.. _hough_circle: Hough Circle Transform *********************** Goal ===== In this tutorial you will learn how to: * Use the OpenCV function :hough_circles: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 :math:(r, \theta). In the circle case, we need three parameters to define a circle: .. math:: C : ( x_{center}, y_{center}, r ) where :math:(x_{center}, y_{center}) define the center position (gree point) and :math:r is the radius, which allows us to completely define a circle, as it can be seen below: .. image:: images/Hough_Circle_Tutorial_Theory_0.jpg :alt: Result of detecting circles with Hough Transform :align: center * 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 ====== #. **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. .. |TutorialHoughCirclesSimpleDownload| replace:: here .. _TutorialHoughCirclesSimpleDownload: https://github.com/opencv/opencv/tree/master/samples/cpp/houghcircles.cpp .. |TutorialHoughCirclesFancyDownload| replace:: here .. _TutorialHoughCirclesFancyDownload: https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp #. The sample code that we will explain can be downloaded from |TutorialHoughCirclesSimpleDownload|_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found |TutorialHoughCirclesFancyDownload|_. .. code-block:: cpp #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include #include 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 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 ============ #. Load an image .. code-block:: cpp src = imread( argv[1], 1 ); if( !src.data ) { return -1; } #. Convert it to grayscale: .. code-block:: cpp cvtColor( src, src_gray, CV_BGR2GRAY ); #. Apply a Gaussian blur to reduce noise and avoid false circle detection: .. code-block:: cpp GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 ); #. Proceed to apply Hough Circle Transform: .. code-block:: cpp vector 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: :math: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 #. Draw the detected circles: .. code-block:: cpp 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 #. Display the detected circle(s): .. code-block:: cpp namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE ); imshow( "Hough Circle Transform Demo", src ); #. Wait for the user to exit the program .. code-block:: cpp waitKey(0); Result ======= The result of running the code above with a test image is shown below: .. image:: images/Hough_Circle_Tutorial_Result.jpg :alt: Result of detecting circles with Hough Transform :align: center