Variational optical flow refinement. More...

#include <opencv2/video/tracking.hpp>

Inheritance diagram for cv::VariationalRefinement:

Collaboration diagram for cv::VariationalRefinement:

Public Member Functions
virtual void	calc (InputArray I0, InputArray I1, InputOutputArray flow)=0
	Calculates an optical flow. More...

virtual void	calcUV (InputArray I0, InputArray I1, InputOutputArray flow_u, InputOutputArray flow_v)=0
	calc function overload to handle separate horizontal (u) and vertical (v) flow components (to avoid extra splits/merges) More...

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

virtual void	collectGarbage ()=0
	Releases all inner buffers. More...

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

virtual float	getAlpha () const =0
	Weight of the smoothness term. More...

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

virtual float	getDelta () const =0
	Weight of the color constancy term. More...

virtual int	getFixedPointIterations () const =0
	Number of outer (fixed-point) iterations in the minimization procedure. More...

virtual float	getGamma () const =0
	Weight of the gradient constancy term. More...

virtual float	getOmega () const =0
	Relaxation factor in SOR. More...

virtual int	getSorIterations () const =0
	Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system. 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	setAlpha (float val)=0
	Weight of the smoothness term. More...

virtual void	setDelta (float val)=0
	Weight of the color constancy term. More...

virtual void	setFixedPointIterations (int val)=0
	Number of outer (fixed-point) iterations in the minimization procedure. More...

virtual void	setGamma (float val)=0
	Weight of the gradient constancy term. More...

virtual void	setOmega (float val)=0
	Relaxation factor in SOR. More...

virtual void	setSorIterations (int val)=0
	Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system. 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
static Ptr< VariationalRefinement >	create ()
	Creates an instance of VariationalRefinement. More...

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

Variational optical flow refinement.

This class implements variational refinement of the input flow field, i.e. it uses input flow to initialize the minimization of the following functional: \(E(U) = \int_{\Omega} \delta \Psi(E_I) + \gamma \Psi(E_G) + \alpha \Psi(E_S) \), where \(E_I,E_G,E_S\) are color constancy, gradient constancy and smoothness terms respectively. \(\Psi(s^2)=\sqrt{s^2+\epsilon^2}\) is a robust penalizer to limit the influence of outliers. A complete formulation and a description of the minimization procedure can be found in [15]

Member Function Documentation

◆ calc()

virtual void cv::DenseOpticalFlow::calc	(	InputArray	I0,
		InputArray	I1,
		InputOutputArray	flow
	)

pure virtualinherited

Calculates an optical flow.

Parameters

I0	first 8-bit single-channel input image.
I1	second input image of the same size and the same type as prev.
flow	computed flow image that has the same size as prev and type CV_32FC2.

◆ calcUV()

virtual void cv::VariationalRefinement::calcUV	(	InputArray	I0,
		InputArray	I1,
		InputOutputArray	flow_u,
		InputOutputArray	flow_v
	)

pure virtual

calc function overload to handle separate horizontal (u) and vertical (v) flow components (to avoid extra splits/merges)

◆ clear()

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

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

◆ collectGarbage()

virtual void cv::DenseOpticalFlow::collectGarbage ( )

pure virtualinherited

Releases all inner buffers.

◆ create()

static Ptr<VariationalRefinement> cv::VariationalRefinement::create ( )

static

Creates an instance of VariationalRefinement.

◆ 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.

◆ getAlpha()

virtual float cv::VariationalRefinement::getAlpha ( ) const

pure virtual

Weight of the smoothness term.

See also: setAlpha

◆ 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.

◆ getDelta()

virtual float cv::VariationalRefinement::getDelta ( ) const

pure virtual

Weight of the color constancy term.

See also: setDelta

◆ getFixedPointIterations()

virtual int cv::VariationalRefinement::getFixedPointIterations ( ) const

pure virtual

Number of outer (fixed-point) iterations in the minimization procedure.

See also: setFixedPointIterations

◆ getGamma()

virtual float cv::VariationalRefinement::getGamma ( ) const

pure virtual

Weight of the gradient constancy term.

See also: setGamma

◆ getOmega()

virtual float cv::VariationalRefinement::getOmega ( ) const

pure virtual

Relaxation factor in SOR.

See also: setOmega

◆ getSorIterations()

virtual int cv::VariationalRefinement::getSorIterations ( ) const

pure virtual

Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system.

See also: setSorIterations

◆ 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.

Here is the call graph for this function:

◆ 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.

Here is the call graph for this function:

◆ 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).

◆ setAlpha()

virtual void cv::VariationalRefinement::setAlpha ( float val )

pure virtual

Weight of the smoothness term.

See also: getAlpha

◆ setDelta()

virtual void cv::VariationalRefinement::setDelta ( float val )

pure virtual

Weight of the color constancy term.

See also: getDelta

◆ setFixedPointIterations()

virtual void cv::VariationalRefinement::setFixedPointIterations ( int val )

pure virtual

Number of outer (fixed-point) iterations in the minimization procedure.

See also: getFixedPointIterations

◆ setGamma()

virtual void cv::VariationalRefinement::setGamma ( float val )

pure virtual

Weight of the gradient constancy term.

See also: getGamma

◆ setOmega()

virtual void cv::VariationalRefinement::setOmega ( float val )

pure virtual

Relaxation factor in SOR.

See also: getOmega

◆ setSorIterations()

virtual void cv::VariationalRefinement::setSorIterations ( int val )

pure virtual

Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system.

See also: getSorIterations

◆ 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().

Here is the caller graph for this function:

◆ 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/tracking.hpp

Public Member Functions

Static Public Member Functions

Protected Member Functions

Detailed Description

Member Function Documentation

◆ calc()

◆ calcUV()

◆ clear()

◆ collectGarbage()

◆ create()

◆ empty()

◆ getAlpha()

◆ getDefaultName()

◆ getDelta()

◆ getFixedPointIterations()

◆ getGamma()

◆ getOmega()

◆ getSorIterations()

◆ load()

◆ loadFromString()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setAlpha()

◆ setDelta()

◆ setFixedPointIterations()

◆ setGamma()

◆ setOmega()

◆ setSorIterations()

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()