cv::saliency::ObjectnessBING Class Reference

Objectness algorithms based on [3] [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014. More...

#include "saliencySpecializedClasses.hpp"

Inheritance diagram for cv::saliency::ObjectnessBING:

Public Member Functions
	ObjectnessBING ()
virtual	~ObjectnessBING ()
bool	computeSaliency (InputArray image, OutputArray saliencyMap)
double	getBase () const
int	getNSS () const
std::vector< float >	getobjectnessValues ()
	Return the list of the rectangles' objectness value,. More...
int	getW () const
void	read ()
void	setBase (double val)
void	setBBResDir (const String &resultsDir)
	This is a utility function that allows to set an arbitrary path in which the algorithm will save the optional results. More...
void	setNSS (int val)
void	setTrainingPath (const String &trainingPath)
	This is a utility function that allows to set the correct path from which the algorithm will load the trained model. More...
void	setW (int val)
void	write () const
Public Member Functions inherited from cv::saliency::Saliency
virtual	~Saliency ()
	Destructor. More...
bool	computeSaliency (InputArray image, OutputArray saliencyMap)
	Compute the saliency. More...
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state. More...
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...
virtual void	save (const String &filename) const
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< ObjectnessBING >	create ()
Static Public Member Functions inherited from cv::Algorithm
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
bool	computeSaliencyImpl (InputArray image, OutputArray objectnessBoundingBox)
	Performs all the operations and calls all internal functions necessary for the accomplishment of the Binarized normed gradients algorithm. More...
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Additional Inherited Members
Protected Attributes inherited from cv::saliency::Saliency
String	className

Detailed Description

Objectness algorithms based on [3] [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.

the Binarized normed gradients algorithm from [31]

Constructor & Destructor Documentation

ObjectnessBING()

cv::saliency::ObjectnessBING::ObjectnessBING

(

)

~ObjectnessBING()

virtual cv::saliency::ObjectnessBING::~ObjectnessBING ( )

virtual

Member Function Documentation

computeSaliency()

bool cv::saliency::ObjectnessBING::computeSaliency ( InputArray  image, OutputArray  saliencyMap  )

inline

Python:
	retval, saliencyMap	=	cv.saliency_ObjectnessBING.computeSaliency(	image[, saliencyMap]	)

computeSaliencyImpl()

bool cv::saliency::ObjectnessBING::computeSaliencyImpl ( InputArray  image, OutputArray  objectnessBoundingBox  )

protectedvirtual

Performs all the operations and calls all internal functions necessary for the accomplishment of the Binarized normed gradients algorithm.

Parameters

image	input image. According to the needs of this specialized algorithm, the param image is a single Mat
objectnessBoundingBox	objectness Bounding Box vector. According to the result given by this specialized algorithm, the objectnessBoundingBox is a vector<Vec4i>. Each bounding box is represented by a Vec4i for (minX, minY, maxX, maxY).

Implements cv::saliency::Objectness.

create()

static Ptr<ObjectnessBING> cv::saliency::ObjectnessBING::create ( )

inlinestatic

Python:
	retval	=	cv.saliency.ObjectnessBING_create(		)

getBase()

double cv::saliency::ObjectnessBING::getBase ( ) const

inline

Python:
	retval	=	cv.saliency_ObjectnessBING.getBase(		)

getNSS()

int cv::saliency::ObjectnessBING::getNSS ( ) const

inline

Python:
	retval	=	cv.saliency_ObjectnessBING.getNSS(		)

getobjectnessValues()

std::vector<float> cv::saliency::ObjectnessBING::getobjectnessValues

(

)

Python:
	retval	=	cv.saliency_ObjectnessBING.getobjectnessValues(		)

Return the list of the rectangles' objectness value,.

in the same order as the vector<Vec4i> objectnessBoundingBox returned by the algorithm (in computeSaliencyImpl function). The bigger value these scores are, it is more likely to be an object window.

getW()

int cv::saliency::ObjectnessBING::getW ( ) const

inline

Python:
	retval	=	cv.saliency_ObjectnessBING.getW(		)

read()

void cv::saliency::ObjectnessBING::read

(

)

Python:
	None	=	cv.saliency_ObjectnessBING.read(		)

setBase()

void cv::saliency::ObjectnessBING::setBase ( double  val )

inline

Python:
	None	=	cv.saliency_ObjectnessBING.setBase(	val	)

setBBResDir()

void cv::saliency::ObjectnessBING::setBBResDir

(

const String &

resultsDir

)

Python:
	None	=	cv.saliency_ObjectnessBING.setBBResDir(	resultsDir	)

This is a utility function that allows to set an arbitrary path in which the algorithm will save the optional results.

(ie writing on file the total number and the list of rectangles returned by objectess, one for each row).

Parameters

resultsDir

results' folder path

setNSS()

void cv::saliency::ObjectnessBING::setNSS ( int  val )

inline

Python:
	None	=	cv.saliency_ObjectnessBING.setNSS(	val	)

setTrainingPath()

void cv::saliency::ObjectnessBING::setTrainingPath

(

const String &

trainingPath

)

Python:
	None	=	cv.saliency_ObjectnessBING.setTrainingPath(	trainingPath	)

This is a utility function that allows to set the correct path from which the algorithm will load the trained model.

Parameters

trainingPath

trained model path

setW()

void cv::saliency::ObjectnessBING::setW ( int  val )

inline

Python:
	None	=	cv.saliency_ObjectnessBING.setW(	val	)

write()

void cv::saliency::ObjectnessBING::write

(

)

const

Python:
	None	=	cv.saliency_ObjectnessBING.write(		)

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

• /build/master-contrib_docs-lin64/opencv_contrib/modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp