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#include "SMCalibrationWorker.h"

#include "SMCalibrationParameters.h"

#include "cvtools.h"

#include <QSettings>

#include <ceres/ceres.h>

struct CircleResidual {

  CircleResidual(std::vector<cv::Point3f> _pointsOnArc)

      : pointsOnArc(_pointsOnArc) {}

  template <typename T>

  bool operator()(const T* point, const T* axis, T* residual) const {

    T axisSqNorm = axis[0]*axis[0] + axis[1]*axis[1] + axis[2]*axis[2];

    unsigned int l = pointsOnArc.size();

    std::vector<T> dI(l);

    // note, this is automatically initialized to 0

    T sum(0.0);

    for(unsigned int i=0; i<l; i++){

      cv::Point3d p = pointsOnArc[i];

      //T p[3] = {pointsOnArc[i].x, pointsOnArc[i].y, pointsOnArc[i].z};

      // point to line distance

      T dotProd = (point[0]-p.x)*axis[0] + (point[1]-p.y)*axis[1] + (point[2]-p.z)*axis[2];

      T dIx = point[0] - p.x - (dotProd*axis[0]/axisSqNorm);

      T dIy = point[1] - p.y - (dotProd*axis[1]/axisSqNorm);

      T dIz = point[2] - p.z - (dotProd*axis[2]/axisSqNorm);

      dI[i] = ceres::sqrt(dIx*dIx + dIy*dIy + dIz*dIz);

      sum += dI[i];

    }

    T mean = sum / double(l);

    for(unsigned int i=0; i<l; i++){

        residual[i] = dI[i] - mean;

    }

    return true;

  }

 private:

  // Observations for one checkerboard corner.

  const std::vector<cv::Point3f> pointsOnArc;

};

// Closed form solution to solve for the rotation axis from sets of 3D point coordinates of flat pattern feature points

// Algorithm according to Chen et al., Rotation axis calibration of a turntable using constrained global optimization, Optik 2014

// DTU, 2014, Jakob Wilm

static void rotationAxisCalibration(const std::vector< std::vector<cv::Point3f> > Qcam, const std::vector<cv::Point3f> Qobj, cv::Vec3f &axis, cv::Vec3f &point, float &error){

    // number of frames (points on each arch)

    int l = Qcam.size();

    // number of points in each frame

    size_t mn = Qobj.size();

    assert(mn == Qcam[0].size());

    // construct matrix for axis determination

    cv::Mat M(6, 6, CV_32F, cv::Scalar(0));

    for(int k=0; k<l; k++){

        for(unsigned int idx=0; idx<mn; idx++){

//            float i = Qobj[idx].x+4;

//            float j = Qobj[idx].y+4;

            float i = Qobj[idx].x;

            float j = Qobj[idx].y;

            float x = Qcam[k][idx].x;

            float y = Qcam[k][idx].y;

            float z = Qcam[k][idx].z;

            M += (cv::Mat_<float>(6,6) << x*x, x*y, x*z, x, i*x, j*x,

                                            0, y*y, y*z, y, i*y, j*y,

                                            0,   0, z*z, z, i*z, j*z,

                                            0,   0,   0, 1,   i,   j,

                                            0,   0,   0, 0, i*i, i*j,

                                            0,   0,   0, 0,   0, j*j);

        }

    }

    cv::completeSymm(M, false);

    // solve for axis

    std::vector<float> lambda;

    cv::Mat u;

    cv::eigen(M, lambda, u);

    float minLambda = std::abs(lambda[0]);

    int idx = 0;

    for(unsigned int i=1; i<lambda.size(); i++){

        if(abs(lambda[i]) < minLambda){

            minLambda = lambda[i];

            idx = i;

        }

    }

    axis = u.row(idx).colRange(0, 3);

    axis = cv::normalize(axis);

    float nx = u.at<float>(idx, 0);

    float ny = u.at<float>(idx, 1);

    float nz = u.at<float>(idx, 2);

    //float d  = u.at<float>(idx, 3);

    float dh = u.at<float>(idx, 4);

    float dv = u.at<float>(idx, 5);

//    // Paper version: c is initially eliminated

//    cv::Mat A(l*mn, mn+2, CV_32F, cv::Scalar(0.0));

//    cv::Mat bb(l*mn, 1, CV_32F);

//    for(int k=0; k<l; k++){

//        for(unsigned int idx=0; idx<mn; idx++){

//            float i = Qobj[idx].x;

//            float j = Qobj[idx].y;

//            float x = Qcam[k][idx].x;

//            float y = Qcam[k][idx].y;

//            float z = Qcam[k][idx].z;

//            float f = x*x + y*y + z*z + (2*x*nx + 2*y*ny + 2*z*nz)*(i*dh + j*dv);

//            int row = k*mn+idx;

//            A.at<float>(row, 0) = 2*x - (2*z*nx)/nz;

//            A.at<float>(row, 1) = 2*y - (2*z*ny)/nz;

//            A.at<float>(row, idx+2) = 1.0;

//            bb.at<float>(row, 0) = f + (2*z*d)/nz;

//        }

//    }

//    // solve for point

//    cv::Mat abe;

//    cv::solve(A, bb, abe, cv::DECOMP_SVD);

//    float a = abe.at<float>(0, 0);

//    float b = abe.at<float>(1, 0);

//    float c = -(nx*a+ny*b+d)/nz;

    // Our version: solve simultanously for a,b,c

    cv::Mat A(l*mn, mn+3, CV_32F, cv::Scalar(0.0));

    cv::Mat bb(l*mn, 1, CV_32F);

    for(int k=0; k<l; k++){

        for(unsigned int idx=0; idx<mn; idx++){

            float i = Qobj[idx].x;

            float j = Qobj[idx].y;

            float x = Qcam[k][idx].x;

            float y = Qcam[k][idx].y;

            float z = Qcam[k][idx].z;

            float f = x*x + y*y + z*z + (2*x*nx + 2*y*ny + 2*z*nz)*(i*dh + j*dv);

            int row = k*mn+idx;

            A.at<float>(row, 0) = 2*x;

            A.at<float>(row, 1) = 2*y;

            A.at<float>(row, 2) = 2*z;

            A.at<float>(row, idx+3) = 1.0;

            bb.at<float>(row, 0) = f;

        }

    }

    // solve for point

    cv::Mat abe;

    cv::solve(A, bb, abe, cv::DECOMP_SVD);

    float a = abe.at<float>(0, 0);

    float b = abe.at<float>(1, 0);

    float c = abe.at<float>(2, 0);

    point[0] = a;

    point[1] = b;

    point[2] = c;

    // Non-linear optimization using Ceres

    double pointArray[] = {point[0], point[1], point[2]};

    double axisArray[] = {axis[0], axis[1], axis[2]};

    ceres::Problem problem;

    // loop through saddle points

    for(unsigned int idx=0; idx<mn; idx++){

        std::vector<cv::Point3f> pointsOnArch(l);

        for(int k=0; k<l; k++){

            pointsOnArch[k] = Qcam[k][idx];

        }

        ceres::CostFunction* cost_function =

           new ceres::AutoDiffCostFunction<CircleResidual, ceres::DYNAMIC, 3, 3>(

               new CircleResidual(pointsOnArch), l);

        problem.AddResidualBlock(cost_function, NULL, pointArray, axisArray);

    }

    // Run the solver!

    ceres::Solver::Options options;

    options.linear_solver_type = ceres::DENSE_QR;

    options.minimizer_progress_to_stdout = true;

    ceres::Solver::Summary summary;

    ceres::Solve(options, &problem, &summary);

    std::cout << summary.BriefReport() << "\n";

    point = cv::Vec3f(pointArray[0], pointArray[1], pointArray[2]);

    axis = cv::Vec3f(axisArray[0], axisArray[1], axisArray[2]);

    axis /= cv::norm(axis);

    // Error estimate (sum of squared differences)

    error = 0;

    // loop through saddle points

    for(unsigned int idx=0; idx<mn; idx++){

        // vector of distances from rotation axis

        std::vector<float> dI(l);

        // loop through angular positions

        for(int k=0; k<l; k++){

            cv::Vec3f p = cv::Vec3f(Qcam[k][idx]);

            // point to line distance

            dI[k] = cv::norm((point-p)-(point-p).dot(axis)*axis);

        }

        float sum = std::accumulate(dI.begin(), dI.end(), 0.0);

        float mean = sum / dI.size();

        float sumSqDev = 0;

        for(int k=0; k<l; k++){

            sumSqDev += (dI[k] - mean)*(dI[k] - mean);

        }

        //meanDev /= l;

        //std::cout << meanDev << std::endl;

        error += sumSqDev;

    }

    //error /= mn;

}

void SMCalibrationWorker::performCalibration(std::vector<SMCalibrationSet> calibrationData){

    QSettings settings;

    // Number of saddle points on calibration pattern

    int checkerCountX = settings.value("calibration/patternSizeX", 22).toInt();

    int checkerCountY = settings.value("calibration/patternSizeY", 13).toInt();

    cv::Size checkerCount(checkerCountX, checkerCountY);

    unsigned int nSets = calibrationData.size();

    // 2D Points collected for OpenCV's calibration procedures

    std::vector< std::vector<cv::Point2f> > qc0, qc1;

    std::vector< std::vector<cv::Point2f> > qc0Stereo, qc1Stereo;

    std::vector<bool> success0(nSets), success1(nSets);

    std::vector<float> angles;

    // Loop through calibration sets

    for(unsigned int i=0; i<nSets; i++){

        SMCalibrationSet SMCalibrationSetI = calibrationData[i];

        if(!SMCalibrationSetI.checked)

            continue;

        // Camera 0

        std::vector<cv::Point2f> qci0;

        // Convert to grayscale

        cv::Mat gray;

        if(SMCalibrationSetI.frame0.channels() == 1)

            cv::cvtColor(SMCalibrationSetI.frame0, gray, CV_BayerBG2GRAY);

        else

            cv::cvtColor(SMCalibrationSetI.frame0, gray, CV_RGB2GRAY);

        // Extract checker corners

        success0[i] = cv::findChessboardCorners(gray, checkerCount, qci0, cv::CALIB_CB_ADAPTIVE_THRESH + cv::CALIB_CB_FAST_CHECK);

        if(success0[i]){

            cv::cornerSubPix(gray, qci0, cv::Size(6, 6), cv::Size(1, 1),cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 20, 0.0001));

            // Draw colored chessboard

            cv::Mat color;

            if(SMCalibrationSetI.frame0.channels() == 1)

                cv::cvtColor(SMCalibrationSetI.frame0, color, CV_BayerBG2RGB);

            else

                color = SMCalibrationSetI.frame0.clone();

            cvtools::drawChessboardCorners(color, checkerCount, qci0, success0[i], 10);

            SMCalibrationSetI.frame0Result = color;

        }

        emit newFrameResult(i, 0, success0[i], SMCalibrationSetI.frame0Result);

        // Camera 1

        std::vector<cv::Point2f> qci1;

        // Convert to grayscale

        if(SMCalibrationSetI.frame1.channels() == 1)

            cv::cvtColor(SMCalibrationSetI.frame1, gray, CV_BayerBG2GRAY);

        else

            cv::cvtColor(SMCalibrationSetI.frame1, gray, CV_RGB2GRAY);

        // Extract checker corners

        success1[i] = cv::findChessboardCorners(gray, checkerCount, qci1, cv::CALIB_CB_ADAPTIVE_THRESH + cv::CALIB_CB_FAST_CHECK);

        if(success1[i]){

            cv::cornerSubPix(gray, qci1, cv::Size(6, 6), cv::Size(1, 1),cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 20, 0.0001));

            // Draw colored chessboard

            cv::Mat color;

            if(SMCalibrationSetI.frame1.channels() == 1)

                cv::cvtColor(SMCalibrationSetI.frame1, color, CV_BayerBG2RGB);

            else

                color = SMCalibrationSetI.frame1.clone();

            cvtools::drawChessboardCorners(color, checkerCount, qci1, success1[i], 10);

            SMCalibrationSetI.frame1Result = color;

        }

        emit newFrameResult(i, 1, success1[i], SMCalibrationSetI.frame1Result);

        if(success0[i])

            qc0.push_back(qci0);

        if(success1[i])

            qc1.push_back(qci1);

        if(success0[i] && success1[i]){

            qc0Stereo.push_back(qci0);

            qc1Stereo.push_back(qci1);

            angles.push_back(SMCalibrationSetI.rotationAngle);

        }

        // Show progress

        emit newSetProcessed(i);

    }

    size_t nValidSets = angles.size();

    if(nValidSets < 2){

        std::cerr << "Not enough valid calibration sequences!" << std::endl;

        emit done();

        return;

    }

    // Generate world object coordinates [mm]

    float checkerSize = settings.value("calibration/squareSize", 10.0).toFloat(); // width and height of one checker square in mm

    std::vector<cv::Point3f> Qi;

    for (int h=0; h<checkerCount.height; h++)

        for (int w=0; w<checkerCount.width; w++)

            Qi.push_back(cv::Point3f(checkerSize * w, checkerSize* h, 0.0));

    std::vector< std::vector<cv::Point3f> > Q0, Q1, QStereo;

    for(unsigned int i=0; i<qc0.size(); i++)

        Q0.push_back(Qi);

    for(unsigned int i=0; i<qc1.size(); i++)

        Q1.push_back(Qi);

    for(unsigned int i=0; i<nValidSets; i++)

        QStereo.push_back(Qi);

    // calibrate the cameras

    SMCalibrationParameters cal;

    cal.frameWidth = calibrationData[0].frame0.cols;

    cal.frameHeight = calibrationData[0].frame0.rows;

    cv::Size frameSize(cal.frameWidth, cal.frameHeight);

    // determine only k1, k2 for lens distortion

    int flags = cv::CALIB_FIX_ASPECT_RATIO + cv::CALIB_FIX_K3 + cv::CALIB_ZERO_TANGENT_DIST + cv::CALIB_FIX_PRINCIPAL_POINT;

    // Note: several of the output arguments below must be cv::Mat, otherwise segfault

    std::vector<cv::Mat> cam_rvecs0, cam_tvecs0;

    cal.cam0_error = cv::calibrateCamera(Q0, qc0, frameSize, cal.K0, cal.k0, cam_rvecs0, cam_tvecs0, flags,

                                         cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, DBL_EPSILON));

//std::cout << cal.k0 << std::endl;

//    // refine extrinsics for camera 0

//    for(int i=0; i<Q.size(); i++)

//        cv::solvePnPRansac(Q[i], qc0[i], cal.K0, cal.k0, cam_rvecs0[i], cam_tvecs0[i], true, 100, 0.05, 100, cv::noArray(), CV_ITERATIVE);

    std::vector<cv::Mat> cam_rvecs1, cam_tvecs1;

    cal.cam1_error = cv::calibrateCamera(Q1, qc1, frameSize, cal.K1, cal.k1, cam_rvecs1, cam_tvecs1, flags,

                                         cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, DBL_EPSILON));

//std::cout << cal.k1 << std::endl;

    // stereo calibration

    int flags_stereo = cv::CALIB_FIX_INTRINSIC;// + cv::CALIB_FIX_K2 + cv::CALIB_FIX_K3 + cv::CALIB_ZERO_TANGENT_DIST + cv::CALIB_FIX_PRINCIPAL_POINT + cv::CALIB_FIX_ASPECT_RATIO;

    cv::Mat E, F, R1, T1;

    cal.stereo_error = cv::stereoCalibrate(QStereo, qc0Stereo, qc1Stereo, cal.K0, cal.k0, cal.K1, cal.k1,

                                              frameSize, R1, T1, E, F,

                                              cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 200, DBL_EPSILON),

                                              flags_stereo);

    cal.R1 = R1;

    cal.T1 = T1;

    cal.E = E;

    cal.F = F;

    // Determine per-view reprojection errors:

    cal.cam0_errors_per_view.resize(nSets);

    int idx = 0;

    for(unsigned int i = 0; i < nSets; ++i){

        if(success0[i]){

            int n = (int)Q0[idx].size();

            std::vector<cv::Point2f> qc_proj;

            cv::projectPoints(cv::Mat(Q0[idx]), cam_rvecs0[idx], cam_tvecs0[idx], cal.K0,  cal.k0, qc_proj);

            float err = 0;

            for(unsigned int j=0; j<qc_proj.size(); j++){

                cv::Point2f d = qc0[idx][j] - qc_proj[j];

                err += cv::sqrt(d.x*d.x + d.y*d.y);

            }

            cal.cam0_errors_per_view[i] = (float)err/n;

            idx++;

        } else

            cal.cam0_errors_per_view[i] = NAN;

    }

    cal.cam1_errors_per_view.resize(nSets);

    idx = 0;

    for(unsigned int i = 0; i < nSets; ++i){

        if(success1[i]){

            int n = (int)Q1[idx].size();

            std::vector<cv::Point2f> qc_proj;

            cv::projectPoints(cv::Mat(Q1[idx]), cam_rvecs1[idx], cam_tvecs1[idx], cal.K1,  cal.k1, qc_proj);

            float err = 0;

            for(unsigned int j=0; j<qc_proj.size(); j++){

                cv::Point2f d = qc1[idx][j] - qc_proj[j];

                err += cv::sqrt(d.x*d.x + d.y*d.y);

            }

            cal.cam1_errors_per_view[i] = (float)err/n;

            idx++;

       } else

            cal.cam1_errors_per_view[i] = NAN;

    }

//    // hand-eye calibration

//    std::vector<cv::Matx33f> Rc(nValidSets - 1); // rotations/translations of the checkerboard in camera 0 reference frame

//    std::vector<cv::Vec3f> Tc(nValidSets - 1);

//    std::vector<cv::Matx33f> Rr(nValidSets - 1); // in rotation stage reference frame

//    std::vector<cv::Vec3f> Tr(nValidSets - 1);

//    for(int i=0; i<nValidSets-1; i++){

//        // relative transformations in camera

//        cv::Mat cRw1, cRw2;

//        cv::Rodrigues(cam_rvecs0[i], cRw1);

//        cv::Rodrigues(cam_rvecs0[i+1], cRw2);

//        cv::Mat cTw1 = cam_tvecs0[i];

//        cv::Mat cTw2 = cam_tvecs0[i+1];

//        cv::Mat w1Rc = cRw1.t();

//        cv::Mat w1Tc = -cRw1.t()*cTw1;

//        Rc[i] = cv::Mat(cRw2*w1Rc);

//        Tc[i] = cv::Mat(cRw2*w1Tc+cTw2);

//        // relative transformations in rotation stage

//        // we define the rotation axis to be in origo, pointing in positive y direction

//        float angleRadians = (angles[i+1]-angles[i])/180.0*M_PI;

//        cv::Vec3f rot_rvec(0.0, -angleRadians, 0.0);

//        cv::Mat Rri;

//        cv::Rodrigues(rot_rvec, Rri);

//        Rr[i] = Rri;

//        Tr[i] = 0.0;

////        std::cout << i << std::endl;

////        std::cout << "cTw1" << cTw1 << std::endl;

////        std::cout << "cTw2" << cTw2 << std::endl;

////        std::cout << "w2Rc" << w2Rc << std::endl;

////        std::cout << "w2Tc" << w2Tc << std::endl;

////        std::cout << "w2Rc" << w2Rc << std::endl;

////        std::cout << "w2Tc" << w2Tc << std::endl;

////        cv::Mat Rci;

////        cv::Rodrigues(Rc[i], Rci);

////        std::cout << "Rci" << Rci << std::endl;

////        std::cout << "Tc[i]" << Tc[i] << std::endl;

////        std::cout << "rot_rvec" << rot_rvec << std::endl;

////        std::cout << "Tr[i]" << Tr[i] << std::endl;

////        std::cout << std::endl;

//    }

//    // determine the transformation from rotation stage to camera 0

//    cvtools::handEyeCalibrationTsai(Rc, Tc, Rr, Tr, cal.Rr, cal.Tr);

//    for(int i=0; i<nValidSets-1; i++){

//        std::cout << i << std::endl;

//        cv::Mat Rci;

//        cv::Rodrigues(Rc[i], Rci);

//        std::cout << "Rc[i]" << Rci << std::endl;

//        std::cout << "Tc[i]" << Tc[i] << std::endl;

//        cv::Mat Rri;

//        cv::Rodrigues(Rr[i], Rri);

//        std::cout << "Rr[i]" << Rri << std::endl;

//        std::cout << "Tr[i]" << Tr[i] << std::endl;

//        cv::Mat Rcr = cv::Mat(cal.Rr)*cv::Mat(Rc[i])*cv::Mat(cal.Rr.t());

//        cv::Rodrigues(Rcr, Rcr);

//        cv::Mat Tcr = -cv::Mat(cal.Rr)*cv::Mat(Rc[i])*cv::Mat(cal.Rr.t())*cv::Mat(cal.Tr) + cv::Mat(cal.Rr)*cv::Mat(Tc[i]) + cv::Mat(cal.Tr);

//        std::cout << "Rcr[i]" << Rcr << std::endl;

//        std::cout << "Tcr[i]" << Tcr << std::endl;

//        std::cout << std::endl;

//    }

    // Direct rotation axis calibration //

    // full camera matrices

    cv::Matx34f P0 = cv::Matx34f::eye();

    cv::Mat RT1(3, 4, CV_32F);

    cv::Mat(cal.R1).copyTo(RT1(cv::Range(0, 3), cv::Range(0, 3)));

    cv::Mat(cal.T1).copyTo(RT1(cv::Range(0, 3), cv::Range(3, 4)));

    cv::Matx34f P1 = cv::Matx34f(RT1);

    // calibration points in camera 0 frame

    std::vector< std::vector<cv::Point3f> > Qcam;

    for(unsigned int i=0; i<nValidSets; i++){

        std::vector<cv::Point2f> qc0i, qc1i;

        cv::undistortPoints(qc0Stereo[i], qc0i, cal.K0, cal.k0);

        cv::undistortPoints(qc1Stereo[i], qc1i, cal.K1, cal.k1);

//        qc0i = qc0[i];

//        qc1i = qc1[i];

        cv::Mat Qhom, Qcami;

        cv::triangulatePoints(P0, P1, qc0i, qc1i, Qhom);

        cvtools::convertMatFromHomogeneous(Qhom, Qcami);

        std::vector<cv::Point3f> QcamiPoints;

        cvtools::matToPoints3f(Qcami, QcamiPoints);

        Qcam.push_back(QcamiPoints);

    }

//    cv::Mat QcamMat(Qcam.size(), Qcam[0].size(), CV_32FC3);

//    for(int i=0; i<Qcam.size(); i++)

//        for(int j=0; j<Qcam[0].size(); j++)

//            QcamMat.at<cv::Point3f>(i,j) = Qcam[i][j];

//    cvtools::writeMat(QcamMat, "Qcam.mat", "Qcam");

    cv::Vec3f axis, point;

    float rot_axis_error;

    rotationAxisCalibration(Qcam, Qi, axis, point, rot_axis_error);

    // construct transformation matrix

    cv::Vec3f ex = axis.cross(cv::Vec3f(0,0,1.0));

    ex = cv::normalize(ex);

    cv::Vec3f ez = ex.cross(axis);

    ez = cv::normalize(ez);

    cv::Mat RrMat(3, 3, CV_32F);

    cv::Mat(ex).copyTo(RrMat.col(0));

    cv::Mat(axis).copyTo(RrMat.col(1));

    cv::Mat(ez).copyTo(RrMat.col(2));

    cal.Rr = cv::Matx33f(RrMat).t();

    cal.Tr = -cv::Matx33f(RrMat).t()*point;

    cal.rot_axis_error = rot_axis_error;

    // Print to std::cout

    cal.print();

    // save to (reentrant qsettings object)

    settings.setValue("calibration/parameters", QVariant::fromValue(cal));

    emit done();

}