51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
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import cv2
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import numpy as np
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import os
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img_path = "seal_cropped.png"
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img = cv2.imread(img_path)
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if img is None:
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print(f"Error: {img_path} not found")
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exit(1)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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gray = cv2.medianBlur(gray, 5)
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# Try HoughCircles
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circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, gray.shape[0] / 8,
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param1=100, param2=30,
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minRadius=int(gray.shape[0] / 4), maxRadius=int(gray.shape[0] / 1.5))
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if circles is not None:
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circles = np.uint16(np.around(circles))
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for i in circles[0, :]:
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# Draw center
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cv2.circle(img, (i[0], i[1]), 2, (0, 255, 0), 3)
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# Draw circumference
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cv2.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 2)
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print(f"Found {len(circles[0])} circles. Best: C={circles[0][0][0]},{circles[0][0][1]} R={circles[0][0][2]}")
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else:
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print("No circles detected via HoughCircles")
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# Fallback: Red Centroid
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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lower_red1 = np.array([0, 50, 50])
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upper_red1 = np.array([10, 255, 255])
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lower_red2 = np.array([160, 50, 50])
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upper_red2 = np.array([180, 255, 255])
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mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
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mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
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mask = cv2.bitwise_or(mask1, mask2)
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M = cv2.moments(mask)
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if M["m00"] != 0:
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cX = int(M["m10"] / M["m00"])
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cY = int(M["m01"] / M["m00"])
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cv2.circle(img, (cX, cY), 5, (255, 0, 0), -1)
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print(f"Red Centroid: {cX}, {cY}")
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else:
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print("No red detected")
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cv2.imwrite("debug_circle_detect_result.png", img)
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print("Saved debug_circle_detect_result.png")
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