使用圆形Hough变换检测圆不正确

Question

我想使用OpenCV和圆形Hough变换（HoughCircles）来检测圆形对象。在图像中我有3个圆圈，但我只能在设置阈值时检测到一个（或两个）。我想有更强大的代码，可以检测所有3个圆圈，然后我将过滤感兴趣的圆（基本上使用半径）。但首先我如何能够一起检测所有3个圆圈。有帮助吗？这是我的代码

    #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 );

    waitKey(0);
    return(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;
                    dilate(canny_output,dil,Mat());
                    erode(dil,erd,Mat());
                    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 );

    }

这里是输入图像

结果图片