Apply Canny Edge Effect on an Image

Steps to Apply Canny Edge Effect on an Image | OpenCV with Visual Studio in Windows 10. In this Tutorial, we are going to apply Canny Edge Effect on an image using OpenCV.

1. First of all, Follow this tutorial to Install & Configure OpenCV with Visual Studio 2015


2. Copy the Full Source Code to Apply Canny Edge on an Image from here:

#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>

#include<iostream>
#include<conio.h>     

int main() {

 cv::Mat imgOriginal;        // input image
 cv::Mat imgGrayscale;       // grayscale image
 cv::Mat imgBlurred;         // blured image
 cv::Mat imgCanny;           // Canny edge image

 imgOriginal = cv::imread("C:/Briefcase/Chess.jpg");        // open image

 if (imgOriginal.empty()) {                                  // if unable to open image
  std::cout << "error: image not read from file\n";       // show error message
  return(0);                                              
 }

 cv::cvtColor(imgOriginal, imgGrayscale, CV_BGR2GRAY);       // convert to grayscale

 cv::GaussianBlur(imgGrayscale,imgBlurred,cv::Size(5, 5),1.5);  // Blur Effect                     

 cv::Canny(imgBlurred,imgCanny,100,200);   //Canny Effect                    

 // declare windows
 cv::namedWindow("imgOriginal", CV_WINDOW_AUTOSIZE);     
 cv::namedWindow("imgCanny", CV_WINDOW_AUTOSIZE);     

 // show windows              
 cv::imshow("imgOriginal", imgOriginal);     
 cv::imshow("imgCanny", imgCanny);

 cv::waitKey(0);

 return(0);
}

OR
Get CannyImg.cpp from Github:
Download CannyImg.cpp


3. Paste full source code and Run it (Ctrl+F5 or F5).

OUTPUT

Load Image OpenCV

Canny Edge Image OpenCV

Explaination : Function used: cv::Canny(imgBlurred,imgCanny,100,200) - This function can process images and implement the Canny Edge Detector Algorithm.

• 1^st parameter is the source image.
• 2^nd parameter is the destination or resultant image.
• 3^rd parameter is the low threshold value.
• 4^th parameter is the high threshold value.

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