圆形非对称靶标相机标定代码

以下是圆形非对称靶标相机标定的Python代码，代码中有详细的注释说明：

import numpy as np
import cv2

# 定义圆形非对称靶标的行列数和每个小格子的大小
rows = 6
cols = 9
square_size = 20  # 单位为毫米

# 生成标定板角点的世界坐标系坐标
objp = np.zeros((rows * cols, 3), np.float32)
objp[:, :2] = np.mgrid[0:cols, 0:rows].T.reshape(-1, 2) * square_size

# 存储所有的角点坐标
objpoints = []  # 世界坐标系中的坐标
imgpoints = []  # 图像坐标系中的坐标

# 读取所有标定图片并提取角点坐标
images = glob.glob('calibration_images/*.jpg')
for fname in images:
    img = cv2.imread(fname)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 查找角点
    ret, corners = cv2.findCirclesGrid(gray, (cols, rows), flags=cv2.CALIB_CB_ASYMMETRIC_GRID)

    # 如果找到了角点，则添加到objpoints和imgpoints中
    if ret == True:
        objpoints.append(objp)
        imgpoints.append(corners)

        # 在图像上绘制角点
        cv2.drawChessboardCorners(img, (cols, rows), corners, ret)
        cv2.imshow('img', img)
        cv2.waitKey(500)

cv2.destroyAllWindows()

# 进行相机标定
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

# 打印标定结果
print("相机内参矩阵：\n", mtx)
print("畸变系数：\n", dist)

# 校正图像
img = cv2.imread('calibration_images/test_image.jpg')
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))

dst = cv2.undistort(img, mtx, dist, None, newcameramtx)

# 显示校正前后的图像
cv2.imshow('original', img)
cv2.imshow('corrected', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()