OpenCV  4.2.0
Open Source Computer Vision
Hough Circle Transform

Prev Tutorial: Hough Line Transform
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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 (green 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, 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

Explanation

The image we used can be found here

Load an image:

Convert it to grayscale:

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

Proceed to apply Hough Circle Transform:

  • with the arguments:
    • gray: Input image (grayscale).
    • circles: A vector that stores sets of 3 values: \(x_{c}, y_{c}, r\) 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:

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:

Result

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

cv::Mat::rows
int rows
the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
Definition: mat.hpp:2086
cv::String
std::string String
Definition: cvstd.hpp:150
cv::Point_< int >
cv::IMREAD_COLOR
If set, always convert image to the 3 channel BGR color image.
Definition: imgcodecs.hpp:67
cv::cvtColor
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0)
Converts an image from one color space to another.
cv::samples::findFile
cv::String findFile(const cv::String &relative_path, bool required=true, bool silentMode=false)
Try to find requested data file.
cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.
cv::Vec3i
Vec< int, 3 > Vec3i
Definition: matx.hpp:413
highgui.hpp
cv::imread
Mat imread(const String &filename, int flags=IMREAD_COLOR)
Loads an image from a file.
cv::Mat::empty
bool empty() const
Returns true if the array has no elements.
cv::HoughCircles
void HoughCircles(InputArray image, OutputArray circles, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0)
Finds circles in a grayscale image using the Hough transform.
cv::Vec< int, 3 >
cv::medianBlur
void medianBlur(InputArray src, OutputArray dst, int ksize)
Blurs an image using the median filter.
imgcodecs.hpp
cv::imshow
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
cv::Scalar
Scalar_< double > Scalar
Definition: types.hpp:669
cv::Point
Point2i Point
Definition: types.hpp:194
cv::HOUGH_GRADIENT
basically 21HT, described in
Definition: imgproc.hpp:476
cv::Mat
n-dimensional dense array class
Definition: mat.hpp:791
cv::imshow
void imshow(const String &winname, const ogl::Texture2D &tex)
Displays OpenGL 2D texture in the specified window.
cv::COLOR_BGR2GRAY
convert between RGB/BGR and grayscale, color conversions
Definition: imgproc.hpp:542
cv
"black box" representation of the file storage associated with a file on disk.
Definition: affine.hpp:51
imgproc.hpp
cv::datasets::circle
Definition: gr_skig.hpp:62
cv::LINE_AA
antialiased line
Definition: imgproc.hpp:807
cv::circle
void circle(InputOutputArray img, Point center, int radius, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
Draws a circle.