Detect Circles in Images using Hough Transform

This function uses the HoughCircles function from the OpenCV library to detect circles in an image. First, the image is converted to grayscale, and GaussianBlur is applied to reduce noise. Then, Hough transform is used to detect circles, and the detected circles are drawn on the original image.

Technology Stack : OpenCV, NumPy

Code Type : Image processing

Code Difficulty : Intermediate

                
                    
def detect_circle(image_path):
    import cv2
    import numpy as np
    
    # Load the image
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Apply GaussianBlur to remove noise
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    
    # Use HoughCircles to detect circles
    circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, dp=1.2, minDist=100,
                               param1=50, param2=30, minRadius=10, maxRadius=0)
    
    # Draw circles on the original image
    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 resulting image
    cv2.imshow('Detected Circles', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()