zoukankan      html  css  js  c++  java
  • python库skimage 绘制直方图;绘制累计直方图;实现直方图匹配(histogram matching)

    绘制直方图

    from skimage import exposure
    # 绘制彩色图像的c通道的直方图
    img_hist, bins = exposure.histogram(img[..., c], source_range='dtype')
    # 以第c行第i列的形式绘制归一化直方图
    axes[c, i].plot(bins, img_hist / img_hist.max())
    

    绘制累积直方图

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

    直方图匹配(histogram matching)

    含义:使源图像的累积直方图和目标图像一致

    from skimage.exposure import match_histograms
    # 参数1:源图像;参数2:目标图像;参数3:多通道匹配
    matched = match_histograms(image, reference, multichannel=True)
    

    实验:直方图匹配效果

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

    实验输出

    左图:源图像;中图:目标图像(参考图像);右图:源图直方图匹配后图像
    直方图匹配操作含义展示:可以看到匹配后,源图像和目标图像的累积直方图趋于一致

  • 相关阅读:
    VUE学习笔记--Vue-cli
    洛谷 P5595 【XR-4】歌唱比赛(构造)
    洛谷 P3658 [USACO17FEB]Why Did the Cow Cross the Road III P(CDQ分治)
    洛谷 P2345 [USACO04OPEN]MooFest G(二维偏序,归并排序)
    洛谷 P1228 地毯填补问题(分治)
    洛谷 P4071 [SDOI2016]排列计数(排列组合、逆元、错排问题)
    初赛选择题知识点整理 & 听课笔记
    洛谷 P6833 [Cnoi2020]雷雨(set优化dijkstra,set、pair、struct的结合)
    洛谷 P1119 灾后重建(Floyd)
    洛谷 P1346 电车(双端队列广搜)
  • 原文地址:https://www.cnblogs.com/wojianxin/p/12641405.html
Copyright © 2011-2022 走看看