K-nearest neighbours - based Background/Foreground Segmentation Algorithm. More...

#include <opencv2/video/background_segm.hpp>

Inheritance diagram for cv::BackgroundSubtractorKNN:

Collaboration diagram for cv::BackgroundSubtractorKNN:

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

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

virtual bool	getDetectShadows () const =0
	Returns the shadow detection flag. More...

virtual double	getDist2Threshold () const =0
	Returns the threshold on the squared distance between the pixel and the sample. More...

virtual int	getHistory () const =0
	Returns the number of last frames that affect the background model. More...

virtual int	getkNNSamples () const =0
	Returns the number of neighbours, the k in the kNN. More...

virtual int	getNSamples () const =0
	Returns the number of data samples in the background model. More...

virtual double	getShadowThreshold () const =0
	Returns the shadow threshold. More...

virtual int	getShadowValue () const =0
	Returns the shadow value. More...

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	setDetectShadows (bool detectShadows)=0
	Enables or disables shadow detection. More...

virtual void	setDist2Threshold (double _dist2Threshold)=0
	Sets the threshold on the squared distance. More...

virtual void	setHistory (int history)=0
	Sets the number of last frames that affect the background model. More...

virtual void	setkNNSamples (int _nkNN)=0
	Sets the k in the kNN. More...

virtual void	setNSamples (int _nN)=0
	Sets the number of data samples in the background model. More...

virtual void	setShadowThreshold (double threshold)=0
	Sets the shadow threshold. More...

virtual void	setShadowValue (int value)=0
	Sets the shadow value. More...

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

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. 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

K-nearest neighbours - based Background/Foreground Segmentation Algorithm.

The class implements the K-nearest neighbours background subtraction described in [109] . Very efficient if number of foreground pixels is low.

Member Function Documentation

◆ apply()

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.

◆ clear()

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

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

◆ empty()

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, and cv::BaseCascadeClassifier.

◆ getBackgroundImage()

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.

◆ getDefaultName()

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.

Reimplemented in cv::AKAZE, cv::KAZE, cv::SimpleBlobDetector, cv::GFTTDetector, cv::AgastFeatureDetector, cv::FastFeatureDetector, cv::MSER, cv::ORB, cv::BRISK, and cv::Feature2D.

◆ getDetectShadows()

virtual bool cv::BackgroundSubtractorKNN::getDetectShadows ( ) const

pure virtual

Returns the shadow detection flag.

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for details.

◆ getDist2Threshold()

virtual double cv::BackgroundSubtractorKNN::getDist2Threshold ( ) const

pure virtual

Returns the threshold on the squared distance between the pixel and the sample.

The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample.

◆ getHistory()

virtual int cv::BackgroundSubtractorKNN::getHistory ( ) const

pure virtual

Returns the number of last frames that affect the background model.

◆ getkNNSamples()

virtual int cv::BackgroundSubtractorKNN::getkNNSamples ( ) const

pure virtual

Returns the number of neighbours, the k in the kNN.

K is the number of samples that need to be within dist2Threshold in order to decide that that pixel is matching the kNN background model.

◆ getNSamples()

virtual int cv::BackgroundSubtractorKNN::getNSamples ( ) const

pure virtual

Returns the number of data samples in the background model.

◆ getShadowThreshold()

virtual double cv::BackgroundSubtractorKNN::getShadowThreshold ( ) const

pure virtual

Returns the shadow threshold.

A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara, Detecting Moving Shadows...*, IEEE PAMI,2003.

◆ getShadowValue()

virtual int cv::BackgroundSubtractorKNN::getShadowValue ( ) const

pure virtual

Returns the shadow value.

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0 in the mask always means background, 255 means foreground.

◆ load()

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_Assert, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::isOpened(), and cv::FileStorage::READ.

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◆ loadFromString()

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::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.

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◆ read() [1/2]

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.

◆ read() [2/2]

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)

◆ save()

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).

◆ setDetectShadows()

virtual void cv::BackgroundSubtractorKNN::setDetectShadows ( bool detectShadows )

pure virtual

Enables or disables shadow detection.

◆ setDist2Threshold()

virtual void cv::BackgroundSubtractorKNN::setDist2Threshold ( double _dist2Threshold )

pure virtual

Sets the threshold on the squared distance.

◆ setHistory()

virtual void cv::BackgroundSubtractorKNN::setHistory ( int history )

pure virtual

Sets the number of last frames that affect the background model.

◆ setkNNSamples()

virtual void cv::BackgroundSubtractorKNN::setkNNSamples ( int _nkNN )

pure virtual

Sets the k in the kNN.

How many nearest neighbours need to match.

◆ setNSamples()

virtual void cv::BackgroundSubtractorKNN::setNSamples ( int _nN )

pure virtual

Sets the number of data samples in the background model.

The model needs to be reinitalized to reserve memory.

◆ setShadowThreshold()

virtual void cv::BackgroundSubtractorKNN::setShadowThreshold ( double threshold )

pure virtual

Sets the shadow threshold.

◆ setShadowValue()

virtual void cv::BackgroundSubtractorKNN::setShadowValue ( int value )

pure virtual

Sets the shadow value.

◆ write() [1/2]

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.

References CV_WRAP.

Referenced by cv::Feature2D::write(), and cv::DescriptorMatcher::write().

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◆ write() [2/2]

void cv::Algorithm::write	(	const Ptr< FileStorage > &	fs,
		const String &	name = `String()`
	)		const

inherited

simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ writeFormat()

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

protectedinherited

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

opencv2/video/background_segm.hpp

Public Member Functions

Static Public Member Functions

Protected Member Functions

Detailed Description

Member Function Documentation

◆ apply()

◆ clear()

◆ empty()

◆ getBackgroundImage()

◆ getDefaultName()

◆ getDetectShadows()

◆ getDist2Threshold()

◆ getHistory()

◆ getkNNSamples()

◆ getNSamples()

◆ getShadowThreshold()

◆ getShadowValue()

◆ load()

◆ loadFromString()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setDetectShadows()

◆ setDist2Threshold()

◆ setHistory()

◆ setkNNSamples()

◆ setNSamples()

◆ setShadowThreshold()

◆ setShadowValue()

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()