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PODetect/Fitting Circles using Hough Transform in OpenCV 2.4.6
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copied!<p>The objective is to detect the 5 white circles in the image.The test image in which the circles have to be detected is the one shown here <a href="http://depot.tu-dortmund.de/get/z4p8g" rel="nofollow">640x480</a><br> Please download the original image here,<a href="http://depot.tu-dortmund.de/get/vnegf" rel="nofollow">1280x1024</a></p> <p>I am using different methods to bring out a evaluation of various circle/ellipse detection methods. But somehow I am not able to fix my simple Hough transform code. It does not detect any circles. I am not clear whether the problem is with pre-processing step, or the parameters of the HoughCircle. I have gone through all the similar questions in the forum, but still not able to fix the issue. This is my code. Please help me in this regards..</p> <p>Header file</p> <pre><code>#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 */ </code></pre> <p>Source file</p> <pre><code>#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; } </code></pre>

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