OpenCV/opencv/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown

Hough Circle Transform {#tutorial_hough_circle}

@tableofcontents

@prev_tutorial{tutorial_hough_lines} @next_tutorial{tutorial_remap}

| | | | -: | :- | | Original author | Ana Huamán | | Compatibility | OpenCV >= 3.0 |

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 \f$(r, \theta)\f$. In the circle case, we need three parameters to define a circle:

  \f[C : ( x{center}, y{center}, r )\f]

  where \f$(x{center}, y{center})\f$ define the center position (green point) and \f$r\f$ 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, which is made up of two main stages. The first stage involves edge detection and finding the possible circle centers and the second stage finds the best radius for each candidate center. For more details, please check the book Learning OpenCV or your favorite Computer Vision bibliography

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.

Code

@add_toggle_cpp The sample code that we will explain can be downloaded from here. A slightly fancier version (which shows trackbars for changing the threshold values) can be found here. @include samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp @end_toggle

@add_toggle_java The sample code that we will explain can be downloaded from here. @include samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java @end_toggle

@add_toggle_python The sample code that we will explain can be downloaded from here. @include samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py @end_toggle

Explanation

The image we used can be found here

Load an image:

@add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp load @end_toggle

@add_toggle_java @snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py load @end_toggle

@add_toggle_python @snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java load @end_toggle

Convert it to grayscale:

@add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp convert_to_gray @end_toggle

@add_toggle_java @snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py convert_to_gray @end_toggle

@add_toggle_python @snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java convert_to_gray @end_toggle

Apply a Median blur to reduce noise and avoid false circle detection:

@add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp reduce_noise @end_toggle

@add_toggle_java @snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py reduce_noise @end_toggle

@add_toggle_python @snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java reduce_noise @end_toggle

Proceed to apply Hough Circle Transform:

@add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp houghcircles @end_toggle

@add_toggle_java @snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py houghcircles @end_toggle

@add_toggle_python @snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java houghcircles @end_toggle

• with the arguments:

  • gray: Input image (grayscale).
  • circles: A vector that stores sets of 3 values: \f$x{c}, y{c}, r\f$ for each detected circle.
  • HOUGH_GRADIENT: Define the detection method. Currently this is the only one available in OpenCV.
  • dp = 1: The inverse ratio of resolution.
  • min_dist = gray.rows/16: 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 radius 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:

@add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp draw @end_toggle

@add_toggle_java @snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py draw @end_toggle

@add_toggle_python @snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java draw @end_toggle

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) and wait for the user to exit the program:

@add_toggle_cpp @snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp display @end_toggle

@add_toggle_java @snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py display @end_toggle

@add_toggle_python @snippet samples/java/tutorial_code/ImgTrans/HoughCircle/HoughCircles.java display @end_toggle

Result

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