Python+OpenCV：基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)

以你之姓@ 2022-12-22 14:30 287阅读 0赞

## Python+OpenCV：基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm) ##

####################################################################################################
    # 基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)
    def lmc_cv_image_segmentation_watershed():
        """
            函数功能: 基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)。
        """
    
        # 读取图像
        image = lmc_cv.imread('D:/99-Research/Python/Image/water_coins.jpg', flags=lmc_cv.IMREAD_UNCHANGED)
        rgb_image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2RGB)
        rgb_image_result = rgb_image.copy()
        gray_image = lmc_cv.cvtColor(rgb_image, lmc_cv.COLOR_RGB2GRAY)
    
        # 基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)
        ret, thresh_image = lmc_cv.threshold(gray_image, 0, 255, lmc_cv.THRESH_BINARY_INV + lmc_cv.THRESH_OTSU)
    
        # noise removal
        kernel = np.ones((3, 3), np.uint8)
        opening_image = lmc_cv.morphologyEx(thresh_image, lmc_cv.MORPH_OPEN, kernel, iterations=2)
        # sure background area
        sure_bg_image = lmc_cv.dilate(opening_image, kernel, iterations=3)
        # Finding sure foreground area
        dist_transform = lmc_cv.distanceTransform(opening_image, lmc_cv.DIST_L2, 5)
        ret, sure_fg_image = lmc_cv.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
        # Finding unknown region
        sure_fg_image = np.uint8(sure_fg_image)
        unknown = lmc_cv.subtract(sure_bg_image, sure_fg_image)
    
        # Marker labelling
        ret, markers_image = lmc_cv.connectedComponents(sure_fg_image)
        # Add one to all labels so that sure background is not 0, but 1
        markers_image = markers_image + 1
        # Now, mark the region of unknown with zero
        markers_image[unknown == 255] = 0
    
        markers_image = lmc_cv.watershed(rgb_image_result, markers_image)
        rgb_image_result[markers_image == -1] = [255, 0, 0]
    
        # 显示图像
        pyplot.figure('Image Display')
        titles = ['Original Image', 'Thresh Image', 'ForeGround Image', 'BackGround Image', 'Distance Transform Image',
                  'Markers Image', 'Result Image']
        images = [rgb_image, thresh_image, sure_fg_image, sure_bg_image, dist_transform, markers_image, rgb_image_result]
        for i in range(7):
            pyplot.subplot(2, 4, i + 1)
            pyplot.imshow(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('all')
        return

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xpdWJpbmc4NjA5_size_16_color_FFFFFF_t_70][]

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