import cv2
import numpy as np

# Load the image
image = cv2.imread('dirty.jpeg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

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

# Detect circles using the Hough Circles method
circles = cv2.HoughCircles(
    gray,
    cv2.HOUGH_GRADIENT,
    dp=1,  # Inverse ratio of accumulator resolution to image resolution
    minDist=20,  # Minimum distance between the centers of detected circles
    param1=50,  # Higher threshold for edge detection
    param2=30,  # Accumulator threshold for circle detection
    minRadius=10,  # Minimum radius
    maxRadius=100  # Maximum radius
)

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

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