OpenCV中的Sobel衍生物 [英] Sobel derivative in OpenCV

问题描述

我的任务是制作我自己的Sobel方法，而不是使用在OpenCV中找到的cv :: Sobel。
我试过实现一个我在编程技巧

I have been tasked with making my own Sobel method, and not use the cv::Sobel found in OpenCV. I tried implementing one I found at Programming techniques

当我运行程序时，cv :: Mat会抛出一个错误。任何人都知道为什么？

When I run the program, cv::Mat throws an error, however. Anyone have any idea why?

Sobel方法：

int sobelCorrelation(Mat InputArray, int x, int y, String xory)
{
    if (xory == "x") {
        return InputArray.at<uchar>(y - 1, x - 1) +
            2 * InputArray.at<uchar>(y, x - 1) +
            InputArray.at<uchar>(y + 1, x - 1) -
            InputArray.at<uchar>(y - 1, x + 1) -
            2 * InputArray.at<uchar>(y, x + 1) -
            InputArray.at<uchar>(y + 1, x + 1);
    }
    else if (xory == "y")
    {
        return InputArray.at<uchar>(y - 1, x - 1) +
            2 * InputArray.at<uchar>(y - 1, x) +
            InputArray.at<uchar>(y - 1, x + 1) -
            InputArray.at<uchar>(y + 1, x - 1) -
            2 * InputArray.at<uchar>(y + 1, x) -
            InputArray.at<uchar>(y + 1, x + 1);
    }
    else
    {
        return 0;
    }
}

在另一个函数中调用和处理它：

Calling and processing it in another function:

void imageOutput(Mat image, String path) {
    image = imread(path, 0);
    Mat dst;
    dst = image.clone();
    int sum, gx, gy;
    if (image.data && !image.empty()){

        for (int y = 0; y < image.rows; y++)
            for (int x = 0; x < image.cols; x++)
                dst.at<uchar>(y, x) = 0.0;

        for (int y = 1; y < image.rows - 1; ++y) {
            for (int x = 1; x < image.cols - 1; ++x){ 
                gx = sobelCorrelation(image, x, y, "x");
                gy = sobelCorrelation(image, x, y, "y");
                sum = absVal(gx) + absVal(gy);
                if (sum > 255)
                    sum = 255;
                else if (sum < 0)
                    sum = 0;
                dst.at<uchar>(x, y) = sum;
            }
        }

        namedWindow("Original");
        imshow("Original", image);

        namedWindow("Diagonal Edges");
        imshow("Diagonal Edges", dst);

    }
    waitKey(0);
}

主要：

int main(int argc, char* argv[]) {

    Mat image;

    imageOutput(image, "C:/Dropbox/2-falling-toast-ted-kinsman.jpg");
    return 0;
}

absVal方法：

int absVal(int v)
{
    return v*((v < 0)*(-1) + (v > 0));
}

运行时会抛出此错误：
0x00007FFC9365A1C8处的未处理异常在Miniproject01.exe中：Microsoft C ++异常：cv ::在内存位置0x000000A780A4F110的异常。
并指向此处：

When run it throws this error: "Unhandled exception at 0x00007FFC9365A1C8 in Miniproject01.exe: Microsoft C++ exception: cv::Exception at memory location 0x000000A780A4F110." and points to here:

template<typename _Tp> inline
_Tp& Mat::at(int i0, int i1)
{
    CV_DbgAssert( dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] &&
        (unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()) &&
        CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1());
    return ((_Tp*)(data + step.p[0] * i0))[i1];
}

如果任何人有任何意见或想法，我做错了，谢谢！

If anyone have any advice or ideas what I am doing wrong it would be greatly appreciated!

推荐答案

此代码片段是演示如何计算Sobel 3x3导数与Sobel内核卷积图像。您可以轻松扩展到不同的内核大小，将内核半径作为 my_sobel 的输入，并创建相应的内核。

This code snippet is to demonstrate how to compute Sobel 3x3 derivatives convolving the image with Sobel kernels. You can easily extend to different kernel sizes giving the kernel radius as input to my_sobel, and creating the appropriate kernel.

#include <opencv2\opencv.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;
}

这篇关于OpenCV中的Sobel衍生物的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！