Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor. More...

#include <opencv2/features2d.hpp>

Inheritance diagram for cv::ORB:

Public Types
enum	{ kBytes = 32, HARRIS_SCORE =0, FAST_SCORE =1 }

Public Member Functions
virtual String	getDefaultName () const CV_OVERRIDE

virtual int	getEdgeThreshold () const =0

virtual int	getFastThreshold () const =0

virtual int	getFirstLevel () const =0

virtual int	getMaxFeatures () const =0

virtual int	getNLevels () const =0

virtual int	getPatchSize () const =0

virtual double	getScaleFactor () const =0

virtual int	getScoreType () const =0

virtual int	getWTA_K () const =0

virtual void	setEdgeThreshold (int edgeThreshold)=0

virtual void	setFastThreshold (int fastThreshold)=0

virtual void	setFirstLevel (int firstLevel)=0

virtual void	setMaxFeatures (int maxFeatures)=0

virtual void	setNLevels (int nlevels)=0

virtual void	setPatchSize (int patchSize)=0

virtual void	setScaleFactor (double scaleFactor)=0

virtual void	setScoreType (int scoreType)=0

virtual void	setWTA_K (int wta_k)=0

Public Member Functions inherited from cv::Feature2D
virtual	~Feature2D ()

virtual void	compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors)
	Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). More...

virtual void	compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)

virtual int	defaultNorm () const

virtual int	descriptorSize () const

virtual int	descriptorType () const

virtual void	detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
	Detects keypoints in an image (first variant) or image set (second variant). More...

virtual void	detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())

virtual void	detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)

virtual bool	empty () const CV_OVERRIDE
	Return true if detector object is empty. More...

void	read (const String &fileName)

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

void	write (const String &fileName) const

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

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

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

virtual void	save (const String &filename) const

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< ORB >	create (int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20)
	The ORB constructor. More...

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...

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Class implementing the ORB (oriented BRIEF) keypoint detector and descriptor extractor.

described in [175] . The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).

Member Enumeration Documentation

§ anonymous enum

anonymous enum

Enumerator
kBytes
HARRIS_SCORE
FAST_SCORE

Member Function Documentation

§ create()

static Ptr<ORB> cv::ORB::create	(	int	nfeatures = `500`,
		float	scaleFactor = `1.2f`,
		int	nlevels = `8`,
		int	edgeThreshold = `31`,
		int	firstLevel = `0`,
		int	WTA_K = `2`,
		int	scoreType = `ORB::HARRIS_SCORE`,
		int	patchSize = `31`,
		int	fastThreshold = `20`
	)

static

Python:
	retval	=	cv.ORB_create(	[, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]	)

The ORB constructor.

Parameters

nfeatures	The maximum number of features to retain.
scaleFactor	Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
nlevels	The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
edgeThreshold	This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel	The level of pyramid to put source image to. Previous layers are filled with upscaled source image.
WTA_K	The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
scoreType	The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
patchSize	size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
fastThreshold	the fast threshold

§ getDefaultName()

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

virtual

Python:
	retval	=	cv.ORB.getDefaultName(		)

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 from cv::Feature2D.

§ getEdgeThreshold()

virtual int cv::ORB::getEdgeThreshold ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getEdgeThreshold(		)

§ getFastThreshold()

virtual int cv::ORB::getFastThreshold ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getFastThreshold(		)

§ getFirstLevel()

virtual int cv::ORB::getFirstLevel ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getFirstLevel(		)

§ getMaxFeatures()

virtual int cv::ORB::getMaxFeatures ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getMaxFeatures(		)

§ getNLevels()

virtual int cv::ORB::getNLevels ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getNLevels(		)

§ getPatchSize()

virtual int cv::ORB::getPatchSize ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getPatchSize(		)

§ getScaleFactor()

virtual double cv::ORB::getScaleFactor ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getScaleFactor(		)

§ getScoreType()

virtual int cv::ORB::getScoreType ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getScoreType(		)

§ getWTA_K()

virtual int cv::ORB::getWTA_K ( ) const

pure virtual

Python:
	retval	=	cv.ORB.getWTA_K(		)

§ setEdgeThreshold()

virtual void cv::ORB::setEdgeThreshold ( int edgeThreshold )

pure virtual

Python:
	None	=	cv.ORB.setEdgeThreshold(	edgeThreshold	)

§ setFastThreshold()

virtual void cv::ORB::setFastThreshold ( int fastThreshold )

pure virtual

Python:
	None	=	cv.ORB.setFastThreshold(	fastThreshold	)

§ setFirstLevel()

virtual void cv::ORB::setFirstLevel ( int firstLevel )

pure virtual

Python:
	None	=	cv.ORB.setFirstLevel(	firstLevel	)

§ setMaxFeatures()

virtual void cv::ORB::setMaxFeatures ( int maxFeatures )

pure virtual

Python:
	None	=	cv.ORB.setMaxFeatures(	maxFeatures	)

§ setNLevels()

virtual void cv::ORB::setNLevels ( int nlevels )

pure virtual

Python:
	None	=	cv.ORB.setNLevels(	nlevels	)

§ setPatchSize()

virtual void cv::ORB::setPatchSize ( int patchSize )

pure virtual

Python:
	None	=	cv.ORB.setPatchSize(	patchSize	)

§ setScaleFactor()

virtual void cv::ORB::setScaleFactor ( double scaleFactor )

pure virtual

Python:
	None	=	cv.ORB.setScaleFactor(	scaleFactor	)

§ setScoreType()

virtual void cv::ORB::setScoreType ( int scoreType )

pure virtual

Python:
	None	=	cv.ORB.setScoreType(	scoreType	)

§ setWTA_K()

virtual void cv::ORB::setWTA_K ( int wta_k )

pure virtual

Python:
	None	=	cv.ORB.setWTA_K(	wta_k	)

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

opencv2/features2d.hpp

Public Types

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

§ anonymous enum

Member Function Documentation

§ create()

§ getDefaultName()

§ getEdgeThreshold()

§ getFastThreshold()

§ getFirstLevel()

§ getMaxFeatures()

§ getNLevels()

§ getPatchSize()

§ getScaleFactor()

§ getScoreType()

§ getWTA_K()

§ setEdgeThreshold()

§ setFastThreshold()

§ setFirstLevel()

§ setMaxFeatures()

§ setNLevels()

§ setPatchSize()

§ setScaleFactor()

§ setScoreType()

§ setWTA_K()