# Hough lines and canny edge, Sobel derivatives Opencv Tutorial

## My favourite

• Opencv tutorial people detection
• Opencv tutorial optical flow
• Opencv Video stabilization
• Opencv videowriter
• Opencv FFMPEG
• Opencv Canny edge and hough lines
• ## 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

### 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.

### 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;

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);
waitKey(10);

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);
waitKey(10000);

imwrite("dy.jpg", dy);
imshow("Sobel in y dirrection", dy);
waitKey(10000);

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);

waitKey(10000);

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

waitKey(10000);

}