在OpenCV 2.4.6中使用霍夫變換檢測/擬合圓

Question

目標是檢測圖像中的5個白色圓圈。其中圓圈必須被檢測到的測試圖像是這裏顯示的圖像640x480
請下載原始圖像在這裏，1280x1024

我正在使用不同的方法來展示各種圓/橢圓檢測方法的評估。但不知何故，我無法修復我簡單的Hough變換代碼。它不檢測任何圈子。我不清楚問題是否與預處理步驟或HoughCircle的參數有關。我已經通過論壇中所有類似的問題，但仍然無法解決問題。這是我的代碼。請幫我在這方面..

頭文件

#ifndef IMGPROCESSOR_H 
    #define IMGPROCESSOR_H 

    // OpenCV Library 
    #include <opencv2\opencv.hpp> 
    #include <iostream> 

    using namespace cv; 
    using namespace std; 

    class ImgProcessor{ 
    public: 
     Mat OpImg ; 
     ImgProcessor(); 
     ~ImgProcessor(); 

     //aquire filter methods to image 
     int Do_Hough(Mat IpImg); 

    }; 
    #endif /* ImgProcessor_H */ 

源文件

#include "ImgProcessor.h" 
#include <opencv2\opencv.hpp> 
#include "opencv2\imgproc\imgproc.hpp" 
#include "opencv2\imgproc\imgproc_c.h" 
#include <vector> 

using namespace cv; 


ImgProcessor::ImgProcessor(){ 
    return; 
} 
ImgProcessor::~ImgProcessor(){ 
    return; 
} 

//Apply filtering for the input image 
int ImgProcessor::Do_Hough(Mat IpImg) 

{ 
    //Parameter Initialization________________________________________________________ 
    double sigma_x, sigma_y, thresh=250, max_thresh = 255; 
    int ksize_w = 5 ; 
    int ksize_h = 5; 
    sigma_x = 0.3*((ksize_w-1)*0.5 - 1) + 0.8 ; 
    sigma_y = 0.3*((ksize_h-1)*0.5 - 1) + 0.8 ; 

    vector<Vec3f> circles; 

    //Read the image as a matrix 
    Mat TempImg; 
    //resize(IpImg, IpImg ,Size(), 0.5,0.5, INTER_AREA); 

    //Preprocessing__________________________________________________________ 

    //Perform initial smoothing 
    GaussianBlur(IpImg, TempImg, Size(ksize_w, ksize_h),2,2); 

    //perform thresholding 
    threshold(TempImg,TempImg, thresh,thresh, 0); 

    //Remove noise by gaussian smoothing 
    GaussianBlur(TempImg, TempImg, Size(ksize_w, ksize_h),2,2); 
    /*imshow("Noisefree Image", TempImg); 
    waitKey(10000);*/ 

    //Obtain edges 
    Canny(TempImg, TempImg, 255,240 , 3); 
    imshow("See Edges", TempImg); 
    waitKey(10000); 

    //Increase the line thickness 
    //dilate(TempImg,TempImg,0,Point(-1,-1),3); 

    //Hough Circle Method______________________________________________________________ 

    // Apply the Hough Transform to find the circles 
    HoughCircles(TempImg, circles, 3, 1, TempImg.rows/32, 255, 240, 5, 0); 
    // Draw the circles detected 
    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(IpImg, center, 3, Scalar(0,255,0), -1, 8, 0); 
     // circle outline 
     circle(IpImg, center, radius, Scalar(0,0,255), 3, 8, 0); 
    } 

    // Show your results 
    namedWindow("Hough Circle Transform", WINDOW_AUTOSIZE); 
    imshow("Hough Circle Transform", IpImg); 

    // waitKey(0); 
    return 0; 





} 

int main(int argc, char** argv) 
{ 
    ImgProcessor Iclass; 
    //char* imageName = argv[1]; 
    string imageName = "D:/Projects/test_2707/test_2707/1.bmp"; 
    Mat IpImg = imread(imageName); 
    cvtColor(IpImg, IpImg,6,CV_8UC1); 
    Iclass.Do_Hough(IpImg); 
    /*Iclass.Do_Contours(IpImg);*/ 
    return 0; 
} 

Answer 1

的代碼似乎罰款，其他比：

HoughCircles(TempImg, circles, 3, 1, TempImg.rows/32, 255, 240, 5, 0); 

數是否在3參數列表對應於CV_HOUGH_GRADIENT？使用定義而不是數字總是更好。

可能你應該首先用大圓圈圖像測試它。一旦你確定代碼的其餘部分是正確的，你可以調整HoughCircles的參數。