Detecting Circles

import cv2
import numpy as np

image = cv2.imread("photo.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (9, 9), 2)

# HoughCircles detects circles directly (no edge detection needed first)
circles = cv2.HoughCircles(
    blurred,
    method=cv2.HOUGH_GRADIENT,
    dp=1,          # Inverse ratio of accumulator resolution
    minDist=50,    # Minimum distance between circle centres
    param1=100,    # Canny high threshold (internal)
    param2=30,     # Accumulator threshold (lower = more circles, less accurate)
    minRadius=10,  # Minimum circle radius in pixels
    maxRadius=200  # Maximum circle radius in pixels
)

result = image.copy()
if circles is not None:
    circles = np.uint16(np.around(circles))
    for circle in circles[0, :]:
        cx, cy, r = circle[0], circle[1], circle[2]
        cv2.circle(result, (cx, cy), r, (0, 255, 0), 2)    # Draw circle
        cv2.circle(result, (cx, cy), 3, (0, 0, 255), -1)   # Draw centre
        print(f"Circle: centre=({cx},{cy}), radius={r}px")

    print(f"Total circles found: {len(circles[0])}")
else:
    print("No circles detected — try adjusting param2 (lower = more sensitive)")

cv2.imwrite("circles_detected.jpg", result)