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Circle Detection

Introduction​

This section teaches how to use OpenCV to detect circles in an image.

Experiment Objective​

Detect circles in an image and draw them for display.

Experiment Explanation​

The OpenCV Python library provides the HoughCircles() function for detecting circles in an image.

HoughCircles() Usage​

circles = cv2.HoughCircles(image, method, dp, minDist, param1, param2, minRadius, maxRadius)

Circle detection. Returns circles as an array of multiple circles (center coordinates + radius). Example: [[x0,y0,r0],[x1,y1,r1]]

  • image: Original image.
  • method: Detection method; defaults to cv2.HOUGH_GRADIENT.
  • dp: Accumulator resolution setting, usually 1.
  • minDist: Minimum distance between circle centers.
  • param1: Maximum threshold for Canny edge detection (optional).
  • param2: The larger the value, the smaller and more precise the detected circles (optional).
  • minRadius: Minimum radius of detected circles (optional).
  • maxRadius: Maximum radius of detected circles (optional).

Read the image, convert it to a grayscale image, and then perform circle detection. The code flow is as follows:


The reference code is as follows:

'''
Experiment Name: Circle Detection
Experiment Platform: WalnutPi 1B
'''

import cv2
import numpy as np

img0 = cv2.imread('circle.jpg') # Read the image
cv2.imshow('circle2', img0) # Display the original image

# Convert the color image to a grayscale image (single channel)
img1 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)
#cv2.imshow('gray', img1) # Display the image

# Detect circles
circles = cv2.HoughCircles(img1, cv2.HOUGH_GRADIENT, 1, 50, 100, 20)

# Convert all coordinate and radius information to integers
circles = np.uint(np.around(circles))

print(circles) # Print circle information

# Draw circles on the original image
for c in circles[0]:    
    x, y, r = c
    cv2.circle(img0, (x, y), 2, (0,255,0), 3) # Draw the center point
    cv2.circle(img0, (x, y), r, (0,255,0), 3) # Draw the circle outline

cv2.imshow('result', img0) # Display the image

cv2.waitKey() # Wait for any keyboard key to be pressed
cv2.destroyAllWindows() # Close the window

Experiment Results​

Run the above code on the WalnutPi; the experiment results are as follows. You can see that the circular objects are detected:

circle_detection