Detect circles with Python
Hough Circle Transform
In this chapter,
- We will learn to use Hough Transform to find circles in an image.
- We will see these functions: cv2.HoughCircles()
A circle is represented mathematically as where is the center of the circle, and 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 cv2.HoughCircles(). It has plenty of arguments which are well explained in the documentation. So we directly go to the code.
import cv2 import numpy as np img = cv2.imread('opencv_logo.png',0) img = cv2.medianBlur(img,5) cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR) circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20, param1=50,param2=30,minRadius=0,maxRadius=0) circles = np.uint16(np.around(circles)) for i in circles[0,:]: # draw the outer circle cv2.circle(cimg,(i,i),i,(0,255,0),2) # draw the center of the circle cv2.circle(cimg,(i,i),2,(0,0,255),3) cv2.imshow('detected circles',cimg) cv2.waitKey(0) cv2.destroyAllWindows()
Result is shown below: