图片存储地址为：C:\Users\Pictures\test.png，该图像图片背景是黑色的，目标区域是亮的，目标区域是两段圆弧和两段曲线构成的封闭区域，其中两段圆弧属于同一个圆，但在目标区域的相对位置，也就是不是相邻的，现在用python+opencv提取轮廓并拟合圆弧的圆心和半径。

1、拟合所有圆弧

import cv2
import numpy as np
​
# 读取图像
image_path = r'C:\Users\Pictures\test.png'
image = cv2.imread(image_path)
​
# 转换为灰度图像
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
​
# 对图像进行二值化处理
_, threshold_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)
​
# 查找图像轮廓
contours, _ = cv2.findContours(threshold_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
​
# 迭代所有轮廓
for contour in contours:# 拟合轮廓为圆弧（仅拟合最小外接圆）(x, y), radius = cv2.minEnclosingCircle(contour)
​# 绘制圆弧cv2.circle(image, (int(x), int(y)), int(radius), (0, 255, 0), 2)
​
# 显示结果图像
cv2.imshow('Detected Arcs', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

2、拟合最大圆弧

import cv2
import numpy as np
​
# 读取图像
image_path = r'C:\Users\Pictures\test.png'
image = cv2.imread(image_path)
​
# 转换为灰度图像
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
​
# 对图像进行二值化处理
_, threshold_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)
​
# 查找图像轮廓
contours, _ = cv2.findContours(threshold_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
​
max_radius = 0
max_circle_center = None
​
# 迭代所有轮廓
for contour in contours:# 拟合轮廓为圆弧（仅拟合最小外接圆）(x, y), radius = cv2.minEnclosingCircle(contour)
​if radius > max_radius:max_radius = radiusmax_circle_center = (int(x), int(y))
​
# 绘制最大半径的圆
cv2.circle(image, max_circle_center, int(max_radius), (0, 255, 0), 2)
​
# 在图像上标注圆心和直径
cv2.putText(image, f"Center: {max_circle_center}", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.putText(image, f"Diameter: {2 * max_radius}", (50, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
​
# 显示结果图像
cv2.imshow('Detected Arcs', image)
cv2.waitKey(0)
cv2.destroyAllWindows()