python opencv 实现hough

Python OpenCV库可以实现Hough变换来检测图像中的直线和圆。下面是使用Python OpenCV实现Hough变换的步骤：

1. 导入必要的库：

import cv2
import numpy as np

2. 读取图像并进行预处理：

image = cv2.imread('image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)

3. 运行Hough变换来检测直线：

lines = cv2.HoughLines(edges, 1, np.pi/180, threshold)

其中，`threshold`是用于筛选直线的阈值参数。

4. 绘制检测到的直线：

if lines is not None:
    for line in lines:
        rho, theta = line[0]
        a = np.cos(theta)
        b = np.sin(theta)
        x0 = a * rho
        y0 = b * rho
        x1 = int(x0 + 1000 * (-b))
        y1 = int(y0 + 1000 * (a))
        x2 = int(x0 - 1000 * (-b))
        y2 = int(y0 - 1000 * (a))
        cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)

5. 运行Hough变换来检测圆：

circles = cv2.HoughCircles(edges, cv2.HOUGH_GRADIENT, dp, minDist, param1=param1, param2=param2, minRadius=minRadius, maxRadius=maxRadius)

其中，`dp`是累加器分辨率与图像分辨率的比值，`minDist`是圆心之间的最小距离，`param1`和`param2`是用于筛选圆的阈值参数，`minRadius`和`maxRadius`是圆的最小半径和最大半径。

6. 绘制检测到的圆：

if circles is not None:
    circles = np.round(circles[0, :]).astype("int")
    for (x, y, r) in circles:
        cv2.circle(image, (x, y), r, (0, 255, 0), 2)

7. 显示结果：

cv2.imshow("Result", image)
cv2.waitKey(0)
cv2.destroyAllWindows()