264 lines
7.5 KiB
C++
264 lines
7.5 KiB
C++
/*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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{
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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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{}
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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 ) : 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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CvEM::~CvEM()
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{
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clear();
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}
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void CvEM::clear()
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{
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emObj.clear();
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}
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void CvEM::read( CvFileStorage* fs, CvFileNode* node )
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{
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FileNode fn(fs, node);
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emObj.read(fn);
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set_mat_hdrs();
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}
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void CvEM::write( CvFileStorage* _fs, const char* name ) const
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{
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FileStorage fs = _fs;
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if(name)
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fs << name << "{";
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emObj.write(fs);
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if(name)
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fs << "}";
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fs.fs.obj = 0;
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}
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double CvEM::calcLikelihood( const Mat &input_sample ) const
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{
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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 = 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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{
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CV_Assert( prbs.size == prbs0.size );
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prbs.convertTo(prbs0, prbs0.type());
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}
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}
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return (float)cls;
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}
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void CvEM::set_mat_hdrs()
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{
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if(emObj.isTrained())
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{
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meansHdr = emObj.get<Mat>("means");
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int K = emObj.get<int>("nclusters");
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covsHdrs.resize(K);
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covsPtrs.resize(K);
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const std::vector<Mat>& covs = emObj.get<vector<Mat> >("covs");
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for(size_t i = 0; i < covsHdrs.size(); i++)
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{
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covsHdrs[i] = covs[i];
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covsPtrs[i] = &covsHdrs[i];
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}
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weightsHdr = emObj.get<Mat>("weights");
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probsHdr = probs;
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}
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}
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static
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void init_params(const CvEMParams& src,
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Mat& prbs, Mat& weights,
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Mat& means, vector<Mat>& covsHdrs)
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{
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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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for(size_t i = 0; i < covsHdrs.size(); i++)
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covsHdrs[i] = src.covs[i];
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}
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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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{
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CV_Assert(_sample_idx == 0);
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Mat samples = cvarrToMat(_samples), labels0, labels;
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if( _labels )
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labels0 = labels = cvarrToMat(_labels);
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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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int CvEM::get_nclusters() const
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{
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return emObj.get<int>("nclusters");
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}
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const CvMat* CvEM::get_means() const
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{
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return emObj.isTrained() ? &meansHdr : 0;
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}
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const CvMat** CvEM::get_covs() const
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{
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return emObj.isTrained() ? (const CvMat**)&covsPtrs[0] : 0;
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}
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const CvMat* CvEM::get_weights() const
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{
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return emObj.isTrained() ? &weightsHdr : 0;
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}
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const CvMat* CvEM::get_probs() const
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{
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return emObj.isTrained() ? &probsHdr : 0;
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}
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using namespace cv;
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CvEM::CvEM( const Mat& samples, const Mat& sample_idx, CvEMParams params )
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{
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train(samples, sample_idx, params, 0);
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}
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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, 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,
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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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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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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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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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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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{
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return emObj.get<int>("nclusters");
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}
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Mat CvEM::getMeans() const
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{
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return emObj.get<Mat>("means");
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}
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void CvEM::getCovs(vector<Mat>& _covs) const
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{
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_covs = emObj.get<vector<Mat> >("covs");
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}
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Mat CvEM::getWeights() const
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{
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return emObj.get<Mat>("weights");
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}
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Mat CvEM::getProbs() const
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{
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return probs;
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}
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/* End of file. */
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