用opencv写一个mark点检测算法

好的，以下是一个简单的基于OpenCV的mark点检测算法：

1. 读取图像并灰度化

import cv2

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

2. 使用高斯滤波器平滑图像

blur = cv2.GaussianBlur(gray, (5, 5), 0)

3. 使用自适应阈值方法二值化图像

thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

4. 使用形态学操作去除噪点

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

5. 检测mark点并绘制

circles = cv2.HoughCircles(opening, cv2.HOUGH_GRADIENT, dp=1, minDist=20, param1=50, param2=30, minRadius=0, maxRadius=0)

if circles is not None:
    circles = np.round(circles[0, :]).astype("int")
 
    for (x, y, r) in circles:
        cv2.circle(img, (x, y), r, (0, 255, 0), 2)
        cv2.putText(img, "mark", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

完整代码如下：

import cv2
import numpy as np

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

blur = cv2.GaussianBlur(gray, (5, 5), 0)

thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

circles = cv2.HoughCircles(opening, cv2.HOUGH_GRADIENT, dp=1, minDist=20, param1=50, param2=30, minRadius=0, maxRadius=0)

if circles is not None:
    circles = np.round(circles[0, :]).astype("int")
 
    for (x, y, r) in circles:
        cv2.circle(img, (x, y), r, (0, 255, 0), 2)
        cv2.putText(img, "mark", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，这只是一个简单的示例。实际上，mark点检测算法可能因应用场景的不同而有所差异，需要根据实际情况进行调整。