#segmentation
import cv2
import numpy as np

image = cv2.imread("circles.jpg")
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

edges = cv2.Canny(gray_image, 50, 150)

circles = cv2.HoughCircles(gray_image, cv2.HOUGH_GRADIENT, dp=1.5, minDist=150, param1=50, param2=30, minRadius=10, maxRadius=50)
circle_image = image.copy()
if circles is not None:
    circles = np.uint16(np.around(circles))
    for circle in circles[0, :]:
        center = (circle[0], circle[1])  
        radius = circle[2]             
        cv2.circle(circle_image, center, radius, (0, 0, 255), 2)  
        cv2.circle(circle_image, center, 2, (255, 0, 0), 3)       

_, binary_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)

num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary_image)

regions_image = np.zeros_like(image)
for i in range(1, num_labels):
    color = tuple(np.random.randint(0, 255, size=3).tolist())
    regions_image[labels == i] = color

cv2.imshow("Original Image", image)
cv2.imshow("Edges (Canny)", edges)
cv2.imshow("Detected Circles", circle_image)
cv2.imshow("Region-Based Segmentation", regions_image)

cv2.waitKey(0)
cv2.destroyAllWindows()