Nested link (inter-layer edge) training class. More...

#include <TrainLinkNested.h>

Inheritance diagram for DirectGraphicalModels::CTrainLinkNested< Trainer >:

Collaboration diagram for DirectGraphicalModels::CTrainLinkNested< Trainer >:

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Public Member Functions
	CTrainLinkNested (byte nStatesBase, byte nStatesOccl, word nFeatures)
	Constructor. More...

template<class TrainerParams >
	CTrainLinkNested (byte nStatesBase, byte nStatesOccl, word nFeatures, TrainerParams params)
	Constructor. More...

virtual	~CTrainLinkNested (void)

virtual void	reset (void)
	Resets class variables. More...

virtual void	save (const std::string &path, const std::string &name=std::string(), short idx=-1) const
	Saves the training data. More...

virtual void	load (const std::string &path, const std::string &name=std::string(), short idx=-1)
	Loads the training data. More...

virtual void	addFeatureVec (const Mat &featureVector, byte gtb, byte gto)
	Adds a feature vector. More...

virtual void	train (bool doClean=false)
	Random model training. More...

Public Member Functions inherited from DirectGraphicalModels::CTrainLink
	CTrainLink (byte nStatesBase, byte nStatesOccl, word nFeatures)
	Constructor. More...

virtual	~CTrainLink (void)=default

void	addFeatureVec (const Mat &featureVectors, const Mat &gtb, const Mat &gto)
	Adds a block of new feature vectors. More...

void	addFeatureVec (const vec_mat_t &featureVectors, const Mat &gtb, const Mat &gto)
	Adds a block of new feature vectors. More...

Mat	getLinkPotentials (const Mat &featureVector, float weight=1.0f) const
	Returns the link potential, based on the feature vector. More...

Public Member Functions inherited from DirectGraphicalModels::ITrain
	ITrain (byte nStates, word nFeatures)
	Constructor. More...

virtual	~ITrain (void)=default

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 states (classes) More...

Protected Member Functions
virtual void	saveFile (FILE *pFile) const
	Saves the random model into the file. More...

virtual void	loadFile (FILE *pFile)
	Loads the random model from the file. More...

virtual Mat	calculateLinkPotentials (const Mat &featureVector) const
	Returns the data-dependent link (inter-layer edge) potentials. 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...

Private Attributes
CPriorNode *	m_pPrior
	Node prior poobability More...

CTrainNode *	m_pTrainer
	Node trainer More...

Additional Inherited Members
Protected Attributes inherited from DirectGraphicalModels::CTrainLink
byte	m_nStatesBase
	Number of states (classes) at the base layer of ML-CRF. More...

byte	m_nStatesOccl
	Number of states (classes) at the occlusion layerts of ML-CRF. More...

Protected Attributes inherited from DirectGraphicalModels::CBaseRandomModel
byte	m_nStates
	The number of states (classes) More...

Detailed Description

template<class Trainer>
class DirectGraphicalModels::CTrainLinkNested< Trainer >

Nested link (inter-layer edge) training class.

This class in order to estimate edge potentials, uses an unary potential trainer, which is learned for nStatesBase \(\times\) nStatesOccl states (classes), corresponding to the base and occlusion layers of the graphical model.

Template Parameters

Trainer The nested node potential trainer, derived from the CTrainNode 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 21 of file TrainLinkNested.h.

Constructor & Destructor Documentation

◆ CTrainLinkNested() [1/2]

template<class Trainer >

DirectGraphicalModels::CTrainLinkNested< Trainer >::CTrainLinkNested	(	byte	nStatesBase,
		byte	nStatesOccl,
		word	nFeatures
	)

inline

Constructor.

Parameters

nStatesBase	Number of states (classes) for the base layer of the graphical model
nStatesOccl	Number of states (classes) for the occlusion layer of the graphical model nStatesBase * nStatesOccl must be less then 256.
nFeatures	Number of features

Definition at line 31 of file TrainLinkNested.h.

◆ CTrainLinkNested() [2/2]

template<class Trainer >

template<class TrainerParams >

DirectGraphicalModels::CTrainLinkNested< Trainer >::CTrainLinkNested	(	byte	nStatesBase,
		byte	nStatesOccl,
		word	nFeatures,
		TrainerParams	params
	)

inline

Constructor.

Template Parameters

TrainerParams Type of the parameters to the nested node potential trainer

Parameters

nStatesBase	Number of states (classes) for the base layer of the graphical model
nStatesOccl	Number of states (classes) for the occlusion layer of the graphical model
nFeatures	Number of features
params	Parameters of the nested node potential trainer

Definition at line 48 of file TrainLinkNested.h.

◆ ~CTrainLinkNested()

template<class Trainer >

virtual DirectGraphicalModels::CTrainLinkNested< Trainer >::~CTrainLinkNested ( void )

inlinevirtual

Definition at line 58 of file TrainLinkNested.h.

Member Function Documentation

◆ addFeatureVec()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::addFeatureVec	(	const Mat &	featureVector,
		byte	gtb,
		byte	gto
	)

inlinevirtual

Adds a feature vector.

Used to add featureVector, corresponding to the ground-truth states (classes) gtb and gto for training. Here the couple {gtb, gto} corresponds to the nodes from base aod occlusion layers.

Parameters

featureVector	Multi-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to both nodes of the link.
gtb	The ground-truth state (class) of the first node of the edge, corresponding to the base layer
gto	The ground-truth state (class) of the second node of the edge, corresponding to the occlusion layer

Implements DirectGraphicalModels::CTrainLink.

Definition at line 68 of file TrainLinkNested.h.

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◆ calculateLinkPotentials()

template<class Trainer >

virtual Mat DirectGraphicalModels::CTrainLinkNested< Trainer >::calculateLinkPotentials ( const Mat & featureVector ) const

inlineprotectedvirtual

Returns the data-dependent link (inter-layer edge) potentials.

This function returns edge potential matrix, which elements are obrained from the unary potential vector:

\[ edgePot[nStatesBase:nStates][0:nStatesBase] = nodePot[nStatesBase \times nStatesOccl] = f(\textbf{f}[nFeatures]). \]

Here \( nStates = nStatesBase + nStatesOccl\).

Parameters

featureVector Multi-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the data site of the both edge nodes

Returns: The edge potential matrix: Mat(size: nStates x nStates; type: CV_32FC1)

Implements DirectGraphicalModels::CTrainLink.

Definition at line 100 of file TrainLinkNested.h.

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◆ load()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::load	(	const std::string &	path,
		const std::string &	name = `std::string()`,
		short	idx = `-1`
	)

inlinevirtual

Loads the training data.

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

Parameters

path	Path to the folder, containing the data file.
name	Name of data file. If empty, will be generated automatically from the class name.
idx	Index of the data file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 66 of file TrainLinkNested.h.

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◆ loadFile()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::loadFile ( FILE * pFile )

inlineprotectedvirtual

Loads the random model from the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for reading.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 91 of file TrainLinkNested.h.

◆ reset()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::reset ( void )

inlinevirtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 64 of file TrainLinkNested.h.

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◆ save()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::save	(	const std::string &	path,
		const std::string &	name = `std::string()`,
		short	idx = `-1`
	)		const

inlinevirtual

Saves the training data.

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

Parameters

path	Path to the destination folder.
name	Name of data file. If empty, will be generated automatically from the class name.
idx	Index of the destination file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 65 of file TrainLinkNested.h.

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◆ saveFile()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::saveFile ( FILE * pFile ) const

inlineprotectedvirtual

Saves the random model into the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for writing.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 90 of file TrainLinkNested.h.

◆ train()

template<class Trainer >

virtual void DirectGraphicalModels::CTrainLinkNested< Trainer >::train ( bool doClean = false )

inlinevirtual

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

doClean Flag indicating if the memory, keeping the trining data should be released after training

Reimplemented from DirectGraphicalModels::CTrainLink.

Definition at line 75 of file TrainLinkNested.h.

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Member Data Documentation

◆ m_pPrior

template<class Trainer >

CPriorNode* DirectGraphicalModels::CTrainLinkNested< Trainer >::m_pPrior

private

Node prior poobability

Definition at line 124 of file TrainLinkNested.h.

◆ m_pTrainer

template<class Trainer >

CTrainNode* DirectGraphicalModels::CTrainLinkNested< Trainer >::m_pTrainer

private

Node trainer

Definition at line 125 of file TrainLinkNested.h.

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

D:/Projects/DGM/modules/DGM/TrainLinkNested.h

Public Member Functions

Protected Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

template<class Trainer> class DirectGraphicalModels::CTrainLinkNested< Trainer >

Constructor & Destructor Documentation

◆ CTrainLinkNested() [1/2]

◆ CTrainLinkNested() [2/2]

◆ ~CTrainLinkNested()

Member Function Documentation

◆ addFeatureVec()

◆ calculateLinkPotentials()

◆ load()

◆ loadFile()

◆ reset()

◆ save()

◆ saveFile()

◆ train()

Member Data Documentation

◆ m_pPrior

◆ m_pTrainer

template<class Trainer>
class DirectGraphicalModels::CTrainLinkNested< Trainer >