class SauvolaTresholding : public Algorithm {
    public:
        inline SauvolaTresholding() {
            k = 0.2;
            R = 128;
        };
        inline cv::Mat Run(const cv::Mat & image) {
            cv::Mat grayImg;
            cv::cvtColor(image, grayImg, cv::COLOR_BGR2GRAY);

            cv::Mat treshImg = grayImg.clone();

            for (int i = 1; i < grayImg.rows - 1; ++i)
            {
                for (int j = 1; j < grayImg.cols - 1; ++j)
                {
                    float sum = grayImg.at<uchar>(i, j - 1) + grayImg.at<uchar>(i, j + 1) + grayImg.at<uchar>(i - 1, j - 1) + grayImg.at<uchar>(i + 1, j - 1) + grayImg.at<uchar>(i - 1, j + 1) + grayImg.at<uchar>(i + 1, j + 1) + grayImg.at<uchar>(i - 1, j) + grayImg.at<uchar>(i + 1, j);
                    float av = sum / 9;

                    float sigma = std::sqrt((std::pow(grayImg.at<uchar>(i, j - 1) - av, 2) + std::pow(grayImg.at<uchar>(i, j + 1) - av, 2) + std::pow(grayImg.at<uchar>(i - 1, j - 1) - av, 2) + std::pow(grayImg.at<uchar>(i + 1, j - 1) - av, 2) + std::pow(grayImg.at<uchar>(i - 1, j + 1) - av, 2) + std::pow(grayImg.at<uchar>(i + 1, j + 1) - av, 2) + std::pow(grayImg.at<uchar>(i - 1, j) - av, 2) + std::pow(grayImg.at<uchar>(i + 1, j) - av, 2)) / (9 - 1));
                    if ( av*(1. + k*((sigma / R )- 1.)) >treshImg.at<uchar>(i, j) ){
                    treshImg.at<uchar>(i, j) = 255}
                    else{
                        treshImg.at<uchar>(i, j) = 255}
                    }
                }
            }

            return treshImg;

        }
        float k;
        float R;
    };