深入解析opencv骨架提取的算法步骤

输入：img(二值图)
输出：out(和img一样shape的图像，初始化是全0)
while img中有像素值为255(在这个循环里面，一直腐蚀我们的二值图，直到全部为黑色):
    腐蚀img图像
    对img开运算
	img2=开运算前的图像减去开运算后的图像
	out+=img2
输出out

'''
用于挑选一个好的二值图
'''
import cv2
import numpy as np
import os

def refine(img_path):
    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
    # thresh, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    thresh, img = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY)
    h, w = img.shape[0:2]
    #前景背景反转
    for i in range(h):
        for j in range(w):
            if img[i, j] == 255:
                img[i, j] = 0
            else:
                img[i, j] = 255
    cv2.namedWindow("binary", 0)
    cv2.resizeWindow("binary", 640, 480)
    cv2.imshow('binary', img)
    dst = img.copy()

    num_erode = 0

    while (True):
        if np.sum(dst) == 0:
            break
        kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
        dst = cv2.erode(dst, kernel)
        cv2.namedWindow("z", 0)
        cv2.resizeWindow("z", 640, 480)
        cv2.imshow('z', dst)
        c = cv2.waiTKEy(0)
        if c == ord("q"):
            print("保存")
            cv2.imwrite("./refine.png", dst)
            break
        num_erode = num_erode + 1

if __name__ == '__main__':
    refine("input.png")

'''
骨架提取
'''
import cv2
import numpy as np

#由于我们经过之前的代码转换到了二值图，所以这里不需要转换
img = cv2.imread('refine.png', cv2.IMREAD_GRAYSCALE)
dst = img.copy()

skeleton = np.zeros(dst.shape, np.uint8)
while (True):
    if np.sum(dst) == 0:
        break
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (7, 7))
    dst = cv2.erode(dst, kernel, None, None, 1)
    open_dst = cv2.morphologyEx(dst, cv2.MORPH_OPEN, kernel)
    result = dst - open_dst
    skeleton = skeleton + result
    cv2.waitKey(1)



cv2.namedWindow("result",0)
cv2.resizeWindow("result",640,480)
cv2.imshow('result', skeleton)
cv2.imwrite("output.png",skeleton)

cv2.waitKey(0)
cv2.destroyAllwindows()