双目摄像机标定矫正的python代码

双目摄像机标定矫正是一个用于将两个摄像机的图像对准以进行三维重建的过程。Python中常用的双目摄像机标定矫正代码库是OpenCV，以下是一个简单的双目摄像机标定和矫正的Python代码示例：

import cv2

# 设置左右摄像机ID
left_cam_id = 0
right_cam_id = 1

# 设置棋盘格尺寸
board_size = (9, 6)

# 设置棋盘格物理尺寸（单位：mm）
square_size = 30

# 设置图像路径
left_path = 'left.jpg'
right_path = 'right.jpg'

# 初始化左右摄像机
left_cam = cv2.VideoCapture(left_cam_id)
right_cam = cv2.VideoCapture(right_cam_id)

# 设置棋盘格角点的三维坐标
obj_points = []
for i in range(board_size * board_size):
    obj_points.append((i // board_size * square_size, i % board_size * square_size, 0))

# 初始化图像点和对象点数组
obj_points_list = []
img_points_left_list = []
img_points_right_list = []

while True:
    # 捕获图像
    ret_l, img_left = left_cam.read()
    ret_r, img_right = right_cam.read()

    # 检测棋盘格角点
    ret_l, corners_left = cv2.findChessboardCorners(img_left, board_size)
    ret_r, corners_right = cv2.findChessboardCorners(img_right, board_size)

    if ret_l and ret_r:
        # 绘制角点
        cv2.drawChessboardCorners(img_left, board_size, corners_left, ret_l)
        cv2.drawChessboardCorners(img_right, board_size, corners_right, ret_r)

        # 添加对象点和图像点到数组中
        obj_points_list.append(obj_points)
        img_points_left_list.append(corners_left)
        img_points_right_list.append(corners_right)

        # 显示图像
        cv2.imshow('Left', img_left)
        cv2.imshow('Right', img_right)

    # 按下 ESC 键退出循环
    if cv2.waitKey(1) == 27:
        break

# 关闭摄像机
left_cam.release()
right_cam.release()

# 计算标定参数
ret_l, mtx_l, dist_l, rvecs_l, tvecs_l = cv2.calibrateCamera(obj_points_list, img_points_left_list, img_left.shape[::-1][1:], None, None)
ret_r, mtx_r, dist_r, rvecs_r, tvecs_r = cv2.calibrateCamera(obj_points_list, img_points_right_list, img_right.shape[::-1][1:], None, None)
ret_s, mtx_l, dist_l, mtx_r, dist_r, R, T, E, F = cv2.stereoCalibrate(obj_points_list, img_points_left_list, img_points_right_list,
                                                                      mtx_l, dist_l, mtx_r, dist_r,
                                                                      img_left.shape[::-1][1:], criteria=criteria)

# 计算矫正参数
R1, R2, P1, P2, Q, valid_roi_left, valid_roi_right = cv2.stereoRectify(mtx_l, dist_l, mtx_r, dist_r,
                                                                        img_left.shape[::-1][1:], R, T,
                                                                        alpha=0)
# 生成映射表
mapx_l, mapy_l = cv2.initUndistortRectifyMap(mtx_l, dist_l, R1, P1,
                                             img_left.shape[::-1][1:], cv2.CV_32FC1)
mapx_r, mapy_r = cv2.initUndistortRectifyMap(mtx_r, dist_r, R2, P2,
                                             img_right.shape[::-1][1:], cv2.CV_32FC1)

# 加载图像
img_left = cv2.imread(left_path)
img_right = cv2.imread(right_path)

# 根据映射表矫正图像
img_rectified_left = cv2.remap(img_left, mapx_l, mapy_l, cv2.INTER_LINEAR)
img_rectified_right = cv2.remap(img_right, mapx_r, mapy_r, cv2.INTER_LINEAR)

# 显示矫正后的图像
cv2.imshow('Left Rectified', img_rectified_left)
cv2.imshow('Right Rectified', img_rectified_right)
cv2.waitKey(0)
cv2.destroyAllWindows()