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CvLevMarq: remove fixed rows instead of setting them to zero

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use the same approach like in fisheye calibration: instead of setting
masked out rows to zero, remove them from the equation system.
This way JtJ does not become singular and we can use the much faster LU
decomposition instead of SVD.

This results in a speedup of the Calibrate unit tests of 3x-10x.
This commit is contained in:
Pavel Rojtberg 2015-11-21 01:30:53 +01:00
parent 9d89c369f8
commit 22b64e2c28
1 changed files with 47 additions and 19 deletions
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66
modules/calib3d/src/compat_ptsetreg.cpp
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@ -93,9 +93,6 @@ void CvLevMarq::init( int nparams, int nerrs, CvTermCriteria criteria0, bool _co
prevParam.reset(cvCreateMat( nparams, 1, CV_64F )); prevParam.reset(cvCreateMat( nparams, 1, CV_64F ));
param.reset(cvCreateMat( nparams, 1, CV_64F )); param.reset(cvCreateMat( nparams, 1, CV_64F ));
JtJ.reset(cvCreateMat( nparams, nparams, CV_64F )); JtJ.reset(cvCreateMat( nparams, nparams, CV_64F ));
JtJN.reset(cvCreateMat( nparams, nparams, CV_64F ));
JtJV.reset(cvCreateMat( nparams, nparams, CV_64F ));
JtJW.reset(cvCreateMat( nparams, 1, CV_64F ));
JtErr.reset(cvCreateMat( nparams, 1, CV_64F )); JtErr.reset(cvCreateMat( nparams, 1, CV_64F ));
if( nerrs > 0 ) if( nerrs > 0 )
{ {
@ -256,6 +253,34 @@ bool CvLevMarq::updateAlt( const CvMat*& _param, CvMat*& _JtJ, CvMat*& _JtErr, d
return true; return true;
} }
namespace {
static void subMatrix(const cv::Mat& src, cv::Mat& dst, const std::vector<uchar>& cols,
const std::vector<uchar>& rows) {
int nonzeros_cols = cv::countNonZero(cols);
cv::Mat tmp(src.rows, nonzeros_cols, CV_64FC1);
for (int i = 0, j = 0; i < (int)cols.size(); i++)
{
if (cols[i])
{
src.col(i).copyTo(tmp.col(j++));
}
}
int nonzeros_rows = cv::countNonZero(rows);
dst.create(nonzeros_rows, nonzeros_cols, CV_64FC1);
for (int i = 0, j = 0; i < (int)rows.size(); i++)
{
if (rows[i])
{
tmp.row(i).copyTo(dst.row(j++));
}
}
}
}
void CvLevMarq::step() void CvLevMarq::step()
{ {
using namespace cv; using namespace cv;
@ -264,33 +289,36 @@ void CvLevMarq::step()
int nparams = param->rows; int nparams = param->rows;
Mat _JtJ = cvarrToMat(JtJ); Mat _JtJ = cvarrToMat(JtJ);
Mat _JtJN = cvarrToMat(JtJN); Mat _mask = cvarrToMat(mask);
Mat _JtJW = cvarrToMat(JtJW);
Mat _JtJV = cvarrToMat(JtJV);
for( int i = 0; i < nparams; i++ ) int nparams_nz = countNonZero(_mask);
if( mask->data.ptr[i] == 0 ) if(!JtJN || JtJN->rows != nparams_nz) {
{ // prevent re-allocation in every step
_JtJ.row(i) = 0; JtJN.reset(cvCreateMat( nparams_nz, nparams_nz, CV_64F ));
_JtJ.col(i) = 0; JtJV.reset(cvCreateMat( nparams_nz, 1, CV_64F ));
JtErr->data.db[i] = 0; JtJW.reset(cvCreateMat( nparams_nz, 1, CV_64F ));
} }
Mat _JtJN = cvarrToMat(JtJN);
Mat _JtErr = cvarrToMat(JtJV);
Mat_<double> nonzero_param = cvarrToMat(JtJW);
subMatrix(cvarrToMat(JtErr), _JtErr, std::vector<uchar>(1, 1), _mask);
subMatrix(_JtJ, _JtJN, _mask, _mask);
if( !err ) if( !err )
completeSymm( _JtJ, completeSymmFlag ); completeSymm( _JtJN, completeSymmFlag );
_JtJ.copyTo(_JtJN);
#if 1 #if 1
_JtJN.diag() *= 1. + lambda; _JtJN.diag() *= 1. + lambda;
#else #else
_JtJN.diag() += lambda; _JtJN.diag() += lambda;
#endif #endif
// solve(JtJN, JtErr, param, DECOMP_SVD); solve(_JtJN, _JtErr, nonzero_param);
SVD::compute(_JtJN, _JtJW, noArray(), _JtJV, SVD::MODIFY_A);
SVD::backSubst(_JtJW, _JtJV.t(), _JtJV, cvarrToMat(JtErr), cvarrToMat(param));
int j = 0;
for( int i = 0; i < nparams; i++ ) for( int i = 0; i < nparams; i++ )
param->data.db[i] = prevParam->data.db[i] - (mask->data.ptr[i] ? param->data.db[i] : 0); param->data.db[i] = prevParam->data.db[i] - (mask->data.ptr[i] ? nonzero_param(j++) : 0);
} }