c++手眼标定opencv代码

下面是基于OpenCV实现手眼标定的C++代码示例：

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    // 读取标定板图像
    vector<Mat> boardImages;
    for (int i = 1; i <= 20; i++) {
        string filename = "board" + to_string(i) + ".jpg";
        Mat img = imread(filename);
        boardImages.push_back(img);
    }

    // 棋盘格参数
    int boardWidth = 9;  // 棋盘格宽度
    int boardHeight = 6;  // 棋盘格高度
    float squareSize = 25.0f;  // 棋盘格大小（毫米）

    // 棋盘格角点坐标
    vector<vector<Point3f>> objectPoints;
    for (int i = 0; i < boardImages.size(); i++) {
        vector<Point3f> obj;
        for (int j = 0; j < boardHeight; j++) {
            for (int k = 0; k < boardWidth; k++) {
                obj.push_back(Point3f(j * squareSize, k * squareSize, 0));
            }
        }
        objectPoints.push_back(obj);
    }

    // 计算棋盘格角点
    vector<vector<Point2f>> imagePoints;
    for (int i = 0; i < boardImages.size(); i++) {
        vector<Point2f> corners;
        bool found = findChessboardCorners(boardImages[i], Size(boardWidth, boardHeight), corners);
        if (found) {
            Mat gray;
            cvtColor(boardImages[i], gray, COLOR_BGR2GRAY);
            cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.1));
            imagePoints.push_back(corners);
        }
    }

    // 相机标定
    Mat cameraMatrix, distCoeffs;
    vector<Mat> rvecs, tvecs;
    calibrateCamera(objectPoints, imagePoints, boardImages[0].size(), cameraMatrix, distCoeffs, rvecs, tvecs);

    // 手眼标定
    Mat R, T;
    solveHandEye(objectPoints, imagePoints, rvecs, tvecs, R, T);

    // 输出结果
    cout << "Camera matrix:" << endl << cameraMatrix << endl;
    cout << "Distortion coefficients:" << endl << distCoeffs << endl;
    cout << "Hand-eye calibration matrix:" << endl << R << endl << T << endl;

    return 0;
}

在这个示例中，我们首先读取了20张棋盘格图像，然后定义了棋盘格的参数和角点坐标，并通过`findChessboardCorners`和`cornerSubPix`函数计算出棋盘格的角点。接着，我们使用`calibrateCamera`函数对相机进行标定，得到相机矩阵和畸变系数。最后，我们使用`solveHandEye`函数对机器人末端执行器和基座之间的转换矩阵进行计算，并输出结果。

需要注意的是，这个示例仅供参考，具体的实现还需要根据实际情况进行调整和优化。