[opencv]自适应阈值二值化

/**自适应阈值二值化*/
Mat ImgProcession::AdaptiveThereshold(Mat src) {
    Mat dst;
    cvtColor(src, dst, COLOR_BGR2GRAY);
    int x1, y1, x2, y2;
    int count = 0;
    long long sum = 0;
    //划分区域的大小S*S
    int S = src.rows >> 3;
    /*百分比，用来最后与阈值的比较.
     * 原文：If the value of the current pixel is t percent less than this average
     then it is set to black, otherwise it is set to white.*/
    int T = 30;//15
    int W = dst.cols;
    int H = dst.rows;
    long long **Argv;
    Argv = new long long *[dst.rows];
    for (int ii = 0; ii < dst.rows; ii++) {
        Argv[ii] = new long long[dst.cols];
    }

    for (int i = 0; i < W; i++) {
        sum = 0;
        for (int j = 0; j < H; j++) {
            sum += dst.at<uchar>(j, i);
            if (i == 0)
                Argv[j][i] = sum;
            else
                Argv[j][i] = Argv[j][i - 1] + sum;
        }
    }

    for (int i = 0; i < W; i++) {
        for (int j = 0; j < H; j++) {
            x1 = i - S / 2;
            x2 = i + S / 2;
            y1 = j - S / 2;
            y2 = j + S / 2;
            if (x1 < 0)
                x1 = 0;
            if (x2 >= W)
                x2 = W - 1;
            if (y1 < 0)
                y1 = 0;
            if (y2 >= H)
                y2 = H - 1;
            count = (x2 - x1) * (y2 - y1);
            sum = Argv[y2][x2] - Argv[y1][x2] - Argv[y2][x1] + Argv[y1][x1];

            if ((long long) (dst.at<uchar>(j, i) * count) < (long long) sum * (100 - T) / 100)
                dst.at<uchar>(j, i) = 0;
            else
                dst.at<uchar>(j, i) = 255;
        }
    }
    for (int i = 0; i < dst.rows; ++i) {
        delete[] Argv[i];
    }
    delete[] Argv;
    return dst;
}