Gaussian Mixture-based Background/Foreground Segmentation Algorithm. More...

#include "cudabgsegm.hpp"

Inheritance diagram for cv::cuda::BackgroundSubtractorMOG:

Collaboration diagram for cv::cuda::BackgroundSubtractorMOG:

Public Member Functions
virtual void	apply (InputArray image, OutputArray fgmask, double learningRate=-1)=0
	Computes a foreground mask. More...

virtual void	apply (InputArray image, OutputArray fgmask, double learningRate, Stream &stream)=0

virtual void	clear ()
	Clears the algorithm state. More...

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. More...

virtual void	getBackgroundImage (OutputArray backgroundImage) const =0
	Computes a background image. More...

virtual void	getBackgroundImage (OutputArray backgroundImage, Stream &stream) const =0

virtual double	getBackgroundRatio () const =0

virtual String	getDefaultName () const
	Returns the algorithm string identifier. More...

virtual int	getHistory () const =0

virtual int	getNMixtures () const =0

virtual double	getNoiseSigma () const =0

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const
	Saves the algorithm to a file. More...

virtual void	setBackgroundRatio (double backgroundRatio)=0

virtual void	setHistory (int nframes)=0

virtual void	setNMixtures (int nmix)=0

virtual void	setNoiseSigma (double noiseSigma)=0

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

Static Public Member Functions
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Protected Member Functions
void	writeFormat (FileStorage &fs) const

Detailed Description

Gaussian Mixture-based Background/Foreground Segmentation Algorithm.

The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [41] .

See also: BackgroundSubtractorMOG

Note

An example on gaussian mixture based background/foreground segmantation can be found at opencv_source_code/samples/gpu/bgfg_segm.cpp

Member Function Documentation

virtual void cv::BackgroundSubtractor::apply	(	InputArray	image,
		OutputArray	fgmask,
		double	learningRate = `-1`
	)

pure virtualinherited

Computes a foreground mask.

Parameters

image	Next video frame.
fgmask	The output foreground mask as an 8-bit binary image.
learningRate	The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.

Implemented in cv::BackgroundSubtractorMOG2.

virtual void cv::cuda::BackgroundSubtractorMOG::apply	(	InputArray	image,
		OutputArray	fgmask,
		double	learningRate,
		Stream &	stream
	)

pure virtual

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::cuda::DescriptorMatcher.

virtual bool cv::Algorithm::empty ( ) const

inlinevirtualinherited

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented in cv::DescriptorMatcher, cv::ml::StatModel, cv::Feature2D, cv::BaseCascadeClassifier, and cv::cuda::DescriptorMatcher.

virtual void cv::BackgroundSubtractor::getBackgroundImage ( OutputArray backgroundImage ) const

pure virtualinherited

Computes a background image.

Parameters

backgroundImage The output background image.

Note: Sometimes the background image can be very blurry, as it contain the average background statistics.

virtual void cv::cuda::BackgroundSubtractorMOG::getBackgroundImage	(	OutputArray	backgroundImage,
		Stream &	stream
	)		const

pure virtual

virtual double cv::cuda::BackgroundSubtractorMOG::getBackgroundRatio ( ) const

pure virtual

virtual String cv::Algorithm::getDefaultName ( ) const

virtualinherited

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

virtual int cv::cuda::BackgroundSubtractorMOG::getHistory ( ) const

pure virtual

virtual int cv::cuda::BackgroundSubtractorMOG::getNMixtures ( ) const

pure virtual

virtual double cv::cuda::BackgroundSubtractorMOG::getNoiseSigma ( ) const

pure virtual

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::load	(	const String &	filename,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from the file.

Parameters

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

References cv::Ptr< T >::empty(), cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), and cv::FileStorage::READ.

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template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::loadFromString	(	const String &	strModel,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from a String.

Parameters

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

References CV_WRAP, cv::Ptr< T >::empty(), cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.

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virtual void cv::Algorithm::read ( const FileNode & fn )

inlinevirtualinherited

Reads algorithm parameters from a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::read ( const FileNode & fn )

inlinestaticinherited

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

cv::FileStorage fsRead("example.xml", FileStorage::READ);

Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

References cv::Ptr< T >::empty().

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virtual void cv::Algorithm::save ( const String & filename ) const

virtualinherited

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

virtual void cv::cuda::BackgroundSubtractorMOG::setBackgroundRatio ( double backgroundRatio )

pure virtual

virtual void cv::cuda::BackgroundSubtractorMOG::setHistory ( int nframes )

pure virtual

virtual void cv::cuda::BackgroundSubtractorMOG::setNMixtures ( int nmix )

pure virtual

virtual void cv::cuda::BackgroundSubtractorMOG::setNoiseSigma ( double noiseSigma )

pure virtual

virtual void cv::Algorithm::write ( FileStorage & fs ) const

inlinevirtualinherited

Stores algorithm parameters in a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

void cv::Algorithm::writeFormat ( FileStorage & fs ) const

protectedinherited

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

cudabgsegm/include/opencv2/cudabgsegm.hpp

Public Member Functions

Static Public Member Functions

Protected Member Functions

Detailed Description

Member Function Documentation