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提交 6569a585 编写于 作者: A Andrey Kamaev 提交者： OpenCV Buildbot
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Merge pull request #592 from vpisarev:c2cpp_calib3d_ptsetreg

上级 816adcfd f303de12
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Showing with 2548 addition and 2344 deletion
modules/calib3d/src/_modelest.h → modules/calib3d/src/calib3d_init.cpp
浏览文件 @ 6569a585
......@@ -7,10 +7,11 @@
// copy or use the software.
//
//
// Intel License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// 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,
......@@ -23,7 +24,7 @@
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// * 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
......@@ -39,44 +40,27 @@
//
//M*/
#include "precomp.hpp"
#ifndef _CV_MODEL_EST_H_
#define _CV_MODEL_EST_H_
using namespace cv;
#include "opencv2/calib3d/calib3d.hpp"
//////////////////////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS CvModelEstimator2
{
public:
CvModelEstimator2(int _modelPoints, CvSize _modelSize, int _maxBasicSolutions);
virtual ~CvModelEstimator2();
virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )=0;
virtual bool runLMeDS( const CvMat* m1, const CvMat* m2, CvMat* model,
CvMat* mask, double confidence=0.99, int maxIters=2000 );
virtual bool runRANSAC( const CvMat* m1, const CvMat* m2, CvMat* model,
CvMat* mask, double threshold,
double confidence=0.99, int maxIters=2000 );
virtual bool refine( const CvMat*, const CvMat*, CvMat*, int ) { return true; }
virtual void setSeed( int64 seed );
//////////////////////////////////////////////////////////////////////////////////////////////////////////
protected:
virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* error ) = 0;
virtual int findInliers( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* error,
CvMat* mask, double threshold );
virtual bool getSubset( const CvMat* m1, const CvMat* m2,
CvMat* ms1, CvMat* ms2, int maxAttempts=1000 );
virtual bool checkSubset( const CvMat* ms1, int count );
virtual bool isMinimalSetConsistent( const CvMat* /*m1*/, const CvMat* /*m2*/ ) { return true; };
CvRNG rng;
int modelPoints;
CvSize modelSize;
int maxBasicSolutions;
bool checkPartialSubsets;
};
///////////////////////////////////////////////////////////////////////////////////////////////////////////
#endif // _CV_MODEL_EST_H_
#if 0
bool cv::initModule_calib3d(void)
{
bool all = true;
all &= !RANSACPointSetRegistrator_info_auto.name().empty();
all &= !LMeDSPointSetRegistrator_info_auto.name().empty();
all &= !LMSolverImpl_info_auto.name().empty();
return all;
}
#endif
modules/calib3d/src/calibration.cpp
浏览文件 @ 6569a585
......@@ -55,247 +55,6 @@
using namespace cv;
CvLevMarq::CvLevMarq()
{
mask = prevParam = param = J = err = JtJ = JtJN = JtErr = JtJV = JtJW = Ptr<CvMat>();
lambdaLg10 = 0; state = DONE;
criteria = cvTermCriteria(0,0,0);
iters = 0;
completeSymmFlag = false;
}
CvLevMarq::CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria0, bool _completeSymmFlag )
{
mask = prevParam = param = J = err = JtJ = JtJN = JtErr = JtJV = JtJW = Ptr<CvMat>();
init(nparams, nerrs, criteria0, _completeSymmFlag);
}
void CvLevMarq::clear()
{
mask.release();
prevParam.release();
param.release();
J.release();
err.release();
JtJ.release();
JtJN.release();
JtErr.release();
JtJV.release();
JtJW.release();
}
CvLevMarq::~CvLevMarq()
{
clear();
}
void CvLevMarq::init( int nparams, int nerrs, CvTermCriteria criteria0, bool _completeSymmFlag )
{
if( !param || param->rows != nparams || nerrs != (err ? err->rows : 0) )
clear();
mask = cvCreateMat( nparams, 1, CV_8U );
cvSet(mask, cvScalarAll(1));
prevParam = cvCreateMat( nparams, 1, CV_64F );
param = cvCreateMat( nparams, 1, CV_64F );
JtJ = cvCreateMat( nparams, nparams, CV_64F );
JtJN = cvCreateMat( nparams, nparams, CV_64F );
JtJV = cvCreateMat( nparams, nparams, CV_64F );
JtJW = cvCreateMat( nparams, 1, CV_64F );
JtErr = cvCreateMat( nparams, 1, CV_64F );
if( nerrs > 0 )
{
J = cvCreateMat( nerrs, nparams, CV_64F );
err = cvCreateMat( nerrs, 1, CV_64F );
}
prevErrNorm = DBL_MAX;
lambdaLg10 = -3;
criteria = criteria0;
if( criteria.type & CV_TERMCRIT_ITER )
criteria.max_iter = MIN(MAX(criteria.max_iter,1),1000);
else
criteria.max_iter = 30;
if( criteria.type & CV_TERMCRIT_EPS )
criteria.epsilon = MAX(criteria.epsilon, 0);
else
criteria.epsilon = DBL_EPSILON;
state = STARTED;
iters = 0;
completeSymmFlag = _completeSymmFlag;
}
bool CvLevMarq::update( const CvMat*& _param, CvMat*& matJ, CvMat*& _err )
{
double change;
matJ = _err = 0;
assert( !err.empty() );
if( state == DONE )
{
_param = param;
return false;
}
if( state == STARTED )
{
_param = param;
cvZero( J );
cvZero( err );
matJ = J;
_err = err;
state = CALC_J;
return true;
}
if( state == CALC_J )
{
cvMulTransposed( J, JtJ, 1 );
cvGEMM( J, err, 1, 0, 0, JtErr, CV_GEMM_A_T );
cvCopy( param, prevParam );
step();
if( iters == 0 )
prevErrNorm = cvNorm(err, 0, CV_L2);
_param = param;
cvZero( err );
_err = err;
state = CHECK_ERR;
return true;
}
assert( state == CHECK_ERR );
errNorm = cvNorm( err, 0, CV_L2 );
if( errNorm > prevErrNorm )
{
if( ++lambdaLg10 <= 16 )
{
step();
_param = param;
cvZero( err );
_err = err;
state = CHECK_ERR;
return true;
}
}
lambdaLg10 = MAX(lambdaLg10-1, -16);
if( ++iters >= criteria.max_iter ||
(change = cvNorm(param, prevParam, CV_RELATIVE_L2)) < criteria.epsilon )
{
_param = param;
state = DONE;
return true;
}
prevErrNorm = errNorm;
_param = param;
cvZero(J);
matJ = J;
_err = err;
state = CALC_J;
return true;
}
bool CvLevMarq::updateAlt( const CvMat*& _param, CvMat*& _JtJ, CvMat*& _JtErr, double*& _errNorm )
{
double change;
CV_Assert( err.empty() );
if( state == DONE )
{
_param = param;
return false;
}
if( state == STARTED )
{
_param = param;
cvZero( JtJ );
cvZero( JtErr );
errNorm = 0;
_JtJ = JtJ;
_JtErr = JtErr;
_errNorm = &errNorm;
state = CALC_J;
return true;
}
if( state == CALC_J )
{
cvCopy( param, prevParam );
step();
_param = param;
prevErrNorm = errNorm;
errNorm = 0;
_errNorm = &errNorm;
state = CHECK_ERR;
return true;
}
assert( state == CHECK_ERR );
if( errNorm > prevErrNorm )
{
if( ++lambdaLg10 <= 16 )
{
step();
_param = param;
errNorm = 0;
_errNorm = &errNorm;
state = CHECK_ERR;
return true;
}
}
lambdaLg10 = MAX(lambdaLg10-1, -16);
if( ++iters >= criteria.max_iter ||
(change = cvNorm(param, prevParam, CV_RELATIVE_L2)) < criteria.epsilon )
{
_param = param;
state = DONE;
return false;
}
prevErrNorm = errNorm;
cvZero( JtJ );
cvZero( JtErr );
_param = param;
_JtJ = JtJ;
_JtErr = JtErr;
state = CALC_J;
return true;
}
void CvLevMarq::step()
{
const double LOG10 = log(10.);
double lambda = exp(lambdaLg10*LOG10);
int i, j, nparams = param->rows;
for( i = 0; i < nparams; i++ )
if( mask->data.ptr[i] == 0 )
{
double *row = JtJ->data.db + i*nparams, *col = JtJ->data.db + i;
for( j = 0; j < nparams; j++ )
row[j] = col[j*nparams] = 0;
JtErr->data.db[i] = 0;
}
if( !err )
cvCompleteSymm( JtJ, completeSymmFlag );
#if 1
cvCopy( JtJ, JtJN );
for( i = 0; i < nparams; i++ )
JtJN->data.db[(nparams+1)*i] *= 1. + lambda;
#else
cvSetIdentity(JtJN, cvRealScalar(lambda));
cvAdd( JtJ, JtJN, JtJN );
#endif
cvSVD( JtJN, JtJW, 0, JtJV, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
cvSVBkSb( JtJW, JtJV, JtJV, JtErr, param, CV_SVD_U_T + CV_SVD_V_T );
for( i = 0; i < nparams; i++ )
param->data.db[i] = prevParam->data.db[i] - (mask->data.ptr[i] ? param->data.db[i] : 0);
}
// reimplementation of dAB.m
CV_IMPL void cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB )
{
......
modules/calib3d/src/circlesgrid.cpp
浏览文件 @ 6569a585
......@@ -402,14 +402,16 @@ void CirclesGridClusterFinder::parsePatternPoints(const std::vector<cv::Point2f>
else
idealPt = Point2f(j*squareSize, i*squareSize);
std::vector<float> query = Mat(idealPt);
int knn = 1;
std::vector<int> indices(knn);
std::vector<float> dists(knn);
Mat query(1, 2, CV_32F, &idealPt);
const int knn = 1;
int indicesbuf[knn] = {0};
float distsbuf[knn] = {0.f};
Mat indices(1, knn, CV_32S, &indicesbuf);
Mat dists(1, knn, CV_32F, &distsbuf);
flannIndex.knnSearch(query, indices, dists, knn, flann::SearchParams());
centers.push_back(patternPoints.at(indices[0]));
centers.push_back(patternPoints.at(indicesbuf[0]));
if(dists[0] > maxRectifiedDistance)
if(distsbuf[0] > maxRectifiedDistance)
{
#ifdef DEBUG_CIRCLES
cout << "Pattern not detected: too large rectified distance" << endl;
......
modules/calib3d/src/compat_ptsetreg.cpp 0 → 100644
浏览文件 @ 6569a585
/*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, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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"
/************************************************************************************\
Some backward compatibility stuff, to be moved to legacy or compat module
\************************************************************************************/
using cv::Ptr;
////////////////// Levenberg-Marquardt engine (the old variant) ////////////////////////
CvLevMarq::CvLevMarq()
{
mask = prevParam = param = J = err = JtJ = JtJN = JtErr = JtJV = JtJW = Ptr<CvMat>();
lambdaLg10 = 0; state = DONE;
criteria = cvTermCriteria(0,0,0);
iters = 0;
completeSymmFlag = false;
}
CvLevMarq::CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria0, bool _completeSymmFlag )
{
mask = prevParam = param = J = err = JtJ = JtJN = JtErr = JtJV = JtJW = Ptr<CvMat>();
init(nparams, nerrs, criteria0, _completeSymmFlag);
}
void CvLevMarq::clear()
{
mask.release();
prevParam.release();
param.release();
J.release();
err.release();
JtJ.release();
JtJN.release();
JtErr.release();
JtJV.release();
JtJW.release();
}
CvLevMarq::~CvLevMarq()
{
clear();
}
void CvLevMarq::init( int nparams, int nerrs, CvTermCriteria criteria0, bool _completeSymmFlag )
{
if( !param || param->rows != nparams || nerrs != (err ? err->rows : 0) )
clear();
mask = cvCreateMat( nparams, 1, CV_8U );
cvSet(mask, cvScalarAll(1));
prevParam = cvCreateMat( nparams, 1, CV_64F );
param = cvCreateMat( nparams, 1, CV_64F );
JtJ = cvCreateMat( nparams, nparams, CV_64F );
JtJN = cvCreateMat( nparams, nparams, CV_64F );
JtJV = cvCreateMat( nparams, nparams, CV_64F );
JtJW = cvCreateMat( nparams, 1, CV_64F );
JtErr = cvCreateMat( nparams, 1, CV_64F );
if( nerrs > 0 )
{
J = cvCreateMat( nerrs, nparams, CV_64F );
err = cvCreateMat( nerrs, 1, CV_64F );
}
prevErrNorm = DBL_MAX;
lambdaLg10 = -3;
criteria = criteria0;
if( criteria.type & CV_TERMCRIT_ITER )
criteria.max_iter = MIN(MAX(criteria.max_iter,1),1000);
else
criteria.max_iter = 30;
if( criteria.type & CV_TERMCRIT_EPS )
criteria.epsilon = MAX(criteria.epsilon, 0);
else
criteria.epsilon = DBL_EPSILON;
state = STARTED;
iters = 0;
completeSymmFlag = _completeSymmFlag;
}
bool CvLevMarq::update( const CvMat*& _param, CvMat*& matJ, CvMat*& _err )
{
double change;
matJ = _err = 0;
assert( !err.empty() );
if( state == DONE )
{
_param = param;
return false;
}
if( state == STARTED )
{
_param = param;
cvZero( J );
cvZero( err );
matJ = J;
_err = err;
state = CALC_J;
return true;
}
if( state == CALC_J )
{
cvMulTransposed( J, JtJ, 1 );
cvGEMM( J, err, 1, 0, 0, JtErr, CV_GEMM_A_T );
cvCopy( param, prevParam );
step();
if( iters == 0 )
prevErrNorm = cvNorm(err, 0, CV_L2);
_param = param;
cvZero( err );
_err = err;
state = CHECK_ERR;
return true;
}
assert( state == CHECK_ERR );
errNorm = cvNorm( err, 0, CV_L2 );
if( errNorm > prevErrNorm )
{
if( ++lambdaLg10 <= 16 )
{
step();
_param = param;
cvZero( err );
_err = err;
state = CHECK_ERR;
return true;
}
}
lambdaLg10 = MAX(lambdaLg10-1, -16);
if( ++iters >= criteria.max_iter ||
(change = cvNorm(param, prevParam, CV_RELATIVE_L2)) < criteria.epsilon )
{
_param = param;
state = DONE;
return true;
}
prevErrNorm = errNorm;
_param = param;
cvZero(J);
matJ = J;
_err = err;
state = CALC_J;
return true;
}
bool CvLevMarq::updateAlt( const CvMat*& _param, CvMat*& _JtJ, CvMat*& _JtErr, double*& _errNorm )
{
double change;
CV_Assert( err.empty() );
if( state == DONE )
{
_param = param;
return false;
}
if( state == STARTED )
{
_param = param;
cvZero( JtJ );
cvZero( JtErr );
errNorm = 0;
_JtJ = JtJ;
_JtErr = JtErr;
_errNorm = &errNorm;
state = CALC_J;
return true;
}
if( state == CALC_J )
{
cvCopy( param, prevParam );
step();
_param = param;
prevErrNorm = errNorm;
errNorm = 0;
_errNorm = &errNorm;
state = CHECK_ERR;
return true;
}
assert( state == CHECK_ERR );
if( errNorm > prevErrNorm )
{
if( ++lambdaLg10 <= 16 )
{
step();
_param = param;
errNorm = 0;
_errNorm = &errNorm;
state = CHECK_ERR;
return true;
}
}
lambdaLg10 = MAX(lambdaLg10-1, -16);
if( ++iters >= criteria.max_iter ||
(change = cvNorm(param, prevParam, CV_RELATIVE_L2)) < criteria.epsilon )
{
_param = param;
state = DONE;
return false;
}
prevErrNorm = errNorm;
cvZero( JtJ );
cvZero( JtErr );
_param = param;
_JtJ = JtJ;
_JtErr = JtErr;
state = CALC_J;
return true;
}
void CvLevMarq::step()
{
const double LOG10 = log(10.);
double lambda = exp(lambdaLg10*LOG10);
int i, j, nparams = param->rows;
for( i = 0; i < nparams; i++ )
if( mask->data.ptr[i] == 0 )
{
double *row = JtJ->data.db + i*nparams, *col = JtJ->data.db + i;
for( j = 0; j < nparams; j++ )
row[j] = col[j*nparams] = 0;
JtErr->data.db[i] = 0;
}
if( !err )
cvCompleteSymm( JtJ, completeSymmFlag );
#if 1
cvCopy( JtJ, JtJN );
for( i = 0; i < nparams; i++ )
JtJN->data.db[(nparams+1)*i] *= 1. + lambda;
#else
cvSetIdentity(JtJN, cvRealScalar(lambda));
cvAdd( JtJ, JtJN, JtJN );
#endif
cvSVD( JtJN, JtJW, 0, JtJV, CV_SVD_MODIFY_A + CV_SVD_U_T + CV_SVD_V_T );
cvSVBkSb( JtJW, JtJV, JtJV, JtErr, param, CV_SVD_U_T + CV_SVD_V_T );
for( i = 0; i < nparams; i++ )
param->data.db[i] = prevParam->data.db[i] - (mask->data.ptr[i] ? param->data.db[i] : 0);
}
CV_IMPL int cvRANSACUpdateNumIters( double p, double ep, int modelPoints, int maxIters )
{
return cv::RANSACUpdateNumIters(p, ep, modelPoints, maxIters);
}
CV_IMPL int cvFindHomography( const CvMat* _src, const CvMat* _dst, CvMat* __H, int method,
double ransacReprojThreshold, CvMat* _mask )
{
cv::Mat src = cv::cvarrToMat(_src), dst = cv::cvarrToMat(_dst);
if( src.channels() == 1 && (src.rows == 2 || src.rows == 3) && src.cols > 3 )
cv::transpose(src, src);
if( dst.channels() == 1 && (dst.rows == 2 || dst.rows == 3) && dst.cols > 3 )
cv::transpose(dst, dst);
const cv::Mat H = cv::cvarrToMat(__H), mask = cv::cvarrToMat(_mask);
cv::Mat H0 = cv::findHomography(src, dst, method, ransacReprojThreshold,
_mask ? cv::_OutputArray(mask) : cv::_OutputArray());
if( H0.empty() )
{
cv::Mat Hz = cv::cvarrToMat(__H);
Hz.setTo(cv::Scalar::all(0));
return 0;
}
H0.convertTo(H, H.type());
return 1;
}
CV_IMPL int cvFindFundamentalMat( const CvMat* points1, const CvMat* points2,
CvMat* fmatrix, int method,
double param1, double param2, CvMat* _mask )
{
cv::Mat m1 = cv::cvarrToMat(points1), m2 = cv::cvarrToMat(points2);
if( m1.channels() == 1 && (m1.rows == 2 || m1.rows == 3) && m1.cols > 3 )
cv::transpose(m1, m1);
if( m2.channels() == 1 && (m2.rows == 2 || m2.rows == 3) && m2.cols > 3 )
cv::transpose(m2, m2);
const cv::Mat FM = cv::cvarrToMat(fmatrix), mask = cv::cvarrToMat(_mask);
cv::Mat FM0 = cv::findFundamentalMat(m1, m2, method, param1, param2,
_mask ? cv::_OutputArray(mask) : cv::_OutputArray());
if( FM0.empty() )
{
cv::Mat FM0z = cv::cvarrToMat(fmatrix);
FM0z.setTo(cv::Scalar::all(0));
return 0;
}
CV_Assert( FM0.cols == 3 && FM0.rows % 3 == 0 && FM.cols == 3 && FM.rows % 3 == 0 && FM.channels() == 1 );
cv::Mat FM1 = FM.rowRange(0, MIN(FM0.rows, FM.rows));
FM0.rowRange(0, FM1.rows).convertTo(FM1, FM1.type());
return FM1.rows / 3;
}
CV_IMPL void cvComputeCorrespondEpilines( const CvMat* points, int pointImageID,
const CvMat* fmatrix, CvMat* _lines )
{
cv::Mat pt = cv::cvarrToMat(points), fm = cv::cvarrToMat(fmatrix);
cv::Mat lines = cv::cvarrToMat(_lines);
const cv::Mat lines0 = lines;
if( pt.channels() == 1 && (pt.rows == 2 || pt.rows == 3) && pt.cols > 3 )
cv::transpose(pt, pt);
cv::computeCorrespondEpilines(pt, pointImageID, fm, lines);
bool tflag = lines0.channels() == 1 && lines0.rows == 3 && lines0.cols > 3;
lines = lines.reshape(lines0.channels(), (tflag ? lines0.cols : lines0.rows));
if( tflag )
{
CV_Assert( lines.rows == lines0.cols && lines.cols == lines0.rows );
if( lines0.type() == lines.type() )
transpose( lines, lines0 );
else
{
transpose( lines, lines );
lines.convertTo( lines0, lines0.type() );
}
}
else
{
CV_Assert( lines.size() == lines0.size() );
if( lines.data != lines0.data )
lines.convertTo(lines0, lines0.type());
}
}
CV_IMPL void cvConvertPointsHomogeneous( const CvMat* _src, CvMat* _dst )
{
cv::Mat src = cv::cvarrToMat(_src), dst = cv::cvarrToMat(_dst);
const cv::Mat dst0 = dst;
int d0 = src.channels() > 1 ? src.channels() : MIN(src.cols, src.rows);
if( src.channels() == 1 && src.cols > d0 )
cv::transpose(src, src);
int d1 = dst.channels() > 1 ? dst.channels() : MIN(dst.cols, dst.rows);
if( d0 == d1 )
src.copyTo(dst);
else if( d0 < d1 )
cv::convertPointsToHomogeneous(src, dst);
else
cv::convertPointsFromHomogeneous(src, dst);
bool tflag = dst0.channels() == 1 && dst0.cols > d1;
dst = dst.reshape(dst0.channels(), (tflag ? dst0.cols : dst0.rows));
if( tflag )
{
CV_Assert( dst.rows == dst0.cols && dst.cols == dst0.rows );
if( dst0.type() == dst.type() )
transpose( dst, dst0 );
else
{
transpose( dst, dst );
dst.convertTo( dst0, dst0.type() );
}
}
else
{
CV_Assert( dst.size() == dst0.size() );
if( dst.data != dst0.data )
dst.convertTo(dst0, dst0.type());
}
}
modules/calib3d/src/five-point.cpp
浏览文件 @ 6569a585
此差异已折叠。
modules/calib3d/src/fundam.cpp
浏览文件 @ 6569a585
此差异已折叠。
modules/calib3d/src/levmarq.cpp 0 → 100644
浏览文件 @ 6569a585
/*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, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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 <stdio.h>
/*
This is translation to C++ of the Matlab's LMSolve package by Miroslav Balda.
Here is the original copyright:
============================================================================
Copyright (c) 2007, Miroslav Balda
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions 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
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 COPYRIGHT OWNER 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.
*/
namespace cv
{
class LMSolverImpl : public LMSolver
{
public:
LMSolverImpl() : maxIters(100) { init(); };
LMSolverImpl(const Ptr<LMSolver::Callback>& _cb, int _maxIters) : cb(_cb), maxIters(_maxIters) { init(); }
void init()
{
epsx = epsf = FLT_EPSILON;
printInterval = 0;
}
int run(InputOutputArray _param0) const
{
Mat param0 = _param0.getMat(), x, xd, r, rd, J, A, Ap, v, temp_d, d;
int ptype = param0.type();
CV_Assert( (param0.cols == 1 || param0.rows == 1) && (ptype == CV_32F || ptype == CV_64F));
CV_Assert( !cb.empty() );
int lx = param0.rows + param0.cols - 1;
param0.convertTo(x, CV_64F);
if( x.cols != 1 )
transpose(x, x);
if( !cb->compute(x, r, J) )
return -1;
double S = norm(r, NORM_L2SQR);
int nfJ = 2;
mulTransposed(J, A, true);
gemm(J, r, 1, noArray(), 0, v, GEMM_1_T);
Mat D = A.diag().clone();
const double Rlo = 0.25, Rhi = 0.75;
double lambda = 1, lc = 0.75;
int i, iter = 0;
if( printInterval != 0 )
{
printf("************************************************************************************\n");
printf("\titr\tnfJ\t\tSUM(r^2)\t\tx\t\tdx\t\tl\t\tlc\n");
printf("************************************************************************************\n");
}
for( ;; )
{
CV_Assert( A.type() == CV_64F && A.rows == lx );
A.copyTo(Ap);
for( i = 0; i < lx; i++ )
Ap.at<double>(i, i) += lambda*D.at<double>(i);
solve(Ap, v, d, DECOMP_EIG);
subtract(x, d, xd);
if( !cb->compute(xd, rd, noArray()) )
return -1;
nfJ++;
double Sd = norm(rd, NORM_L2SQR);
gemm(A, d, -1, v, 2, temp_d);
double dS = d.dot(temp_d);
double R = (S - Sd)/(fabs(dS) > DBL_EPSILON ? dS : 1);
if( R > Rhi )
{
lambda *= 0.5;
if( lambda < lc )
lambda = 0;
}
else if( R < Rlo )
{
// find new nu if R too low
double t = d.dot(v);
double nu = (Sd - S)/(fabs(t) > DBL_EPSILON ? t : 1) + 2;
nu = std::min(std::max(nu, 2.), 10.);
if( lambda == 0 )
{
invert(A, Ap, DECOMP_EIG);
double maxval = DBL_EPSILON;
for( i = 0; i < lx; i++ )
maxval = std::max(maxval, std::abs(Ap.at<double>(i,i)));
lambda = lc = 1./maxval;
nu *= 0.5;
}
lambda *= nu;
}
if( Sd < S )
{
nfJ++;
S = Sd;
std::swap(x, xd);
if( !cb->compute(x, r, J) )
return -1;
mulTransposed(J, A, true);
gemm(J, r, 1, noArray(), 0, v, GEMM_1_T);
}
iter++;
bool proceed = iter < maxIters && norm(d, NORM_INF) >= epsx && norm(r, NORM_INF) >= epsf;
if( printInterval != 0 && (iter % printInterval == 0 || iter == 1 || !proceed) )
{
printf("%c%10d %10d %15.4e %16.4e %17.4e %16.4e %17.4e\n",
(proceed ? ' ' : '*'), iter, nfJ, S, x.at<double>(0), d.at<double>(0), lambda, lc);
}
if(!proceed)
break;
}
if( param0.size != x.size )
transpose(x, x);
x.convertTo(param0, ptype);
if( iter == maxIters )
iter = -iter;
return iter;
}
void setCallback(const Ptr<LMSolver::Callback>& _cb) { cb = _cb; }
AlgorithmInfo* info() const;
Ptr<LMSolver::Callback> cb;
double epsx;
double epsf;
int maxIters;
int printInterval;
};
CV_INIT_ALGORITHM(LMSolverImpl, "LMSolver",
obj.info()->addParam(obj, "epsx", obj.epsx);
obj.info()->addParam(obj, "epsf", obj.epsf);
obj.info()->addParam(obj, "maxIters", obj.maxIters);
obj.info()->addParam(obj, "printInterval", obj.printInterval));
Ptr<LMSolver> createLMSolver(const Ptr<LMSolver::Callback>& cb, int maxIters)
{
CV_Assert( !LMSolverImpl_info_auto.name().empty() );
return new LMSolverImpl(cb, maxIters);
}
}
modules/calib3d/src/modelest.cpp 已删除 100644 → 0
浏览文件 @ 816adcfd
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 "_modelest.h"
#include <algorithm>
#include <iterator>
#include <limits>
CvModelEstimator2::CvModelEstimator2(int _modelPoints, CvSize _modelSize, int _maxBasicSolutions)
{
modelPoints = _modelPoints;
modelSize = _modelSize;
maxBasicSolutions = _maxBasicSolutions;
checkPartialSubsets = true;
rng = cvRNG(-1);
}
CvModelEstimator2::~CvModelEstimator2()
{
}
void CvModelEstimator2::setSeed( int64 seed )
{
rng = cvRNG(seed);
}
int CvModelEstimator2::findInliers( const CvMat* m1, const CvMat* m2,
const CvMat* model, CvMat* _err,
CvMat* _mask, double threshold )
{
int i, count = _err->rows*_err->cols, goodCount = 0;
const float* err = _err->data.fl;
uchar* mask = _mask->data.ptr;
computeReprojError( m1, m2, model, _err );
threshold *= threshold;
for( i = 0; i < count; i++ )
goodCount += mask[i] = err[i] <= threshold;
return goodCount;
}
CV_IMPL int
cvRANSACUpdateNumIters( double p, double ep,
int model_points, int max_iters )
{
if( model_points <= 0 )
CV_Error( CV_StsOutOfRange, "the number of model points should be positive" );
p = MAX(p, 0.);
p = MIN(p, 1.);
ep = MAX(ep, 0.);
ep = MIN(ep, 1.);
// avoid inf's & nan's
double num = MAX(1. - p, DBL_MIN);
double denom = 1. - pow(1. - ep,model_points);
if( denom < DBL_MIN )
return 0;
num = log(num);
denom = log(denom);
return denom >= 0 || -num >= max_iters*(-denom) ?
max_iters : cvRound(num/denom);
}
bool CvModelEstimator2::runRANSAC( const CvMat* m1, const CvMat* m2, CvMat* model,
CvMat* mask0, double reprojThreshold,
double confidence, int maxIters )
{
bool result = false;
cv::Ptr<CvMat> mask = cvCloneMat(mask0);
cv::Ptr<CvMat> models, err, tmask;
cv::Ptr<CvMat> ms1, ms2;
int iter, niters = maxIters;
int count = m1->rows*m1->cols, maxGoodCount = 0;
CV_Assert( CV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask) );
if( count < modelPoints )
return false;
models = cvCreateMat( modelSize.height*maxBasicSolutions, modelSize.width, CV_64FC1 );
err = cvCreateMat( 1, count, CV_32FC1 );
tmask = cvCreateMat( 1, count, CV_8UC1 );
if( count > modelPoints )
{
ms1 = cvCreateMat( 1, modelPoints, m1->type );
ms2 = cvCreateMat( 1, modelPoints, m2->type );
}
else
{
niters = 1;
ms1 = cvCloneMat(m1);
ms2 = cvCloneMat(m2);
}
for( iter = 0; iter < niters; iter++ )
{
int i, goodCount, nmodels;
if( count > modelPoints )
{
bool found = getSubset( m1, m2, ms1, ms2, 300 );
if( !found )
{
if( iter == 0 )
return false;
break;
}
// Here we check for model specific geometrical
// constraints that allow to avoid "runKernel"
// and not checking for inliers if not fulfilled.
//
// The usefullness of this constraint for homographies is explained in the paper:
//
// "Speeding-up homography estimation in mobile devices"
// Journal of Real-Time Image Processing. 2013. DOI: 10.1007/s11554-012-0314-1
// Pablo Márquez-Neila, Javier López-Alberca, José M. Buenaposada, Luis Baumela
if ( !isMinimalSetConsistent( ms1, ms2 ) )
continue;
}
nmodels = runKernel( ms1, ms2, models );
if( nmodels <= 0 )
continue;
for( i = 0; i < nmodels; i++ )
{
CvMat model_i;
cvGetRows( models, &model_i, i*modelSize.height, (i+1)*modelSize.height );
goodCount = findInliers( m1, m2, &model_i, err, tmask, reprojThreshold );
if( goodCount > MAX(maxGoodCount, modelPoints-1) )
{
std::swap(tmask, mask);
cvCopy( &model_i, model );
maxGoodCount = goodCount;
niters = cvRANSACUpdateNumIters( confidence,
(double)(count - goodCount)/count, modelPoints, niters );
}
}
}
if( maxGoodCount > 0 )
{
if( mask != mask0 )
cvCopy( mask, mask0 );
result = true;
}
return result;
}
static CV_IMPLEMENT_QSORT( icvSortDistances, int, CV_LT )
bool CvModelEstimator2::runLMeDS( const CvMat* m1, const CvMat* m2, CvMat* model,
CvMat* mask, double confidence, int maxIters )
{
const double outlierRatio = 0.45;
bool result = false;
cv::Ptr<CvMat> models;
cv::Ptr<CvMat> ms1, ms2;
cv::Ptr<CvMat> err;
int iter, niters = maxIters;
int count = m1->rows*m1->cols;
double minMedian = DBL_MAX, sigma;
CV_Assert( CV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask) );
if( count < modelPoints )
return false;
models = cvCreateMat( modelSize.height*maxBasicSolutions, modelSize.width, CV_64FC1 );
err = cvCreateMat( 1, count, CV_32FC1 );
if( count > modelPoints )
{
ms1 = cvCreateMat( 1, modelPoints, m1->type );
ms2 = cvCreateMat( 1, modelPoints, m2->type );
}
else
{
niters = 1;
ms1 = cvCloneMat(m1);
ms2 = cvCloneMat(m2);
}
niters = cvRound(log(1-confidence)/log(1-pow(1-outlierRatio,(double)modelPoints)));
niters = MIN( MAX(niters, 3), maxIters );
for( iter = 0; iter < niters; iter++ )
{
int i, nmodels;
if( count > modelPoints )
{
bool found = getSubset( m1, m2, ms1, ms2, 300 );
if( !found )
{
if( iter == 0 )
return false;
break;
}
}
nmodels = runKernel( ms1, ms2, models );
if( nmodels <= 0 )
continue;
for( i = 0; i < nmodels; i++ )
{
CvMat model_i;
cvGetRows( models, &model_i, i*modelSize.height, (i+1)*modelSize.height );
computeReprojError( m1, m2, &model_i, err );
icvSortDistances( err->data.i, count, 0 );
double median = count % 2 != 0 ?
err->data.fl[count/2] : (err->data.fl[count/2-1] + err->data.fl[count/2])*0.5;
if( median < minMedian )
{
minMedian = median;
cvCopy( &model_i, model );
}
}
}
if( minMedian < DBL_MAX )
{
sigma = 2.5*1.4826*(1 + 5./(count - modelPoints))*std::sqrt(minMedian);
sigma = MAX( sigma, 0.001 );
count = findInliers( m1, m2, model, err, mask, sigma );
result = count >= modelPoints;
}
return result;
}
bool CvModelEstimator2::getSubset( const CvMat* m1, const CvMat* m2,
CvMat* ms1, CvMat* ms2, int maxAttempts )
{
cv::AutoBuffer<int> _idx(modelPoints);
int* idx = _idx;
int i = 0, j, k, idx_i, iters = 0;
int type = CV_MAT_TYPE(m1->type), elemSize = CV_ELEM_SIZE(type);
const int *m1ptr = m1->data.i, *m2ptr = m2->data.i;
int *ms1ptr = ms1->data.i, *ms2ptr = ms2->data.i;
int count = m1->cols*m1->rows;
assert( CV_IS_MAT_CONT(m1->type & m2->type) && (elemSize % sizeof(int) == 0) );
elemSize /= sizeof(int);
for(; iters < maxAttempts; iters++)
{
for( i = 0; i < modelPoints && iters < maxAttempts; )
{
idx[i] = idx_i = cvRandInt(&rng) % count;
for( j = 0; j < i; j++ )
if( idx_i == idx[j] )
break;
if( j < i )
continue;
for( k = 0; k < elemSize; k++ )
{
ms1ptr[i*elemSize + k] = m1ptr[idx_i*elemSize + k];
ms2ptr[i*elemSize + k] = m2ptr[idx_i*elemSize + k];
}
if( checkPartialSubsets && (!checkSubset( ms1, i+1 ) || !checkSubset( ms2, i+1 )))
{
iters++;
continue;
}
i++;
}
if( !checkPartialSubsets && i == modelPoints &&
(!checkSubset( ms1, i ) || !checkSubset( ms2, i )))
continue;
break;
}
return i == modelPoints && iters < maxAttempts;
}
bool CvModelEstimator2::checkSubset( const CvMat* m, int count )
{
if( count <= 2 )
return true;
int j, k, i, i0, i1;
CvPoint2D64f* ptr = (CvPoint2D64f*)m->data.ptr;
assert( CV_MAT_TYPE(m->type) == CV_64FC2 );
if( checkPartialSubsets )
i0 = i1 = count - 1;
else
i0 = 0, i1 = count - 1;
for( i = i0; i <= i1; i++ )
{
// check that the i-th selected point does not belong
// to a line connecting some previously selected points
for( j = 0; j < i; j++ )
{
double dx1 = ptr[j].x - ptr[i].x;
double dy1 = ptr[j].y - ptr[i].y;
for( k = 0; k < j; k++ )
{
double dx2 = ptr[k].x - ptr[i].x;
double dy2 = ptr[k].y - ptr[i].y;
if( fabs(dx2*dy1 - dy2*dx1) <= FLT_EPSILON*(fabs(dx1) + fabs(dy1) + fabs(dx2) + fabs(dy2)))
break;
}
if( k < j )
break;
}
if( j < i )
break;
}
return i > i1;
}
namespace cv
{
class Affine3DEstimator : public CvModelEstimator2
{
public:
Affine3DEstimator() : CvModelEstimator2(4, cvSize(4, 3), 1) {}
virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
protected:
virtual void computeReprojError( const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error );
virtual bool checkSubset( const CvMat* ms1, int count );
};
}
int cv::Affine3DEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
{
const Point3d* from = reinterpret_cast<const Point3d*>(m1->data.ptr);
const Point3d* to = reinterpret_cast<const Point3d*>(m2->data.ptr);
Mat A(12, 12, CV_64F);
Mat B(12, 1, CV_64F);
A = Scalar(0.0);
for(int i = 0; i < modelPoints; ++i)
{
*B.ptr<Point3d>(3*i) = to[i];
double *aptr = A.ptr<double>(3*i);
for(int k = 0; k < 3; ++k)
{
aptr[3] = 1.0;
*reinterpret_cast<Point3d*>(aptr) = from[i];
aptr += 16;
}
}
CvMat cvA = A;
CvMat cvB = B;
CvMat cvX;
cvReshape(model, &cvX, 1, 12);
cvSolve(&cvA, &cvB, &cvX, CV_SVD );
return 1;
}
void cv::Affine3DEstimator::computeReprojError( const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error )
{
int count = m1->rows * m1->cols;
const Point3d* from = reinterpret_cast<const Point3d*>(m1->data.ptr);
const Point3d* to = reinterpret_cast<const Point3d*>(m2->data.ptr);
const double* F = model->data.db;
float* err = error->data.fl;
for(int i = 0; i < count; i++ )
{
const Point3d& f = from[i];
const Point3d& t = to[i];
double a = F[0]*f.x + F[1]*f.y + F[ 2]*f.z + F[ 3] - t.x;
double b = F[4]*f.x + F[5]*f.y + F[ 6]*f.z + F[ 7] - t.y;
double c = F[8]*f.x + F[9]*f.y + F[10]*f.z + F[11] - t.z;
err[i] = (float)std::sqrt(a*a + b*b + c*c);
}
}
bool cv::Affine3DEstimator::checkSubset( const CvMat* ms1, int count )
{
CV_Assert( CV_MAT_TYPE(ms1->type) == CV_64FC3 );
int j, k, i = count - 1;
const Point3d* ptr = reinterpret_cast<const Point3d*>(ms1->data.ptr);
// check that the i-th selected point does not belong
// to a line connecting some previously selected points
for(j = 0; j < i; ++j)
{
Point3d d1 = ptr[j] - ptr[i];
double n1 = norm(d1);
for(k = 0; k < j; ++k)
{
Point3d d2 = ptr[k] - ptr[i];
double n = norm(d2) * n1;
if (fabs(d1.dot(d2) / n) > 0.996)
break;
}
if( k < j )
break;
}
return j == i;
}
int cv::estimateAffine3D(InputArray _from, InputArray _to,
OutputArray _out, OutputArray _inliers,
double param1, double param2)
{
Mat from = _from.getMat(), to = _to.getMat();
int count = from.checkVector(3);
CV_Assert( count >= 0 && to.checkVector(3) == count );
_out.create(3, 4, CV_64F);
Mat out = _out.getMat();
Mat inliers(1, count, CV_8U);
inliers = Scalar::all(1);
Mat dFrom, dTo;
from.convertTo(dFrom, CV_64F);
to.convertTo(dTo, CV_64F);
dFrom = dFrom.reshape(3, 1);
dTo = dTo.reshape(3, 1);
CvMat F3x4 = out;
CvMat mask = inliers;
CvMat m1 = dFrom;
CvMat m2 = dTo;
const double epsilon = std::numeric_limits<double>::epsilon();
param1 = param1 <= 0 ? 3 : param1;
param2 = (param2 < epsilon) ? 0.99 : (param2 > 1 - epsilon) ? 0.99 : param2;
int ok = Affine3DEstimator().runRANSAC(&m1, &m2, &F3x4, &mask, param1, param2 );
if( _inliers.needed() )
transpose(inliers, _inliers);
return ok;
}
modules/calib3d/src/precomp.hpp
浏览文件 @ 6569a585
......@@ -59,4 +59,51 @@
#define GET_OPTIMIZED(func) (func)
#endif
namespace cv
{
int RANSACUpdateNumIters( double p, double ep, int modelPoints, int maxIters );
class CV_EXPORTS LMSolver : public Algorithm
{
public:
class CV_EXPORTS Callback
{
public:
virtual ~Callback() {}
virtual bool compute(InputArray param, OutputArray err, OutputArray J) const = 0;
};
virtual void setCallback(const Ptr<LMSolver::Callback>& cb) = 0;
virtual int run(InputOutputArray _param0) const = 0;
};
CV_EXPORTS Ptr<LMSolver> createLMSolver(const Ptr<LMSolver::Callback>& cb, int maxIters);
class CV_EXPORTS PointSetRegistrator : public Algorithm
{
public:
class CV_EXPORTS Callback
{
public:
virtual ~Callback() {}
virtual int runKernel(InputArray m1, InputArray m2, OutputArray model) const = 0;
virtual void computeError(InputArray m1, InputArray m2, InputArray model, OutputArray err) const = 0;
virtual bool checkSubset(InputArray, InputArray, int) const { return true; }
};
virtual void setCallback(const Ptr<PointSetRegistrator::Callback>& cb) = 0;
virtual bool run(InputArray m1, InputArray m2, OutputArray model, OutputArray mask) const = 0;
};
CV_EXPORTS Ptr<PointSetRegistrator> createRANSACPointSetRegistrator(const Ptr<PointSetRegistrator::Callback>& cb,
int modelPoints, double threshold,
double confidence=0.99, int maxIters=1000 );
CV_EXPORTS Ptr<PointSetRegistrator> createLMeDSPointSetRegistrator(const Ptr<PointSetRegistrator::Callback>& cb,
int modelPoints, double confidence=0.99, int maxIters=1000 );
}
#endif
modules/calib3d/src/ptsetreg.cpp 0 → 100644
浏览文件 @ 6569a585
此差异已折叠。
modules/calib3d/test/test_affine3d_estimator.cpp
浏览文件 @ 6569a585
......@@ -163,6 +163,8 @@ bool CV_Affine3D_EstTest::testNPoints()
const double thres = 1e-4;
if (norm(aff_est, aff, NORM_INF) > thres)
{
cout << "aff est: " << aff_est << endl;
cout << "aff ref: " << aff << endl;
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
return false;
}
......
modules/calib3d/test/test_fundam.cpp
浏览文件 @ 6569a585
......@@ -1020,7 +1020,7 @@ void CV_FundamentalMatTest::prepare_to_validation( int test_case_idx )
F0 *= 1./f0[8];
uchar* status = test_mat[TEMP][1].data;
double err_level = get_success_error_level( test_case_idx, OUTPUT, 1 );
double err_level = method <= CV_FM_8POINT ? 1 : get_success_error_level( test_case_idx, OUTPUT, 1 );
uchar* mtfm1 = test_mat[REF_OUTPUT][1].data;
uchar* mtfm2 = test_mat[OUTPUT][1].data;
double* f_prop1 = (double*)test_mat[REF_OUTPUT][0].data;
......
modules/calib3d/test/test_modelest.cpp
浏览文件 @ 6569a585
......@@ -40,6 +40,8 @@
//M*/
#include "test_precomp.hpp"
#if 0
#include "_modelest.h"
using namespace std;
......@@ -225,3 +227,6 @@ void CV_ModelEstimator2_Test::run_func()
}
TEST(Calib3d_ModelEstimator2, accuracy) { CV_ModelEstimator2_Test test; test.safe_run(); }
#endif
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