OpenCV中的Sobel导数

此代码片段演示如何计算将图像与索贝尔核卷积的索贝尔3x3导数。您可以很容易地扩展到不同的内核大小，将内核半径作为my_sobel的输入，并创建适当的内核。

#include <opencv2opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;

void my_sobel(const Mat1b& src, Mat1s& dst, int direction)
{
    Mat1s kernel;
    int radius = 0;
    // Create the kernel
    if (direction == 0)
    {
        // Sobel 3x3 X kernel
        kernel = (Mat1s(3,3) << -1, 0, +1, -2, 0, +2, -1, 0, +1);
        radius = 1;
    }
    else
    {
        // Sobel 3x3 Y kernel
        kernel = (Mat1s(3, 3) << -1, -2, -1, 0, 0, 0, +1, +2, +1);
        radius = 1;
    }
    // Handle border issues
    Mat1b _src;
    copyMakeBorder(src, _src, radius, radius, radius, radius, BORDER_REFLECT101);
    // Create output matrix
    dst.create(src.rows, src.cols);
    // Convolution loop
    // Iterate on image 
    for (int r = radius; r < _src.rows - radius; ++r)
    {
        for (int c = radius; c < _src.cols - radius; ++c)
        {
            short s = 0;
            // Iterate on kernel
            for (int i = -radius; i <= radius; ++i)
            {
                for (int j = -radius; j <= radius; ++j)
                {
                    s += _src(r + i, c + j) * kernel(i + radius, j + radius);
                }
            }
            dst(r - radius, c - radius) = s;
        }
    }
}
int main(void)
{
    Mat1b img = imread("path_to_image", IMREAD_GRAYSCALE);
    // Compute custom Sobel 3x3 derivatives
    Mat1s sx, sy;
    my_sobel(img, sx, 0);
    my_sobel(img, sy, 1);
    // Edges L1 norm
    Mat1b edges_L1;
    absdiff(sx, sy, edges_L1);

    // Check results against OpenCV
    Mat1s cvsx,cvsy;
    Sobel(img, cvsx, CV_16S, 1, 0);
    Sobel(img, cvsy, CV_16S, 0, 1);
    Mat1b cvedges_L1;
    absdiff(cvsx, cvsy, cvedges_L1);
    Mat diff_L1;
    absdiff(edges_L1, cvedges_L1, diff_L1);
    cout << "Number of different pixels: " << countNonZero(diff_L1) << endl;
    return 0;
}

如果我是你，我几乎总是避免使用for循环(如果可能的话)。不必要的for循环往往会减慢执行速度。相反，尽可能重用。例如，下面的代码使用filter2D给出2d关联结果:

Mat kern = (Mat_<float>(3,3)<<-1,0,1,-2,0,2,-1,0,1);
Mat dest;
cv::filter2D(src,dest,src.type(),kern);

如果你想得到卷积结果，你需要在过滤之前翻转内核'kern'。

cv::flip(kern,kern, -1);

如果你想挤出更多的性能，你可以使用可分离过滤器'sepFilter2D'

谢谢!我能够使用上面的内核生成渐变地图，并使用openCV代码filter2D从使用自定义内核在opencv 2DFilter -导致崩溃…卷积如何?

将图像与内核进行卷积。我使用的代码是

    #include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char** argv) {
    //Loading the source image
    Mat src;
    //src = imread("1.png");
    src = cv::imread("E:\Gray_Image.bmp", 0);
    //Output image of the same size and the same number of channels as src.
    Mat dst1,dst2,grad;
    //Mat dst = src.clone();   //didn't help...
    //desired depth of the destination image
    //negative so dst will be the same as src.depth()
    int ddepth = -1;
    //the convolution kernel, a single-channel floating point matrix:

    //Mat kernel = imread("kernel.png");
    Mat kernel_x = (Mat_<float>(3, 3) << -1, 0, 1, -2, 0, 2, -1, 0, 1);
    Mat kernel_y = (Mat_<float>(3, 3) << -1, -2, -1, 0, 0, 0, 1, 2, 1);
    kernel_x.convertTo(kernel_x, CV_32F);  kernel_y.convertTo(kernel_y, CV_32F);   //<<not working
                                          //normalize(kernel, kernel, 1.0, 0.0, 4, -1, noArray());  //doesn't help
                                          //cout << kernel.size() << endl;  // ... gives 11, 11
                                          //however, the example from tutorial that does work:
                                          //kernel = Mat::ones( 11, 11, CV_32F )/ (float)(11*11);
                                          //default value (-1,-1) here means that the anchor is at the kernel center.
    Point anchor = Point(-1, -1);
    //value added to the filtered pixels before storing them in dst.
    double delta = 0;
    //alright, let's do this...
    filter2D(src, dst1, ddepth, kernel_x, anchor, delta, BORDER_DEFAULT);
    filter2D(src, dst2, ddepth, kernel_y, anchor, delta, BORDER_DEFAULT);
    imshow("Source", src);     //<< unhandled exception here
    //imshow("Kernel1", kernel_x);  imshow("Kernel2", kernel_y);
    imshow("Destination1", dst1);
    imshow("Destination2", dst2);
    addWeighted(dst1, 0.5, dst2, 0.5, 0, grad);
    imshow("Destination3", grad);
    waitKey(1000000);
    return 0;
}