OpenCV  4.10.0-dev Open Source Computer Vision
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Hough Circle Transform

# Goal

In this chapter,

• We will learn to use Hough Transform to find circles in an image.
• We will see 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.

The function we use here is cv.HoughCircles(). It has plenty of arguments which are well explained in the documentation. So we directly go to the code.

import numpy as np
import cv2 as cv
assert img is not None, "file could not be read, check with os.path.exists()"
img = cv.medianBlur(img,5)
cimg = cv.cvtColor(img,cv.COLOR_GRAY2BGR)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
# draw the center of the circle
cv.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
cv.imshow('detected circles',cimg)
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
int waitKey(int delay=0)
Waits for a pressed key.
void destroyAllWindows()
Destroys all of the HighGUI windows.
Loads an image from a file.
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0)
Converts an image from one color space to another.
void circle(InputOutputArray img, Point center, int radius, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
Draws a circle.
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.
void medianBlur(InputArray src, OutputArray dst, int ksize)
Blurs an image using the median filter.

Result is shown below:

image