Hough lines and canny edge, Sobel derivatives Opencv Tutorial


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  • Hough lines, Canny edges and Sobel derivatives

    HoughLines, Canny edges, for OpenCV line detection  edges detection in symple described C++ code, where all the steps are visualize and exmplayn. In this tutorial is used Visual studio 2015 instalation by nuget packages. Easy and fast without usual problems with version, dll, and environmental vatiables. Check this tutorial here

    HoughLines hough lines

    Sobel derivatives

    Sobel derivatives is convolution of image parts with kernel that represent sobel derivative approximation. The upper image is our sobel kernel. Simple 3 x 3 matrices with this parameters. This configurations can detect edges or changes which is vertically oriented. How? 
    Convolution of source image 3x3 part with this kernel generates a number.

    Kernel Convolution wiki

    Use this kernel with  3x3 image part 1. This image matrices has constant values 1. There is no edges in x direction. Number generates by convolution is 0. If you convolve kernel with image part 2. There is edges in x direction from 1 to 5. Convolution of the same kernel with this part generates number 16. See the example.

    Try to think how simple is this in all directions. 

    Sobel derivatives convolution

    Canny edges, sobel and hough lines code

    #include <Windows.h>
    #include "opencv2\highgui.hpp"
    #include "opencv2\imgproc.hpp"
    #include "opencv2/imgcodecs/imgcodecs.hpp"
    #include "opencv2/videoio/videoio.hpp"

    using namespace cv;
    using namespace std;

    int main(int argc, const char** argv)

    Mat image;

    // Load an image
    canny hough lines

    image = imread("1.jpg", 0);
    resize(image, image, Size(800, 600));

    cv::Mat edges;

    // Canny edge 
    cv::Canny(image, edges, 95, 100);

    imwrite("edges.jpg", edges);
    imshow("Canny edges", edges);
    Canny edges

    cv::Mat dx, dy;

    // sobel derivative approximation X direction of edges image
    cv::Sobel(edges, dx, CV_32F, 1, 0);

    // sobel derivative approximation Y direction of edges image
    cv::Sobel(edges, dy, CV_32F, 0, 1);

    imwrite("dx.jpg", dx);
    imshow("Sobel in x dirrection", dx);
    sobel derivatives

    imwrite("dy.jpg", dy);
    imshow("Sobel in y dirrection", dy);
    sobel derivatives

        vector<Vec4i> lines;
               // Find hough lines 
       HoughLinesP(edges, lines, 1, CV_PI / 180, 100, 100, 10);

               // Prepare blank mat with same sizes as image
       Mat Blank(image.rows, image.cols, CV_8UC3, Scalar(0, 0, 0));

           // Draw lines into image and Blank images
           for (size_t i = 0; i < lines.size(); i++)
          Vec4i l = lines[i];

          line(image, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 0), 2, CV_AA);

          line(Blank, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(255, 255, 255), 2, CV_AA);


    imwrite("houg.jpg", image);
    imshow("Edges", image);

    Hough lines


    imwrite("houg2.jpg", Blank);
    imshow("Edges Structure", Blank);

    Hough lines


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