/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" //#include "upnp.h" #include "dls.h" #include "epnp.h" #include "p3p.h" #include "ap3p.h" #include "ippe.hpp" #include "opencv2/calib3d/calib3d_c.h" #include namespace cv { #if defined _DEBUG || defined CV_STATIC_ANALYSIS static bool isPlanarObjectPoints(InputArray _objectPoints, double threshold) { CV_CheckType(_objectPoints.type(), _objectPoints.type() == CV_32FC3 || _objectPoints.type() == CV_64FC3, "Type of _objectPoints must be CV_32FC3 or CV_64FC3"); Mat objectPoints; if (_objectPoints.type() == CV_32FC3) { _objectPoints.getMat().convertTo(objectPoints, CV_64F); } else { objectPoints = _objectPoints.getMat(); } Scalar meanValues = mean(objectPoints); int nbPts = objectPoints.checkVector(3, CV_64F); Mat objectPointsCentred = objectPoints - meanValues; objectPointsCentred = objectPointsCentred.reshape(1, nbPts); Mat w, u, vt; Mat MM = objectPointsCentred.t() * objectPointsCentred; SVDecomp(MM, w, u, vt); return (w.at(2) < w.at(1) * threshold); } static bool approxEqual(double a, double b, double eps) { return std::fabs(a-b) < eps; } #endif void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs, InputArray rvec, InputArray tvec, float length, int thickness) { CV_INSTRUMENT_REGION(); int type = image.type(); int cn = CV_MAT_CN(type); CV_CheckType(type, cn == 1 || cn == 3 || cn == 4, "Number of channels must be 1, 3 or 4" ); CV_Assert(image.getMat().total() > 0); CV_Assert(length > 0); // project axes points vector axesPoints; axesPoints.push_back(Point3f(0, 0, 0)); axesPoints.push_back(Point3f(length, 0, 0)); axesPoints.push_back(Point3f(0, length, 0)); axesPoints.push_back(Point3f(0, 0, length)); vector imagePoints; projectPoints(axesPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints); // draw axes lines line(image, imagePoints[0], imagePoints[1], Scalar(0, 0, 255), thickness); line(image, imagePoints[0], imagePoints[2], Scalar(0, 255, 0), thickness); line(image, imagePoints[0], imagePoints[3], Scalar(255, 0, 0), thickness); } bool solvePnP( InputArray opoints, InputArray ipoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess, int flags ) { CV_INSTRUMENT_REGION(); vector rvecs, tvecs; int solutions = solvePnPGeneric(opoints, ipoints, cameraMatrix, distCoeffs, rvecs, tvecs, useExtrinsicGuess, (SolvePnPMethod)flags, rvec, tvec); if (solutions > 0) { int rdepth = rvec.empty() ? CV_64F : rvec.depth(); int tdepth = tvec.empty() ? CV_64F : tvec.depth(); rvecs[0].convertTo(rvec, rdepth); tvecs[0].convertTo(tvec, tdepth); } return solutions > 0; } class PnPRansacCallback CV_FINAL : public PointSetRegistrator::Callback { public: PnPRansacCallback(Mat _cameraMatrix=Mat(3,3,CV_64F), Mat _distCoeffs=Mat(4,1,CV_64F), int _flags=SOLVEPNP_ITERATIVE, bool _useExtrinsicGuess=false, Mat _rvec=Mat(), Mat _tvec=Mat() ) : cameraMatrix(_cameraMatrix), distCoeffs(_distCoeffs), flags(_flags), useExtrinsicGuess(_useExtrinsicGuess), rvec(_rvec), tvec(_tvec) {} /* Pre: True */ /* Post: compute _model with given points and return number of found models */ int runKernel( InputArray _m1, InputArray _m2, OutputArray _model ) const CV_OVERRIDE { Mat opoints = _m1.getMat(), ipoints = _m2.getMat(); bool correspondence = solvePnP( _m1, _m2, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess, flags ); Mat _local_model; hconcat(rvec, tvec, _local_model); _local_model.copyTo(_model); return correspondence; } /* Pre: True */ /* Post: fill _err with projection errors */ void computeError( InputArray _m1, InputArray _m2, InputArray _model, OutputArray _err ) const CV_OVERRIDE { Mat opoints = _m1.getMat(), ipoints = _m2.getMat(), model = _model.getMat(); int i, count = opoints.checkVector(3); Mat _rvec = model.col(0); Mat _tvec = model.col(1); Mat projpoints(count, 2, CV_32FC1); projectPoints(opoints, _rvec, _tvec, cameraMatrix, distCoeffs, projpoints); const Point2f* ipoints_ptr = ipoints.ptr(); const Point2f* projpoints_ptr = projpoints.ptr(); _err.create(count, 1, CV_32FC1); float* err = _err.getMat().ptr(); for ( i = 0; i < count; ++i) err[i] = (float)norm( Matx21f(ipoints_ptr[i] - projpoints_ptr[i]), NORM_L2SQR ); } Mat cameraMatrix; Mat distCoeffs; int flags; bool useExtrinsicGuess; Mat rvec; Mat tvec; }; bool solvePnPRansac(InputArray _opoints, InputArray _ipoints, InputArray _cameraMatrix, InputArray _distCoeffs, OutputArray _rvec, OutputArray _tvec, bool useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, OutputArray _inliers, int flags) { CV_INSTRUMENT_REGION(); Mat opoints0 = _opoints.getMat(), ipoints0 = _ipoints.getMat(); Mat opoints, ipoints; if( opoints0.depth() == CV_64F || !opoints0.isContinuous() ) opoints0.convertTo(opoints, CV_32F); else opoints = opoints0; if( ipoints0.depth() == CV_64F || !ipoints0.isContinuous() ) ipoints0.convertTo(ipoints, CV_32F); else ipoints = ipoints0; int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); CV_Assert( npoints >= 4 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) ); CV_Assert(opoints.isContinuous()); CV_Assert(opoints.depth() == CV_32F || opoints.depth() == CV_64F); CV_Assert((opoints.rows == 1 && opoints.channels() == 3) || opoints.cols*opoints.channels() == 3); CV_Assert(ipoints.isContinuous()); CV_Assert(ipoints.depth() == CV_32F || ipoints.depth() == CV_64F); CV_Assert((ipoints.rows == 1 && ipoints.channels() == 2) || ipoints.cols*ipoints.channels() == 2); _rvec.create(3, 1, CV_64FC1); _tvec.create(3, 1, CV_64FC1); Mat rvec = useExtrinsicGuess ? _rvec.getMat() : Mat(3, 1, CV_64FC1); Mat tvec = useExtrinsicGuess ? _tvec.getMat() : Mat(3, 1, CV_64FC1); Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat(); int model_points = 5; int ransac_kernel_method = SOLVEPNP_EPNP; if( flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) { model_points = 4; ransac_kernel_method = flags; } else if( npoints == 4 ) { model_points = 4; ransac_kernel_method = SOLVEPNP_P3P; } if( model_points == npoints ) { opoints = opoints.reshape(3); ipoints = ipoints.reshape(2); bool result = solvePnP(opoints, ipoints, cameraMatrix, distCoeffs, _rvec, _tvec, useExtrinsicGuess, ransac_kernel_method); if(!result) { if( _inliers.needed() ) _inliers.release(); return false; } if(_inliers.needed()) { _inliers.create(npoints, 1, CV_32S); Mat _local_inliers = _inliers.getMat(); for(int i = 0; i < npoints; i++) { _local_inliers.at(i) = i; } } return true; } Ptr cb; // pointer to callback cb = makePtr( cameraMatrix, distCoeffs, ransac_kernel_method, useExtrinsicGuess, rvec, tvec); double param1 = reprojectionError; // reprojection error double param2 = confidence; // confidence int param3 = iterationsCount; // number maximum iterations Mat _local_model(3, 2, CV_64FC1); Mat _mask_local_inliers(1, opoints.rows, CV_8UC1); // call Ransac int result = createRANSACPointSetRegistrator(cb, model_points, param1, param2, param3)->run(opoints, ipoints, _local_model, _mask_local_inliers); if( result <= 0 || _local_model.rows <= 0) { _rvec.assign(rvec); // output rotation vector _tvec.assign(tvec); // output translation vector if( _inliers.needed() ) _inliers.release(); return false; } vector opoints_inliers; vector ipoints_inliers; opoints = opoints.reshape(3); ipoints = ipoints.reshape(2); opoints.convertTo(opoints_inliers, CV_64F); ipoints.convertTo(ipoints_inliers, CV_64F); const uchar* mask = _mask_local_inliers.ptr(); int npoints1 = compressElems(&opoints_inliers[0], mask, 1, npoints); compressElems(&ipoints_inliers[0], mask, 1, npoints); opoints_inliers.resize(npoints1); ipoints_inliers.resize(npoints1); result = solvePnP(opoints_inliers, ipoints_inliers, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess, (flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) ? SOLVEPNP_EPNP : flags) ? 1 : -1; if( result <= 0 ) { _rvec.assign(_local_model.col(0)); // output rotation vector _tvec.assign(_local_model.col(1)); // output translation vector if( _inliers.needed() ) _inliers.release(); return false; } else { _rvec.assign(rvec); // output rotation vector _tvec.assign(tvec); // output translation vector } if(_inliers.needed()) { Mat _local_inliers; for (int i = 0; i < npoints; ++i) { if((int)_mask_local_inliers.at(i) != 0) // inliers mask _local_inliers.push_back(i); // output inliers vector } _local_inliers.copyTo(_inliers); } return true; } int solveP3P( InputArray _opoints, InputArray _ipoints, InputArray _cameraMatrix, InputArray _distCoeffs, OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs, int flags) { CV_INSTRUMENT_REGION(); Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat(); int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); CV_Assert( npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) ); CV_Assert( npoints == 3 || npoints == 4 ); CV_Assert( flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P ); if (opoints.cols == 3) opoints = opoints.reshape(3); if (ipoints.cols == 2) ipoints = ipoints.reshape(2); Mat cameraMatrix0 = _cameraMatrix.getMat(); Mat distCoeffs0 = _distCoeffs.getMat(); Mat cameraMatrix = Mat_(cameraMatrix0); Mat distCoeffs = Mat_(distCoeffs0); Mat undistortedPoints; undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); std::vector Rs, ts, rvecs; int solutions = 0; if (flags == SOLVEPNP_P3P) { p3p P3Psolver(cameraMatrix); solutions = P3Psolver.solve(Rs, ts, opoints, undistortedPoints); } else if (flags == SOLVEPNP_AP3P) { ap3p P3Psolver(cameraMatrix); solutions = P3Psolver.solve(Rs, ts, opoints, undistortedPoints); } if (solutions == 0) { return 0; } Mat objPts, imgPts; opoints.convertTo(objPts, CV_64F); ipoints.convertTo(imgPts, CV_64F); if (imgPts.cols > 1) { imgPts = imgPts.reshape(1); imgPts = imgPts.t(); } else imgPts = imgPts.reshape(1, 2*imgPts.rows); vector reproj_errors(solutions); for (size_t i = 0; i < reproj_errors.size(); i++) { Mat rvec; Rodrigues(Rs[i], rvec); rvecs.push_back(rvec); Mat projPts; projectPoints(objPts, rvec, ts[i], _cameraMatrix, _distCoeffs, projPts); projPts = projPts.reshape(1, 2*projPts.rows); Mat err = imgPts - projPts; err = err.t() * err; reproj_errors[i] = err.at(0,0); } //sort the solutions for (int i = 1; i < solutions; i++) { for (int j = i; j > 0 && reproj_errors[j-1] > reproj_errors[j]; j--) { std::swap(reproj_errors[j], reproj_errors[j-1]); std::swap(rvecs[j], rvecs[j-1]); std::swap(ts[j], ts[j-1]); } } int depthRot = _rvecs.fixedType() ? _rvecs.depth() : CV_64F; int depthTrans = _tvecs.fixedType() ? _tvecs.depth() : CV_64F; _rvecs.create(solutions, 1, CV_MAKETYPE(depthRot, _rvecs.fixedType() && _rvecs.kind() == _InputArray::STD_VECTOR ? 3 : 1)); _tvecs.create(solutions, 1, CV_MAKETYPE(depthTrans, _tvecs.fixedType() && _tvecs.kind() == _InputArray::STD_VECTOR ? 3 : 1)); for (int i = 0; i < solutions; i++) { Mat rvec0, tvec0; if (depthRot == CV_64F) rvec0 = rvecs[i]; else rvecs[i].convertTo(rvec0, depthRot); if (depthTrans == CV_64F) tvec0 = ts[i]; else ts[i].convertTo(tvec0, depthTrans); if (_rvecs.fixedType() && _rvecs.kind() == _InputArray::STD_VECTOR) { Mat rref = _rvecs.getMat_(); if (_rvecs.depth() == CV_32F) rref.at(0,i) = Vec3f(rvec0.at(0,0), rvec0.at(1,0), rvec0.at(2,0)); else rref.at(0,i) = Vec3d(rvec0.at(0,0), rvec0.at(1,0), rvec0.at(2,0)); } else { _rvecs.getMatRef(i) = rvec0; } if (_tvecs.fixedType() && _tvecs.kind() == _InputArray::STD_VECTOR) { Mat tref = _tvecs.getMat_(); if (_tvecs.depth() == CV_32F) tref.at(0,i) = Vec3f(tvec0.at(0,0), tvec0.at(1,0), tvec0.at(2,0)); else tref.at(0,i) = Vec3d(tvec0.at(0,0), tvec0.at(1,0), tvec0.at(2,0)); } else { _tvecs.getMatRef(i) = tvec0; } } return solutions; } class SolvePnPRefineLMCallback CV_FINAL : public LMSolver::Callback { public: SolvePnPRefineLMCallback(InputArray _opoints, InputArray _ipoints, InputArray _cameraMatrix, InputArray _distCoeffs) { objectPoints = _opoints.getMat(); imagePoints = _ipoints.getMat(); npoints = std::max(objectPoints.checkVector(3, CV_32F), objectPoints.checkVector(3, CV_64F)); imagePoints0 = imagePoints.reshape(1, npoints*2); cameraMatrix = _cameraMatrix.getMat(); distCoeffs = _distCoeffs.getMat(); } bool compute(InputArray _param, OutputArray _err, OutputArray _Jac) const CV_OVERRIDE { Mat param = _param.getMat(); _err.create(npoints*2, 1, CV_64FC1); if(_Jac.needed()) { _Jac.create(npoints*2, param.rows, CV_64FC1); } Mat rvec = param(Rect(0, 0, 1, 3)), tvec = param(Rect(0, 3, 1, 3)); Mat J, projectedPts; projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, projectedPts, _Jac.needed() ? J : noArray()); if (_Jac.needed()) { Mat Jac = _Jac.getMat(); for (int i = 0; i < Jac.rows; i++) { for (int j = 0; j < Jac.cols; j++) { Jac.at(i,j) = J.at(i,j); } } } Mat err = _err.getMat(); projectedPts = projectedPts.reshape(1, npoints*2); err = projectedPts - imagePoints0; return true; } Mat objectPoints, imagePoints, imagePoints0; Mat cameraMatrix, distCoeffs; int npoints; }; /** * @brief Compute the Interaction matrix and the residuals for the current pose. * @param objectPoints 3D object points. * @param R Current estimated rotation matrix. * @param tvec Current estimated translation vector. * @param L Interaction matrix for a vector of point features. * @param s Residuals. */ static void computeInteractionMatrixAndResiduals(const Mat& objectPoints, const Mat& R, const Mat& tvec, Mat& L, Mat& s) { Mat objectPointsInCam; int npoints = objectPoints.rows; for (int i = 0; i < npoints; i++) { Mat curPt = objectPoints.row(i); objectPointsInCam = R * curPt.t() + tvec; double Zi = objectPointsInCam.at(2,0); double xi = objectPointsInCam.at(0,0) / Zi; double yi = objectPointsInCam.at(1,0) / Zi; s.at(2*i,0) = xi; s.at(2*i+1,0) = yi; L.at(2*i,0) = -1 / Zi; L.at(2*i,1) = 0; L.at(2*i,2) = xi / Zi; L.at(2*i,3) = xi*yi; L.at(2*i,4) = -(1 + xi*xi); L.at(2*i,5) = yi; L.at(2*i+1,0) = 0; L.at(2*i+1,1) = -1 / Zi; L.at(2*i+1,2) = yi / Zi; L.at(2*i+1,3) = 1 + yi*yi; L.at(2*i+1,4) = -xi*yi; L.at(2*i+1,5) = -xi; } } /** * @brief The exponential map from se(3) to SE(3). * @param twist A twist (v, w) represents the velocity of a rigid body as an angular velocity * around an axis and a linear velocity along this axis. * @param R1 Resultant rotation matrix from the twist. * @param t1 Resultant translation vector from the twist. */ static void exponentialMapToSE3Inv(const Mat& twist, Mat& R1, Mat& t1) { //see Exponential Map in http://ethaneade.com/lie.pdf /* \begin{align*} \boldsymbol{\delta} &= \left( \mathbf{u}, \boldsymbol{\omega} \right ) \in se(3) \\ \mathbf{u}, \boldsymbol{\omega} &\in \mathbb{R}^3 \\ \theta &= \sqrt{ \boldsymbol{\omega}^T \boldsymbol{\omega} } \\ A &= \frac{\sin \theta}{\theta} \\ B &= \frac{1 - \cos \theta}{\theta^2} \\ C &= \frac{1-A}{\theta^2} \\ \mathbf{R} &= \mathbf{I} + A \boldsymbol{\omega}_{\times} + B \boldsymbol{\omega}_{\times}^2 \\ \mathbf{V} &= \mathbf{I} + B \boldsymbol{\omega}_{\times} + C \boldsymbol{\omega}_{\times}^2 \\ \exp \begin{pmatrix} \mathbf{u} \\ \boldsymbol{\omega} \end{pmatrix} &= \left( \begin{array}{c|c} \mathbf{R} & \mathbf{V} \mathbf{u} \\ \hline \mathbf{0} & 1 \end{array} \right ) \end{align*} */ double vx = twist.at(0,0); double vy = twist.at(1,0); double vz = twist.at(2,0); double wx = twist.at(3,0); double wy = twist.at(4,0); double wz = twist.at(5,0); Matx31d rvec(wx, wy, wz); Mat R; Rodrigues(rvec, R); double theta = sqrt(wx*wx + wy*wy + wz*wz); double sinc = std::fabs(theta) < 1e-8 ? 1 : sin(theta) / theta; double mcosc = (std::fabs(theta) < 1e-8) ? 0.5 : (1-cos(theta)) / (theta*theta); double msinc = (std::abs(theta) < 1e-8) ? (1/6.0) : (1-sinc) / (theta*theta); Matx31d dt; dt(0) = vx*(sinc + wx*wx*msinc) + vy*(wx*wy*msinc - wz*mcosc) + vz*(wx*wz*msinc + wy*mcosc); dt(1) = vx*(wx*wy*msinc + wz*mcosc) + vy*(sinc + wy*wy*msinc) + vz*(wy*wz*msinc - wx*mcosc); dt(2) = vx*(wx*wz*msinc - wy*mcosc) + vy*(wy*wz*msinc + wx*mcosc) + vz*(sinc + wz*wz*msinc); R1 = R.t(); t1 = -R1 * dt; } enum SolvePnPRefineMethod { SOLVEPNP_REFINE_LM = 0, SOLVEPNP_REFINE_VVS = 1 }; static void solvePnPRefine(InputArray _objectPoints, InputArray _imagePoints, InputArray _cameraMatrix, InputArray _distCoeffs, InputOutputArray _rvec, InputOutputArray _tvec, SolvePnPRefineMethod _flags, TermCriteria _criteria=TermCriteria(TermCriteria::EPS+TermCriteria::COUNT, 20, FLT_EPSILON), double _vvslambda=1) { CV_INSTRUMENT_REGION(); Mat opoints_ = _objectPoints.getMat(), ipoints_ = _imagePoints.getMat(); Mat opoints, ipoints; opoints_.convertTo(opoints, CV_64F); ipoints_.convertTo(ipoints, CV_64F); int npoints = opoints.checkVector(3, CV_64F); CV_Assert( npoints >= 3 && npoints == ipoints.checkVector(2, CV_64F) ); CV_Assert( !_rvec.empty() && !_tvec.empty() ); int rtype = _rvec.type(), ttype = _tvec.type(); Size rsize = _rvec.size(), tsize = _tvec.size(); CV_Assert( (rtype == CV_32FC1 || rtype == CV_64FC1) && (ttype == CV_32FC1 || ttype == CV_64FC1) ); CV_Assert( (rsize == Size(1, 3) || rsize == Size(3, 1)) && (tsize == Size(1, 3) || tsize == Size(3, 1)) ); Mat cameraMatrix0 = _cameraMatrix.getMat(); Mat distCoeffs0 = _distCoeffs.getMat(); Mat cameraMatrix = Mat_(cameraMatrix0); Mat distCoeffs = Mat_(distCoeffs0); if (_flags == SOLVEPNP_REFINE_LM) { Mat rvec0 = _rvec.getMat(), tvec0 = _tvec.getMat(); Mat rvec, tvec; rvec0.convertTo(rvec, CV_64F); tvec0.convertTo(tvec, CV_64F); Mat params(6, 1, CV_64FC1); for (int i = 0; i < 3; i++) { params.at(i,0) = rvec.at(i,0); params.at(i+3,0) = tvec.at(i,0); } createLMSolver(makePtr(opoints, ipoints, cameraMatrix, distCoeffs), _criteria.maxCount, _criteria.epsilon)->run(params); params.rowRange(0, 3).convertTo(rvec0, rvec0.depth()); params.rowRange(3, 6).convertTo(tvec0, tvec0.depth()); } else if (_flags == SOLVEPNP_REFINE_VVS) { Mat rvec0 = _rvec.getMat(), tvec0 = _tvec.getMat(); Mat rvec, tvec; rvec0.convertTo(rvec, CV_64F); tvec0.convertTo(tvec, CV_64F); vector ipoints_normalized; undistortPoints(ipoints, ipoints_normalized, cameraMatrix, distCoeffs); Mat sd = Mat(ipoints_normalized).reshape(1, npoints*2); Mat objectPoints0 = opoints.reshape(1, npoints); Mat imagePoints0 = ipoints.reshape(1, npoints*2); Mat L(npoints*2, 6, CV_64FC1), s(npoints*2, 1, CV_64FC1); double residuals_1 = std::numeric_limits::max(), residuals = 0; Mat err; Mat R; Rodrigues(rvec, R); for (int iter = 0; iter < _criteria.maxCount; iter++) { computeInteractionMatrixAndResiduals(objectPoints0, R, tvec, L, s); err = s - sd; Mat Lp = L.inv(cv::DECOMP_SVD); Mat dq = -_vvslambda * Lp * err; Mat R1, t1; exponentialMapToSE3Inv(dq, R1, t1); R = R1 * R; tvec = R1 * tvec + t1; residuals_1 = residuals; Mat res = err.t()*err; residuals = res.at(0,0); if (std::fabs(residuals - residuals_1) < _criteria.epsilon) break; } Rodrigues(R, rvec); rvec.convertTo(rvec0, rvec0.depth()); tvec.convertTo(tvec0, tvec0.depth()); } } void solvePnPRefineLM(InputArray _objectPoints, InputArray _imagePoints, InputArray _cameraMatrix, InputArray _distCoeffs, InputOutputArray _rvec, InputOutputArray _tvec, TermCriteria _criteria) { CV_INSTRUMENT_REGION(); solvePnPRefine(_objectPoints, _imagePoints, _cameraMatrix, _distCoeffs, _rvec, _tvec, SOLVEPNP_REFINE_LM, _criteria); } void solvePnPRefineVVS(InputArray _objectPoints, InputArray _imagePoints, InputArray _cameraMatrix, InputArray _distCoeffs, InputOutputArray _rvec, InputOutputArray _tvec, TermCriteria _criteria, double _VVSlambda) { CV_INSTRUMENT_REGION(); solvePnPRefine(_objectPoints, _imagePoints, _cameraMatrix, _distCoeffs, _rvec, _tvec, SOLVEPNP_REFINE_VVS, _criteria, _VVSlambda); } int solvePnPGeneric( InputArray _opoints, InputArray _ipoints, InputArray _cameraMatrix, InputArray _distCoeffs, OutputArrayOfArrays _rvecs, OutputArrayOfArrays _tvecs, bool useExtrinsicGuess, SolvePnPMethod flags, InputArray _rvec, InputArray _tvec, OutputArray reprojectionError) { CV_INSTRUMENT_REGION(); Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat(); int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F)); CV_Assert( ( (npoints >= 4) || (npoints == 3 && flags == SOLVEPNP_ITERATIVE && useExtrinsicGuess) ) && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) ); opoints = opoints.reshape(3, npoints); ipoints = ipoints.reshape(2, npoints); if( flags != SOLVEPNP_ITERATIVE ) useExtrinsicGuess = false; if (useExtrinsicGuess) CV_Assert( !_rvec.empty() && !_tvec.empty() ); if( useExtrinsicGuess ) { int rtype = _rvec.type(), ttype = _tvec.type(); Size rsize = _rvec.size(), tsize = _tvec.size(); CV_Assert( (rtype == CV_32FC1 || rtype == CV_64FC1) && (ttype == CV_32FC1 || ttype == CV_64FC1) ); CV_Assert( (rsize == Size(1, 3) || rsize == Size(3, 1)) && (tsize == Size(1, 3) || tsize == Size(3, 1)) ); } Mat cameraMatrix0 = _cameraMatrix.getMat(); Mat distCoeffs0 = _distCoeffs.getMat(); Mat cameraMatrix = Mat_(cameraMatrix0); Mat distCoeffs = Mat_(distCoeffs0); vector vec_rvecs, vec_tvecs; if (flags == SOLVEPNP_EPNP || flags == SOLVEPNP_DLS || flags == SOLVEPNP_UPNP) { if (flags == SOLVEPNP_DLS) { CV_LOG_DEBUG(NULL, "Broken implementation for SOLVEPNP_DLS. Fallback to EPnP."); } else if (flags == SOLVEPNP_UPNP) { CV_LOG_DEBUG(NULL, "Broken implementation for SOLVEPNP_UPNP. Fallback to EPnP."); } Mat undistortedPoints; undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); epnp PnP(cameraMatrix, opoints, undistortedPoints); Mat rvec, tvec, R; PnP.compute_pose(R, tvec); Rodrigues(R, rvec); vec_rvecs.push_back(rvec); vec_tvecs.push_back(tvec); } else if (flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) { vector rvecs, tvecs; solveP3P(opoints, ipoints, _cameraMatrix, _distCoeffs, rvecs, tvecs, flags); vec_rvecs.insert(vec_rvecs.end(), rvecs.begin(), rvecs.end()); vec_tvecs.insert(vec_tvecs.end(), tvecs.begin(), tvecs.end()); } else if (flags == SOLVEPNP_ITERATIVE) { Mat rvec, tvec; if (useExtrinsicGuess) { rvec = _rvec.getMat(); tvec = _tvec.getMat(); } else { rvec.create(3, 1, CV_64FC1); tvec.create(3, 1, CV_64FC1); } CvMat c_objectPoints = cvMat(opoints), c_imagePoints = cvMat(ipoints); CvMat c_cameraMatrix = cvMat(cameraMatrix), c_distCoeffs = cvMat(distCoeffs); CvMat c_rvec = cvMat(rvec), c_tvec = cvMat(tvec); cvFindExtrinsicCameraParams2(&c_objectPoints, &c_imagePoints, &c_cameraMatrix, (c_distCoeffs.rows && c_distCoeffs.cols) ? &c_distCoeffs : 0, &c_rvec, &c_tvec, useExtrinsicGuess ); vec_rvecs.push_back(rvec); vec_tvecs.push_back(tvec); } else if (flags == SOLVEPNP_IPPE) { CV_DbgAssert(isPlanarObjectPoints(opoints, 1e-3)); Mat undistortedPoints; undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); IPPE::PoseSolver poseSolver; Mat rvec1, tvec1, rvec2, tvec2; float reprojErr1, reprojErr2; try { poseSolver.solveGeneric(opoints, undistortedPoints, rvec1, tvec1, reprojErr1, rvec2, tvec2, reprojErr2); if (reprojErr1 < reprojErr2) { vec_rvecs.push_back(rvec1); vec_tvecs.push_back(tvec1); vec_rvecs.push_back(rvec2); vec_tvecs.push_back(tvec2); } else { vec_rvecs.push_back(rvec2); vec_tvecs.push_back(tvec2); vec_rvecs.push_back(rvec1); vec_tvecs.push_back(tvec1); } } catch (...) { } } else if (flags == SOLVEPNP_IPPE_SQUARE) { CV_Assert(npoints == 4); #if defined _DEBUG || defined CV_STATIC_ANALYSIS double Xs[4][3]; if (opoints.depth() == CV_32F) { for (int i = 0; i < 4; i++) { for (int j = 0; j < 3; j++) { Xs[i][j] = opoints.ptr(0)[i](j); } } } else { for (int i = 0; i < 4; i++) { for (int j = 0; j < 3; j++) { Xs[i][j] = opoints.ptr(0)[i](j); } } } const double equalThreshold = 1e-9; //Z must be zero for (int i = 0; i < 4; i++) { CV_DbgCheck(Xs[i][2], approxEqual(Xs[i][2], 0, equalThreshold), "Z object point coordinate must be zero!"); } //Y0 == Y1 && Y2 == Y3 CV_DbgCheck(Xs[0][1], approxEqual(Xs[0][1], Xs[1][1], equalThreshold), "Object points must be: Y0 == Y1!"); CV_DbgCheck(Xs[2][1], approxEqual(Xs[2][1], Xs[3][1], equalThreshold), "Object points must be: Y2 == Y3!"); //X0 == X3 && X1 == X2 CV_DbgCheck(Xs[0][0], approxEqual(Xs[0][0], Xs[3][0], equalThreshold), "Object points must be: X0 == X3!"); CV_DbgCheck(Xs[1][0], approxEqual(Xs[1][0], Xs[2][0], equalThreshold), "Object points must be: X1 == X2!"); //X1 == Y1 && X3 == Y3 CV_DbgCheck(Xs[1][0], approxEqual(Xs[1][0], Xs[1][1], equalThreshold), "Object points must be: X1 == Y1!"); CV_DbgCheck(Xs[3][0], approxEqual(Xs[3][0], Xs[3][1], equalThreshold), "Object points must be: X3 == Y3!"); #endif Mat undistortedPoints; undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); IPPE::PoseSolver poseSolver; Mat rvec1, tvec1, rvec2, tvec2; float reprojErr1, reprojErr2; try { poseSolver.solveSquare(opoints, undistortedPoints, rvec1, tvec1, reprojErr1, rvec2, tvec2, reprojErr2); if (reprojErr1 < reprojErr2) { vec_rvecs.push_back(rvec1); vec_tvecs.push_back(tvec1); vec_rvecs.push_back(rvec2); vec_tvecs.push_back(tvec2); } else { vec_rvecs.push_back(rvec2); vec_tvecs.push_back(tvec2); vec_rvecs.push_back(rvec1); vec_tvecs.push_back(tvec1); } } catch (...) { } } /*else if (flags == SOLVEPNP_DLS) { Mat undistortedPoints; undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); dls PnP(opoints, undistortedPoints); Mat rvec, tvec, R; bool result = PnP.compute_pose(R, tvec); if (result) { Rodrigues(R, rvec); vec_rvecs.push_back(rvec); vec_tvecs.push_back(tvec); } } else if (flags == SOLVEPNP_UPNP) { upnp PnP(cameraMatrix, opoints, ipoints); Mat rvec, tvec, R; PnP.compute_pose(R, tvec); Rodrigues(R, rvec); vec_rvecs.push_back(rvec); vec_tvecs.push_back(tvec); }*/ else CV_Error(CV_StsBadArg, "The flags argument must be one of SOLVEPNP_ITERATIVE, SOLVEPNP_P3P, SOLVEPNP_EPNP or SOLVEPNP_DLS"); CV_Assert(vec_rvecs.size() == vec_tvecs.size()); int solutions = static_cast(vec_rvecs.size()); int depthRot = _rvecs.fixedType() ? _rvecs.depth() : CV_64F; int depthTrans = _tvecs.fixedType() ? _tvecs.depth() : CV_64F; _rvecs.create(solutions, 1, CV_MAKETYPE(depthRot, _rvecs.fixedType() && _rvecs.kind() == _InputArray::STD_VECTOR ? 3 : 1)); _tvecs.create(solutions, 1, CV_MAKETYPE(depthTrans, _tvecs.fixedType() && _tvecs.kind() == _InputArray::STD_VECTOR ? 3 : 1)); for (int i = 0; i < solutions; i++) { Mat rvec0, tvec0; if (depthRot == CV_64F) rvec0 = vec_rvecs[i]; else vec_rvecs[i].convertTo(rvec0, depthRot); if (depthTrans == CV_64F) tvec0 = vec_tvecs[i]; else vec_tvecs[i].convertTo(tvec0, depthTrans); if (_rvecs.fixedType() && _rvecs.kind() == _InputArray::STD_VECTOR) { Mat rref = _rvecs.getMat_(); if (_rvecs.depth() == CV_32F) rref.at(0,i) = Vec3f(rvec0.at(0,0), rvec0.at(1,0), rvec0.at(2,0)); else rref.at(0,i) = Vec3d(rvec0.at(0,0), rvec0.at(1,0), rvec0.at(2,0)); } else { _rvecs.getMatRef(i) = rvec0; } if (_tvecs.fixedType() && _tvecs.kind() == _InputArray::STD_VECTOR) { Mat tref = _tvecs.getMat_(); if (_tvecs.depth() == CV_32F) tref.at(0,i) = Vec3f(tvec0.at(0,0), tvec0.at(1,0), tvec0.at(2,0)); else tref.at(0,i) = Vec3d(tvec0.at(0,0), tvec0.at(1,0), tvec0.at(2,0)); } else { _tvecs.getMatRef(i) = tvec0; } } if (reprojectionError.needed()) { int type = (reprojectionError.fixedType() || !reprojectionError.empty()) ? reprojectionError.type() : (max(_ipoints.depth(), _opoints.depth()) == CV_64F ? CV_64F : CV_32F); reprojectionError.create(solutions, 1, type); CV_CheckType(reprojectionError.type(), type == CV_32FC1 || type == CV_64FC1, "Type of reprojectionError must be CV_32FC1 or CV_64FC1!"); Mat objectPoints, imagePoints; if (opoints.depth() == CV_32F) { opoints.convertTo(objectPoints, CV_64F); } else { objectPoints = opoints; } if (ipoints.depth() == CV_32F) { ipoints.convertTo(imagePoints, CV_64F); } else { imagePoints = ipoints; } for (size_t i = 0; i < vec_rvecs.size(); i++) { vector projectedPoints; projectPoints(objectPoints, vec_rvecs[i], vec_tvecs[i], cameraMatrix, distCoeffs, projectedPoints); double rmse = norm(Mat(projectedPoints, false), imagePoints, NORM_L2) / sqrt(2*projectedPoints.size()); Mat err = reprojectionError.getMat(); if (type == CV_32F) { err.at(static_cast(i)) = static_cast(rmse); } else { err.at(static_cast(i)) = rmse; } } } return solutions; } }