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Canny 边缘算法的C++ 实现

Canny 边缘算法的C++ 实现

作者: ticks | 来源:发表于2018-11-26 14:53 被阅读0次

    lena_gray_512

    lena_gray_512

    高斯滤波

    高斯滤波
    我之前写过高斯滤波,现在只写代码

    cv::Mat Gauss(const cv::Mat& img, double sigma)
{  // 高斯模糊
    int rows = img.rows, cols = img.cols;
    cv::Mat temp(rows, cols, CV_8U, cv::Scalar(0));
    int n = ceil(6 * sigma);
    if (n % 2 == 0)
        n++;
    double** gauss = new double*[n];  //高斯模板
    double sum = 0;
    for (int i = 0; i < n; i++)
        gauss[i] = new double[n];
    for (int i = 0; i < n; i++)
    {
        int x = i - n / 2;
        for (int j = 0; j < n; j++)
        {
            int y = j - n / 2;
            gauss[i][j] = exp(-(pow(x, 2) + pow(y, 2)) / (2 * pow(sigma, 2)));
            sum += gauss[i][j];
        }
    }
    for (int i = 0; i < n; i++)  // 归一化
        for (int j = 0; j < n; j++)
            gauss[i][j] /= sum;
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < cols; j++)
        {
            for (int m = 0; m < n; m++)
                for (int k = 0; k < n; k++)
                    if (i - n / 2 + m >= 0 && i - n / 2 + m < rows && j - n / 2 + k >= 0 && j - n / 2 + k < cols)
                        temp.ptr(i)[j] += gauss[m][k] * img.ptr(i - n / 2 + m)[j - n / 2 + k];
                    else
                        temp.ptr(i)[j] += gauss[m][k] * img.ptr(i)[j];
        }
    return temp;
}
    return temp;
}

    高斯滤波后的lena图

    sigma=0.8

    求梯度

    Sobel算子

    g_x=\begin{array}{|c|c|c|}\hline -1&0&1\\ \hline -2&0&2\\ \hline -1&0&1\\ \hline \end{array} \quad g_y=\begin{array}{|c|c|c|} \hline -1&-2&-1\\ \hline 0&0&0\\ \hline 1&2&1\\ \hline \end{array}
    M(x,y)=|g_x|+|g_y|
    \theta(x,y)=arctan(gy/gx)
    将方向的值统一到-\pi/4,0,\pi/4,\pi/2上

    cv::Mat Sobel(const cv::Mat& img, int** theta)
{  //同时计算角度
    const int rows = img.rows;
    const int cols = img.cols;
    cv::Mat temp(rows, cols, CV_8U, cv::Scalar(0));
    int M[rows][cols];
    int gx, gy;
    int min = 1000, max = 0;
    for (int i = 1; i < rows - 1; i++)
        for (int j = 1; j < cols - 1; j++)
        {
            gx = img.ptr(i + 1)[j - 1] + 2 * img.ptr(i + 1)[j] + img.ptr(i + 1)[j + 1] - img.ptr(i - 1)[j - 1] - 2 * img.ptr(i - 1)[j] - img.ptr(i - 1)[j + 1];
            gy = img.ptr(i + 1)[j + 1] + 2 * img.ptr(i)[j + 1] + img.ptr(i - 1)[j + 1] - img.ptr(i - 1)[j - 1] - 2 * img.ptr(i)[j - 1] - img.ptr(i + 1)[j - 1];
            M[i][j] = abs(gx) + abs(gy);
            if (temp.ptr(i)[j] > max)
                max = M[i][j];
            if (temp.ptr(i)[j] < min)
                min = M[i][j];
            if (gx < 0)
            {
                gx = -gx;
                gy = -gy;
            }
            gy = gy << 16;
            int tanpi_8gx = gx * 27146;    // 27146 是tan(pi/8)*(1<<16),使用整形可以加快运算
            int tan3pi_8gx = gx * 158218;  // 158218 是tan(3pi/8)*(1<<16)
            if (abs(gy) > tan3pi_8gx)
                theta[i][j] = 0;
            else if (gy > tanpi_8gx)
                theta[i][j] = 1;
            else if (gy > -tanpi_8gx)
                theta[i][j] = 2;
            else
                theta[i][j] = 3;
        }
    if (max != min)
        for (int i = 1; i < rows - 1; i++)
            for (int j = 1; j < cols - 1; j++)
                M[i][j] = 255 * (M[i][j] - min) / (max - min);
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < cols; j++)
            temp.ptr(i)[j] = M[i][j];
    return temp;
}
    Sobel

    非极大值抑制

    如果梯度赋值在它的方向上不是最大值,将其设0

        int direc_base[4][2] = {{0, 1}, {1, 1}, {1, 0}, {0, -1}};
    for (int i = 1; i < rows - 1; i++)  //非极大值抑制
        for (int j = 1; j < cols - 1; j++)
        {
            if (M.ptr(i)[j] < M.ptr(i + direc_base[alpha[i][j]][0])[j + direc_base[alpha[i][j]][1]] || M.ptr(i)[j] < M.ptr(i - direc_base[alpha[i][j]][0])[j - direc_base[alpha[i][j]][1]])
                M.ptr(i)[j] = 0;
        }
    非极大值抑制

    双阈值处理

    输入高阈值与低阈值
    将非极大值抑制的图像分为两个边缘图像MH,ML


    canny tl=45,th=100

    全部代码

    cv::Mat Gauss(const cv::Mat& img, double sigma)
{  // 高斯模糊
    int rows = img.rows, cols = img.cols;
    cv::Mat temp(rows, cols, CV_8U, cv::Scalar(0));
    int n = ceil(6 * sigma);
    if (n % 2 == 0)
        n++;
    double** gauss = new double*[n];  //高斯模板
    double sum = 0;
    for (int i = 0; i < n; i++)
        gauss[i] = new double[n];
    for (int i = 0; i < n; i++)
    {
        int x = i - n / 2;
        for (int j = 0; j < n; j++)
        {
            int y = j - n / 2;
            gauss[i][j] = exp(-(pow(x, 2) + pow(y, 2)) / (2 * pow(sigma, 2)));
            sum += gauss[i][j];
        }
    }
    for (int i = 0; i < n; i++)  // 归一化
        for (int j = 0; j < n; j++)
            gauss[i][j] /= sum;
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < cols; j++)
        {
            for (int m = 0; m < n; m++)
                for (int k = 0; k < n; k++)
                    if (i - n / 2 + m >= 0 && i - n / 2 + m < rows && j - n / 2 + k >= 0 && j - n / 2 + k < cols)
                        temp.ptr(i)[j] += gauss[m][k] * img.ptr(i - n / 2 + m)[j - n / 2 + k];
                    else
                        temp.ptr(i)[j] += gauss[m][k] * img.ptr(i)[j];
        }
    return temp;
}
cv::Mat Sobel(const cv::Mat& img, int** theta)
{  //同时计算角度
    const int rows = img.rows;
    const int cols = img.cols;
    cv::Mat temp(rows, cols, CV_8U, cv::Scalar(0));
    int M[rows][cols];
    int gx, gy;
    int min = 1000, max = 0;
    for (int i = 1; i < rows - 1; i++)
        for (int j = 1; j < cols - 1; j++)
        {
            gx = img.ptr(i + 1)[j - 1] + 2 * img.ptr(i + 1)[j] + img.ptr(i + 1)[j + 1] - img.ptr(i - 1)[j - 1] - 2 * img.ptr(i - 1)[j] - img.ptr(i - 1)[j + 1];
            gy = img.ptr(i + 1)[j + 1] + 2 * img.ptr(i)[j + 1] + img.ptr(i - 1)[j + 1] - img.ptr(i - 1)[j - 1] - 2 * img.ptr(i)[j - 1] - img.ptr(i + 1)[j - 1];
            M[i][j] = abs(gx) + abs(gy);
            if (temp.ptr(i)[j] > max)
                max = M[i][j];
            if (temp.ptr(i)[j] < min)
                min = M[i][j];
            if (gx < 0)
            {
                gx = -gx;
                gy = -gy;
            }
            gy = gy << 16;
            int tanpi_8gx = gx * 27146;    // 27146 是tan(pi/8)*(1<<16),使用整形可以加快运算
            int tan3pi_8gx = gx * 158218;  // 158218 是tan(3pi/8)*(1<<16)
            if (abs(gy) > tan3pi_8gx)
                theta[i][j] = 0;
            else if (gy > tanpi_8gx)
                theta[i][j] = 1;
            else if (gy > -tanpi_8gx)
                theta[i][j] = 2;
            else
                theta[i][j] = 3;
        }
    if (max != min)
        for (int i = 1; i < rows - 1; i++)
            for (int j = 1; j < cols - 1; j++)
                M[i][j] = 255 * (M[i][j] - min) / (max - min);
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < cols; j++)
            temp.ptr(i)[j] = M[i][j];
    return temp;
}
cv::Mat Canny(const cv::Mat& img)
{  // Canny边缘检测
    int rows = img.rows, cols = img.cols;
    int direc_base[4][2] = {{0, 1}, {1, 1}, {1, 0}, {0, -1}};
    cv::Mat temp(rows, cols, CV_8U, cv::Scalar(0));
    cv::Mat M(rows, cols, CV_8U, cv::Scalar(0));
    cv::Mat ML(rows, cols, CV_8U, cv::Scalar(0));
    cv::Mat MH(rows, cols, CV_8U, cv::Scalar(0));
    int** alpha = new int*[rows];
    for (int i = 0; i < rows; i++)
        alpha[i] = new int[cols];
    std::cout << "Canny 边缘检测" << std::endl;
    std::cout << "输入高斯模板的sigma" << std::endl;
    double sigma;
    std::cin >> sigma;
    M = Gauss(img, sigma);  // 高斯滤波
    cv::imwrite("gauss.png", M);
    std::cout << "求梯度" << std::endl;
    M = Sobel(M, alpha);
    cv::imwrite("weifen.png", M);
    for (int i = 1; i < rows - 1; i++)  //极大值抑制
        for (int j = 1; j < cols - 1; j++)
        {
            if (M.ptr(i)[j] < M.ptr(i + direc_base[alpha[i][j]][0])[j + direc_base[alpha[i][j]][1]] || M.ptr(i)[j] < M.ptr(i - direc_base[alpha[i][j]][0])[j - direc_base[alpha[i][j]][1]])
                M.ptr(i)[j] = 0;
        }
    for (int i = 0; i < rows; i++)
        delete[] alpha[i];
    delete[] alpha;
    cv::imwrite("yizhi.png", M);
    // 阈值处理
    int tl = 0, th = 0;
    std::cout << "输入tl,th" << std::endl;
    std::cin >> tl >> th;
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < cols; j++)
        {
            if (M.ptr(i)[j] > th)
                MH.ptr(i)[j] = 255;
            else if (M.ptr(i)[j] > tl)
                ML.ptr(i)[j] = 255;
        }
    cv::imwrite("MH.png", MH);
    cv::imwrite("ML.png", ML);
    for (int i = 0; i < rows; i++)
        for (int j = 0; j < cols; j++)
        {
            if (MH.ptr(i)[j] != 0)
            {  //检测弱边缘是否连通
                MH.ptr(i)[j] = 0;
                temp.ptr(i)[j] = 255;
                bool flg = true;
                int* point = new int[2];
                point[0] = i;
                point[1] = j;
                std::stack<int*> S;
                S.push(point);
                while (flg || !S.empty())
                {
                    flg = false;
                    point = S.top();
                part1:
                    for (int m = -1; m <= 1; m++)
                        for (int k = -1; k <= 1; k++)
                        {
                            if (point[0] + m >= 0 && point[0] + m < rows && point[1] + k >= 0 && point[1] + k < cols && ML.ptr(point[0] + m)[point[1] + k] != 0)
                            {
                                temp.ptr(point[0] + m)[point[1] + k] = ML.ptr(point[0] + m)[point[1] + k];
                                ML.ptr(point[0] + m)[point[1] + k] = 0;
                                point = new int[2];
                                point[0] = S.top()[0] + m;
                                point[1] = S.top()[1] + k;
                                S.push(point);
                                flg = true;
                                goto part1;
                            }
                        }
                    if (!flg && !S.empty())
                    {
                        S.pop();
                        delete[] point;
                    }
                }
            }
        }
    cv::imwrite("canny.png", temp);
    // ********************

    return temp;
}

    PS:
    与opencv自带的Canny相比还有很大的差距,但是不知到如何改进, 希望高手指教

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