对二值化图像进行骨架提取 c++实现

以下是基于OpenCV的C++代码实现对二值化图像进行骨架提取：

#include <opencv2/opencv.hpp>
using namespace cv;

void thinningIteration(Mat& img, int iter)
{
    Mat marker = Mat::zeros(img.size(), CV_8UC1);

    for (int i = 1; i < img.rows - 1; i++)
    {
        for (int j = 1; j < img.cols - 1; j++)
        {
            uchar p2 = img.at<uchar>(i - 1, j);
            uchar p3 = img.at<uchar>(i - 1, j + 1);
            uchar p4 = img.at<uchar>(i, j + 1);
            uchar p5 = img.at<uchar>(i + 1, j + 1);
            uchar p6 = img.at<uchar>(i + 1, j);
            uchar p7 = img.at<uchar>(i + 1, j - 1);
            uchar p8 = img.at<uchar>(i, j - 1);
            uchar p9 = img.at<uchar>(i - 1, j - 1);

            int A = (p2 == 0 && p3 == 1) + (p3 == 0 && p4 == 1) +
                (p4 == 0 && p5 == 1) + (p5 == 0 && p6 == 1) +
                (p6 == 0 && p7 == 1) + (p7 == 0 && p8 == 1) +
                (p8 == 0 && p9 == 1) + (p9 == 0 && p2 == 1);
            int B = p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9;
            int m1 = iter == 0 ? (p2 * p4 * p6) : (p2 * p4 * p8);
            int m2 = iter == 0 ? (p4 * p6 * p8) : (p2 * p6 * p8);

            if (A == 1 && (B >= 2 && B <= 6) && m1 == 0 && m2 == 0)
            {
                marker.at<uchar>(i, j) = 1;
            }
        }
    }

    img &= ~marker;
}

void thinning(Mat& img)
{
    img /= 255;

    Mat prev = Mat::zeros(img.size(), CV_8UC1);
    Mat diff;

    do {
        thinningIteration(img, 0);
        thinningIteration(img, 1);
        absdiff(img, prev, diff);
        img.copyTo(prev);
    } while (countNonZero(diff) > 0);

    img *= 255;
}

int main()
{
    Mat src = imread("binary_image.png", IMREAD_GRAYSCALE);

    if (src.empty())
    {
        return -1;
    }

    Mat dst = src.clone();

    thinning(dst);

    imshow("Thinning", dst);
    waitKey(0);

    return 0;
}

该代码实现了Zhang-Suen骨架提取算法的过程，将二值化图像进行骨架提取，并通过可视化展示提取结果。需要注意的是，在实际使用中，还需要根据具体情况进行调整和优化。