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opencv/modules/3d/src/solvepnp.cpp
Alexander Alekhin 899b4d1452 Merge branch 4.x
2022-02-22 19:55:26 +00:00

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/*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
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// 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
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//M*/
#include "precomp.hpp"
//#include "upnp.h"
#include "dls.h"
#include "epnp.h"
#include "p3p.h"
#include "ap3p.h"
#include "ippe.hpp"
#include "sqpnp.hpp"
#include "usac.hpp"
#include <opencv2/core/utils/logger.hpp>
namespace cv {
using namespace std;
#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<double>(2) < w.at<double>(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
std::vector<Point3f> 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));
std::vector<Point2f> 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();
std::vector<Mat> 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<Point2f>();
const Point2f* projpoints_ptr = projpoints.ptr<Point2f>();
_err.create(count, 1, CV_32FC1);
float* err = _err.getMat().ptr<float>();
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;
};
UsacParams::UsacParams()
{
confidence = 0.99;
isParallel = false;
loIterations = 5;
loMethod = LocalOptimMethod::LOCAL_OPTIM_INNER_LO;
loSampleSize = 14;
maxIterations = 5000;
neighborsSearch = NeighborSearchMethod::NEIGH_GRID;
randomGeneratorState = 0;
sampler = SamplingMethod::SAMPLING_UNIFORM;
score = ScoreMethod::SCORE_METHOD_MSAC;
threshold = 1.5;
}
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();
if (flags >= USAC_DEFAULT && flags <= USAC_MAGSAC)
return usac::solvePnPRansac(_opoints, _ipoints, _cameraMatrix, _distCoeffs,
_rvec, _tvec, useExtrinsicGuess, iterationsCount, reprojectionError,
confidence, _inliers, flags);
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<int>(i) = i;
}
}
return true;
}
Ptr<PointSetRegistrator::Callback> cb; // pointer to callback
cb = makePtr<PnPRansacCallback>( 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;
}
std::vector<Point3d> opoints_inliers;
std::vector<Point2d> 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<uchar>();
int npoints1 = compressElems(&opoints_inliers[0], mask, 1, npoints);
compressElems(&ipoints_inliers[0], mask, 1, npoints);
opoints_inliers.resize(npoints1);
ipoints_inliers.resize(npoints1);
try
{
result = solvePnP(opoints_inliers, ipoints_inliers, cameraMatrix,
distCoeffs, rvec, tvec, useExtrinsicGuess,
(flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) ? SOLVEPNP_EPNP : flags) ? 1 : -1;
}
catch (const cv::Exception& e)
{
if (flags == SOLVEPNP_ITERATIVE &&
npoints1 == 5 &&
e.what() &&
std::string(e.what()).find("DLT algorithm needs at least 6 points") != std::string::npos
)
{
CV_LOG_INFO(NULL, "solvePnPRansac(): solvePnP stage to compute the final pose using points "
"in the consensus set raised DLT 6 points exception, use result from MSS (Minimal Sample Sets) stage instead.");
rvec = _local_model.col(0); // output rotation vector
tvec = _local_model.col(1); // output translation vector
result = 1;
}
else
{
// raise other exceptions
throw;
}
}
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();
CV_LOG_DEBUG(NULL, "solvePnPRansac(): solvePnP stage to compute the final pose using points in the consensus set failed. Return false");
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<uchar>(i) != 0) // inliers mask
_local_inliers.push_back(i); // output inliers vector
}
_local_inliers.copyTo(_inliers);
}
return true;
}
bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints,
InputOutputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec, OutputArray inliers,
const UsacParams &params) {
Ptr<usac::Model> model_params;
usac::setParameters(model_params, cameraMatrix.empty() ? usac::EstimationMethod::P6P :
usac::EstimationMethod::P3P, params, inliers.needed());
Ptr<usac::RansacOutput> ransac_output;
if (usac::run(model_params, imagePoints, objectPoints, model_params->getRandomGeneratorState(),
ransac_output, cameraMatrix, noArray(), distCoeffs, noArray())) {
if (inliers.needed()) {
const auto &inliers_mask = ransac_output->getInliersMask();
Mat inliers_;
for (int i = 0; i < (int)inliers_mask.size(); i++)
if (inliers_mask[i])
inliers_.push_back(i);
inliers_.copyTo(inliers);
}
const Mat &model = ransac_output->getModel();
model.col(0).copyTo(rvec);
model.col(1).copyTo(tvec);
if (cameraMatrix.empty())
model.colRange(2, 5).copyTo(cameraMatrix);
return true;
} else return false;
}
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_<double>(cameraMatrix0);
Mat distCoeffs = Mat_<double>(distCoeffs0);
Mat undistortedPoints;
undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
std::vector<Mat> 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);
std::vector<double> 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<double>(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<Vec3f>(0,i) = Vec3f(rvec0.at<float>(0,0), rvec0.at<float>(1,0), rvec0.at<float>(2,0));
else
rref.at<Vec3d>(0,i) = Vec3d(rvec0.at<double>(0,0), rvec0.at<double>(1,0), rvec0.at<double>(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<Vec3f>(0,i) = Vec3f(tvec0.at<float>(0,0), tvec0.at<float>(1,0), tvec0.at<float>(2,0));
else
tref.at<Vec3d>(0,i) = Vec3d(tvec0.at<double>(0,0), tvec0.at<double>(1,0), tvec0.at<double>(2,0));
}
else
{
_tvecs.getMatRef(i) = tvec0;
}
}
return solutions;
}
/**
* @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<double>(2,0);
double xi = objectPointsInCam.at<double>(0,0) / Zi;
double yi = objectPointsInCam.at<double>(1,0) / Zi;
s.at<double>(2*i,0) = xi;
s.at<double>(2*i+1,0) = yi;
L.at<double>(2*i,0) = -1 / Zi;
L.at<double>(2*i,1) = 0;
L.at<double>(2*i,2) = xi / Zi;
L.at<double>(2*i,3) = xi*yi;
L.at<double>(2*i,4) = -(1 + xi*xi);
L.at<double>(2*i,5) = yi;
L.at<double>(2*i+1,0) = 0;
L.at<double>(2*i+1,1) = -1 / Zi;
L.at<double>(2*i+1,2) = yi / Zi;
L.at<double>(2*i+1,3) = 1 + yi*yi;
L.at<double>(2*i+1,4) = -xi*yi;
L.at<double>(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<double>(0,0);
double vy = twist.at<double>(1,0);
double vz = twist.at<double>(2,0);
double wx = twist.at<double>(3,0);
double wy = twist.at<double>(4,0);
double wz = twist.at<double>(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 : std::sin(theta) / theta;
double mcosc = (std::fabs(theta) < 1e-8) ? 0.5 : (1- std::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_<double>(cameraMatrix0);
Mat distCoeffs = Mat_<double>(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<double>(i,0) = rvec.at<double>(i,0);
params.at<double>(i+3,0) = tvec.at<double>(i,0);
}
int npts = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
Mat imagePoints0 = ipoints.reshape(1, npts * 2);
auto solvePnPRefineLMCallback = [opoints, imagePoints0, npts, cameraMatrix, distCoeffs]
(InputOutputArray _param, OutputArray _err, OutputArray _Jac) -> bool
{
Mat param = _param.getMat();
_err.create(npts * 2, 1, CV_64FC1);
if (_Jac.needed())
{
_Jac.create(npts * 2, param.rows, CV_64FC1);
}
Mat prvec = param(Rect(0, 0, 1, 3)), ptvec = param(Rect(0, 3, 1, 3));
Mat J, projectedPts;
projectPoints(opoints, prvec, ptvec, 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<double>(i, j) = J.at<double>(i, j);
}
}
}
Mat err = _err.getMat();
projectedPts = projectedPts.reshape(1, npts * 2);
err = projectedPts - imagePoints0;
return true;
};
LevMarq solver(params, solvePnPRefineLMCallback,
LevMarq::Settings()
.setMaxIterations((unsigned int)_criteria.maxCount)
.setStepNormTolerance((double)_criteria.epsilon)
.setSmallEnergyTolerance((double)_criteria.epsilon * (double)_criteria.epsilon)
.setGeodesic(true));
solver.optimize();
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);
std::vector<Point2d> 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<double>::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<double>(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, int 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 >= 3 && flags == SOLVEPNP_SQPNP) )
&& 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_<double>(cameraMatrix0);
Mat distCoeffs = Mat_<double>(distCoeffs0);
std::vector<Mat> 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)
{
std::vector<Mat> 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);
}
findExtrinsicCameraParams2(opoints, ipoints, cameraMatrix, distCoeffs,
rvec, 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<Vec3f>(0)[i](j);
}
}
}
else
{
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 3; j++)
{
Xs[i][j] = opoints.ptr<Vec3d>(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_SQPNP)
{
Mat undistortedPoints;
undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
sqpnp::PoseSolver solver;
solver.solve(opoints, undistortedPoints, vec_rvecs, vec_tvecs);
}
/*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, SOLVEPNP_DLS, SOLVEPNP_UPNP, SOLVEPNP_AP3P, SOLVEPNP_IPPE, SOLVEPNP_IPPE_SQUARE or SOLVEPNP_SQPNP");
CV_Assert(vec_rvecs.size() == vec_tvecs.size());
int solutions = static_cast<int>(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<Vec3f>(0,i) = Vec3f(rvec0.at<float>(0,0), rvec0.at<float>(1,0), rvec0.at<float>(2,0));
else
rref.at<Vec3d>(0,i) = Vec3d(rvec0.at<double>(0,0), rvec0.at<double>(1,0), rvec0.at<double>(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<Vec3f>(0,i) = Vec3f(tvec0.at<float>(0,0), tvec0.at<float>(1,0), tvec0.at<float>(2,0));
else
tref.at<Vec3d>(0,i) = Vec3d(tvec0.at<double>(0,0), tvec0.at<double>(1,0), tvec0.at<double>(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++)
{
std::vector<Point2d> 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<float>(static_cast<int>(i)) = static_cast<float>(rmse);
}
else
{
err.at<double>(static_cast<int>(i)) = rmse;
}
}
}
return solutions;
}
}
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