Hough Circle Transform

Goal

• We will learn to use Hough Transform to find circles in an image.
• We will learn these functions: cv.HoughCircles()

Theory

A circle is represented mathematically as \((x-x_{center})^2 + (y - y_{center})^2 = r^2\) where \((x_{center},y_{center})\) is the center of the circle, and \(r\) is the radius of the circle. From equation, we can see we have 3 parameters, so we need a 3D accumulator for hough transform, which would be highly ineffective. So OpenCV uses more trickier method, Hough Gradient Method which uses the gradient information of edges.

We use the function: cv.HoughCircles (image, circles, method, dp, minDist, param1 = 100, param2 = 100, minRadius = 0, maxRadius = 0)

Parameters

image	8-bit, single-channel, grayscale input image.
circles	output vector of found circles(cv.CV_32FC3 type). Each vector is encoded as a 3-element floating-point vector (x,y,radius) .
method	detection method(see cv.HoughModes). Currently, the only implemented method is HOUGH_GRADIENT
dp	inverse ratio of the accumulator resolution to the image resolution. For example, if dp = 1 , the accumulator has the same resolution as the input image. If dp = 2 , the accumulator has half as big width and height.
minDist	minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1	first method-specific parameter. In case of HOUGH_GRADIENT , it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
param2	second method-specific parameter. In case of HOUGH_GRADIENT , it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first.
minRadius	minimum circle radius.
maxRadius	maximum circle radius.

Try it