#ifndef HANDEYECALIB_LIBRARY
#define HANDEYECALIB_LIBRARY
#endif

#include "HandEyeCalib.h"
#include "VrError.h"
#include "VrLog.h"
#include <Eigen/Dense>
#include <Eigen/SVD>
#include <cmath>

HandEyeCalib::HandEyeCalib()
{
}

HandEyeCalib::~HandEyeCalib()
{
}

int HandEyeCalib::CalculateRT(
    const std::vector<HECPoint3D>& eyePoints,
    const std::vector<HECPoint3D>& robotPoints,
    HECCalibResult& result)
{
    // 检查输入参数
    if (eyePoints.size() != robotPoints.size()) {
        return ERR_CODE(APP_ERR_PARAM);
    }

    int N = static_cast<int>(eyePoints.size());
    if (N < 3) {
        return ERR_CODE(APP_ERR_PARAM);  // 至少需要3个点
    }

    // 【1】计算质心
    HECPoint3D p1, p2;
    for (int i = 0; i < N; i++) {
        p1 += eyePoints[i];
        p2 += robotPoints[i];
    }
    p1 = p1 / static_cast<double>(N);
    p2 = p2 / static_cast<double>(N);
    result.centerEye = p1;
    result.centerRobot = p2;

    // 【2】计算去中心坐标
    std::vector<HECPoint3D> q1(N), q2(N);
    for (int i = 0; i < N; i++) {
        q1[i] = eyePoints[i] - p1;
        q2[i] = robotPoints[i] - p2;
    }

    // 【3】计算协方差矩阵 W = sum(q1 * q2^T)
    Eigen::Matrix3d W = Eigen::Matrix3d::Zero();
    for (int i = 0; i < N; i++) {
        Eigen::Vector3d v1(q1[i].x, q1[i].y, q1[i].z);
        Eigen::Vector3d v2(q2[i].x, q2[i].y, q2[i].z);
        W += v1 * v2.transpose();
    }

    // 【4】对W进行SVD分解
    Eigen::JacobiSVD<Eigen::Matrix3d> svd(W, Eigen::ComputeFullU | Eigen::ComputeFullV);
    Eigen::Matrix3d U = svd.matrixU();
    Eigen::Matrix3d V = svd.matrixV();

    // 【5】计算旋转矩阵 R = V * M * U^T
    // M用于处理反射情况，确保R是正交旋转矩阵
    double det = (U * V.transpose()).determinant();
    Eigen::Matrix3d M;
    M << 1, 0, 0,
         0, 1, 0,
         0, 0, det;

    Eigen::Matrix3d R_eigen = V * M * U.transpose();

    // 【6】计算平移向量 T = p2 - R * p1
    Eigen::Vector3d p1_eigen(p1.x, p1.y, p1.z);
    Eigen::Vector3d p2_eigen(p2.x, p2.y, p2.z);
    Eigen::Vector3d T_eigen = p2_eigen - R_eigen * p1_eigen;

    // 【7】转换为输出格式
    for (int i = 0; i < 3; ++i) {
        for (int j = 0; j < 3; ++j) {
            result.R.at(i, j) = R_eigen(i, j);
        }
    }
    result.T = HECTranslationVector(T_eigen(0), T_eigen(1), T_eigen(2));

    // 【8】计算标定误差
    result.error = CalculateError(eyePoints, robotPoints, result);

    return SUCCESS;
}

int HandEyeCalib::CalculateRT(
    const std::vector<HECCalibPointPair>& pointPairs,
    HECCalibResult& result)
{
    std::vector<HECPoint3D> eyePoints;
    std::vector<HECPoint3D> robotPoints;

    eyePoints.reserve(pointPairs.size());
    robotPoints.reserve(pointPairs.size());

    for (const auto& pair : pointPairs) {
        eyePoints.push_back(pair.eyePoint);
        robotPoints.push_back(pair.robotPoint);
    }

    return CalculateRT(eyePoints, robotPoints, result);
}

void HandEyeCalib::TransformPoint(
    const HECRotationMatrix& R,
    const HECTranslationVector& T,
    const HECPoint3D& srcPoint,
    HECPoint3D& dstPoint)
{
    // 使用Eigen进行计算
    Eigen::Matrix3d R_eigen;
    for (int i = 0; i < 3; ++i) {
        for (int j = 0; j < 3; ++j) {
            R_eigen(i, j) = R.at(i, j);
        }
    }
    Eigen::Vector3d T_eigen(T.at(0), T.at(1), T.at(2));
    Eigen::Vector3d src_eigen(srcPoint.x, srcPoint.y, srcPoint.z);

    Eigen::Vector3d dst_eigen = R_eigen * src_eigen + T_eigen;

    dstPoint.x = dst_eigen(0);
    dstPoint.y = dst_eigen(1);
    dstPoint.z = dst_eigen(2);
}

void HandEyeCalib::RotatePoint(
    const HECRotationMatrix& R,
    const HECPoint3D& srcPoint,
    HECPoint3D& dstPoint)
{
    // 使用Eigen进行计算
    Eigen::Matrix3d R_eigen;
    for (int i = 0; i < 3; ++i) {
        for (int j = 0; j < 3; ++j) {
            R_eigen(i, j) = R.at(i, j);
        }
    }
    Eigen::Vector3d src_eigen(srcPoint.x, srcPoint.y, srcPoint.z);

    Eigen::Vector3d dst_eigen = R_eigen * src_eigen;

    dstPoint.x = dst_eigen(0);
    dstPoint.y = dst_eigen(1);
    dstPoint.z = dst_eigen(2);
}

void HandEyeCalib::RotationMatrixToEuler(
    const HECRotationMatrix& R,
    HECEulerOrder order,
    HECEulerAngles& angles)
{
    // 将 HECRotationMatrix 转换为 Eigen::Matrix3d
    Eigen::Matrix3d R_eigen;
    for (int i = 0; i < 3; ++i)
        for (int j = 0; j < 3; ++j)
            R_eigen(i, j) = R.at(i, j);

    // 外旋 ABC = 内旋 CBA，使用 Eigen 的 eulerAngles（内旋模式）
    Eigen::Vector3d ea;
    switch (order) {
    case HECEulerOrder::XYZ:
        // 外旋 XYZ = 内旋 ZYX
        ea = R_eigen.eulerAngles(2, 1, 0);  // 返回 [yaw, pitch, roll]
        angles.yaw = ea[0]; angles.pitch = ea[1]; angles.roll = ea[2];
        break;
    case HECEulerOrder::XZY:
        // 外旋 XZY = 内旋 YZX
        ea = R_eigen.eulerAngles(1, 2, 0);  // 返回 [pitch, yaw, roll]
        angles.pitch = ea[0]; angles.yaw = ea[1]; angles.roll = ea[2];
        break;
    case HECEulerOrder::YXZ:
        // 外旋 YXZ = 内旋 ZXY
        ea = R_eigen.eulerAngles(2, 0, 1);  // 返回 [yaw, roll, pitch]
        angles.yaw = ea[0]; angles.roll = ea[1]; angles.pitch = ea[2];
        break;
    case HECEulerOrder::YZX:
        // 外旋 YZX = 内旋 XZY
        ea = R_eigen.eulerAngles(0, 2, 1);  // 返回 [roll, yaw, pitch]
        angles.roll = ea[0]; angles.yaw = ea[1]; angles.pitch = ea[2];
        break;
    case HECEulerOrder::ZXY:
        // 外旋 ZXY = 内旋 YXZ
        ea = R_eigen.eulerAngles(1, 0, 2);  // 返回 [pitch, roll, yaw]
        angles.pitch = ea[0]; angles.roll = ea[1]; angles.yaw = ea[2];
        break;
    case HECEulerOrder::ZYX:
        // 外旋 ZYX = 内旋 XYZ
        ea = R_eigen.eulerAngles(0, 1, 2);  // 返回 [roll, pitch, yaw]
        angles.roll = ea[0]; angles.pitch = ea[1]; angles.yaw = ea[2];
        break;
    }
}

void HandEyeCalib::EulerToRotationMatrix(
    const HECEulerAngles& angles,
    HECEulerOrder order,
    HECRotationMatrix& R)
{
    double roll  = angles.roll;   // 绕X轴
    double pitch = angles.pitch;  // 绕Y轴
    double yaw   = angles.yaw;   // 绕Z轴

    // 使用 Eigen::AngleAxisd 构造各轴旋转矩阵
    Eigen::Matrix3d Rx = Eigen::AngleAxisd(roll,  Eigen::Vector3d::UnitX()).toRotationMatrix();
    Eigen::Matrix3d Ry = Eigen::AngleAxisd(pitch, Eigen::Vector3d::UnitY()).toRotationMatrix();
    Eigen::Matrix3d Rz = Eigen::AngleAxisd(yaw,   Eigen::Vector3d::UnitZ()).toRotationMatrix();

    // 外旋 ABC：矩阵公式为 R = Rc · Rb · Ra（右乘，反向）
    Eigen::Matrix3d R_eigen;
    switch (order) {
    case HECEulerOrder::XYZ: R_eigen = Rz * Ry * Rx; break;  // 外旋 XYZ
    case HECEulerOrder::XZY: R_eigen = Ry * Rz * Rx; break;  // 外旋 XZY
    case HECEulerOrder::YXZ: R_eigen = Rz * Rx * Ry; break;  // 外旋 YXZ
    case HECEulerOrder::YZX: R_eigen = Rx * Rz * Ry; break;  // 外旋 YZX
    case HECEulerOrder::ZXY: R_eigen = Ry * Rx * Rz; break;  // 外旋 ZXY
    case HECEulerOrder::ZYX: R_eigen = Rx * Ry * Rz; break;  // 外旋 ZYX
    }

    // 转换回 HECRotationMatrix
    for (int i = 0; i < 3; ++i)
        for (int j = 0; j < 3; ++j)
            R.at(i, j) = R_eigen(i, j);
}

bool HandEyeCalib::TransformPose(
    const HECCalibResult& calibResult,
    const HECPoint3D& eyeCenter,
    const HECPoint3D& longAxisDir,
    const HECPoint3D& normalDir,
    int dirVectorInvert,
    HECEulerOrder eulerOrder,
    HECLongAxisDir longAxisMapping,
    HECPoseResult& poseResult)
{
    auto cross = [](const HECPoint3D& a, const HECPoint3D& b) {
        return HECPoint3D(a.y*b.z - a.z*b.y, a.z*b.x - a.x*b.z, a.x*b.y - a.y*b.x);
    };
    auto dot = [](const HECPoint3D& a, const HECPoint3D& b) {
        return a.x*b.x + a.y*b.y + a.z*b.z;
    };

    HECPoint3D xAxis, yAxis, zAxis;

    if (longAxisMapping == HECLongAxisDir::AxisY) {
        // Long axis maps to Y: Y = longAxisDir, Z = normalDir, X = cross(Y, Z)
        yAxis = longAxisDir.normalized();
        zAxis = normalDir.normalized();
        if (yAxis.norm() < 1e-6 || zAxis.norm() < 1e-6) return false;

        zAxis = (zAxis - yAxis * dot(yAxis, zAxis)).normalized();
        if (zAxis.norm() < 1e-6) return false;

        xAxis = cross(yAxis, zAxis).normalized();
        if (xAxis.norm() < 1e-6) return false;

        zAxis = cross(xAxis, yAxis).normalized();
        if (zAxis.norm() < 1e-6) return false;
    } else {
        // Long axis maps to X (default): X = longAxisDir, Z = normalDir, Y = cross(Z, X)
        xAxis = longAxisDir.normalized();
        zAxis = normalDir.normalized();
        if (xAxis.norm() < 1e-6 || zAxis.norm() < 1e-6) return false;

        zAxis = (zAxis - xAxis * dot(xAxis, zAxis)).normalized();
        if (zAxis.norm() < 1e-6) return false;

        yAxis = cross(zAxis, xAxis).normalized();
        if (yAxis.norm() < 1e-6) return false;

        zAxis = cross(xAxis, yAxis).normalized();
        if (zAxis.norm() < 1e-6) return false;
    }

    // Compute eye-frame Euler angles before flip
    HECRotationMatrix eyeR;
    eyeR.at(0, 0) = xAxis.x; eyeR.at(0, 1) = yAxis.x; eyeR.at(0, 2) = zAxis.x;
    eyeR.at(1, 0) = xAxis.y; eyeR.at(1, 1) = yAxis.y; eyeR.at(1, 2) = zAxis.y;
    eyeR.at(2, 0) = xAxis.z; eyeR.at(2, 1) = yAxis.z; eyeR.at(2, 2) = zAxis.z;
    HECEulerAngles eyeEuler;
    RotationMatrixToEuler(eyeR, eulerOrder, eyeEuler);
    double eyeRollDeg, eyePitchDeg, eyeYawDeg;
    eyeEuler.toDegrees(eyeRollDeg, eyePitchDeg, eyeYawDeg);
    LOG_INFO("[HandEyeCalib] Eye Frame (longAxisMapping=%d) Euler (deg): Roll=%.6f, Pitch=%.6f, Yaw=%.6f\n",
             static_cast<int>(longAxisMapping), eyeRollDeg, eyePitchDeg, eyeYawDeg);

    std::vector<HECPoint3D> dirVectors = { xAxis, yAxis, zAxis };

    // Apply axis flip
    switch (dirVectorInvert) {
    case 1:
        dirVectors[0] = dirVectors[0] * (-1.0);
        dirVectors[1] = dirVectors[1] * (-1.0);
        break;
    case 2:
        dirVectors[0] = dirVectors[0] * (-1.0);
        dirVectors[2] = dirVectors[2] * (-1.0);
        break;
    case 3:
        dirVectors[1] = dirVectors[1] * (-1.0);
        dirVectors[2] = dirVectors[2] * (-1.0);
        break;
    default:
        break;
    }

    // Transform position
    TransformPoint(calibResult.R, calibResult.T, eyeCenter, poseResult.position);

    // Rotate direction vectors and build rotation matrix
    std::vector<HECPoint3D> robotDirs(3);
    for (int i = 0; i < 3; ++i) {
        RotatePoint(calibResult.R, dirVectors[i], robotDirs[i]);
    }

    HECRotationMatrix R_pose;
    R_pose.at(0, 0) = robotDirs[0].x; R_pose.at(0, 1) = robotDirs[1].x; R_pose.at(0, 2) = robotDirs[2].x;
    R_pose.at(1, 0) = robotDirs[0].y; R_pose.at(1, 1) = robotDirs[1].y; R_pose.at(1, 2) = robotDirs[2].y;
    R_pose.at(2, 0) = robotDirs[0].z; R_pose.at(2, 1) = robotDirs[1].z; R_pose.at(2, 2) = robotDirs[2].z;

    // Decompose to Euler angles (radians), then convert to degrees
    HECEulerAngles euler;
    RotationMatrixToEuler(R_pose, eulerOrder, euler);
    double rollDeg, pitchDeg, yawDeg;
    euler.toDegrees(rollDeg, pitchDeg, yawDeg);
    poseResult.angles = HECEulerAngles::fromDegrees(rollDeg, pitchDeg, yawDeg);

    return true;
}

double HandEyeCalib::CalculateError(
    const std::vector<HECPoint3D>& eyePoints,
    const std::vector<HECPoint3D>& robotPoints,
    const HECCalibResult& calibResult)
{
    if (eyePoints.size() != robotPoints.size() || eyePoints.empty()) {
        return -1.0;
    }

    double totalError = 0.0;
    double maxErr = 0.0;
    double minErr = std::numeric_limits<double>::max();
    int N = static_cast<int>(eyePoints.size());

    for (int i = 0; i < N; i++) {
        HECPoint3D transformedPoint;
        TransformPoint(calibResult.R, calibResult.T, eyePoints[i], transformedPoint);

        HECPoint3D diff = transformedPoint - robotPoints[i];
        double error = diff.norm();
        totalError += error;

        if (error > maxErr) maxErr = error;
        if (error < minErr) minErr = error;
    }

    // 更新 calibResult 中的误差信息（需要强制转换为非 const）
    HECCalibResult& result = const_cast<HECCalibResult&>(calibResult);
    result.maxError = maxErr;
    result.minError = minErr;

    return totalError / N;
}

int HandEyeCalib::CalculateEyeInHand(
    const std::vector<HECEyeInHandData>& calibData,
    HECCalibResult& result)
{
    int N = static_cast<int>(calibData.size());
    if (N < 3) {
        return ERR_CODE(APP_ERR_PARAM);
    }

    // ================================================================
    // 预处理：提取末端位姿和相机观测点
    // ================================================================
    // 眼在手上：相机安装在机器人末端，标定靶固定
    // 约束：T_end_i * X * P_cam_i = P_base（对所有 i，P_base 相同）
    // 其中 X = [R_x | t_x] 是相机到末端的变换（待求）

    std::vector<Eigen::Matrix3d> R_ends(N);
    std::vector<Eigen::Vector3d> t_ends(N);
    std::vector<Eigen::Vector3d> p_cams(N);

    for (int i = 0; i < N; i++) {
        Eigen::Matrix4d T = Eigen::Matrix4d::Identity();
        for (int r = 0; r < 4; r++)
            for (int c = 0; c < 4; c++)
                T(r, c) = calibData[i].endPose.at(r, c);
        R_ends[i] = T.block<3, 3>(0, 0);
        t_ends[i] = T.block<3, 1>(0, 3);
        p_cams[i] = Eigen::Vector3d(
            calibData[i].targetInCamera.x,
            calibData[i].targetInCamera.y,
            calibData[i].targetInCamera.z);
    }

    int numPairs = N * (N - 1) / 2;

    // ================================================================
    // 第一阶段：线性初始化
    // ================================================================
    // 对两组 (i, j)：
    //   R_i*(R_x*p_i + t_x) + t_i = R_j*(R_x*p_j + t_x) + t_j
    // 展开：
    //   (R_i-R_j)*t_x + R_i*M*p_i - R_j*M*p_j = t_j - t_i
    // 其中 M = R_x，对 [t_x; vec(M)] 线性

    Eigen::MatrixXd C(3 * numPairs, 12);
    Eigen::VectorXd d_vec(3 * numPairs);

    int row = 0;
    for (int i = 0; i < N; i++) {
        for (int j = i + 1; j < N; j++) {
            // (R_i - R_j) * t_x
            C.block<3, 3>(row, 0) = R_ends[i] - R_ends[j];

            // d(R*M*p)_k / dM_{l,m} = R_{k,l} * p_m (row-major vec)
            for (int k = 0; k < 3; k++) {
                for (int l = 0; l < 3; l++) {
                    for (int m = 0; m < 3; m++) {
                        C(row + k, 3 + l * 3 + m) =
                            R_ends[i](k, l) * p_cams[i](m)
                          - R_ends[j](k, l) * p_cams[j](m);
                    }
                }
            }

            d_vec.segment<3>(row) = t_ends[j] - t_ends[i];
            row += 3;
        }
    }

    Eigen::VectorXd x_lin = C.bdcSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(d_vec);

    // 提取平移
    Eigen::Vector3d t_x = x_lin.head<3>();

    // 提取 M 并投影到最近旋转矩阵
    Eigen::Matrix3d M_mat;
    for (int l = 0; l < 3; l++)
        for (int m = 0; m < 3; m++)
            M_mat(l, m) = x_lin(3 + l * 3 + m);

    Eigen::JacobiSVD<Eigen::Matrix3d> svd_M(M_mat, Eigen::ComputeFullU | Eigen::ComputeFullV);
    Eigen::Matrix3d R_x = svd_M.matrixU() * svd_M.matrixV().transpose();
    if (R_x.determinant() < 0) {
        Eigen::Matrix3d V = svd_M.matrixV();
        V.col(2) *= -1;
        R_x = svd_M.matrixU() * V.transpose();
    }

    // 已知旋转后重新线性求解平移
    Eigen::MatrixXd C_t(3 * numPairs, 3);
    Eigen::VectorXd d_t(3 * numPairs);
    row = 0;
    for (int i = 0; i < N; i++) {
        for (int j = i + 1; j < N; j++) {
            C_t.block<3, 3>(row, 0) = R_ends[i] - R_ends[j];
            d_t.segment<3>(row) = t_ends[j] - t_ends[i]
                                 - R_ends[i] * R_x * p_cams[i]
                                 + R_ends[j] * R_x * p_cams[j];
            row += 3;
        }
    }
    t_x = C_t.bdcSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(d_t);

    // ================================================================
    // 第二阶段：Levenberg-Marquardt 非线性优化
    // ================================================================
    // 参数化：角轴 w(3) + 平移 t(3) = 6 参数
    // 残差：r_ij = T_end_i * X * p_i - T_end_j * X * p_j
    // 最小化 sum ||r_ij||^2

    auto skew = [](const Eigen::Vector3d& v) -> Eigen::Matrix3d {
        Eigen::Matrix3d S;
        S <<     0, -v(2),  v(1),
             v(2),     0, -v(0),
            -v(1),  v(0),     0;
        return S;
    };

    auto paramsToRot = [](const Eigen::Vector3d& w) -> Eigen::Matrix3d {
        double theta = w.norm();
        if (theta < 1e-10) return Eigen::Matrix3d::Identity();
        return Eigen::AngleAxisd(theta, w / theta).toRotationMatrix();
    };

    auto computeCost = [&](const Eigen::Matrix3d& R, const Eigen::Vector3d& t) -> double {
        double cost = 0;
        for (int i = 0; i < N; i++) {
            Eigen::Vector3d base_i = R_ends[i] * (R * p_cams[i] + t) + t_ends[i];
            for (int j = i + 1; j < N; j++) {
                Eigen::Vector3d base_j = R_ends[j] * (R * p_cams[j] + t) + t_ends[j];
                cost += (base_i - base_j).squaredNorm();
            }
        }
        return 0.5 * cost;
    };

    // 初始化参数
    Eigen::AngleAxisd aa_init(R_x);
    Eigen::VectorXd params(6);
    if (aa_init.angle() > 1e-10) {
        params.head<3>() = aa_init.angle() * aa_init.axis();
    } else {
        params.head<3>().setZero();
    }
    params.tail<3>() = t_x;

    double lambda = 1e-3;

    for (int iter = 0; iter < 100; iter++) {
        Eigen::Matrix3d R_curr = paramsToRot(params.head<3>());
        Eigen::Vector3d t_curr = params.tail<3>();

        // 残差和 Jacobian
        Eigen::VectorXd residual(3 * numPairs);
        Eigen::MatrixXd J(3 * numPairs, 6);

        row = 0;
        for (int i = 0; i < N; i++) {
            Eigen::Vector3d Rp_i = R_curr * p_cams[i];
            Eigen::Vector3d base_i = R_ends[i] * (Rp_i + t_curr) + t_ends[i];

            for (int j = i + 1; j < N; j++) {
                Eigen::Vector3d Rp_j = R_curr * p_cams[j];
                Eigen::Vector3d base_j = R_ends[j] * (Rp_j + t_curr) + t_ends[j];

                residual.segment<3>(row) = base_i - base_j;

                // dr/dw = -R_i * skew(Rp_i) + R_j * skew(Rp_j)
                J.block<3, 3>(row, 0) = -R_ends[i] * skew(Rp_i) + R_ends[j] * skew(Rp_j);
                // dr/dt = R_i - R_j
                J.block<3, 3>(row, 3) = R_ends[i] - R_ends[j];

                row += 3;
            }
        }

        double cost = 0.5 * residual.squaredNorm();
        Eigen::MatrixXd JtJ = J.transpose() * J;
        Eigen::VectorXd Jtr = J.transpose() * residual;
        Eigen::VectorXd prev_params = params;

        // LM 自适应阻尼更新
        bool accepted = false;
        for (int lm = 0; lm < 8; lm++) {
            Eigen::MatrixXd damped = JtJ;
            damped.diagonal() += lambda * JtJ.diagonal();
            Eigen::VectorXd delta = damped.ldlt().solve(Jtr);
            Eigen::VectorXd trial = prev_params - delta;

            Eigen::Matrix3d R_trial = paramsToRot(trial.head<3>());
            double newCost = computeCost(R_trial, trial.tail<3>());

            if (newCost < cost) {
                params = trial;
                lambda = std::max(lambda * 0.1, 1e-12);
                accepted = true;
                break;
            } else {
                lambda *= 10;
            }
        }

        if (!accepted) break;
        if ((params - prev_params).norm() < 1e-10) break;
    }

    // 提取最终结果
    Eigen::Matrix3d R_final = paramsToRot(params.head<3>());
    Eigen::Vector3d t_final = params.tail<3>();

    for (int i = 0; i < 3; i++)
        for (int j = 0; j < 3; j++)
            result.R.at(i, j) = R_final(i, j);
    result.T = HECTranslationVector(t_final(0), t_final(1), t_final(2));

    // ================================================================
    // 误差计算：各观测映射到基座坐标系的离散程度
    // ================================================================
    Eigen::Matrix4d T_result = Eigen::Matrix4d::Identity();
    T_result.block<3, 3>(0, 0) = R_final;
    T_result.block<3, 1>(0, 3) = t_final;

    std::vector<Eigen::Vector3d> basePoints(N);
    for (int i = 0; i < N; i++) {
        Eigen::Vector4d ph(p_cams[i](0), p_cams[i](1), p_cams[i](2), 1.0);
        Eigen::Matrix4d T_end = Eigen::Matrix4d::Identity();
        for (int r = 0; r < 4; r++)
            for (int c = 0; c < 4; c++)
                T_end(r, c) = calibData[i].endPose.at(r, c);
        basePoints[i] = (T_end * T_result * ph).head<3>();
    }

    Eigen::Vector3d meanBase = Eigen::Vector3d::Zero();
    for (const auto& p : basePoints) meanBase += p;
    meanBase /= N;

    double totalError = 0, maxErr = 0, minErr = std::numeric_limits<double>::max();
    for (const auto& p : basePoints) {
        double err = (p - meanBase).norm();
        totalError += err;
        if (err > maxErr) maxErr = err;
        if (err < minErr) minErr = err;
    }
    result.error = totalError / N;
    result.maxError = maxErr;
    result.minError = minErr;
    result.centerEye = HECPoint3D(0, 0, 0);
    result.centerRobot = HECPoint3D(meanBase(0), meanBase(1), meanBase(2));

    return SUCCESS;
}

Eigen::Matrix3d HandEyeCalib::eulerToRotationMatrix(double rx, double ry, double rz, HECEulerOrder order)
{
    // 将角度转为弧度
    const double deg2rad = M_PI / 180.0;
    double roll  = rx * deg2rad;
    double pitch = ry * deg2rad;
    double yaw   = rz * deg2rad;

    // 复用现有的 EulerToRotationMatrix 逻辑
    HECEulerAngles angles(roll, pitch, yaw);
    HECRotationMatrix R;
    EulerToRotationMatrix(angles, order, R);

    // 转换为 Eigen 矩阵
    Eigen::Matrix3d result;
    for (int i = 0; i < 3; ++i) {
        for (int j = 0; j < 3; ++j) {
            result(i, j) = R.at(i, j);
        }
    }
    return result;
}

HECTCPCalibResult HandEyeCalib::CalculateTCP(const HECTCPCalibData& data)
{
    HECTCPCalibResult result;

    // 参数校验
    int N = static_cast<int>(data.poses.size());
    if (N < 3) {
        result.success = false;
        result.errorMessage = "至少需要3组法兰位姿数据";
        return result;
    }

    // 构建每组位姿的旋转矩阵和位置向量
    std::vector<Eigen::Matrix3d> rotations(N);
    std::vector<Eigen::Vector3d> positions(N);

    for (int i = 0; i < N; ++i) {
        const auto& pose = data.poses[i];
        rotations[i] = eulerToRotationMatrix(pose.rx, pose.ry, pose.rz, pose.eulerOrder);
        positions[i] = Eigen::Vector3d(pose.x, pose.y, pose.z);
    }

    // === 3-DOF 位置求解 ===
    // 约束：R_i * t_tcp + p_i = P_tcp（常数）
    // 对相邻位姿对：(R_i - R_j) * t_tcp = p_j - p_i
    int numPairs = N - 1;
    Eigen::MatrixXd A(3 * numPairs, 3);
    Eigen::VectorXd b(3 * numPairs);

    for (int i = 0; i < numPairs; ++i) {
        Eigen::Matrix3d dR = rotations[i] - rotations[i + 1];
        Eigen::Vector3d dp = positions[i + 1] - positions[i];

        A.block<3, 3>(i * 3, 0) = dR;
        b.segment<3>(i * 3) = dp;
    }

    // SVD 最小二乘求解
    Eigen::JacobiSVD<Eigen::MatrixXd> svd(A, Eigen::ComputeThinU | Eigen::ComputeThinV);

    // 检查奇异值，判断退化情况
    Eigen::VectorXd singularValues = svd.singularValues();
    if (singularValues.size() < 3 || singularValues(2) < 1e-6) {
        result.success = false;
        result.errorMessage = "位姿数据退化，无法求解（可能所有位姿过于相似）";
        return result;
    }

    Eigen::Vector3d t_tcp = svd.solve(b);

    result.tx = t_tcp(0);
    result.ty = t_tcp(1);
    result.tz = t_tcp(2);

    // 计算残差：所有位姿计算 R_i * t_tcp + p_i，应趋近同一点
    Eigen::Vector3d meanTCPPoint = Eigen::Vector3d::Zero();
    for (int i = 0; i < N; ++i) {
        meanTCPPoint += rotations[i] * t_tcp + positions[i];
    }
    meanTCPPoint /= N;

    double totalResidual = 0.0;
    for (int i = 0; i < N; ++i) {
        Eigen::Vector3d tcpPoint = rotations[i] * t_tcp + positions[i];
        totalResidual += (tcpPoint - meanTCPPoint).squaredNorm();
    }
    result.residualError = std::sqrt(totalResidual / N);

    // === 6-DOF 姿态求解 ===
    if (data.mode == HECTCPCalibMode::Full6DOF) {
        int refIdx = data.referencePoseIndex;
        if (refIdx < 0 || refIdx >= N) {
            result.success = false;
            result.errorMessage = "参考位姿索引越界";
            return result;
        }

        // 参考位姿的法兰旋转矩阵
        Eigen::Matrix3d R_ref = rotations[refIdx];

        // 期望的世界坐标系朝向旋转矩阵
        Eigen::Matrix3d R_world = eulerToRotationMatrix(
            data.worldRx, data.worldRy, data.worldRz, data.worldEulerOrder);

        // TCP 相对于法兰的旋转：R_tcp = R_ref^T * R_world
        Eigen::Matrix3d R_tcp = R_ref.transpose() * R_world;

        // 将 R_tcp 转换为欧拉角（度）输出
        HECRotationMatrix R_out;
        for (int i = 0; i < 3; ++i) {
            for (int j = 0; j < 3; ++j) {
                R_out.at(i, j) = R_tcp(i, j);
            }
        }

        HECEulerAngles tcpAngles;
        RotationMatrixToEuler(R_out, data.worldEulerOrder, tcpAngles);

        double rxDeg, ryDeg, rzDeg;
        tcpAngles.toDegrees(rxDeg, ryDeg, rzDeg);
        result.rx = rxDeg;
        result.ry = ryDeg;
        result.rz = rzDeg;
    }

    result.success = true;
    return result;
}

int HandEyeCalib::CalculateEyeToHandWithPose(
    const std::vector<HECEyeToHandData>& calibData,
    HECCalibResult& result)
{
    int N = static_cast<int>(calibData.size());
    if (N < 3) {
        return ERR_CODE(APP_ERR_PARAM);
    }

    // Eye-To-Hand 标定（Park 方法）
    // 相机固定，标定板在末端移动
    //
    // 对于每个位姿 i：
    //   T_board_in_cam_i = T_cam_to_base^{-1} * T_base_to_end_i * T_end_to_board
    //
    // 其中 T_end_to_board 是标定板相对末端的固定变换（未知）
    //
    // 对于两个位姿 i 和 j：
    //   T_board_i = T_cam_to_base^{-1} * T_end_i * T_end_to_board
    //   T_board_j = T_cam_to_base^{-1} * T_end_j * T_end_to_board
    //
    // 消去 T_end_to_board：
    //   T_board_i * T_board_j^{-1} = T_cam_to_base^{-1} * T_end_i * T_end_j^{-1} * T_cam_to_base
    //
    // 即 XA = BX 形式（注意顺序！）：
    //   A = T_end_i * T_end_j^{-1} (末端位姿的相对变换)
    //   B = T_board_i * T_board_j^{-1} (标定板在相机坐标系的相对变换)
    //   X = T_cam_to_base^{-1} (基座到相机的变换)
    //
    // 转换为 AX = XB 形式：X^{-1}A = BX^{-1}
    // 令 Y = X^{-1} = T_cam_to_base，则：YA = BY
    // 即 A^{-1}Y = YB^{-1}，或 AY = YB (取逆)

    // 构建相对变换（使用所有唯一位姿对，提供更多约束）
    std::vector<Eigen::Matrix3d> A_rot, B_rot;
    std::vector<Eigen::Vector3d> A_trans, B_trans;

    for (int i = 0; i < N; i++) {
        for (int j = i + 1; j < N; j++) {
            // 获取末端位姿 T_end_i 和 T_end_j
            Eigen::Matrix4d T_end_i = Eigen::Matrix4d::Identity();
            Eigen::Matrix4d T_end_j = Eigen::Matrix4d::Identity();

            for (int r = 0; r < 4; r++) {
                for (int c = 0; c < 4; c++) {
                    T_end_i(r, c) = calibData[i].endPose.at(r, c);
                    T_end_j(r, c) = calibData[j].endPose.at(r, c);
                }
            }

            // A = T_end_i * T_end_j^{-1}
            Eigen::Matrix4d A = T_end_i * T_end_j.inverse();

            // 获取标定板在相机坐标系的位姿
            Eigen::Matrix4d T_board_i = Eigen::Matrix4d::Identity();
            Eigen::Matrix4d T_board_j = Eigen::Matrix4d::Identity();

            for (int r = 0; r < 4; r++) {
                for (int c = 0; c < 4; c++) {
                    T_board_i(r, c) = calibData[i].boardInCamera.at(r, c);
                    T_board_j(r, c) = calibData[j].boardInCamera.at(r, c);
                }
            }

            // B = T_board_i * T_board_j^{-1}
            Eigen::Matrix4d B = T_board_i * T_board_j.inverse();

            A_rot.push_back(A.block<3, 3>(0, 0));
            A_trans.push_back(A.block<3, 1>(0, 3));
            B_rot.push_back(B.block<3, 3>(0, 0));
            B_trans.push_back(B.block<3, 1>(0, 3));
        }
    }

    // 使用 Tsai-Lenz 方法求解旋转矩阵
    // AY = YB 的旋转部分：R_A * R_Y = R_Y * R_B
    // 展开为 Kronecker 积：(I ⊗ R_A - R_B^T ⊗ I) * vec(R_Y) = 0
    int numPairs = static_cast<int>(A_rot.size());
    Eigen::MatrixXd M(9 * numPairs, 9);
    M.setZero();

    for (int i = 0; i < numPairs; i++) {
        Eigen::Matrix3d Ai = A_rot[i];
        Eigen::Matrix3d Bi = B_rot[i];

        for (int r = 0; r < 3; r++) {
            for (int c = 0; c < 3; c++) {
                int row = i * 9 + r * 3 + c;
                for (int k = 0; k < 3; k++) {
                    M(row, k * 3 + c) += Ai(r, k);  // R_A * R_Y 项
                    M(row, r * 3 + k) -= Bi(k, c);  // R_Y * R_B 项
                }
            }
        }
    }

    // SVD 求解
    Eigen::JacobiSVD<Eigen::MatrixXd> svd(M, Eigen::ComputeFullV);
    Eigen::VectorXd x = svd.matrixV().col(8);

    // 重构旋转矩阵
    Eigen::Matrix3d R_raw;
    R_raw << x(0), x(1), x(2),
             x(3), x(4), x(5),
             x(6), x(7), x(8);

    // 正交化旋转矩阵
    Eigen::JacobiSVD<Eigen::Matrix3d> svd_R(R_raw, Eigen::ComputeFullU | Eigen::ComputeFullV);
    Eigen::Matrix3d R_cam = svd_R.matrixU() * svd_R.matrixV().transpose();

    // 确保行列式为1
    if (R_cam.determinant() < 0) {
        R_cam = -R_cam;
    }

    // 求解平移向量
    // (R_A - I) * t_Y = R_Y * t_B - t_A
    Eigen::MatrixXd C(3 * numPairs, 3);
    Eigen::VectorXd d(3 * numPairs);

    for (int i = 0; i < numPairs; i++) {
        C.block<3, 3>(i * 3, 0) = A_rot[i] - Eigen::Matrix3d::Identity();
        d.segment<3>(i * 3) = R_cam * B_trans[i] - A_trans[i];
    }

    // 最小二乘求解
    Eigen::Vector3d t_cam = C.bdcSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(d);

    // 输出结果
    for (int i = 0; i < 3; i++) {
        for (int j = 0; j < 3; j++) {
            result.R.at(i, j) = R_cam(i, j);
        }
    }
    result.T = HECTranslationVector(t_cam(0), t_cam(1), t_cam(2));

    // 计算误差
    double totalError = 0.0;
    double maxErr = 0.0;
    double minErr = std::numeric_limits<double>::max();
    Eigen::Matrix4d T_result = Eigen::Matrix4d::Identity();
    T_result.block<3, 3>(0, 0) = R_cam;
    T_result.block<3, 1>(0, 3) = t_cam;

    // 计算标定板相对末端的位置（应该一致）
    std::vector<Eigen::Vector3d> boardInEndPoints;
    for (int i = 0; i < N; i++) {
        Eigen::Matrix4d T_end = Eigen::Matrix4d::Identity();
        Eigen::Matrix4d T_board = Eigen::Matrix4d::Identity();

        for (int r = 0; r < 4; r++) {
            for (int c = 0; c < 4; c++) {
                T_end(r, c) = calibData[i].endPose.at(r, c);
                T_board(r, c) = calibData[i].boardInCamera.at(r, c);
            }
        }

        // T_board_in_end = T_end^{-1} * T_cam_to_base * T_board_in_cam
        Eigen::Matrix4d T_board_in_end = T_end.inverse() * T_result * T_board;
        boardInEndPoints.push_back(T_board_in_end.block<3, 1>(0, 3));
    }

    // 计算标定板在末端坐标系下位置的离散程度作为误差
    Eigen::Vector3d meanBoardInEnd = Eigen::Vector3d::Zero();
    for (const auto& p : boardInEndPoints) {
        meanBoardInEnd += p;
    }
    meanBoardInEnd /= N;

    for (const auto& p : boardInEndPoints) {
        double error = (p - meanBoardInEnd).norm();
        totalError += error;
        if (error > maxErr) maxErr = error;
        if (error < minErr) minErr = error;
    }
    result.error = totalError / N;
    result.maxError = maxErr;
    result.minError = minErr;

    result.centerEye = HECPoint3D(0, 0, 0);
    result.centerRobot = HECPoint3D(meanBoardInEnd(0), meanBoardInEnd(1), meanBoardInEnd(2));

    return SUCCESS;
}

// 导出函数实现
IHandEyeCalib* CreateHandEyeCalibInstance()
{
    return new HandEyeCalib();
}

void DestroyHandEyeCalibInstance(IHandEyeCalib* instance)
{
    delete instance;
}