opencv/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp

/**
 * @file HoughCircle_Demo.cpp
 * @brief Demo code for Hough Transform
 * @author OpenCV team
 */

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>

using namespace std;
using namespace cv;

namespace
{
    // windows and trackbars name
    const std::string windowName = "Hough Circle Detection Demo";
    const std::string cannyThresholdTrackbarName = "Canny threshold";
    const std::string accumulatorThresholdTrackbarName = "Accumulator Threshold";
    const std::string usage = "Usage : tutorial_HoughCircle_Demo <path_to_input_image>\n";

    // initial and max values of the parameters of interests.
    const int cannyThresholdInitialValue = 100;
    const int accumulatorThresholdInitialValue = 50;
    const int maxAccumulatorThreshold = 200;
    const int maxCannyThreshold = 255;

    void HoughDetection(const Mat& src_gray, const Mat& src_display, int cannyThreshold, int accumulatorThreshold)
    {
        // will hold the results of the detection
        std::vector<Vec3f> circles;
        // runs the actual detection
        HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, src_gray.rows/8, cannyThreshold, accumulatorThreshold, 0, 0 );

        // clone the colour, input image for displaying purposes
        Mat display = src_display.clone();
        for( size_t i = 0; i < circles.size(); i++ )
        {
            Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
            int radius = cvRound(circles[i][2]);
            // circle center
            circle( display, center, 3, Scalar(0,255,0), -1, 8, 0 );
            // circle outline
            circle( display, center, radius, Scalar(0,0,255), 3, 8, 0 );
        }

        // shows the results
        imshow( windowName, display);
    }
}


int main(int argc, char** argv)
{
    Mat src, src_gray;

    // Read the image
    String imageName("../data/stuff.jpg"); // by default
    if (argc > 1)
    {
       imageName = argv[1];
    }
    src = imread( imageName, IMREAD_COLOR );

    if( src.empty() )
    {
        std::cerr<<"Invalid input image\n";
        std::cout<<usage;
        return -1;
    }

    // Convert it to gray
    cvtColor( src, src_gray, COLOR_BGR2GRAY );

    // Reduce the noise so we avoid false circle detection
    GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );

    //declare and initialize both parameters that are subjects to change
    int cannyThreshold = cannyThresholdInitialValue;
    int accumulatorThreshold = accumulatorThresholdInitialValue;

    // create the main window, and attach the trackbars
    namedWindow( windowName, WINDOW_AUTOSIZE );
    createTrackbar(cannyThresholdTrackbarName, windowName, &cannyThreshold,maxCannyThreshold);
    createTrackbar(accumulatorThresholdTrackbarName, windowName, &accumulatorThreshold, maxAccumulatorThreshold);

    // infinite loop to display
    // and refresh the content of the output image
    // until the user presses q or Q
    char key = 0;
    while(key != 'q' && key != 'Q')
    {
        // those parameters cannot be =0
        // so we must check here
        cannyThreshold = std::max(cannyThreshold, 1);
        accumulatorThreshold = std::max(accumulatorThreshold, 1);

        //runs the detection, and update the display
        HoughDetection(src_gray, src, cannyThreshold, accumulatorThreshold);

        // get user key
        key = (char)waitKey(10);
    }

    return 0;
}