opencv双目标定 python

在Python中使用OpenCV进行双目标定的过程如下：

1. 导入必要的库和模块：

import cv2
import numpy as np

2. 读取左右相机的图像：

left_image = cv2.imread('left_image.jpg')
right_image = cv2.imread('right_image.jpg')

3. 定义相机的内参矩阵和畸变系数：

# 左相机内参矩阵和畸变系数
left_camera_matrix = np.array([[fx, 0, cx],
                               [0, fy, cy],
                               [0, 0, 1]])
left_distortion_coeffs = np.array([k1, k2, p1, p2, k3])

# 右相机内参矩阵和畸变系数
right_camera_matrix = np.array([[fx, 0, cx],
                                [0, fy, cy],
                                [0, 0, 1]])
right_distortion_coeffs = np.array([k1, k2, p1, p2, k3])

其中，fx、fy是焦距，cx、cy是光心的坐标，k1、k2、p1、p2、k3是畸变系数。

4. 定义棋盘格的尺寸和角点数量：

pattern_size = (9, 6)  # 棋盘格内角点的数量
square_size = 1.0  # 棋盘格方块的尺寸（单位：毫米）

5. 检测棋盘格的角点：

left_found, left_corners = cv2.findChessboardCorners(left_image, pattern_size)
right_found, right_corners = cv2.findChessboardCorners(right_image, pattern_size)

6. 对角点进行亚像素级别的精确化：

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
cv2.cornerSubPix(left_image, left_corners, (11, 11), (-1, -1), criteria)
cv2.cornerSubPix(right_image, right_corners, (11, 11), (-1, -1), criteria)

7. 进行双目标定：

flags = cv2.CALIB_FIX_INTRINSIC + cv2.CALIB_USE_INTRINSIC_GUESS
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
retval, camera_matrix1, dist_coeffs1, camera_matrix2, dist_coeffs2, R, T, E, F = cv2.stereoCalibrate(
    object_points, left_corners, right_corners, left_camera_matrix, left_distortion_coeffs,
    right_camera_matrix, right_distortion_coeffs, left_image.shape[:2], criteria=criteria, flags=flags)

其中，object_points是棋盘格的三维坐标。

8. 输出结果：

print("左相机内参矩阵：\n", camera_matrix1)
print("左相机畸变系数：\n", dist_coeffs1)
print("右相机内参矩阵：\n", camera_matrix2)
print("右相机畸变系数：\n", dist_coeffs2)
print("旋转矩阵：\n", R)
print("平移向量：\n", T)
print("本质矩阵：\n", E)
print("基础矩阵：\n", F)