|
|
|
/*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 "opencv2/calib3d/calib3d_c.h"
|
|
|
|
|
|
|
|
#include <iostream>
|
|
|
|
using namespace cv;
|
|
|
|
|
|
|
|
bool cv::solvePnP( InputArray _opoints, InputArray _ipoints,
|
|
|
|
InputArray _cameraMatrix, InputArray _distCoeffs,
|
|
|
|
OutputArray _rvec, OutputArray _tvec, bool useExtrinsicGuess, int flags )
|
|
|
|
{
|
|
|
|
Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat();
|
|
|
|
int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
|
|
|
|
CV_Assert( npoints >= 0 && npoints == std::max(ipoints.checkVector(2, CV_32F), ipoints.checkVector(2, CV_64F)) );
|
|
|
|
_rvec.create(3, 1, CV_64F);
|
|
|
|
_tvec.create(3, 1, CV_64F);
|
|
|
|
Mat cameraMatrix = _cameraMatrix.getMat(), distCoeffs = _distCoeffs.getMat();
|
|
|
|
|
|
|
|
if (flags == SOLVEPNP_EPNP)
|
|
|
|
{
|
|
|
|
cv::Mat undistortedPoints;
|
|
|
|
cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
|
|
|
|
epnp PnP(cameraMatrix, opoints, undistortedPoints);
|
|
|
|
|
|
|
|
cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
|
|
|
|
PnP.compute_pose(R, tvec);
|
|
|
|
cv::Rodrigues(R, rvec);
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
else if (flags == SOLVEPNP_P3P)
|
|
|
|
{
|
|
|
|
CV_Assert( npoints == 4);
|
|
|
|
cv::Mat undistortedPoints;
|
|
|
|
cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
|
|
|
|
p3p P3Psolver(cameraMatrix);
|
|
|
|
|
|
|
|
cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
|
|
|
|
bool result = P3Psolver.solve(R, tvec, opoints, undistortedPoints);
|
|
|
|
if (result)
|
|
|
|
cv::Rodrigues(R, rvec);
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
else if (flags == SOLVEPNP_ITERATIVE)
|
|
|
|
{
|
|
|
|
CvMat c_objectPoints = opoints, c_imagePoints = ipoints;
|
|
|
|
CvMat c_cameraMatrix = cameraMatrix, c_distCoeffs = distCoeffs;
|
|
|
|
CvMat c_rvec = _rvec.getMat(), c_tvec = _tvec.getMat();
|
|
|
|
cvFindExtrinsicCameraParams2(&c_objectPoints, &c_imagePoints, &c_cameraMatrix,
|
|
|
|
c_distCoeffs.rows*c_distCoeffs.cols ? &c_distCoeffs : 0,
|
|
|
|
&c_rvec, &c_tvec, useExtrinsicGuess );
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
else if (flags == SOLVEPNP_DLS)
|
|
|
|
{
|
|
|
|
cv::Mat undistortedPoints;
|
|
|
|
cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs);
|
|
|
|
|
|
|
|
dls PnP(opoints, undistortedPoints);
|
|
|
|
|
|
|
|
cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
|
|
|
|
bool result = PnP.compute_pose(R, tvec);
|
|
|
|
if (result)
|
|
|
|
cv::Rodrigues(R, rvec);
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
else if (flags == SOLVEPNP_UPNP)
|
|
|
|
{
|
|
|
|
upnp PnP(cameraMatrix, opoints, ipoints);
|
|
|
|
|
|
|
|
cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat();
|
|
|
|
double f = PnP.compute_pose(R, tvec);
|
|
|
|
cv::Rodrigues(R, rvec);
|
|
|
|
cameraMatrix.at<double>(0,0) = cameraMatrix.at<double>(1,1) = f;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
CV_Error(CV_StsBadArg, "The flags argument must be one of SOLVEPNP_ITERATIVE, SOLVEPNP_P3P, SOLVEPNP_EPNP or SOLVEPNP_DLS");
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
class PnPRansacCallback : public PointSetRegistrator::Callback
|
|
|
|
{
|
|
|
|
|
|
|
|
public:
|
|
|
|
|
|
|
|
PnPRansacCallback(Mat _cameraMatrix=Mat(3,3,CV_64F), Mat _distCoeffs=Mat(4,1,CV_64F), int _flags=cv::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 an return number of found models */
|
|
|
|
int runKernel( InputArray _m1, InputArray _m2, OutputArray _model ) const
|
|
|
|
{
|
|
|
|
Mat opoints = _m1.getMat(), ipoints = _m2.getMat();
|
|
|
|
|
|
|
|
|
|
|
|
bool correspondence = cv::solvePnP( _m1, _m2, cameraMatrix, distCoeffs,
|
|
|
|
rvec, tvec, useExtrinsicGuess, flags );
|
|
|
|
|
|
|
|
Mat _local_model;
|
|
|
|
cv::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
|
|
|
|
{
|
|
|
|
|
|
|
|
Mat opoints = _m1.getMat(), ipoints = _m2.getMat(), model = _model.getMat();
|
|
|
|
|
|
|
|
int i, count = opoints.cols;
|
|
|
|
Mat _rvec = model.col(0);
|
|
|
|
Mat _tvec = model.col(1);
|
|
|
|
|
|
|
|
|
|
|
|
Mat projpoints(count, 2, CV_32FC1);
|
|
|
|
cv::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)cv::norm( ipoints_ptr[i] - projpoints_ptr[i] );
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
Mat cameraMatrix;
|
|
|
|
Mat distCoeffs;
|
|
|
|
int flags;
|
|
|
|
bool useExtrinsicGuess;
|
|
|
|
Mat rvec;
|
|
|
|
Mat tvec;
|
|
|
|
};
|
|
|
|
|
|
|
|
bool cv::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)
|
|
|
|
{
|
|
|
|
|
|
|
|
Mat opoints = _opoints.getMat(), ipoints = _ipoints.getMat();
|
|
|
|
|
|
|
|
int npoints = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
|
|
|
|
CV_Assert( npoints >= 0 && 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();
|
|
|
|
|
|
|
|
Ptr<PointSetRegistrator::Callback> cb; // pointer to callback
|
|
|
|
cb = makePtr<PnPRansacCallback>( cameraMatrix, distCoeffs, flags, useExtrinsicGuess, rvec, tvec);
|
|
|
|
|
|
|
|
int model_points = 4; // minimum of number of model points
|
|
|
|
if( flags == cv::SOLVEPNP_ITERATIVE ) model_points = 6;
|
|
|
|
else if( flags == cv::SOLVEPNP_UPNP ) model_points = 6;
|
|
|
|
else if( flags == cv::SOLVEPNP_EPNP ) model_points = 5;
|
|
|
|
|
|
|
|
double param1 = reprojectionError; // reprojection error
|
|
|
|
double param2 = confidence; // confidence
|
|
|
|
int param3 = iterationsCount; // number maximum iterations
|
|
|
|
|
|
|
|
cv::Mat _local_model(3, 2, CV_64FC1);
|
|
|
|
cv::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;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
{
|
|
|
|
_rvec.assign(_local_model.col(0)); // output rotation vector
|
|
|
|
_tvec.assign(_local_model.col(1)); // output translation vector
|
|
|
|
}
|
|
|
|
|
|
|
|
if(_inliers.needed())
|
|
|
|
{
|
|
|
|
Mat _local_inliers;
|
|
|
|
int count = 0;
|
|
|
|
for (int i = 0; i < _mask_local_inliers.rows; ++i)
|
|
|
|
{
|
|
|
|
if((int)_mask_local_inliers.at<uchar>(i) == 1) // inliers mask
|
|
|
|
{
|
|
|
|
_local_inliers.push_back(count); // output inliers vector
|
|
|
|
count++;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
_local_inliers.copyTo(_inliers);
|
|
|
|
}
|
|
|
|
return true;
|
|
|
|
}
|