Python+OpenCV：图像对比度受限自适应直方图均衡化(CLAHE, Contrast Limited Adaptive Histogram Equalization)

小鱼儿 2022-12-21 06:21 111阅读 0赞

## Python+OpenCV：图像对比度受限自适应直方图均衡化(CLAHE, Contrast Limited Adaptive Histogram Equalization) ##

####################################################################################################
    # 图像对比度受限自适应直方图均衡化(CLAHE, Contrast Limited Adaptive Histogram Equalization)
    def lmc_cv_image_clahe():
        """
            函数功能: 图像对比度受限自适应直方图均衡化(CLAHE, Contrast Limited Adaptive Histogram Equalization)。
        """
    
        stacking_images = []
        # 图像对比度受限自适应直方图均衡化(CLAHE, Contrast Limited Adaptive Histogram Equalization)
        image_file_name = ['D:/99-Research/Python/Image/Photo1.jpg', 'D:/99-Research/Python/Image/Photo2.jpg',
                           'D:/99-Research/Python/Image/Photo3.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])
            image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2GRAY)
    
            # create a CLAHE object (Arguments are optional).
            clahe = lmc_cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
            clahe_image = clahe.apply(image)
    
            # stacking images side-by-side
            stacking_image = np.hstack((image, clahe_image))
            stacking_images.append(stacking_image)
    
        # 显示图像
        pyplot.figure('CLAHE, Contrast Limited Adaptive Histogram Equalization')
        for i in range(len(stacking_images)):
            pyplot.subplot(2, 2, i + 1)
            pyplot.imshow(stacking_images[i], 'gray')
            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

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