#3c: Detecting any kind of edges: Canny Edge Detection Algorithm

Note: This tutorial is optional. Don't worry if you are unable to understand properly.

You may refer HERE for further insight of this algorithm.

The following is the code for your reference.

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Input Image:

#include
#include
#include
using namespace std;
using namespace cv;

 Mat src, src_gray;
 Mat dst, detected_edges;

 int edgeThresh = 1;
 int lowThreshold;
 int const max_lowThreshold = 100;
 int ratio = 3;
 int kernel_size = 3;
 char* window_name = "Edge Map";

 /**
  * @function CannyThreshold
  * @brief Trackbar callback - Canny thresholds input with a ratio 1:3
  */
 void CannyThreshold(int, void*)
 {
   /// Reduce noise with a kernel 3x3
   blur( src_gray, detected_edges, Size(3,3) );

   /// Canny detector
   Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );

   /// Using Canny's output as a mask, we display our result
   dst = Scalar::all(0);

   src.copyTo( dst, detected_edges);
   imshow( window_name, dst );
  }


 /** @function main */
 int main( int argc, char** argv )
 {
   /// Load an image
   src = imread("images/windows.jpg");

   if( !src.data )
   { return -1; }

   /// Create a matrix of the same type and size as src (for dst)
   dst.create( src.size(), src.type() );

   /// Convert the image to grayscale
   cvtColor( src, src_gray, CV_BGR2GRAY );

   /// Create a window
   namedWindow( window_name, CV_WINDOW_AUTOSIZE );

   /// Create a Trackbar for user to enter threshold
   createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );

   /// Show the image
   CannyThreshold(0, 0);

   /// Wait until user exit program by pressing a key

 while(char(waitKey(0))!='q'){}
 return 0;
}

Result: