Detecting and Drawing Circles in Images with HoughCircles

This code uses OpenCV's HoughCircles function to detect circles in an image. It first converts the image to grayscale and applies Gaussian blur to reduce noise. Then, it uses Hough transform to detect circles and draws the detected circles and their boundaries on the original image.

Technology Stack : OpenCV, NumPy

Code Type : Image processing

Code Difficulty : Intermediate

                
                    
def find_circle(image):
    """
    Find circles in the given image using HoughCircles method from OpenCV.

    :param image: The input image where circles will be detected.
    :return: None
    """
    import cv2
    import numpy as np

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

    # Apply GaussianBlur to reduce noise
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)

    # Detect circles
    circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, dp=1.2, minDist=100, param1=50, param2=30, minRadius=10, maxRadius=0)

    if circles is not None:
        circles = np.round(circles[0, :]).astype("int")
        for (x, y, r) in circles:
            cv2.circle(image, (x, y), r, (0, 255, 0), 4)
            cv2.rectangle(image, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)

    # Display the image with detected circles
    cv2.imshow("Detected Circles", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Example usage
# find_circle(cv2.imread('path_to_image.jpg'))