Goal¶

In this tutorial you will learn how to:

Theory¶

1. TheCanny Edge detector was developed by John F. Canny in 1986. Also known to many as theoptimal detector, Canny algorithm aims to satisfy three main criteria:
   • Low error rate: Meaning a good detection of only existent edges.
   • Good localization: The distance between edge pixels detected and real edge pixels have to be minimized.
   • Minimal response: Only one detector response per edge.

Code¶

1. What does this program do?
   • Asks the user to enter a numerical value to set the lower threshold for ourCanny Edge Detector (by means of a Trackbar)
   • Applies theCanny Detector and generates amask (bright lines representing the edges on a black background).
   • Applies the mask obtained on the original image and display it in a window.
2. The tutorial code’s is shown lines below. You can also download it fromhere

#include"opencv2/imgproc/imgproc.hpp"#include"opencv2/highgui/highgui.hpp"#include<stdlib.h>#include<stdio.h>usingnamespacecv;/// Global variablesMatsrc,src_gray;Matdst,detected_edges;intedgeThresh=1;intlowThreshold;intconstmax_lowThreshold=100;intratio=3;intkernel_size=3;char*window_name="Edge Map";/** * @function CannyThreshold * @brief Trackbar callback - Canny thresholds input with a ratio 1:3 */voidCannyThreshold(int,void*){/// Reduce noise with a kernel 3x3blur(src_gray,detected_edges,Size(3,3));/// Canny detectorCanny(detected_edges,detected_edges,lowThreshold,lowThreshold*ratio,kernel_size);/// Using Canny's output as a mask, we display our resultdst=Scalar::all(0);src.copyTo(dst,detected_edges);imshow(window_name,dst);}/** @function main */intmain(intargc,char**argv){/// Load an imagesrc=imread(argv[1]);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 grayscalecvtColor(src,src_gray,CV_BGR2GRAY);/// Create a windownamedWindow(window_name,CV_WINDOW_AUTOSIZE);/// Create a Trackbar for user to enter thresholdcreateTrackbar("Min Threshold:",window_name,&lowThreshold,max_lowThreshold,CannyThreshold);/// Show the imageCannyThreshold(0,0);/// Wait until user exit program by pressing a keywaitKey(0);return0;}

Explanation¶

1. Create some needed variables:

     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";Note the following:a. We establish a ratio of lower:upper threshold of 3:1 (with the variable *ratio*)b. We set the kernel size of :math:`3` (for the Sobel operations to be performed internally by the Canny function)c. We set a maximum value for the lower Threshold of :math:`100`.

2. Loads the source image:

   /// Load an imagesrc=imread(argv[1]);if(!src.data){return-1;}

3. Create a matrix of the same type and size ofsrc (to bedst)

   dst.create(src.size(),src.type());

4. Convert the image to grayscale (using the functioncvtColor:

   cvtColor(src,src_gray,CV_BGR2GRAY);

5. Create a window to display the results

   namedWindow(window_name,CV_WINDOW_AUTOSIZE);

6. Create a Trackbar for the user to enter the lower threshold for our Canny detector:

   createTrackbar("Min Threshold:",window_name,&lowThreshold,max_lowThreshold,CannyThreshold);

   Observe the following:

   1. The variable to be controlled by the Trackbar islowThreshold with a limit ofmax_lowThreshold (which we set to 100 previously)
   2. Each time the Trackbar registers an action, the callback functionCannyThreshold will be invoked.

7. Let’s check theCannyThreshold function, step by step:

   1. First, we blur the image with a filter of kernel size 3:

      blur(src_gray,detected_edges,Size(3,3));

   2. Second, we apply the OpenCV functionCanny:

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

      where the arguments are:

      • detected_edges: Source image, grayscale
      • detected_edges: Output of the detector (can be the same as the input)
      • lowThreshold: The value entered by the user moving the Trackbar
      • highThreshold: Set in the program as three times the lower threshold (following Canny’s recommendation)
      • kernel_size: We defined it to be 3 (the size of the Sobel kernel to be used internally)

8. We fill adst image with zeros (meaning the image is completely black).

   dst=Scalar::all(0);

9. Finally, we will use the functioncopyTo to map only the areas of the image that are identified as edges (on a black background).

   src.copyTo(dst,detected_edges);

   copyTo copy thesrc image ontodst. However, it will only copy the pixels in the locations where they have non-zero values. Since the output of the Canny detector is the edge contours on a black background, the resultingdst will be black in all the area but the detected edges.

10. We display our result:

    imshow(window_name,dst);

Result¶

• After compiling the code above, we can run it giving as argument the path to an image. For example, using as an input the following image:

  

• Moving the slider, trying different threshold, we obtain the following result:

  

• Notice how the image is superposed to the black background on the edge regions.