mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
464 lines
17 KiB
464 lines
17 KiB
/*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 "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 == 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 == 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 == 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 == DLS) |
|
{ |
|
std::cout << "DLS" << std::endl; |
|
cv::Mat undistortedPoints; |
|
cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); |
|
|
|
|
|
dls PnP(opoints, undistortedPoints); |
|
// DO SOMETHING |
|
|
|
cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat(); |
|
|
|
return true; |
|
} |
|
else |
|
CV_Error(CV_StsBadArg, "The flags argument must be one of CV_ITERATIVE, CV_P3P or CV_EPNP"); |
|
return false; |
|
} |
|
|
|
/*namespace cv |
|
{ |
|
namespace pnpransac |
|
{ |
|
const int MIN_POINTS_COUNT = 4; |
|
|
|
static void project3dPoints(const Mat& points, const Mat& rvec, const Mat& tvec, Mat& modif_points) |
|
{ |
|
modif_points.create(1, points.cols, CV_32FC3); |
|
Mat R(3, 3, CV_64FC1); |
|
Rodrigues(rvec, R); |
|
Mat transformation(3, 4, CV_64F); |
|
Mat r = transformation.colRange(0, 3); |
|
R.copyTo(r); |
|
Mat t = transformation.colRange(3, 4); |
|
tvec.copyTo(t); |
|
transform(points, modif_points, transformation); |
|
} |
|
|
|
struct CameraParameters |
|
{ |
|
void init(Mat _intrinsics, Mat _distCoeffs) |
|
{ |
|
_intrinsics.copyTo(intrinsics); |
|
_distCoeffs.copyTo(distortion); |
|
} |
|
|
|
Mat intrinsics; |
|
Mat distortion; |
|
}; |
|
|
|
struct Parameters |
|
{ |
|
int iterationsCount; |
|
float reprojectionError; |
|
int minInliersCount; |
|
bool useExtrinsicGuess; |
|
int flags; |
|
CameraParameters camera; |
|
}; |
|
|
|
static void pnpTask(const std::vector<char>& pointsMask, const Mat& objectPoints, const Mat& imagePoints, |
|
const Parameters& params, std::vector<int>& inliers, Mat& rvec, Mat& tvec, |
|
const Mat& rvecInit, const Mat& tvecInit, Mutex& resultsMutex) |
|
{ |
|
Mat modelObjectPoints(1, MIN_POINTS_COUNT, CV_32FC3), modelImagePoints(1, MIN_POINTS_COUNT, CV_32FC2); |
|
for (int i = 0, colIndex = 0; i < (int)pointsMask.size(); i++) |
|
{ |
|
if (pointsMask[i]) |
|
{ |
|
Mat colModelImagePoints = modelImagePoints(Rect(colIndex, 0, 1, 1)); |
|
imagePoints.col(i).copyTo(colModelImagePoints); |
|
Mat colModelObjectPoints = modelObjectPoints(Rect(colIndex, 0, 1, 1)); |
|
objectPoints.col(i).copyTo(colModelObjectPoints); |
|
colIndex = colIndex+1; |
|
} |
|
} |
|
|
|
//filter same 3d points, hang in solvePnP |
|
double eps = 1e-10; |
|
int num_same_points = 0; |
|
for (int i = 0; i < MIN_POINTS_COUNT; i++) |
|
for (int j = i + 1; j < MIN_POINTS_COUNT; j++) |
|
{ |
|
if (norm(modelObjectPoints.at<Vec3f>(0, i) - modelObjectPoints.at<Vec3f>(0, j)) < eps) |
|
num_same_points++; |
|
} |
|
if (num_same_points > 0) |
|
return; |
|
|
|
Mat localRvec, localTvec; |
|
rvecInit.copyTo(localRvec); |
|
tvecInit.copyTo(localTvec); |
|
|
|
solvePnP(modelObjectPoints, modelImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, |
|
params.useExtrinsicGuess, params.flags); |
|
|
|
|
|
std::vector<Point2f> projected_points; |
|
projected_points.resize(objectPoints.cols); |
|
projectPoints(objectPoints, localRvec, localTvec, params.camera.intrinsics, params.camera.distortion, projected_points); |
|
|
|
Mat rotatedPoints; |
|
project3dPoints(objectPoints, localRvec, localTvec, rotatedPoints); |
|
|
|
std::vector<int> localInliers; |
|
for (int i = 0; i < objectPoints.cols; i++) |
|
{ |
|
Point2f p(imagePoints.at<Vec2f>(0, i)[0], imagePoints.at<Vec2f>(0, i)[1]); |
|
if ((norm(p - projected_points[i]) < params.reprojectionError) |
|
&& (rotatedPoints.at<Vec3f>(0, i)[2] > 0)) //hack |
|
{ |
|
localInliers.push_back(i); |
|
} |
|
} |
|
|
|
if (localInliers.size() > inliers.size()) |
|
{ |
|
resultsMutex.lock(); |
|
|
|
inliers.clear(); |
|
inliers.resize(localInliers.size()); |
|
memcpy(&inliers[0], &localInliers[0], sizeof(int) * localInliers.size()); |
|
localRvec.copyTo(rvec); |
|
localTvec.copyTo(tvec); |
|
|
|
resultsMutex.unlock(); |
|
} |
|
} |
|
|
|
class PnPSolver |
|
{ |
|
public: |
|
void operator()( const BlockedRange& r ) const |
|
{ |
|
std::vector<char> pointsMask(objectPoints.cols, 0); |
|
memset(&pointsMask[0], 1, MIN_POINTS_COUNT ); |
|
for( int i=r.begin(); i!=r.end(); ++i ) |
|
{ |
|
generateVar(pointsMask); |
|
pnpTask(pointsMask, objectPoints, imagePoints, parameters, |
|
inliers, rvec, tvec, initRvec, initTvec, syncMutex); |
|
if ((int)inliers.size() >= parameters.minInliersCount) |
|
{ |
|
#ifdef HAVE_TBB |
|
tbb::task::self().cancel_group_execution(); |
|
#else |
|
break; |
|
#endif |
|
} |
|
} |
|
} |
|
PnPSolver(const Mat& _objectPoints, const Mat& _imagePoints, const Parameters& _parameters, |
|
Mat& _rvec, Mat& _tvec, std::vector<int>& _inliers): |
|
objectPoints(_objectPoints), imagePoints(_imagePoints), parameters(_parameters), |
|
rvec(_rvec), tvec(_tvec), inliers(_inliers) |
|
{ |
|
rvec.copyTo(initRvec); |
|
tvec.copyTo(initTvec); |
|
|
|
generator.state = theRNG().state; //to control it somehow... |
|
} |
|
private: |
|
PnPSolver& operator=(const PnPSolver&); |
|
|
|
const Mat& objectPoints; |
|
const Mat& imagePoints; |
|
const Parameters& parameters; |
|
Mat &rvec, &tvec; |
|
std::vector<int>& inliers; |
|
Mat initRvec, initTvec; |
|
|
|
static RNG generator; |
|
static Mutex syncMutex; |
|
|
|
void generateVar(std::vector<char>& mask) const |
|
{ |
|
int size = (int)mask.size(); |
|
for (int i = 0; i < size; i++) |
|
{ |
|
int i1 = generator.uniform(0, size); |
|
int i2 = generator.uniform(0, size); |
|
char curr = mask[i1]; |
|
mask[i1] = mask[i2]; |
|
mask[i2] = curr; |
|
} |
|
} |
|
}; |
|
|
|
Mutex PnPSolver::syncMutex; |
|
RNG PnPSolver::generator; |
|
|
|
} |
|
}*/ |
|
|
|
class PnPRansacCallback : public PointSetRegistrator::Callback |
|
{ |
|
public: |
|
|
|
PnPRansacCallback(Mat _cameraMatrix=Mat(3,3,CV_64F), Mat _distCoeffs=Mat(4,1,CV_64F), int _flags=cv::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] = 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, float 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 = flags == cv::P3P ? 4 : 6; // minimum of number of model points |
|
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); |
|
} |
|
|
|
// OLD IMPLEMENTATION |
|
|
|
/*std::vector<int> localInliers; |
|
Mat localRvec, localTvec; |
|
rvec.copyTo(localRvec); |
|
tvec.copyTo(localTvec); |
|
|
|
if (objectPoints.cols >= pnpransac::MIN_POINTS_COUNT) |
|
{ |
|
parallel_for(BlockedRange(0,iterationsCount), cv::pnpransac::PnPSolver(objectPoints, imagePoints, params, |
|
localRvec, localTvec, localInliers)); |
|
} |
|
|
|
if (localInliers.size() >= (size_t)pnpransac::MIN_POINTS_COUNT) |
|
{ |
|
if (flags != P3P) |
|
{ |
|
int i, pointsCount = (int)localInliers.size(); |
|
Mat inlierObjectPoints(1, pointsCount, CV_32FC3), inlierImagePoints(1, pointsCount, CV_32FC2); |
|
for (i = 0; i < pointsCount; i++) |
|
{ |
|
int index = localInliers[i]; |
|
Mat colInlierImagePoints = inlierImagePoints(Rect(i, 0, 1, 1)); |
|
imagePoints.col(index).copyTo(colInlierImagePoints); |
|
Mat colInlierObjectPoints = inlierObjectPoints(Rect(i, 0, 1, 1)); |
|
objectPoints.col(index).copyTo(colInlierObjectPoints); |
|
} |
|
solvePnP(inlierObjectPoints, inlierImagePoints, params.camera.intrinsics, params.camera.distortion, localRvec, localTvec, true, flags); |
|
} |
|
localRvec.copyTo(rvec); |
|
localTvec.copyTo(tvec); |
|
if (_inliers.needed()) |
|
Mat(localInliers).copyTo(_inliers); |
|
} |
|
else |
|
{ |
|
tvec.setTo(Scalar(0)); |
|
Mat R = Mat::eye(3, 3, CV_64F); |
|
Rodrigues(R, rvec); |
|
if( _inliers.needed() ) |
|
_inliers.release(); |
|
}*/ |
|
return true; |
|
}
|
|
|