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Commit 85fa0e77 authored by Maria Dimashova's avatar Maria Dimashova
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added cv::EM, moved CvEM to legacy, added/updated tests

parent cdc5bbc0
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modules/contrib/include/opencv2/contrib/hybridtracker.hpp
View file @ 85fa0e77
...@@ -66,7 +66,7 @@ struct CV_EXPORTS CvMotionModel ...@@ -66,7 +66,7 @@ struct CV_EXPORTS CvMotionModel
} }
float low_pass_gain; // low pass gain float low_pass_gain; // low pass gain
CvEMParams em_params; // EM parameters cv::EM::Params em_params; // EM parameters
}; };
// Mean Shift Tracker parameters for specifying use of HSV channel and CamShift parameters. // Mean Shift Tracker parameters for specifying use of HSV channel and CamShift parameters.
...@@ -109,7 +109,7 @@ struct CV_EXPORTS CvHybridTrackerParams ...@@ -109,7 +109,7 @@ struct CV_EXPORTS CvHybridTrackerParams
float ms_tracker_weight; float ms_tracker_weight;
CvFeatureTrackerParams ft_params; CvFeatureTrackerParams ft_params;
CvMeanShiftTrackerParams ms_params; CvMeanShiftTrackerParams ms_params;
CvEMParams em_params; cv::EM::Params em_params;
int motion_model; int motion_model;
float low_pass_gain; float low_pass_gain;
}; };
...@@ -182,7 +182,7 @@ private: ...@@ -182,7 +182,7 @@ private:
CvMat* samples; CvMat* samples;
CvMat* labels; CvMat* labels;
CvEM em_model; cv::EM em_model;
Rect prev_window; Rect prev_window;
Point2f prev_center; Point2f prev_center;
......
modules/contrib/src/hybridtracker.cpp
View file @ 85fa0e77
...@@ -137,11 +137,11 @@ void CvHybridTracker::newTracker(Mat image, Rect selection) { ...@@ -137,11 +137,11 @@ void CvHybridTracker::newTracker(Mat image, Rect selection) {
params.em_params.probs = NULL; params.em_params.probs = NULL;
params.em_params.nclusters = 1; params.em_params.nclusters = 1;
params.em_params.weights = NULL; params.em_params.weights = NULL;
params.em_params.cov_mat_type = CvEM::COV_MAT_SPHERICAL; params.em_params.covMatType = cv::EM::COV_MAT_SPHERICAL;
params.em_params.start_step = CvEM::START_AUTO_STEP; params.em_params.startStep = cv::EM::START_AUTO_STEP;
params.em_params.term_crit.max_iter = 10000; params.em_params.termCrit.maxCount = 10000;
params.em_params.term_crit.epsilon = 0.001; params.em_params.termCrit.epsilon = 0.001;
params.em_params.term_crit.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS; params.em_params.termCrit.type = cv::TermCriteria::COUNT + cv::TermCriteria::EPS;
samples = cvCreateMat(2, 1, CV_32FC1); samples = cvCreateMat(2, 1, CV_32FC1);
labels = cvCreateMat(2, 1, CV_32SC1); labels = cvCreateMat(2, 1, CV_32SC1);
...@@ -221,7 +221,10 @@ void CvHybridTracker::updateTrackerWithEM(Mat image) { ...@@ -221,7 +221,10 @@ void CvHybridTracker::updateTrackerWithEM(Mat image) {
count++; count++;
} }
em_model.train(samples, 0, params.em_params, labels); cv::Mat lbls;
em_model.train(samples, cv::Mat(), params.em_params, &lbls);
if(labels)
*labels = lbls;
curr_center.x = (float)em_model.getMeans().at<double> (0, 0); curr_center.x = (float)em_model.getMeans().at<double> (0, 0);
curr_center.y = (float)em_model.getMeans().at<double> (0, 1); curr_center.y = (float)em_model.getMeans().at<double> (0, 1);
......
modules/legacy/CMakeLists.txt
View file @ 85fa0e77
ocv_define_module(legacy opencv_calib3d opencv_highgui opencv_video) ocv_define_module(legacy opencv_calib3d opencv_highgui opencv_video opencv_ml)
modules/legacy/include/opencv2/legacy/legacy.hpp
View file @ 85fa0e77
...@@ -46,6 +46,7 @@ ...@@ -46,6 +46,7 @@
#include "opencv2/imgproc/imgproc_c.h" #include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/features2d/features2d.hpp" #include "opencv2/features2d/features2d.hpp"
#include "opencv2/calib3d/calib3d.hpp" #include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/ml/ml.hpp"
#ifdef __cplusplus #ifdef __cplusplus
extern "C" { extern "C" {
...@@ -1761,10 +1762,106 @@ protected: ...@@ -1761,10 +1762,106 @@ protected:
IplImage* m_mask; IplImage* m_mask;
}; };
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
struct CV_EXPORTS_W_MAP CvEMParams
{
CvEMParams();
CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
int start_step=0/*CvEM::START_AUTO_STEP*/,
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );
CV_PROP_RW int nclusters;
CV_PROP_RW int cov_mat_type;
CV_PROP_RW int start_step;
const CvMat* probs;
const CvMat* weights;
const CvMat* means;
const CvMat** covs;
CV_PROP_RW CvTermCriteria term_crit;
};
class CV_EXPORTS_W CvEM : public CvStatModel
{
public:
// Type of covariation matrices
enum { COV_MAT_SPHERICAL=cv::EM::COV_MAT_SPHERICAL,
COV_MAT_DIAGONAL =cv::EM::COV_MAT_DIAGONAL,
COV_MAT_GENERIC =cv::EM::COV_MAT_GENERIC };
// The initial step
enum { START_E_STEP=cv::EM::START_E_STEP,
START_M_STEP=cv::EM::START_M_STEP,
START_AUTO_STEP=cv::EM::START_AUTO_STEP };
CV_WRAP CvEM();
CvEM( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual ~CvEM();
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs, bool isNormalize=true ) const;
#ifndef SWIG
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams() );
CV_WRAP virtual bool train( const cv::Mat& samples,
const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(),
CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0, bool isNormalize=true ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const;
CV_WRAP const cv::Mat& getMeans() const;
CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
CV_WRAP const cv::Mat& getWeights() const;
CV_WRAP const cv::Mat& getProbs() const;
CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? likelihood : DBL_MAX; }
#endif
CV_WRAP virtual void clear();
int get_nclusters() const;
const CvMat* get_means() const;
const CvMat** get_covs() const;
const CvMat* get_weights() const;
const CvMat* get_probs() const;
inline double get_log_likelihood() const { return getLikelihood(); }
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
protected:
void set_mat_hdrs();
cv::EM emObj;
cv::Mat probs;
double likelihood;
CvMat meansHdr;
std::vector<CvMat> covsHdrs;
std::vector<CvMat*> covsPtrs;
CvMat weightsHdr;
CvMat probsHdr;
};
namespace cv namespace cv
{ {
typedef CvEMParams EMParams;
typedef CvEM ExpectationMaximization;
/*! /*!
The Patch Generator class The Patch Generator class
*/ */
......
modules/legacy/src/em.cpp 0 → 100644
View file @ 85fa0e77
/*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"
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() : likelihood(DBL_MAX)
{
}
CvEM::CvEM( const CvMat* samples, const CvMat* sample_idx,
CvEMParams params, CvMat* labels ) : likelihood(DBL_MAX)
{
train(samples, sample_idx, params, labels);
}
CvEM::~CvEM()
{
clear();
}
void CvEM::clear()
{
emObj.clear();
}
void CvEM::read( CvFileStorage* fs, CvFileNode* node )
{
cv::FileNode fn(fs, node);
emObj.read(fn);
set_mat_hdrs();
}
void CvEM::write( CvFileStorage* _fs, const char* name ) const
{
cv::FileStorage fs = _fs;
if(name)
fs << name << "{";
emObj.write(fs);
if(name)
fs << "}";
}
double CvEM::calcLikelihood( const cv::Mat &input_sample ) const
{
double likelihood;
emObj.predict(input_sample, 0, &likelihood);
return likelihood;
}
float
CvEM::predict( const CvMat* _sample, CvMat* _probs, bool isNormalize ) const
{
cv::Mat prbs;
int cls = emObj.predict(_sample, _probs ? &prbs : 0);
if(_probs)
{
if(isNormalize)
cv::normalize(prbs, prbs, 1, 0, cv::NORM_L1);
*_probs = prbs;
}
return (float)cls;
}
void CvEM::set_mat_hdrs()
{
if(emObj.isTrained())
{
meansHdr = emObj.getMeans();
covsHdrs.resize(emObj.getNClusters());
covsPtrs.resize(emObj.getNClusters());
const std::vector<cv::Mat>& covs = emObj.getCovs();
for(size_t i = 0; i < covsHdrs.size(); i++)
{
covsHdrs[i] = covs[i];
covsPtrs[i] = &covsHdrs[i];
}
weightsHdr = emObj.getWeights();
probsHdr = probs;
}
}
static
void init_params(const CvEMParams& src, cv::EM::Params& dst,
cv::Mat& prbs, cv::Mat& weights,
cv::Mat& means, cv::vector<cv::Mat>& covsHdrs)
{
dst.nclusters = src.nclusters;
dst.covMatType = src.cov_mat_type;
dst.startStep = src.start_step;
dst.termCrit = src.term_crit;
prbs = src.probs;
dst.probs = &prbs;
weights = src.weights;
dst.weights = &weights;
means = src.means;
dst.means = &means;
if(src.covs)
{
covsHdrs.resize(src.nclusters);
for(size_t i = 0; i < covsHdrs.size(); i++)
covsHdrs[i] = src.covs[i];
dst.covs = &covsHdrs;
}
}
bool CvEM::train( const CvMat* _samples, const CvMat* _sample_idx,
CvEMParams _params, CvMat* _labels )
{
cv::EM::Params params;
cv::Mat prbs, weights, means;
std::vector<cv::Mat> covsHdrs;
init_params(_params, params, prbs, weights, means, covsHdrs);
cv::Mat lbls;
cv::Mat likelihoods;
bool isOk = emObj.train(_samples, _sample_idx, params, _labels ? &lbls : 0, &probs, &likelihoods );
if(isOk)
{
if(_labels)
*_labels = lbls;
likelihood = cv::sum(likelihoods)[0];
set_mat_hdrs();
}
return isOk;
}
int CvEM::get_nclusters() const
{
return emObj.getNClusters();
}
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::EM::Params params;
cv::Mat prbs, weights, means;
std::vector<cv::Mat> covsHdrs;
init_params(_params, params, prbs, weights, means, covsHdrs);
cv::Mat likelihoods;
bool isOk = emObj.train(_samples, _sample_idx, params, _labels, &probs, &likelihoods);
if(isOk)
{
likelihoods = cv::sum(likelihoods).val[0];
set_mat_hdrs();
}
return isOk;
}
float
CvEM::predict( const Mat& _sample, Mat* _probs, bool isNormalize ) const
{
int cls = emObj.predict(_sample, _probs);
if(_probs && isNormalize)
cv::normalize(*_probs, *_probs, 1, 0, cv::NORM_L1);
return (float)cls;
}
int CvEM::getNClusters() const
{
return emObj.getNClusters();
}
const Mat& CvEM::getMeans() const
{
return emObj.getMeans();
}
void CvEM::getCovs(vector<Mat>& _covs) const
{
_covs = emObj.getCovs();
}
const Mat& CvEM::getWeights() const
{
return emObj.getWeights();
}
const Mat& CvEM::getProbs() const
{
return probs;
}
/* End of file. */
modules/legacy/test/test_em.cpp 0 → 100644
View file @ 85fa0e77
This diff is collapsed.
modules/ml/include/opencv2/ml/ml.hpp
View file @ 85fa0e77
...@@ -46,6 +46,10 @@ ...@@ -46,6 +46,10 @@
#ifdef __cplusplus #ifdef __cplusplus
#include <map>
#include <string>
#include <iostream>
// Apple defines a check() macro somewhere in the debug headers // Apple defines a check() macro somewhere in the debug headers
// that interferes with a method definiton in this header // that interferes with a method definiton in this header
#undef check #undef check
...@@ -549,114 +553,93 @@ protected: ...@@ -549,114 +553,93 @@ protected:
/****************************************************************************************\ /****************************************************************************************\
* Expectation - Maximization * * Expectation - Maximization *
\****************************************************************************************/ \****************************************************************************************/
namespace cv
struct CV_EXPORTS_W_MAP CvEMParams
{ {
CvEMParams(); class CV_EXPORTS_W EM : public Algorithm
CvEMParams( int nclusters, int cov_mat_type=1/*CvEM::COV_MAT_DIAGONAL*/,
int start_step=0/*CvEM::START_AUTO_STEP*/,
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );
CV_PROP_RW int nclusters;
CV_PROP_RW int cov_mat_type;
CV_PROP_RW int start_step;
const CvMat* probs;
const CvMat* weights;
const CvMat* means;
const CvMat** covs;
CV_PROP_RW CvTermCriteria term_crit;
};
class CV_EXPORTS_W CvEM : public CvStatModel
{ {
public: public:
// Type of covariation matrices // Type of covariation matrices
enum { COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2 }; enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2};
// The initial step // The initial step
enum { START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0 }; enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
CV_WRAP CvEM(); class CV_EXPORTS_W Params
CvEM( const CvMat* samples, const CvMat* sampleIdx=0, {
CvEMParams params=CvEMParams(), CvMat* labels=0 ); public:
//CvEM (CvEMParams params, CvMat * means, CvMat ** covs, CvMat * weights, Params(int nclusters=10, int covMatType=EM::COV_MAT_DIAGONAL, int startStep=EM::START_AUTO_STEP,
// CvMat * probs, CvMat * log_weight_div_det, CvMat * inv_eigen_values, CvMat** cov_rotate_mats); const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* probs=0, const cv::Mat* weights=0,
const cv::Mat* means=0, const std::vector<cv::Mat>* covs=0);
int nclusters;
int covMatType;
int startStep;
// all 4 following matrices should have type CV_32FC1
const cv::Mat* probs;
const cv::Mat* weights;
const cv::Mat* means;
const std::vector<cv::Mat>* covs;
cv::TermCriteria termCrit;
};
virtual ~CvEM(); EM();
EM(const cv::Mat& samples, const cv::Mat samplesMask=cv::Mat(),
const EM::Params& params=EM::Params(), cv::Mat* labels=0, cv::Mat* probs=0, cv::Mat* likelihoods=0);
virtual ~EM();
virtual void clear();
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0, virtual bool train(const cv::Mat& samples, const cv::Mat& samplesMask=cv::Mat(),
CvEMParams params=CvEMParams(), CvMat* labels=0 ); const EM::Params& params=EM::Params(), cv::Mat* labels=0, cv::Mat* probs=0, cv::Mat* likelihoods=0);
int predict(const cv::Mat& sample, cv::Mat* probs=0, double* likelihood=0) const;
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const; bool isTrained() const;
int getNClusters() const;
int getCovMatType() const;
#ifndef SWIG const cv::Mat& getWeights() const;
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& getMeans() const;
CvEMParams params=CvEMParams() ); const std::vector<cv::Mat>& getCovs() const;
CV_WRAP virtual bool train( const cv::Mat& samples,
const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(),
CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const;
CV_WRAP cv::Mat getMeans() const;
CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
CV_WRAP cv::Mat getWeights() const;
CV_WRAP cv::Mat getProbs() const;
CV_WRAP inline double getLikelihood() const { return log_likelihood; }
CV_WRAP inline double getLikelihoodDelta() const { return log_likelihood_delta; }
#endif
CV_WRAP virtual void clear();
int get_nclusters() const; AlgorithmInfo* info() const;
const CvMat* get_means() const; virtual void read(const FileNode& fn);
const CvMat** get_covs() const;
const CvMat* get_weights() const;
const CvMat* get_probs() const;
inline double get_log_likelihood() const { return log_likelihood; } protected:
inline double get_log_likelihood_delta() const { return log_likelihood_delta; } virtual void setTrainData(const cv::Mat& samples, const cv::Mat& samplesMask, const EM::Params& params);
// inline const CvMat * get_log_weight_div_det () const { return log_weight_div_det; };
// inline const CvMat * get_inv_eigen_values () const { return inv_eigen_values; };
// inline const CvMat ** get_cov_rotate_mats () const { return cov_rotate_mats; };
virtual void read( CvFileStorage* fs, CvFileNode* node ); bool doTrain(const cv::TermCriteria& termCrit);
virtual void write( CvFileStorage* fs, const char* name ) const; virtual void eStep();
virtual void mStep();
virtual void write_params( CvFileStorage* fs ) const; void clusterTrainSamples();
virtual void read_params( CvFileStorage* fs, CvFileNode* node ); void decomposeCovs();
void computeLogWeightDivDet();
protected: void computeProbabilities(const cv::Mat& sample, int& label, cv::Mat* probs, float* likelihood) const;
virtual void set_params( const CvEMParams& params, // all inner matrices have type CV_32FC1
const CvVectors& train_data ); int nclusters;
virtual void init_em( const CvVectors& train_data ); int covMatType;
virtual double run_em( const CvVectors& train_data ); int startStep;
virtual void init_auto( const CvVectors& samples );
virtual void kmeans( const CvVectors& train_data, int nclusters,
CvMat* labels, CvTermCriteria criteria,
const CvMat* means );
CvEMParams params;
double log_likelihood;
double log_likelihood_delta;
CvMat* means;
CvMat** covs;
CvMat* weights;
CvMat* probs;
CvMat* log_weight_div_det; cv::Mat trainSamples;
CvMat* inv_eigen_values; cv::Mat trainProbs;
CvMat** cov_rotate_mats; cv::Mat trainLikelihoods;
cv::Mat trainLabels;
cv::Mat trainCounts;
cv::Mat weights;
cv::Mat means;
std::vector<cv::Mat> covs;
std::vector<cv::Mat> covsEigenValues;
std::vector<cv::Mat> covsRotateMats;
std::vector<cv::Mat> invCovsEigenValues;
cv::Mat logWeightDivDet;
}; };
} // namespace cv
/****************************************************************************************\ /****************************************************************************************\
* Decision Tree * * Decision Tree *
...@@ -2012,17 +1995,10 @@ CVAPI(void) cvCreateTestSet( int type, CvMat** samples, ...@@ -2012,17 +1995,10 @@ CVAPI(void) cvCreateTestSet( int type, CvMat** samples,
CvMat** responses, CvMat** responses,
int num_classes, ... ); int num_classes, ... );
#endif
/****************************************************************************************\ /****************************************************************************************\
* Data * * Data *
\****************************************************************************************/ \****************************************************************************************/
#include <map>
#include <string>
#include <iostream>
#define CV_COUNT 0 #define CV_COUNT 0
#define CV_PORTION 1 #define CV_PORTION 1
...@@ -2133,8 +2109,6 @@ typedef CvSVMParams SVMParams; ...@@ -2133,8 +2109,6 @@ typedef CvSVMParams SVMParams;
typedef CvSVMKernel SVMKernel; typedef CvSVMKernel SVMKernel;
typedef CvSVMSolver SVMSolver; typedef CvSVMSolver SVMSolver;
typedef CvSVM SVM; typedef CvSVM SVM;
typedef CvEMParams EMParams;
typedef CvEM ExpectationMaximization;
typedef CvDTreeParams DTreeParams; typedef CvDTreeParams DTreeParams;
typedef CvMLData TrainData; typedef CvMLData TrainData;
typedef CvDTree DecisionTree; typedef CvDTree DecisionTree;
...@@ -2156,5 +2130,7 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj(); ...@@ -2156,5 +2130,7 @@ template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
} }
#endif #endif // __cplusplus
#endif // __OPENCV_ML_HPP__
/* End of file. */ /* End of file. */
modules/ml/src/em.cpp
View file @ 85fa0e77
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modules/ml/test/test_emknearestkmeans.cpp
View file @ 85fa0e77
This diff is collapsed.
modules/ml/test/test_mltests2.cpp
View file @ 85fa0e77
...@@ -451,7 +451,6 @@ CV_MLBaseTest::CV_MLBaseTest(const char* _modelName) ...@@ -451,7 +451,6 @@ CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
nbayes = 0; nbayes = 0;
knearest = 0; knearest = 0;
svm = 0; svm = 0;
em = 0;
ann = 0; ann = 0;
dtree = 0; dtree = 0;
boost = 0; boost = 0;
...@@ -463,8 +462,6 @@ CV_MLBaseTest::CV_MLBaseTest(const char* _modelName) ...@@ -463,8 +462,6 @@ CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
knearest = new CvKNearest; knearest = new CvKNearest;
else if( !modelName.compare(CV_SVM) ) else if( !modelName.compare(CV_SVM) )
svm = new CvSVM; svm = new CvSVM;
else if( !modelName.compare(CV_EM) )
em = new CvEM;
else if( !modelName.compare(CV_ANN) ) else if( !modelName.compare(CV_ANN) )
ann = new CvANN_MLP; ann = new CvANN_MLP;
else if( !modelName.compare(CV_DTREE) ) else if( !modelName.compare(CV_DTREE) )
...@@ -487,8 +484,6 @@ CV_MLBaseTest::~CV_MLBaseTest() ...@@ -487,8 +484,6 @@ CV_MLBaseTest::~CV_MLBaseTest()
delete knearest; delete knearest;
if( svm ) if( svm )
delete svm; delete svm;
if( em )
delete em;
if( ann ) if( ann )
delete ann; delete ann;
if( dtree ) if( dtree )
...@@ -756,8 +751,6 @@ void CV_MLBaseTest::save( const char* filename ) ...@@ -756,8 +751,6 @@ void CV_MLBaseTest::save( const char* filename )
knearest->save( filename ); knearest->save( filename );
else if( !modelName.compare(CV_SVM) ) else if( !modelName.compare(CV_SVM) )
svm->save( filename ); svm->save( filename );
else if( !modelName.compare(CV_EM) )
em->save( filename );
else if( !modelName.compare(CV_ANN) ) else if( !modelName.compare(CV_ANN) )
ann->save( filename ); ann->save( filename );
else if( !modelName.compare(CV_DTREE) ) else if( !modelName.compare(CV_DTREE) )
...@@ -778,8 +771,6 @@ void CV_MLBaseTest::load( const char* filename ) ...@@ -778,8 +771,6 @@ void CV_MLBaseTest::load( const char* filename )
knearest->load( filename ); knearest->load( filename );
else if( !modelName.compare(CV_SVM) ) else if( !modelName.compare(CV_SVM) )
svm->load( filename ); svm->load( filename );
else if( !modelName.compare(CV_EM) )
em->load( filename );
else if( !modelName.compare(CV_ANN) ) else if( !modelName.compare(CV_ANN) )
ann->load( filename ); ann->load( filename );
else if( !modelName.compare(CV_DTREE) ) else if( !modelName.compare(CV_DTREE) )
......
modules/ml/test/test_precomp.hpp
View file @ 85fa0e77
...@@ -44,7 +44,6 @@ protected: ...@@ -44,7 +44,6 @@ protected:
CvNormalBayesClassifier* nbayes; CvNormalBayesClassifier* nbayes;
CvKNearest* knearest; CvKNearest* knearest;
CvSVM* svm; CvSVM* svm;
CvEM* em;
CvANN_MLP* ann; CvANN_MLP* ann;
CvDTree* dtree; CvDTree* dtree;
CvBoost* boost; CvBoost* boost;
......
samples/cpp/em.cpp
View file @ 85fa0e77
#include "opencv2/ml/ml.hpp" #include "opencv2/legacy/legacy.hpp"
#include "opencv2/highgui/highgui.hpp" #include "opencv2/highgui/highgui.hpp"
using namespace cv; using namespace cv;
......
samples/cpp/points_classifier.cpp
View file @ 85fa0e77
...@@ -11,7 +11,6 @@ const Scalar WHITE_COLOR = CV_RGB(255,255,255); ...@@ -11,7 +11,6 @@ const Scalar WHITE_COLOR = CV_RGB(255,255,255);
const string winName = "points"; const string winName = "points";
const int testStep = 5; const int testStep = 5;
Mat img, imgDst; Mat img, imgDst;
RNG rng; RNG rng;
...@@ -19,16 +18,16 @@ vector<Point> trainedPoints; ...@@ -19,16 +18,16 @@ vector<Point> trainedPoints;
vector<int> trainedPointsMarkers; vector<int> trainedPointsMarkers;
vector<Scalar> classColors; vector<Scalar> classColors;
#define NBC 0 // normal Bayessian classifier #define _NBC_ 0 // normal Bayessian classifier
#define KNN 0 // k nearest neighbors classifier #define _KNN_ 0 // k nearest neighbors classifier
#define SVM 0 // support vectors machine #define _SVM_ 0 // support vectors machine
#define DT 1 // decision tree #define _DT_ 1 // decision tree
#define BT 0 // ADA Boost #define _BT_ 0 // ADA Boost
#define GBT 0 // gradient boosted trees #define _GBT_ 0 // gradient boosted trees
#define RF 0 // random forest #define _RF_ 0 // random forest
#define ERT 0 // extremely randomized trees #define _ERT_ 0 // extremely randomized trees
#define ANN 0 // artificial neural networks #define _ANN_ 0 // artificial neural networks
#define EM 0 // expectation-maximization #define _EM_ 0 // expectation-maximization
void on_mouse( int event, int x, int y, int /*flags*/, void* ) void on_mouse( int event, int x, int y, int /*flags*/, void* )
{ {
...@@ -48,13 +47,13 @@ void on_mouse( int event, int x, int y, int /*flags*/, void* ) ...@@ -48,13 +47,13 @@ void on_mouse( int event, int x, int y, int /*flags*/, void* )
} }
else if( event == CV_EVENT_RBUTTONUP ) else if( event == CV_EVENT_RBUTTONUP )
{ {
#if BT #if _BT_
if( classColors.size() < 2 ) if( classColors.size() < 2 )
{ {
#endif #endif
classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) ); classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) );
updateFlag = true; updateFlag = true;
#if BT #if _BT_
} }
else else
cout << "New class can not be added, because CvBoost can only be used for 2-class classification" << endl; cout << "New class can not be added, because CvBoost can only be used for 2-class classification" << endl;
...@@ -98,7 +97,7 @@ void prepare_train_data( Mat& samples, Mat& classes ) ...@@ -98,7 +97,7 @@ void prepare_train_data( Mat& samples, Mat& classes )
samples.convertTo( samples, CV_32FC1 ); samples.convertTo( samples, CV_32FC1 );
} }
#if NBC #if _NBC_
void find_decision_boundary_NBC() void find_decision_boundary_NBC()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -125,7 +124,7 @@ void find_decision_boundary_NBC() ...@@ -125,7 +124,7 @@ void find_decision_boundary_NBC()
#endif #endif
#if KNN #if _KNN_
void find_decision_boundary_KNN( int K ) void find_decision_boundary_KNN( int K )
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -151,7 +150,7 @@ void find_decision_boundary_KNN( int K ) ...@@ -151,7 +150,7 @@ void find_decision_boundary_KNN( int K )
} }
#endif #endif
#if SVM #if _SVM_
void find_decision_boundary_SVM( CvSVMParams params ) void find_decision_boundary_SVM( CvSVMParams params )
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -185,7 +184,7 @@ void find_decision_boundary_SVM( CvSVMParams params ) ...@@ -185,7 +184,7 @@ void find_decision_boundary_SVM( CvSVMParams params )
} }
#endif #endif
#if DT #if _DT_
void find_decision_boundary_DT() void find_decision_boundary_DT()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -225,7 +224,7 @@ void find_decision_boundary_DT() ...@@ -225,7 +224,7 @@ void find_decision_boundary_DT()
} }
#endif #endif
#if BT #if _BT_
void find_decision_boundary_BT() void find_decision_boundary_BT()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -265,7 +264,7 @@ void find_decision_boundary_BT() ...@@ -265,7 +264,7 @@ void find_decision_boundary_BT()
#endif #endif
#if GBT #if _GBT_
void find_decision_boundary_GBT() void find_decision_boundary_GBT()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -305,7 +304,7 @@ void find_decision_boundary_GBT() ...@@ -305,7 +304,7 @@ void find_decision_boundary_GBT()
#endif #endif
#if RF #if _RF_
void find_decision_boundary_RF() void find_decision_boundary_RF()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -346,7 +345,7 @@ void find_decision_boundary_RF() ...@@ -346,7 +345,7 @@ void find_decision_boundary_RF()
#endif #endif
#if ERT #if _ERT_
void find_decision_boundary_ERT() void find_decision_boundary_ERT()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -390,7 +389,7 @@ void find_decision_boundary_ERT() ...@@ -390,7 +389,7 @@ void find_decision_boundary_ERT()
} }
#endif #endif
#if ANN #if _ANN_
void find_decision_boundary_ANN( const Mat& layer_sizes ) void find_decision_boundary_ANN( const Mat& layer_sizes )
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -435,7 +434,7 @@ void find_decision_boundary_ANN( const Mat& layer_sizes ) ...@@ -435,7 +434,7 @@ void find_decision_boundary_ANN( const Mat& layer_sizes )
} }
#endif #endif
#if EM #if _EM_
void find_decision_boundary_EM() void find_decision_boundary_EM()
{ {
img.copyTo( imgDst ); img.copyTo( imgDst );
...@@ -443,19 +442,12 @@ void find_decision_boundary_EM() ...@@ -443,19 +442,12 @@ void find_decision_boundary_EM()
Mat trainSamples, trainClasses; Mat trainSamples, trainClasses;
prepare_train_data( trainSamples, trainClasses ); prepare_train_data( trainSamples, trainClasses );
CvEM em; cv::EM em;
CvEMParams params; cv::EM::Params params;
params.covs = NULL;
params.means = NULL;
params.weights = NULL;
params.probs = NULL;
params.nclusters = classColors.size(); params.nclusters = classColors.size();
params.cov_mat_type = CvEM::COV_MAT_GENERIC; params.covMatType = cv::EM::COV_MAT_GENERIC;
params.start_step = CvEM::START_AUTO_STEP; params.startStep = cv::EM::START_AUTO_STEP;
params.term_crit.max_iter = 10; params.termCrit = cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::COUNT, 10, 0.1);
params.term_crit.epsilon = 0.1;
params.term_crit.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
// learn classifier // learn classifier
em.train( trainSamples, Mat(), params, &trainClasses ); em.train( trainSamples, Mat(), params, &trainClasses );
...@@ -509,12 +501,12 @@ int main() ...@@ -509,12 +501,12 @@ int main()
if( key == 'r' ) // run if( key == 'r' ) // run
{ {
#if NBC #if _NBC_
find_decision_boundary_NBC(); find_decision_boundary_NBC();
cvNamedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE ); cvNamedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
imshow( "NormalBayesClassifier", imgDst ); imshow( "NormalBayesClassifier", imgDst );
#endif #endif
#if KNN #if _KNN_
int K = 3; int K = 3;
find_decision_boundary_KNN( K ); find_decision_boundary_KNN( K );
namedWindow( "kNN", WINDOW_AUTOSIZE ); namedWindow( "kNN", WINDOW_AUTOSIZE );
...@@ -526,7 +518,7 @@ int main() ...@@ -526,7 +518,7 @@ int main()
imshow( "kNN2", imgDst ); imshow( "kNN2", imgDst );
#endif #endif
#if SVM #if _SVM_
//(1)-(2)separable and not sets //(1)-(2)separable and not sets
CvSVMParams params; CvSVMParams params;
params.svm_type = CvSVM::C_SVC; params.svm_type = CvSVM::C_SVC;
...@@ -549,37 +541,37 @@ int main() ...@@ -549,37 +541,37 @@ int main()
imshow( "classificationSVM2", imgDst ); imshow( "classificationSVM2", imgDst );
#endif #endif
#if DT #if _DT_
find_decision_boundary_DT(); find_decision_boundary_DT();
namedWindow( "DT", WINDOW_AUTOSIZE ); namedWindow( "DT", WINDOW_AUTOSIZE );
imshow( "DT", imgDst ); imshow( "DT", imgDst );
#endif #endif
#if BT #if _BT_
find_decision_boundary_BT(); find_decision_boundary_BT();
namedWindow( "BT", WINDOW_AUTOSIZE ); namedWindow( "BT", WINDOW_AUTOSIZE );
imshow( "BT", imgDst); imshow( "BT", imgDst);
#endif #endif
#if GBT #if _GBT_
find_decision_boundary_GBT(); find_decision_boundary_GBT();
namedWindow( "GBT", WINDOW_AUTOSIZE ); namedWindow( "GBT", WINDOW_AUTOSIZE );
imshow( "GBT", imgDst); imshow( "GBT", imgDst);
#endif #endif
#if RF #if _RF_
find_decision_boundary_RF(); find_decision_boundary_RF();
namedWindow( "RF", WINDOW_AUTOSIZE ); namedWindow( "RF", WINDOW_AUTOSIZE );
imshow( "RF", imgDst); imshow( "RF", imgDst);
#endif #endif
#if ERT #if _ERT_
find_decision_boundary_ERT(); find_decision_boundary_ERT();
namedWindow( "ERT", WINDOW_AUTOSIZE ); namedWindow( "ERT", WINDOW_AUTOSIZE );
imshow( "ERT", imgDst); imshow( "ERT", imgDst);
#endif #endif
#if ANN #if _ANN_
Mat layer_sizes1( 1, 3, CV_32SC1 ); Mat layer_sizes1( 1, 3, CV_32SC1 );
layer_sizes1.at<int>(0) = 2; layer_sizes1.at<int>(0) = 2;
layer_sizes1.at<int>(1) = 5; layer_sizes1.at<int>(1) = 5;
...@@ -589,7 +581,7 @@ int main() ...@@ -589,7 +581,7 @@ int main()
imshow( "ANN", imgDst ); imshow( "ANN", imgDst );
#endif #endif
#if EM #if _EM_
find_decision_boundary_EM(); find_decision_boundary_EM();
namedWindow( "EM", WINDOW_AUTOSIZE ); namedWindow( "EM", WINDOW_AUTOSIZE );
imshow( "EM", imgDst ); imshow( "EM", imgDst );
......
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