OpenCV Nearest Neighbor training class. More...

#include <TrainNodeCvKNN.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeCvKNN:

Collaboration diagram for DirectGraphicalModels::CTrainNodeCvKNN:

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Public Member Functions
	CTrainNodeCvKNN (byte nStates, word nFeatures, TrainNodeCvKNNParams params=TRAIN_NODE_CV_KNN_PARAMS_DEFAULT)
	Constructor. More...

	CTrainNodeCvKNN (byte nStates, word nFeatures, size_t maxSamples)
	Constructor. More...

virtual	~CTrainNodeCvKNN (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)=default

void	addFeatureVecs (const Mat &featureVectors, const Mat &gt)
	Adds a block of new feature vectors. More...

void	addFeatureVecs (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)=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
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
Ptr< ml::KNearest >	m_pKNN
	k-Nearest Neighbors More...

CSamplesAccumulator *	m_pSamplesAcc
	Samples Accumulator. More...

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

Private Member Functions
void	init (TrainNodeCvKNNParams params)

Private Attributes
TrainNodeCvKNNParams	m_params

Additional Inherited Members
Static Public Member Functions inherited from DirectGraphicalModels::CTrainNode
static std::shared_ptr< CTrainNode >	create (byte nodeRandomModel, byte nStates, word nFeatures)
	Factory method returning node trainer object. More...

Detailed Description

OpenCV Nearest Neighbor training class.

This class implements the k-nearest neighbors classifier (k-NN), where the input consists of the k closest training samples in the feature space and the output depends on k-Nearest Neighbors.

This trainer is especially effective for low-dimentional feature spaces.

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 36 of file TrainNodeCvKNN.h.

Constructor & Destructor Documentation

◆ CTrainNodeCvKNN() [1/2]

DirectGraphicalModels::CTrainNodeCvKNN::CTrainNodeCvKNN	(	byte	nStates,
		word	nFeatures,
		TrainNodeCvKNNParams	params = `TRAIN_NODE_CV_KNN_PARAMS_DEFAULT`
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
params	k-Nearest Neighbors parameters (Ref. TrainNodeCvKNNParams)

Definition at line 7 of file TrainNodeCvKNN.cpp.

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◆ CTrainNodeCvKNN() [2/2]

DirectGraphicalModels::CTrainNodeCvKNN::CTrainNodeCvKNN	(	byte	nStates,
		word	nFeatures,
		size_t	maxSamples
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
maxSamples	Maximum number of samples to be used in training Default value 0 means using all the samples. If another value is specified, the class for training will use maxSamples random samples from the whole amount of samples, added via addFeatureVec() function

Definition at line 13 of file TrainNodeCvKNN.cpp.

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◆ ~CTrainNodeCvKNN()

DirectGraphicalModels::CTrainNodeCvKNN::~CTrainNodeCvKNN ( void )

virtual

Definition at line 32 of file TrainNodeCvKNN.cpp.

Member Function Documentation

◆ addFeatureVec()

void DirectGraphicalModels::CTrainNodeCvKNN::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

featureVector	Multi-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1)
gt	Corresponding ground-truth state (class)

Implements DirectGraphicalModels::CTrainNode.

Definition at line 55 of file TrainNodeCvKNN.cpp.

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◆ calculateNodePotentials()

void DirectGraphicalModels::CTrainNodeCvKNN::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]	featureVector	Multi-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_{XX}C1)
[in,out]	potential	Node potentials: Mat(size: nStates x 1; type: CV_32FC1). This parameter should be preinitialized and set to value 0.
[in,out]	mask	Relevant 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 94 of file TrainNodeCvKNN.cpp.

◆ init()

void DirectGraphicalModels::CTrainNodeCvKNN::init ( TrainNodeCvKNNParams params )

private

Definition at line 20 of file TrainNodeCvKNN.cpp.

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◆ load()

void DirectGraphicalModels::CTrainNodeCvKNN::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

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 49 of file TrainNodeCvKNN.cpp.

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◆ loadFile()

void DirectGraphicalModels::CTrainNodeCvKNN::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 67 of file TrainNodeCvKNN.h.

◆ reset()

void DirectGraphicalModels::CTrainNodeCvKNN::reset ( void )

virtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 37 of file TrainNodeCvKNN.cpp.

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◆ save()

void DirectGraphicalModels::CTrainNodeCvKNN::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

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 43 of file TrainNodeCvKNN.cpp.

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◆ saveFile()

void DirectGraphicalModels::CTrainNodeCvKNN::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 66 of file TrainNodeCvKNN.h.

◆ train()

void DirectGraphicalModels::CTrainNodeCvKNN::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

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

Reimplemented from DirectGraphicalModels::CTrainNode.

Definition at line 60 of file TrainNodeCvKNN.cpp.

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Member Data Documentation

◆ m_params

TrainNodeCvKNNParams DirectGraphicalModels::CTrainNodeCvKNN::m_params

private

Definition at line 81 of file TrainNodeCvKNN.h.

◆ m_pKNN

Ptr<ml::KNearest> DirectGraphicalModels::CTrainNodeCvKNN::m_pKNN

protected

k-Nearest Neighbors

Definition at line 72 of file TrainNodeCvKNN.h.

◆ m_pSamplesAcc

CSamplesAccumulator* DirectGraphicalModels::CTrainNodeCvKNN::m_pSamplesAcc

protected

Samples Accumulator.

Definition at line 73 of file TrainNodeCvKNN.h.

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

D:/Projects/DGM/modules/DGM/TrainNodeCvKNN.h
D:/Projects/DGM/modules/DGM/TrainNodeCvKNN.cpp

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ CTrainNodeCvKNN() [1/2]

◆ CTrainNodeCvKNN() [2/2]

◆ ~CTrainNodeCvKNN()

Member Function Documentation

◆ addFeatureVec()

◆ calculateNodePotentials()

◆ init()

◆ load()

◆ loadFile()

◆ reset()

◆ save()

◆ saveFile()

◆ train()

Member Data Documentation

◆ m_params

◆ m_pKNN

◆ m_pSamplesAcc