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merged 2.4 into trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-30 14:33:52 +00:00
parent 3f1c6d7357
commit d5a0088bbe
194 changed files with 10158 additions and 8225 deletions
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84
modules/legacy/src/bgfg_gaussmix.cpp
View File

@@ -50,10 +50,9 @@ icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
if( *bg_model )
{
delete (cv::Mat*)((*bg_model)->g_point);
delete (cv::BackgroundSubtractorMOG*)((*bg_model)->mog);
cvReleaseImage( &(*bg_model)->background );
cvReleaseImage( &(*bg_model)->foreground );
cvReleaseMemStorage(&(*bg_model)->storage);
memset( *bg_model, 0, sizeof(**bg_model) );
delete *bg_model;
*bg_model = 0;
@@ -64,70 +63,15 @@ icvReleaseGaussianBGModel( CvGaussBGModel** bg_model )
static int CV_CDECL
icvUpdateGaussianBGModel( IplImage* curr_frame, CvGaussBGModel* bg_model, double learningRate )
{
int region_count = 0;
cv::Mat image = cv::cvarrToMat(curr_frame), mask = cv::cvarrToMat(bg_model->foreground);
cv::BackgroundSubtractorMOG mog;
mog.bgmodel = *(cv::Mat*)bg_model->g_point;
mog.frameSize = mog.bgmodel.data ? cv::Size(cvGetSize(curr_frame)) : cv::Size();
mog.frameType = image.type();
cv::BackgroundSubtractorMOG* mog = (cv::BackgroundSubtractorMOG*)(bg_model->mog);
CV_Assert(mog != 0);
mog.nframes = bg_model->countFrames;
mog.history = bg_model->params.win_size;
mog.nmixtures = bg_model->params.n_gauss;
mog.varThreshold = bg_model->params.std_threshold*bg_model->params.std_threshold;
mog.backgroundRatio = bg_model->params.bg_threshold;
(*mog)(image, mask, learningRate);
bg_model->countFrames++;
mog(image, mask, learningRate);
bg_model->countFrames = mog.nframes;
if( ((cv::Mat*)bg_model->g_point)->data != mog.bgmodel.data )
*((cv::Mat*)bg_model->g_point) = mog.bgmodel;
//foreground filtering
//filter small regions
cvClearMemStorage(bg_model->storage);
//cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_OPEN, 1 );
//cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_CLOSE, 1 );
#if 0
CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
cvFindContours( bg_model->foreground, bg_model->storage, &first_seq, sizeof(CvContour), CV_RETR_LIST );
for( seq = first_seq; seq; seq = seq->h_next )
{
CvContour* cnt = (CvContour*)seq;
if( cnt->rect.width * cnt->rect.height < bg_model->params.minArea )
{
//delete small contour
prev_seq = seq->h_prev;
if( prev_seq )
{
prev_seq->h_next = seq->h_next;
if( seq->h_next ) seq->h_next->h_prev = prev_seq;
}
else
{
first_seq = seq->h_next;
if( seq->h_next ) seq->h_next->h_prev = NULL;
}
}
else
{
region_count++;
}
}
bg_model->foreground_regions = first_seq;
cvZero(bg_model->foreground);
cvDrawContours(bg_model->foreground, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1);
#endif
CvMat _mask = mask;
cvCopy(&_mask, bg_model->foreground);
return region_count;
return 0;
}
CV_IMPL CvBGStatModel*
@@ -161,15 +105,17 @@ cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parame
bg_model->params = params;
//prepare storages
bg_model->g_point = (CvGaussBGPoint*)new cv::Mat();
cv::BackgroundSubtractorMOG* mog =
new cv::BackgroundSubtractorMOG(params.win_size,
params.n_gauss,
params.bg_threshold,
params.variance_init);
bg_model->background = cvCreateImage(cvSize(first_frame->width,
first_frame->height), IPL_DEPTH_8U, first_frame->nChannels);
bg_model->foreground = cvCreateImage(cvSize(first_frame->width,
first_frame->height), IPL_DEPTH_8U, 1);
bg_model->mog = mog;
bg_model->storage = cvCreateMemStorage();
CvSize sz = cvGetSize(first_frame);
bg_model->background = cvCreateImage(sz, IPL_DEPTH_8U, first_frame->nChannels);
bg_model->foreground = cvCreateImage(sz, IPL_DEPTH_8U, 1);
bg_model->countFrames = 0;

125
modules/legacy/src/em.cpp
View File

@@ -1,67 +1,67 @@
/*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 ifadvised of the possibility of such damage.
//
//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 ifadvised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
CvEMParams::CvEMParams() : nclusters(10), cov_mat_type(CvEM::COV_MAT_DIAGONAL),
start_step(CvEM::START_AUTO_STEP), probs(0), weights(0), means(0), covs(0)
start_step(CvEM::START_AUTO_STEP), probs(0), weights(0), means(0), covs(0)
{
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
}
CvEMParams::CvEMParams( int _nclusters, int _cov_mat_type, int _start_step,
CvTermCriteria _term_crit, const CvMat* _probs,
const CvMat* _weights, const CvMat* _means, const CvMat** _covs ) :
nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
CvTermCriteria _term_crit, const CvMat* _probs,
const CvMat* _weights, const CvMat* _means, const CvMat** _covs ) :
nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
{}
CvEM::CvEM() : likelihood(DBL_MAX)
CvEM::CvEM() : logLikelihood(DBL_MAX)
{
}
CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx,
CvEMParams params, CvMat* labels ) : likelihood(DBL_MAX)
CvEMParams params, CvMat* labels ) : logLikelihood(DBL_MAX)
{
train(samples, sample_idx, params, labels);
}
@@ -96,16 +96,14 @@ void CvEM::write( CvFileStorage* _fs, const char* name ) const
double CvEM::calcLikelihood( const Mat &input_sample ) const
{
double likelihood;
emObj.predict(input_sample, noArray(), &likelihood);
return likelihood;
return emObj.predict(input_sample)[0];
}
float
CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
{
Mat prbs0 = cvarrToMat(_probs), prbs = prbs0, sample = cvarrToMat(_sample);
int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray());
int cls = static_cast<int>(emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray())[1]);
if(_probs)
{
if( prbs.data != prbs0.data )
@@ -144,7 +142,7 @@ void init_params(const CvEMParams& src,
prbs = src.probs;
weights = src.weights;
means = src.means;
if(src.covs)
{
covsHdrs.resize(src.nclusters);
@@ -154,7 +152,7 @@ void init_params(const CvEMParams& src,
}
bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
CvEMParams _params, CvMat* _labels )
CvEMParams _params, CvMat* _labels )
{
CV_Assert(_sample_idx == 0);
Mat samples = cvarrToMat(_samples), labels0, labels;
@@ -163,7 +161,7 @@ bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
bool isOk = train(samples, Mat(), _params, _labels ? &labels : 0);
CV_Assert( labels0.data == labels.data );
return isOk;
}
@@ -203,40 +201,37 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
CvEMParams _params, Mat* _labels )
{
CV_Assert(_sample_idx.empty());
Mat prbs, weights, means, likelihoods;
Mat prbs, weights, means, logLikelihoods;
std::vector<Mat> covsHdrs;
init_params(_params, prbs, weights, means, covsHdrs);
emObj = EM(_params.nclusters, _params.cov_mat_type, _params.term_crit);
bool isOk = false;
if( _params.start_step == EM::START_AUTO_STEP )
isOk = emObj.train(_samples, _labels ? _OutputArray(*_labels) : cv::noArray(),
probs, likelihoods);
isOk = emObj.train(_samples,
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else if( _params.start_step == EM::START_E_STEP )
isOk = emObj.trainE(_samples, means, covsHdrs, weights,
_labels ? _OutputArray(*_labels) : cv::noArray(),
probs, likelihoods);
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else if( _params.start_step == EM::START_M_STEP )
isOk = emObj.trainM(_samples, prbs,
_labels ? _OutputArray(*_labels) : cv::noArray(),
probs, likelihoods);
logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
else
CV_Error(CV_StsBadArg, "Bad start type of EM algorithm");
if(isOk)
{
likelihoods = sum(likelihoods).val[0];
logLikelihood = sum(logLikelihoods).val[0];
set_mat_hdrs();
}
return isOk;
}
float
CvEM::predict( const Mat& _sample, Mat* _probs ) const
{
int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray());
return (float)cls;
return static_cast<float>(emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray())[1]);
}
int CvEM::getNClusters() const

2
modules/legacy/src/features2d.cpp
View File

@@ -95,7 +95,7 @@ cvExtractSURF( const CvArr* _img, const CvArr* _mask,
{
if( _keypoints )
{
CvSURFPoint pt = cvSURFPoint(kpt[i].pt, kpt[i].class_id, cvRound(kpt[i].size));
CvSURFPoint pt = cvSURFPoint(kpt[i].pt, kpt[i].class_id, cvRound(kpt[i].size), kpt[i].angle, kpt[i].response);
cvSeqPush(*_keypoints, &pt);
}
if( _descriptors )