Python OpenCV cv2.HoughCircles() Guide

Circle detection is a common task in image processing. OpenCV provides the cv2.HoughCircles() function to detect circles in an image. This guide will explain how to use it effectively.

What is cv2.HoughCircles()?

The cv2.HoughCircles() function is used to detect circles in an image. It uses the Hough Transform, a popular technique for detecting shapes. This method is particularly useful for detecting circular objects in images.

Before using cv2.HoughCircles(), you may need to preprocess the image. For example, you can use cv2.Canny() for edge detection or cv2.GaussianBlur() to reduce noise.

How to Use cv2.HoughCircles()

Here’s a step-by-step guide to using cv2.HoughCircles():

import cv2
import numpy as np

# Load an image
image = cv2.imread('circles.jpg', cv2.IMREAD_COLOR)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply GaussianBlur to reduce noise
gray = cv2.GaussianBlur(gray, (9, 9), 2)

# Detect circles using HoughCircles
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp=1, minDist=20,
                           param1=50, param2=30, minRadius=0, maxRadius=0)

# Draw detected circles
if circles is not None:
    circles = np.uint16(np.around(circles))
    for i in circles[0, :]:
        # Draw the outer circle
        cv2.circle(image, (i[0], i[1]), i[2], (0, 255, 0), 2)
        # Draw the center of the circle
        cv2.circle(image, (i[0], i[1]), 2, (0, 0, 255), 3)

# Display the result
cv2.imshow('Detected Circles', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Code Explanation

The code above loads an image, converts it to grayscale, and applies a Gaussian blur. Then, it uses cv2.HoughCircles() to detect circles. Finally, it draws the detected circles on the original image.

Parameters of cv2.HoughCircles():

• dp: Inverse ratio of the accumulator resolution to the image resolution.
• minDist: Minimum distance between the centers of detected circles.
• param1: Upper threshold for the internal Canny edge detector.
• param2: Threshold for center detection.
• minRadius: Minimum radius of the circles to detect.
• maxRadius: Maximum radius of the circles to detect.

Example Output

After running the code, you will see an image with detected circles highlighted. The outer circle is drawn in green, and the center is marked with a red dot.

Detected Circles: [Output Image]

Tips for Better Results

To improve circle detection, adjust the parameters of cv2.HoughCircles(). For example, increase param2 to reduce false positives. You can also preprocess the image using cv2.GaussianBlur() to reduce noise.

If you need to draw other shapes, check out our guides on cv2.line() and cv2.rectangle().

Conclusion

Detecting circles in images is easy with OpenCV's cv2.HoughCircles() function. By understanding its parameters and preprocessing the image, you can achieve accurate results. Experiment with different settings to optimize detection for your specific use case.