scikit-image库--直方图均衡化（四）

痛定思痛。 2021-10-18 11:22 330阅读 0赞

#### 直方图均衡化 ####

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
    
    #画直方图
    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
        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')
        ax_cdf.set_yticks([])
    
        return ax_img, ax_hist, ax_cdf
    
    
    # Load an example image
    img = data.moon()   #低对比度图像
    
    # Contrast stretching #对比度拉伸
    p2, p98 = np.percentile(img, (2, 98))
    img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
    #in_range 表示输入图片的强度范围，默认为'image', 表示用图像的最大/最小像素值作为范围
    
    # Equalization
    img_eq = exposure.equalize_hist(img)   #直方图均衡化
    
    # Adaptive Equalization
    img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)  # 自适应均衡
    
    # 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')
    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')
    ax_cdf.set_yticks(np.linspace(0, 1, 5))
    
    # prevent overlap of y-axis labels
    fig.tight_layout()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]: /images/20211018/737e4fb03b9f43eca999846e1a020ff0.png