1Hough Circle Transform {#tutorial_hough_circle}
2======================
3
4Goal
5----
6
7In this tutorial you will learn how to:
8
9-   Use the OpenCV function @ref cv::HoughCircles to detect circles in an image.
10
11Theory
12------
13
14### Hough Circle Transform
15
16-   The Hough Circle Transform works in a *roughly* analogous way to the Hough Line Transform
17    explained in the previous tutorial.
18-   In the line detection case, a line was defined by two parameters \f$(r, \theta)\f$. In the circle
19    case, we need three parameters to define a circle:
20
21    \f[C : ( x_{center}, y_{center}, r )\f]
22
23    where \f$(x_{center}, y_{center})\f$ define the center position (green point) and \f$r\f$ is the radius,
24    which allows us to completely define a circle, as it can be seen below:
25
26    ![](images/Hough_Circle_Tutorial_Theory_0.jpg)
27
28-   For sake of efficiency, OpenCV implements a detection method slightly trickier than the standard
29    Hough Transform: *The Hough gradient method*, which is made up of two main stages. The first
30    stage involves edge detection and finding the possible circle centers and the second stage finds
31    the best radius for each candidate center. For more details, please check the book *Learning
32    OpenCV* or your favorite Computer Vision bibliography
33
34Code
35----
36
37-#  **What does this program do?**
38    -   Loads an image and blur it to reduce the noise
39    -   Applies the *Hough Circle Transform* to the blurred image .
40    -   Display the detected circle in a window.
41
42-#  The sample code that we will explain can be downloaded from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/houghcircles.cpp).
43    A slightly fancier version (which shows trackbars for
44    changing the threshold values) can be found [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp).
45    @include samples/cpp/houghcircles.cpp
46
47Explanation
48-----------
49
50-#  Load an image
51    @code{.cpp}
52    src = imread( argv[1], 1 );
53
54    if( !src.data )
55      { return -1; }
56    @endcode
57-#  Convert it to grayscale:
58    @code{.cpp}
59    cvtColor( src, src_gray, COLOR_BGR2GRAY );
60    @endcode
61-#  Apply a Gaussian blur to reduce noise and avoid false circle detection:
62    @code{.cpp}
63    GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
64    @endcode
65-#  Proceed to apply Hough Circle Transform:
66    @code{.cpp}
67    vector<Vec3f> circles;
68
69    HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );
70    @endcode
71    with the arguments:
72
73    -   *src_gray*: Input image (grayscale).
74    -   *circles*: A vector that stores sets of 3 values: \f$x_{c}, y_{c}, r\f$ for each detected
75        circle.
76    -   *HOUGH_GRADIENT*: Define the detection method. Currently this is the only one available in
77        OpenCV.
78    -   *dp = 1*: The inverse ratio of resolution.
79    -   *min_dist = src_gray.rows/8*: Minimum distance between detected centers.
80    -   *param_1 = 200*: Upper threshold for the internal Canny edge detector.
81    -   *param_2* = 100\*: Threshold for center detection.
82    -   *min_radius = 0*: Minimum radio to be detected. If unknown, put zero as default.
83    -   *max_radius = 0*: Maximum radius to be detected. If unknown, put zero as default.
84
85-#  Draw the detected circles:
86    @code{.cpp}
87    for( size_t i = 0; i < circles.size(); i++ )
88    {
89       Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
90       int radius = cvRound(circles[i][2]);
91       // circle center
92       circle( src, center, 3, Scalar(0,255,0), -1, 8, 0 );
93       // circle outline
94       circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );
95     }
96    @endcode
97    You can see that we will draw the circle(s) on red and the center(s) with a small green dot
98
99-#  Display the detected circle(s):
100    @code{.cpp}
101    namedWindow( "Hough Circle Transform Demo", WINDOW_AUTOSIZE );
102    imshow( "Hough Circle Transform Demo", src );
103    @endcode
104-#  Wait for the user to exit the program
105    @code{.cpp}
106    waitKey(0);
107    @endcode
108
109Result
110------
111
112The result of running the code above with a test image is shown below:
113
114![](images/Hough_Circle_Tutorial_Result.jpg)
115