merged 2.4 into trunk
This commit is contained in:
Vadim Pisarevsky
@@ -1,67 +1,67 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright( C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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//(including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort(including negligence or otherwise) arising in any way out of
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// the use of this software, even ifadvised of the possibility of such damage.
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//
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//M*/
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright( C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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//(including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort(including negligence or otherwise) arising in any way out of
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// the use of this software, even ifadvised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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using namespace cv;
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CvEMParams::CvEMParams() : nclusters(10), cov_mat_type(CvEM::COV_MAT_DIAGONAL),
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start_step(CvEM::START_AUTO_STEP), probs(0), weights(0), means(0), covs(0)
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start_step(CvEM::START_AUTO_STEP), probs(0), weights(0), means(0), covs(0)
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{
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term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
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}
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CvEMParams::CvEMParams( int _nclusters, int _cov_mat_type, int _start_step,
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CvTermCriteria _term_crit, const CvMat* _probs,
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const CvMat* _weights, const CvMat* _means, const CvMat** _covs ) :
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nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
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probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
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CvTermCriteria _term_crit, const CvMat* _probs,
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const CvMat* _weights, const CvMat* _means, const CvMat** _covs ) :
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nclusters(_nclusters), cov_mat_type(_cov_mat_type), start_step(_start_step),
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probs(_probs), weights(_weights), means(_means), covs(_covs), term_crit(_term_crit)
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{}
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CvEM::CvEM() : likelihood(DBL_MAX)
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CvEM::CvEM() : logLikelihood(DBL_MAX)
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{
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}
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CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx,
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CvEMParams params, CvMat* labels ) : likelihood(DBL_MAX)
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CvEMParams params, CvMat* labels ) : logLikelihood(DBL_MAX)
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{
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train(samples, sample_idx, params, labels);
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}
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@@ -96,16 +96,14 @@ void CvEM::write( CvFileStorage* _fs, const char* name ) const
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double CvEM::calcLikelihood( const Mat &input_sample ) const
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{
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double likelihood;
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emObj.predict(input_sample, noArray(), &likelihood);
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return likelihood;
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return emObj.predict(input_sample)[0];
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}
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float
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CvEM::predict( const CvMat* _sample, CvMat* _probs ) const
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{
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Mat prbs0 = cvarrToMat(_probs), prbs = prbs0, sample = cvarrToMat(_sample);
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int cls = emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray());
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int cls = static_cast<int>(emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray())[1]);
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if(_probs)
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{
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if( prbs.data != prbs0.data )
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@@ -144,7 +142,7 @@ void init_params(const CvEMParams& src,
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prbs = src.probs;
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weights = src.weights;
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means = src.means;
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if(src.covs)
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{
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covsHdrs.resize(src.nclusters);
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@@ -154,7 +152,7 @@ void init_params(const CvEMParams& src,
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}
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bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
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CvEMParams _params, CvMat* _labels )
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CvEMParams _params, CvMat* _labels )
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{
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CV_Assert(_sample_idx == 0);
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Mat samples = cvarrToMat(_samples), labels0, labels;
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@@ -163,7 +161,7 @@ bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
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bool isOk = train(samples, Mat(), _params, _labels ? &labels : 0);
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CV_Assert( labels0.data == labels.data );
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return isOk;
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}
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@@ -203,40 +201,37 @@ bool CvEM::train( const Mat& _samples, const Mat& _sample_idx,
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CvEMParams _params, Mat* _labels )
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{
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CV_Assert(_sample_idx.empty());
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Mat prbs, weights, means, likelihoods;
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Mat prbs, weights, means, logLikelihoods;
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std::vector<Mat> covsHdrs;
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init_params(_params, prbs, weights, means, covsHdrs);
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emObj = EM(_params.nclusters, _params.cov_mat_type, _params.term_crit);
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bool isOk = false;
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if( _params.start_step == EM::START_AUTO_STEP )
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isOk = emObj.train(_samples, _labels ? _OutputArray(*_labels) : cv::noArray(),
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probs, likelihoods);
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isOk = emObj.train(_samples,
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logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
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else if( _params.start_step == EM::START_E_STEP )
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isOk = emObj.trainE(_samples, means, covsHdrs, weights,
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_labels ? _OutputArray(*_labels) : cv::noArray(),
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probs, likelihoods);
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logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
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else if( _params.start_step == EM::START_M_STEP )
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isOk = emObj.trainM(_samples, prbs,
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_labels ? _OutputArray(*_labels) : cv::noArray(),
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probs, likelihoods);
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logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs);
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else
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CV_Error(CV_StsBadArg, "Bad start type of EM algorithm");
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if(isOk)
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{
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likelihoods = sum(likelihoods).val[0];
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logLikelihood = sum(logLikelihoods).val[0];
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set_mat_hdrs();
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}
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return isOk;
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}
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float
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CvEM::predict( const Mat& _sample, Mat* _probs ) const
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{
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int cls = emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray());
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return (float)cls;
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return static_cast<float>(emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray())[1]);
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}
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int CvEM::getNClusters() const
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