/*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) { 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) {} CvEM::CvEM() : logLikelihood(DBL_MAX) { } CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx, CvEMParams params, CvMat* labels ) : logLikelihood(DBL_MAX) { train(samples, sample_idx, params, labels); } CvEM::~CvEM() { clear(); } void CvEM::clear() { emObj.clear(); } void CvEM::read( CvFileStorage* fs, CvFileNode* node ) { FileNode fn(fs, node); emObj.read(fn); set_mat_hdrs(); } void CvEM::write( CvFileStorage* _fs, const char* name ) const { FileStorage fs = _fs; if(name) fs << name << "{"; emObj.write(fs); if(name) fs << "}"; fs.fs.obj = 0; } double CvEM::calcLikelihood( const Mat &input_sample ) const { 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 = static_cast(emObj.predict(sample, _probs ? _OutputArray(prbs) : cv::noArray())[1]); if(_probs) { if( prbs.data != prbs0.data ) { CV_Assert( prbs.size == prbs0.size ); prbs.convertTo(prbs0, prbs0.type()); } } return (float)cls; } void CvEM::set_mat_hdrs() { if(emObj.isTrained()) { meansHdr = emObj.get("means"); int K = emObj.get("nclusters"); covsHdrs.resize(K); covsPtrs.resize(K); const std::vector& covs = emObj.get >("covs"); for(size_t i = 0; i < covsHdrs.size(); i++) { covsHdrs[i] = covs[i]; covsPtrs[i] = &covsHdrs[i]; } weightsHdr = emObj.get("weights"); probsHdr = probs; } } static void init_params(const CvEMParams& src, Mat& prbs, Mat& weights, Mat& means, vector& covsHdrs) { prbs = src.probs; weights = src.weights; means = src.means; if(src.covs) { covsHdrs.resize(src.nclusters); for(size_t i = 0; i < covsHdrs.size(); i++) covsHdrs[i] = src.covs[i]; } } bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx, CvEMParams _params, CvMat* _labels ) { CV_Assert(_sample_idx == 0); Mat samples = cvarrToMat(_samples), labels0, labels; if( _labels ) labels0 = labels = cvarrToMat(_labels); bool isOk = train(samples, Mat(), _params, _labels ? &labels : 0); CV_Assert( labels0.data == labels.data ); return isOk; } int CvEM::get_nclusters() const { return emObj.get("nclusters"); } const CvMat* CvEM::get_means() const { return emObj.isTrained() ? &meansHdr : 0; } const CvMat** CvEM::get_covs() const { return emObj.isTrained() ? (const CvMat**)&covsPtrs[0] : 0; } const CvMat* CvEM::get_weights() const { return emObj.isTrained() ? &weightsHdr : 0; } const CvMat* CvEM::get_probs() const { return emObj.isTrained() ? &probsHdr : 0; } using namespace cv; CvEM::CvEM( const Mat& samples, const Mat& sample_idx, CvEMParams params ) { train(samples, sample_idx, params, 0); } bool CvEM::train( const Mat& _samples, const Mat& _sample_idx, CvEMParams _params, Mat* _labels ) { CV_Assert(_sample_idx.empty()); Mat prbs, weights, means, logLikelihoods; std::vector 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, logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs); else if( _params.start_step == EM::START_E_STEP ) isOk = emObj.trainE(_samples, means, covsHdrs, weights, logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs); else if( _params.start_step == EM::START_M_STEP ) isOk = emObj.trainM(_samples, prbs, logLikelihoods, _labels ? _OutputArray(*_labels) : cv::noArray(), probs); else CV_Error(CV_StsBadArg, "Bad start type of EM algorithm"); if(isOk) { logLikelihood = sum(logLikelihoods).val[0]; set_mat_hdrs(); } return isOk; } float CvEM::predict( const Mat& _sample, Mat* _probs ) const { return static_cast(emObj.predict(_sample, _probs ? _OutputArray(*_probs) : cv::noArray())[1]); } int CvEM::getNClusters() const { return emObj.get("nclusters"); } Mat CvEM::getMeans() const { return emObj.get("means"); } void CvEM::getCovs(vector& _covs) const { _covs = emObj.get >("covs"); } Mat CvEM::getWeights() const { return emObj.get("weights"); } Mat CvEM::getProbs() const { return probs; } /* End of file. */