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# Hough Circle Transform

## Goal

In this tutorial you will learn how to:

- Use the OpenCV function HoughCircles to detect circles in an image.

## Theory

### Hough Circle Transform

- The Hough Circle Transform works in a roughly analogous way to the Hough Line Transform explained in the previous tutorial.

- In the line detection case, a line was defined by two parameters (r, \theta). In the circle case, we need three parameters to define a circle:

  C : ( x_{center}, y_{center}, r )

  where (x_{center}, y_{center}) define the center position (gree point) and r is the radius, which allows us to completely define a circle, as it can be seen below:

- For sake of efficiency, OpenCV implements a detection method slightly trickier than the standard Hough Transform: The Hough gradient method. For more details, please check the book Learning OpenCV or your favorite Computer Vision bibliography

## Code

1. What does this program do?
   - Loads an image and blur it to reduce the noise
   - Applies the Hough Circle Transform to the blurred image .
   - Display the detected circle in a window.
2. The sample code that we will explain can be downloaded from here. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found here.

    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/imgproc/imgproc.hpp"
    #include <iostream>
    #include <stdio.h>

    using namespace cv;

    /** @function main */
    int main(int argc, char** argv)
    {
      Mat src, src_gray;

      /// Read the image
      src = imread( argv[1], 1 );

      if( !src.data )
        { return -1; }

      /// Convert it to gray
      cvtColor( src, src_gray, CV_BGR2GRAY );

      /// Reduce the noise so we avoid false circle detection
      GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );

      vector<Vec3f> circles;

      /// Apply the Hough Transform to find the circles
      HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 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( src, center, 3, Scalar(0,255,0), -1, 8, 0 );
          // circle outline
          circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );
       }

      /// Show your results
      namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );
      imshow( "Hough Circle Transform Demo", src );

      waitKey(0);
      return 0;
    }

## Explanation

1. Load an image

       src = imread( argv[1], 1 );

       if( !src.data )
         { return -1; }

2. Convert it to grayscale:

       cvtColor( src, src_gray, CV_BGR2GRAY );

3. Apply a Gaussian blur to reduce noise and avoid false circle detection:

       GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );

4. Proceed to apply Hough Circle Transform:

       vector<Vec3f> circles;

       HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );

   with the arguments:

   - src_gray: Input image (grayscale)
   - circles: A vector that stores sets of 3 values: x_{c}, y_{c}, r for each detected circle.
   - CV_HOUGH_GRADIENT: Define the detection method. Currently this is the only one available in OpenCV
   - dp = 1: The inverse ratio of resolution
   - min_dist = src_gray.rows/8: Minimum distance between detected centers
   - param_1 = 200: Upper threshold for the internal Canny edge detector
   - param_2 = 100*: Threshold for center detection.
   - min_radius = 0: Minimum radio to be detected. If unknown, put zero as default.
   - max_radius = 0: Maximum radius to be detected. If unknown, put zero as default

5. Draw the detected circles:

       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( src, center, 3, Scalar(0,255,0), -1, 8, 0 );
          // circle outline
          circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );
        }

   You can see that we will draw the circle(s) on red and the center(s) with a small green dot

6. Display the detected circle(s):

       namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );
       imshow( "Hough Circle Transform Demo", src );

7. Wait for the user to exit the program

       waitKey(0);

## Result

The result of running the code above with a test image is shown below:

## Help and Feedback

You did not find what you were looking for?

- Ask a question on the Q&A forum.
- If you think something is missing or wrong in the documentation, please file a bug report.

### Navigation

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