Use of Hough circles in OpenCV

OpenCV provides and inbuilt function to detect circles in your image. The Hough transform is a technique which can be used to isolate features of a particular shape within an image. The implementation of the circle  transform in OpenCV uses a tricky method called the Hough gradient method ( Link to Google Books ).

cvHoughCircles( ) is the function provided in OpenCV.

CvSeq* cvHoughCircles( 
        CvArr* image,         
        void*  circle_storage, 
        int    method, 
        double dp, 
        double min_dist, 
        double param1 = 100, 
        double param2 = 300, 
        int    min_radius = 0, 
        int    max_radius = 0 

Following is a example code snippet.

//  Allocating memory dynamically to store the circle regions detected
CvMemStorage* storage_var = cvCreateMemStorage(0);

//  A sequence of circles will get stored in the results variable
CvSeq* results = cvHoughCircles(img, storage_var , CV_HOUGH_GRADIENT , 2 , img->height/3 );

//  Using a for loop to access individual circles; draw a circle in the input image
for( int i = 0; i < results->total; i++ )
      float* p = (float*) cvGetSeqElem( results, i );
      CvPoint pt = cvPoint( cvRound( p[0] ), cvRound( p[1] ));
      cvCircle(img,pt,cvRound( p[2] ),cvScalar(0,0,255),1.8);

Some things to note before using cvHoughTransform( )
  • The image used should be in grey-scale.
  • Reduce noise in the image as much as possible. You can use cvSmooth( ) function with CV_GRADIENT or CV_MEDIAN types to achieve this. Also many other filters could be used depending on the image. 
cvHoughCircles( ) is a very inefficient method! I had tried using it to detect the pupil in the eyes. It was a very lame try because pupil detection actually turned out to be a very complex procedure! I would like to share some of the experiments i tried by implementing hough transform.

Initially i converted image to grey scale and applied gaussian smooth. Implemeted cvHoughCircles( ) on this image. You can see that the circle detection has been all over the place. Even the pupil which i wanted to detect is not accurate! So basically i had to filter more and reduce features in the image. 

So next i just increased the intensity levels of the pixels. I can say that's one method which worked for me in this case. The number of circles detected was reduced and also a more accurate pupil boundary was detected. Similarly you can try various other filters.


Андрей said...
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Adithya said...

Ya right..

ratna paul said...

Can you give me some details regarding the filters we can use ??

Exorcismus said...

try median filter , or gaussian

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Suraj C K said...

How will i access the source code of the function HoughCircles?

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