Python+OpenCV：直方图均衡化(Histogram Equalization)

Python+OpenCV：直方图均衡化(Histogram Equalization)

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# 图像直方图均衡化(Image Histogram Equalization)
def lmc_cv_image_histogram_equalization():
    """
        函数功能: 图像直方图均衡化(Image Histogram Equalization)。
    """

    stacking_images = []

    # 灰色图像直方图均衡化
    image = lmc_cv.imread('D:/99-Research/Python/Image/CountryRoad.jpg')
    image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2GRAY)
    equalize_image = lmc_cv.equalizeHist(image)
    # stacking images side-by-side
    stacking_image = np.hstack((image, equalize_image))
    stacking_images.append(stacking_image)

    # 彩色图像直方图均衡化
    image_file_name = ['D:/99-Research/Python/Image/hist_low.jpg', 'D:/99-Research/Python/Image/hist_high.jpg',
                       'D:/99-Research/Python/Image/Lena.jpg']
    for i in range(len(image_file_name)):
        # 读取图像
        image = lmc_cv.imread(image_file_name[i])
        ycrcb = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2YCR_CB)
        channels = lmc_cv.split(ycrcb)
        channels[0] = lmc_cv.equalizeHist(channels[0])
        ycrcb = lmc_cv.merge(channels)
        equalize_image = lmc_cv.cvtColor(ycrcb, lmc_cv.COLOR_YCR_CB2BGR)
        image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2RGB)
        equalize_image = lmc_cv.cvtColor(equalize_image, lmc_cv.COLOR_BGR2RGB)
        # stacking images side-by-side
        stacking_image = np.hstack((image, equalize_image))
        stacking_images.append(stacking_image)

    # 显示图像
    pyplot.figure('Image Display')
    titles = ['Gray Image Histogram Equalization', 'Low Histogram Image Histogram Equalization',
              'High Histogram Image Histogram Equalization', 'Nomal Histogram Image Histogram Equalization']
    for i in range(len(stacking_images)):
        pyplot.subplot(2, 2, i + 1)
        pyplot.imshow(stacking_images[i], 'gray')
        pyplot.title(titles[i])
        pyplot.xticks([])
        pyplot.yticks([])
    pyplot.show()

    # 根据用户输入保存图像
    if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
        # 销毁窗口
        pyplot.close()
    return