Direct Graphical Models  v.1.5.2
DirectGraphicalModels::CTrainNodeCvGMM Class Reference

OpenCV Gaussian Mixture Model training class. More...

#include <TrainNodeCvGMM.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeCvGMM:
Collaboration diagram for DirectGraphicalModels::CTrainNodeCvGMM:

Public Member Functions

 CTrainNodeCvGMM (byte nStates, word nFeatures, TrainNodeCvGMMParams params=TRAIN_NODE_CV_GMM_PARAMS_DEFAULT)
 Constructor. More...
 
 CTrainNodeCvGMM (byte nStates, word nFeatures, size_t maxSamples, byte nGausses=TRAIN_NODE_CV_GMM_PARAMS_DEFAULT.numGausses)
 Constructor. More...
 
virtual ~CTrainNodeCvGMM (void)
 
void reset (void)
 Resets class variables. More...
 
void save (const std::string &path, const std::string &name=std::string(), short idx=-1) const
 Saves the training data. More...
 
void load (const std::string &path, const std::string &name=std::string(), short idx=-1)
 Loads the training data. More...
 
void addFeatureVec (const Mat &featureVector, byte gt)
 Adds new feature vector. More...
 
void train (bool doClean=false)
 Random model training. More...
 
- Public Member Functions inherited from DirectGraphicalModels::CTrainNode
 CTrainNode (byte nStates, word nFeatures)
 Constructor. More...
 
virtual ~CTrainNode (void)
 
void addFeatureVec (const Mat &featureVectors, const Mat &gt)
 Adds a block of new feature vectors. More...
 
void addFeatureVec (const vec_mat_t &featureVectors, const Mat &gt)
 Adds a block of new feature vectors. More...
 
Mat getNodePotentials (const Mat &featureVectors, const Mat &weights=Mat(), float Z=0.0f) const
 Returns a block of node potentials, based on the block of feature vector. More...
 
Mat getNodePotentials (const vec_mat_t &featureVectors, const Mat &weights=Mat(), float Z=0.0f) const
 Returns a block of node potentials, based on the block of feature vector. More...
 
Mat getNodePotentials (const Mat &featureVector, float weight, float Z=0.0f) const
 Returns the node potential, based on the feature vector. More...
 
- Public Member Functions inherited from DirectGraphicalModels::ITrain
 ITrain (byte nStates, word nFeatures)
 Constructor. More...
 
virtual ~ITrain (void)
 
word getNumFeatures (void) const
 Returns number of features. More...
 
- Public Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
 CBaseRandomModel (byte nStates)
 Constructor. More...
 
virtual ~CBaseRandomModel (void)
 
byte getNumStates (void) const
 Returns number of features. More...
 

Protected Member Functions

void saveFile (FILE *pFile) const
 Saves the random model into the file. More...
 
void loadFile (FILE *pFile)
 Loads the random model from the file. More...
 
void calculateNodePotentials (const Mat &featureVector, Mat &potential, Mat &mask) const
 Calculates the node potential, based on the feature vector. More...
 
- Protected Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
std::string generateFileName (const std::string &path, const std::string &name, short idx) const
 Generates name of the data file for storing random model parameters. More...
 

Protected Attributes

std::vector< Ptr< ml::EM > > m_vpEM
 Expectation Maximization for GMM parameters estimation. More...
 
CSamplesAccumulatorm_pSamplesAcc
 Samples Accumulator. More...
 
- Protected Attributes inherited from DirectGraphicalModels::ITrain
word m_nFeatures
 The number of features (length of the feature vector) More...
 
- Protected Attributes inherited from DirectGraphicalModels::CBaseRandomModel
byte m_nStates
 The number of states (classes) More...
 

Detailed Description

OpenCV Gaussian Mixture Model training class.

Author
Sergey G. Kosov, serge.nosp@m.y.ko.nosp@m.sov@p.nosp@m.roje.nosp@m.ct-10.nosp@m..de

Definition at line 39 of file TrainNodeCvGMM.h.

Constructor & Destructor Documentation

◆ CTrainNodeCvGMM() [1/2]

DirectGraphicalModels::CTrainNodeCvGMM::CTrainNodeCvGMM ( byte  nStates,
word  nFeatures,
TrainNodeCvGMMParams  params = TRAIN_NODE_CV_GMM_PARAMS_DEFAULT 
)

Constructor.

Parameters
nStatesNumber of states (classes)
nFeaturesNumber of features
paramsExpectation Maximization parameters (Ref. TrainNodeCvGMMParams)

Definition at line 12 of file TrainNodeCvGMM.cpp.

◆ CTrainNodeCvGMM() [2/2]

DirectGraphicalModels::CTrainNodeCvGMM::CTrainNodeCvGMM ( byte  nStates,
word  nFeatures,
size_t  maxSamples,
byte  nGausses = TRAIN_NODE_CV_GMM_PARAMS_DEFAULT.numGausses 
)

Constructor.

Parameters
nStatesNumber of states (classes)
nFeaturesNumber of features
maxSamplesMaximum number of samples to be used in training
nGaussesThe number of mixture components in the Gaussian Mixture Model per state (class)

Definition at line 18 of file TrainNodeCvGMM.cpp.

◆ ~CTrainNodeCvGMM()

DirectGraphicalModels::CTrainNodeCvGMM::~CTrainNodeCvGMM ( void  )
virtual

Definition at line 40 of file TrainNodeCvGMM.cpp.

Member Function Documentation

◆ addFeatureVec()

void DirectGraphicalModels::CTrainNodeCvGMM::addFeatureVec ( const Mat &  featureVector,
byte  gt 
)
virtual

Adds new feature vector.

Used to add a featureVector, corresponding to the ground-truth state (class) gt for training

Parameters
featureVectorMulti-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1)
gtCorresponding ground-truth state (class)

Implements DirectGraphicalModels::CTrainNode.

Definition at line 74 of file TrainNodeCvGMM.cpp.

Here is the call graph for this function:

◆ calculateNodePotentials()

void DirectGraphicalModels::CTrainNodeCvGMM::calculateNodePotentials ( const Mat &  featureVector,
Mat &  potential,
Mat &  mask 
) const
protectedvirtual

Calculates the node potential, based on the feature vector.

This function calculates the potentials of the node, described with the sample featureVector, being in each state (belonging to each class). These potentials are united in the node potential vector:

\[nodePot[nStates] = f(\textbf{f}[nFeatures]).\]

Functions \( f \) must be implemented in derived classes.

Parameters
[in]featureVectorMulti-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_{XX}C1)
[in,out]potentialNode potentials: Mat(size: nStates x 1; type: CV_32FC1). This parameter should be preinitialized and set to value 0.
[in,out]maskRelevant Node potentials: Mat(size: nStates x 1; type: CV_8UC1). This parameter should be preinitialized and set to value 1 (all potentials are relevant).

Implements DirectGraphicalModels::CTrainNode.

Definition at line 98 of file TrainNodeCvGMM.cpp.

◆ load()

void DirectGraphicalModels::CTrainNodeCvGMM::load ( const std::string &  path,
const std::string &  name = std::string(),
short  idx = -1 
)
virtual

Loads the training data.

Allows to re-use the class. Loads data to the file: "<path><name>_<idx>.dat".

Parameters
pathPath to the folder, containing the data file.
nameName of data file. If empty, will be generated automatically from the class name.
idxIndex of the data file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 60 of file TrainNodeCvGMM.cpp.

Here is the call graph for this function:

◆ loadFile()

void DirectGraphicalModels::CTrainNodeCvGMM::loadFile ( FILE *  pFile)
inlineprotectedvirtual

Loads the random model from the file.

Allows to re-use the class.

Parameters
pFilePointer to the file, opened for reading.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 70 of file TrainNodeCvGMM.h.

◆ reset()

void DirectGraphicalModels::CTrainNodeCvGMM::reset ( void  )
virtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 46 of file TrainNodeCvGMM.cpp.

Here is the call graph for this function:

◆ save()

void DirectGraphicalModels::CTrainNodeCvGMM::save ( const std::string &  path,
const std::string &  name = std::string(),
short  idx = -1 
) const
virtual

Saves the training data.

Allows to re-use the class. Stores data to the file: "<path><name>_<idx>.dat".

Parameters
pathPath to the destination folder.
nameName of data file. If empty, will be generated automatically from the class name.
idxIndex of the destination file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 52 of file TrainNodeCvGMM.cpp.

Here is the call graph for this function:

◆ saveFile()

void DirectGraphicalModels::CTrainNodeCvGMM::saveFile ( FILE *  pFile) const
inlineprotectedvirtual

Saves the random model into the file.

Allows to re-use the class.

Parameters
pFilePointer to the file, opened for writing.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 69 of file TrainNodeCvGMM.h.

◆ train()

void DirectGraphicalModels::CTrainNodeCvGMM::train ( bool  doClean = false)
virtual

Random model training.

Auxilary function for training - some derived classes may use this function inbetween training and classification phases

Note
This function must be called inbetween the training and classification phases
Parameters
doCleanFlag indicating if the memory, keeping the trining data should be released after training

Reimplemented from DirectGraphicalModels::CTrainNode.

Definition at line 79 of file TrainNodeCvGMM.cpp.

Here is the call graph for this function:

Member Data Documentation

◆ m_pSamplesAcc

CSamplesAccumulator* DirectGraphicalModels::CTrainNodeCvGMM::m_pSamplesAcc
protected

Samples Accumulator.

Definition at line 84 of file TrainNodeCvGMM.h.

◆ m_vpEM

std::vector<Ptr<ml::EM> > DirectGraphicalModels::CTrainNodeCvGMM::m_vpEM
protected

Expectation Maximization for GMM parameters estimation.

Definition at line 83 of file TrainNodeCvGMM.h.


The documentation for this class was generated from the following files: