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Detect circlues using Circular Hough transform does not work correct

asked 2016-10-06 04:15:47 -0500

astronaut gravatar image

I would like to detect circular objects using OpenCV and Circular Hough transform (HoughCircles). In the image I have 3 circles, but Im only able to detect one (or two of them) when set up the threshold. I would like to have more robust code that can detect all 3 circle and after that I will filter the interested circle( basically using the radius). But first how can I detect all 3 circles together. Any help?Here my code

#include <opencv2/opencv.hpp>
    #include <iostream>
    #include <string>
    #include <vector>

    using namespace cv;
    using namespace std;

    int thresh = 100;
    int max_thresh = 9000;
    Mat src;
    void thresh_callback(int, void* );

    int main()

                 cv::Mat src = cv::imread("w1.png");
                 resize(src, src, Size(640,480), 0, 0, INTER_CUBIC);
                  char* source_window = "Source";
                  namedWindow( source_window, CV_WINDOW_AUTOSIZE );
                  imshow( source_window, src );

                  createTrackbar( " Canny thresh:", "Source", &thresh, max_thresh, thresh_callback );
                  thresh_callback( 0, 0 );


    void thresh_callback(int, void* ) {

          Mat src_gray, gray;
          cv::Mat bgr_image = cv::imread( "w1.png");
          cv::Mat orig_image = bgr_image.clone();

         //blur( bgr_image, src_gray, Size(1,1) );
           medianBlur(bgr_image, src_gray,1);
          //cv::GaussianBlur(bgr_image, src_gray, cv::Size(1, 1), 1, 1);
          cvtColor( src_gray,gray, CV_BGR2GRAY );

        Mat canny_output;
        Canny( gray, canny_output, thresh, thresh*1, 5,true );

                    Mat bw,dil,bw1,erd;
                    Mat tmp=canny_output.clone();
                    Size kernalSize (15,15);
                    Mat element = getStructuringElement (MORPH_RECT, kernalSize, Point(9,9)  );
                    morphologyEx( canny_output, bw1, MORPH_CLOSE, element );

            // Use the Hough transform to detect circles in the combined threshold image

std::vector<cv::Vec3f> circles;
            cv::HoughCircles(canny_output, circles, CV_HOUGH_GRADIENT, 1, canny_output.rows/1.1, 10, 100, 10, 0);
            //cv::HoughCircles(blue_hue_image, circles, CV_HOUGH_GRADIENT, 1, blue_hue_image.rows/1, 10, 100, 10, 0);
            // Loop over all detected circles and outline them on the original image
            if(circles.size() == 0) std::exit(-1);
            for(size_t current_circle = 0; current_circle < circles.size(); ++current_circle) {
                Point center(cvRound(circles[current_circle][0]), cvRound(circles[current_circle][1]));
                int radius = cvRound(circles[current_circle][2]);
                cv::circle(orig_image, center, radius, cv::Scalar(0, 255, 0), 10);
            // Show images

             resize(orig_image, orig_image, Size(640,480), 0, 0, INTER_CUBIC);
             char* source_window4 = "Detected window on the input image";
             namedWindow( source_window4, CV_WINDOW_AUTOSIZE );
             imshow( source_window4, orig_image );


Here the input image Input file

And here the result by 4600 threshold detecting one circle result

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answered 2016-10-07 11:43:36 -0500

matman gravatar image

updated 2016-10-07 11:45:51 -0500

Don't use Canny before HoughCircles. HoughCircles needs a grayscale image because it uses gradients for processing. There will be a Canny edge detection inside HoughCircles using param1 as higher threshold. Looking at the tutorial shows that a grayscale image is passed, so why do you think there should be an edge detection before?

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Asked: 2016-10-06 04:15:47 -0500

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Last updated: Oct 07 '16