直方图全局均衡化

from skimage import exposure# Equalizationimg_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 stretchingp2, 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 intensityvalues" in an image. The equalized image has a roughly linear cumulativedistribution 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 intensitiesthat fall within the 2nd and 98th percentiles."""import matplotlibimport matplotlib.pyplot as pltimport numpy as npfrom skimage import data, img_as_floatfrom skimage import exposurematplotlib.rcParams['font.size'] = 8def 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 imageax_img.imshow(image, cmap=plt.cm.gray)ax_img.set_axis_off()# Display histogramax_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 distributionimg_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 imageimg = data.moon()# Contrast stretchingp2, p98 = np.percentile(img, (2, 98))img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))# Equalizationimg_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 resultsfig = 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 labelsfig.tight_layout()plt.show()