分类：这个函数，主要用来做对比度调整，利用 gamma 曲线 或者 log 函数曲线，
gamma 函数的表达式：
y=xγ,
其中， x 是输入的像素值，取值范围为 [0−1], y 是输出的像素值，通过调整γ 值，改变图像的像素值的分布，进而改变图像的对比度。
log 函数的表达式:
y=alog(1+x), a 是一个放大系数，x 同样是输入的像素值，取值范围为 [0−1], y 是输出的像素值。
inverse log 的表达式:
y=a(2x−1),
这些变换都是从 [0−1] 变到 [0−1] 。
"""
=================================
Gamma and log contrast adjustment
=================================
This example adjusts image contrast by performing a Gamma and a Logarithmic
correction on the input image.
"""
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(img, axes, bins=256):
"""Plot an image along with its histogram and cumulative histogram.
"""
img = img_as_float(img)
ax_img, ax_hist = axes
ax_cdf = ax_hist.twinx()
# Display image
ax_img.imshow(img, cmap=plt.cm.gray)
ax_img.set_axis_off()
# Display histogram
ax_hist.hist(img.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(img, bins)
ax_cdf.plot(bins, img_cdf, 'r')
ax_cdf.set_yticks([])
return ax_img, ax_hist, ax_cdf
# Load an example image
img = data.moon()
# Gamma
gamma_corrected = exposure.adjust_gamma(img, 2)
# Logarithmic
logarithmic_corrected = exposure.adjust_log(img, 1)
# Display results
fig = plt.figure(figsize=(8, 5))
axes = np.zeros((2, 3), dtype=np.object)
axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced')
axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0],
adjustable='box-forced')
axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0],
adjustable='box-forced')
axes[1, 0] = plt.subplot(2, 3, 4)
axes[1, 1] = plt.subplot(2, 3, 5)
axes[1, 2] = plt.subplot(2, 3, 6)
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')
ax_hist.set_yticks(np.linspace(0, y_max, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected, axes[:, 1])
ax_img.set_title('Gamma correction')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected, axes[:, 2])
ax_img.set_title('Logarithmic correction')
ax_cdf.set_ylabel('Fraction of total intensity')
ax_cdf.set_yticks(np.linspace(0, 1, 5))
# prevent overlap of y-axis labels
fig.tight_layout()
plt.show()
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