python zhang-suen骨架算法实现

以下是Python实现的Zhang-Suen骨架算法的示例代码：

import numpy as np

def ZhangSuen(image):
    """
    Implementation of Zhang-Suen thinning algorithm for binary images.
    """
    # Define the 8-neighborhood of a pixel
    neighbors = ((-1,-1),(-1,0),(-1,1),(0,-1),(0,1),(1,-1),(1,0),(1,1))
    
    # Initialize the image to be thinned
    thinned_image = image.copy()
    
    # Initialize flag to indicate if a pixel was removed
    pixel_removed = True
    
    while pixel_removed:
        pixel_removed = False
        
        # Step 1: Mark pixels to be removed
        marked_pixels = []
        for x in range(1, image.shape[0]-1):
            for y in range(1, image.shape[1]-1):
                if image[x,y] == 0:
                    continue
                neighbors_count = sum([image[x+i,y+j] == 255 for i,j in neighbors])
                if neighbors_count < 2 or neighbors_count > 6:
                    continue
                transitions_count = sum([1 for i in range(8) if image[x+neighbors[i][0],y+neighbors[i][1]] == 0 and image[x+neighbors[i+1 if i<7 else 0][0],y+neighbors[i+1 if i<7 else 0][1]] == 255])
                if transitions_count != 1:
                    continue
                marked_pixels.append((x,y))
        
        # Step 2: Remove marked pixels
        for x,y in marked_pixels:
            thinned_image[x,y] = 0
            pixel_removed = True
        
        # Step 3: Mark pixels to be removed
        marked_pixels = []
        for x in range(1, image.shape[0]-1):
            for y in range(1, image.shape[1]-1):
                if image[x,y] == 0:
                    continue
                neighbors_count = sum([image[x+i,y+j] == 255 for i,j in neighbors])
                if neighbors_count < 2 or neighbors_count > 6:
                    continue
                transitions_count = sum([1 for i in range(8) if image[x+neighbors[i][0],y+neighbors[i][1]] == 0 and image[x+neighbors[i+1 if i<7 else 0][0],y+neighbors[i+1 if i<7 else 0][1]] == 255])
                if transitions_count != 1:
                    continue
                marked_pixels.append((x,y))
        
        # Step 4: Remove marked pixels
        for x,y in marked_pixels:
            thinned_image[x,y] = 0
            pixel_removed = True
    
    return thinned_image

解释一下上面的代码：

首先，我们定义了一个8邻域的元组neighbors，表示一个像素的8个邻居像素；

然后，我们初始化一个待细化的二值图像thinned_image，复制输入的二值图像；

接着，我们开始迭代细化过程，直到没有像素可以被删除为止；

在每一轮迭代中，我们执行两个步骤：标记像素和删除像素。标记像素的过程先扫描一遍图像，找到满足一定条件的像素，并将它们加入到一个列表marked_pixels中。删除像素的过程再扫描一遍图像，将marked_pixels中的像素删除。这两个步骤分别执行两次，以保证算法的正确性；

标记像素的条件包括：它周围有2到6个前景像素；它周围的前景像素中只有一个像素的背景像素是它的邻居；它不是端点像素（即它的周围不是一个完整的线段）；

删除像素的条件与标记像素的条件相同。

最后，我们返回细化后的二值图像。