`cv::ximgproc::thinning` is too slow

C++
1

cv::ximgproc::thinning is too slow. Even though the thinningIteration function can use parallel processing, it is still much slower compared to Halcon, with 2000ms (OpenCV) vs 3ms (Halcon)!

namespace cv {
namespace ximgproc {

// Applies a thinning iteration to a binary image
static void thinningIteration(Mat img, int iter, int thinningType){
    Mat marker = Mat::zeros(img.size(), CV_8UC1);

    if(thinningType == THINNING_ZHANGSUEN){
        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;
            }
        }
    }
    if(thinningType == THINNING_GUOHALL){
        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 C  = ((!p2) & (p3 | p4)) + ((!p4) & (p5 | p6)) +
                         ((!p6) & (p7 | p8)) + ((!p8) & (p9 | p2));
                int N1 = (p9 | p2) + (p3 | p4) + (p5 | p6) + (p7 | p8);
                int N2 = (p2 | p3) + (p4 | p5) + (p6 | p7) + (p8 | p9);
                int N  = N1 < N2 ? N1 : N2;
                int m  = iter == 0 ? ((p6 | p7 | (!p9)) & p8) : ((p2 | p3 | (!p5)) & p4);

                if ((C == 1) && ((N >= 2) && ((N <= 3)) & (m == 0)))
                    marker.at<uchar>(i,j) = 1;
            }
        }
    }

    img &= ~marker;
}

// Apply the thinning procedure to a given image
void thinning(InputArray input, OutputArray output, int thinningType){
    Mat processed = input.getMat().clone();
    CV_CheckTypeEQ(processed.type(), CV_8UC1, "");
    // Enforce the range of the input image to be in between 0 - 255
    processed /= 255;

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

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

    processed *= 255;

    output.assign(processed);
}

} //namespace ximgproc
} //namespace cv