Circle detection using Hough transform in OpenCV

Hough transform

A mathematical method called the Hough transform is used in computer vision and image analysis to find basic geometric shapes like circles, lines, and ellipses. The Hough transform primarily represents an image’s lines or curves as points in parameters space. For line and curve detection in images, the Hough transform is an effective tool, especially in situations when more conventional edge detection methods fall short.

Note: You can read more on the following topics by going to their respective links:
Hough transform OpenCV
What is computer vision?

Required library

Before we dive into feature matching, it’s important to have OpenCV installed. We can install it using pip:

Here are the details of the parameters:

image: Grayscale input image
method: The detection method that we'll use to detect the circles (the only implemented method is HOUGH_GRADIENT)
dp: Inverse ratio of the accumulator resolution to the image resolution
minDist: The smallest distance between the circles’ centres
param1: Upper threshold for the internal Canny edge detector
param2: Threshold for center detection
minRadius: Minimum radius of the circles
maxRadius: Maximum radius of the circles

Note: You’re encouraged to read more about Canny edge detection.

Complete code

Here’s the complete code for circle detection in an image. This is made executable in the “Live demo” section.

import cv2
import numpy as np
import matplotlib.pyplot as plt
# Read the image
image = cv2.imread('img1.jpg', cv2.IMREAD_COLOR)
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur to reduce noise and improve circle detection
blurred = cv2.GaussianBlur(gray, (9, 9), 2)
# Use Hough Circle Transform for circle detection
circles = cv2.HoughCircles(
    blurred,
    cv2.HOUGH_GRADIENT,
    dp=1,
    minDist=20,
    param1=50,
    param2=30,
    minRadius=50,
    maxRadius=70  # Adjust maxRadius based on the expected size of the circles
)
# Convert the (x, y) coordinates and radius of the circles to integers
if circles is not None:
    circles = np.uint16(np.around(circles))
    for i in circles[0, :]:
        # Draw the outer circle in red
        cv2.circle(image, (i[0], i[1]), i[2], (0, 0, 255), 2)
        # Draw the center of the circle
         cv2.circle(image, (i[0], i[1]), 2, (0, 0, 255), 3)
# Display the result using matplotlib with the updated title
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.title('Detected Circles')
plt.axis('off')
plt.show()

Code explanation

Lines 1–3: We import the required libraries. OpenCV (cv2) is used for image processing, NumPy (np) for numerical operations, and matplotlib.pyplot (plt) for visualizing the images.
Lines 6–9: We load the image using the cv2.imread() method and then convert it to grayscale using the cv2.cvtColor() method. Grayscale is used because the Gaussian blur requires a grayscale image as input.
Lines 11–12: We use the cv2.GaussianBlur() method to reduce the noise and to detect the circle more accurately. It uses a $9\times9$ kernel and a standard deviation of 2.
Lines 15–24: Here, we use the cv2.HoughCircles() method for the preprocessed blurred image using cv2.HOUGH_GRADIENT as the detection method. Furthermore, we the minRadius and maxRadius as 50 and 70, respectively, separated by a minimum distance of 20 pixels. The parameters param1 and param2 influence the edge detection and circle center detection thresholds, respectively.
Lines 27–33: We process and visualize circles detected in an image by the cv2.HoughCircles() method by drawing red circles around the detected outlines and optionally marking their centers.
Lines 36–39: We use the plt.imshow() method to show the image and the cv2.cvtColor() to convert the image from the OpenCV BGR format to the RGB format expected by matplotlib.

Live demo

Run the notebook given in the widget below to execute the code you just saw!

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