用python的opencv写一个自动找圆的代码

在Python中使用OpenCV（cv2库）来检测圆形物体，通常我们会利用Hough Circle Transform算法。这是一个示例代码片段，它可以帮助你理解如何实现这个功能：

import cv2
import numpy as np

def find_circles(image_path):
    # 读取图像
    image = cv2.imread(image_path, 0)  # 将彩色图片转为灰度图像
    if image is None:
        print("无法打开图片")
        return

    # 使用Canny边缘检测
    edges = cv2.Canny(image, low_threshold=50, high_threshold=150)

    # 初始化参数
    min_radius = 10
    max_radius = int(np.sqrt(image.shape[0] * image.shape[1])) // 2  # 圆的直径不能超过图像边长的一半
    circles = []

    # Hough Circle Transform
    method = cv2.HOUGH_GRADIENT
    dp = 1  # 边缘像素连接的距离间隔
    min_dist = 10  # 最小距离避免检测到太近的圆
    param1 = 50  # 第一阶差分阈值
    param2 = 30  # 第二阶差分阈值
    min_radius, max_radius, _ = cv2.HoughCircles(edges, method, dp, min_dist, param1, param2, minRadius=min_radius, maxRadius=max_radius)

    # 如果找到圆
    if circles is not None:
        circles = np.round(circles[0, :]).astype("int")  # 取出最有可能的结果
        for (x, y, r) in circles:
            # 绘制圆
            cv2.circle(image, (x, y), r, (0, 255, 0), 2)
            cv2.putText(image, "Circle", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    return image

# 使用函数并显示结果
result = find_circles('input_image.jpg')
cv2.imshow("Detected Circles", result)
cv2.waitKey(0)
cv2.destroyAllWindows()