比

"""

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

Histogram Equalization

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

This examples enhances an image with low contrast, using a method called

*histogram equalization*, which "spreads out the most frequent intensity

values" in an image. The equalized image has a roughly linear cumulative

distribution function.

While histogram equalization has the advantage that it requires no parameters,

it sometimes yields unnatural looking images. An alternative method is

*contrast stretching*, where the image is rescaled to include all intensities

that fall within the 2nd and 98th percentiles.

"""

import matplotlib

import matplotlib.pyplot as plt

import numpy as np

from skimage import data, img_as_float

from skimage import exposure

matplotlib.rcParams['font.size'] = 8

def plot_img_and_hist(image, axes, bins=256):

"""Plot an image along with its histogram and cumulative histogram.

"""

image = img_as_float(image)

ax_img, ax_hist = axes

# 共用x轴

ax_cdf = ax_hist.twinx()

# Display image

ax_img.imshow(image, cmap=plt.cm.gray)

ax_img.set_axis_off()

# Display histogram

ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')

ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))

ax_hist.set_xlabel('Pixel intensity')

ax_hist.set_xlim(0, 1)

ax_hist.set_yticks([])

# Display cumulative distribution

img_cdf, bins = exposure.cumulative_distribution(image, bins)

ax_cdf.plot(bins, img_cdf, 'r')

# 设置右侧坐标轴为空

ax_cdf.set_yticks([])

return ax_img, ax_hist, ax_cdf

# Load an example image

img = data.moon()

# Contrast stretching

p2, p98 = np.percentile(img, (2, 98))

img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))

# Equalization

img_eq = exposure.equalize_hist(img)

# Adaptive Equalization

# 参数2：Clipping limit, normalized between 0 and 1 (higher values give more contrast).

img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

# Display results

fig = plt.figure(figsize=(8, 5))

axes = np.zeros((2, 4), dtype=np.object)

axes[0, 0] = fig.add_subplot(2, 4, 1)

for i in range(1, 4):

axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])

for i in range(0, 4):

axes[1, i] = fig.add_subplot(2, 4, 5+i)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])

ax_img.set_title('Low contrast image')

y_min, y_max = ax_hist.get_ylim()

ax_hist.set_ylabel('Number of pixels')

# 左侧y轴范围为0到y_max，5个刻度

ax_hist.set_yticks(np.linspace(0, y_max, 5))

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])

ax_img.set_title('Contrast stretching')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])

ax_img.set_title('Histogram equalization')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])

ax_img.set_title('Adaptive equalization')

ax_cdf.set_ylabel('Fraction of total intensity')

# 右侧y轴范围为0到1,5个刻度

ax_cdf.set_yticks(np.linspace(0, 1, 5))

# prevent overlap of y-axis labels

fig.tight_layout()

plt.show()

对比度增强效果对比图