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

#include <opencv2/features2d.hpp>

Inheritance diagram for cv::ORB:

Collaboration diagram for cv::ORB:

Public Types
enum	ScoreType { HARRIS_SCORE =0, FAST_SCORE =1 }

Public Member Functions
virtual void	clear ()
	Clears the algorithm state. More...

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

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

virtual void	detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
	Detects keypoints and computes the descriptors. More...

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

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

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 ORB::ScoreType	getScoreType () const =0

virtual int	getWTA_K () const =0

void	read (const String &fileName)

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

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

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 (ORB::ScoreType scoreType)=0

virtual void	setWTA_K (int wta_k)=0

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

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, ORB::ScoreType scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20)
	The ORB constructor. 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...

Static Public Attributes
static const int	kBytes = 32

Protected Member Functions
void	writeFormat (FileStorage &fs) const

Detailed Description

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

described in [81] . 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

◆ ScoreType

enum cv::ORB::ScoreType

Enumerator
HARRIS_SCORE
FAST_SCORE

Member Function Documentation

◆ clear()

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

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

◆ compute() [1/2]

virtual void cv::Feature2D::compute	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		OutputArray	descriptors
	)

virtualinherited

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

Parameters

image	Image.
keypoints	Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors	Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

◆ compute() [2/2]

virtual void cv::Feature2D::compute	(	InputArrayOfArrays	images,
		std::vector< std::vector< KeyPoint > > &	keypoints,
		OutputArrayOfArrays	descriptors
	)

virtualinherited

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

Parameters

images	Image set.
keypoints	Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors	Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

◆ 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`,
		ORB::ScoreType	scoreType = `ORB::HARRIS_SCORE`,
		int	patchSize = `31`,
		int	fastThreshold = `20`
	)

static

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

◆ defaultNorm()

virtual int cv::Feature2D::defaultNorm ( ) const

virtualinherited

◆ descriptorSize()

virtual int cv::Feature2D::descriptorSize ( ) const

virtualinherited

◆ descriptorType()

virtual int cv::Feature2D::descriptorType ( ) const

virtualinherited

◆ detect() [1/2]

virtual void cv::Feature2D::detect	(	InputArray	image,
		std::vector< KeyPoint > &	keypoints,
		InputArray	mask = `noArray()`
	)

virtualinherited

Detects keypoints in an image (first variant) or image set (second variant).

Parameters

image	Image.
keypoints	The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
mask	Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.

◆ detect() [2/2]

virtual void cv::Feature2D::detect	(	InputArrayOfArrays	images,
		std::vector< std::vector< KeyPoint > > &	keypoints,
		InputArrayOfArrays	masks = `noArray()`
	)

virtualinherited

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

Parameters

images	Image set.
keypoints	The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
masks	Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

◆ detectAndCompute()

virtual void cv::Feature2D::detectAndCompute	(	InputArray	image,
		InputArray	mask,
		std::vector< KeyPoint > &	keypoints,
		OutputArray	descriptors,
		bool	useProvidedKeypoints = `false`
	)

virtualinherited

Detects keypoints and computes the descriptors.

◆ empty()

virtual bool cv::Feature2D::empty ( ) const

virtualinherited

Return true if detector object is empty.

Reimplemented from cv::Algorithm.

◆ getDefaultName()

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

virtual

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

◆ getFastThreshold()

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

pure virtual

◆ getFirstLevel()

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

pure virtual

◆ getMaxFeatures()

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

pure virtual

◆ getNLevels()

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

pure virtual

◆ getPatchSize()

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

pure virtual

◆ getScaleFactor()

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

pure virtual

◆ getScoreType()

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

pure virtual

◆ getWTA_K()

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

pure virtual

◆ 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]

void cv::Feature2D::read ( const String & fileName )

inherited

◆ read() [2/2]

virtual void cv::Feature2D::read ( const FileNode & fn )

virtualinherited

Reads algorithm parameters from a file storage.

Reimplemented from cv::Algorithm.

◆ 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).

◆ setEdgeThreshold()

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

pure virtual

◆ setFastThreshold()

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

pure virtual

◆ setFirstLevel()

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

pure virtual

◆ setMaxFeatures()

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

pure virtual

◆ setNLevels()

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

pure virtual

◆ setPatchSize()

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

pure virtual

◆ setScaleFactor()

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

pure virtual

◆ setScoreType()

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

pure virtual

◆ setWTA_K()

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

pure virtual

◆ write() [1/3]

void cv::Feature2D::write ( const String & fileName ) const

inherited

◆ write() [2/3]

virtual void cv::Feature2D::write ( FileStorage & fs ) const

virtualinherited

Stores algorithm parameters in a file storage.

Reimplemented from cv::Algorithm.

◆ write() [3/3]

void cv::Feature2D::write	(	const Ptr< FileStorage > &	fs,
		const String &	name = `String()`
	)		const

inlineinherited

References cv::Algorithm::write().

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◆ writeFormat()

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

protectedinherited

Member Data Documentation

◆ kBytes

const int cv::ORB::kBytes = 32

static

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

opencv2/features2d.hpp

Public Types

Public Member Functions

Static Public Member Functions

Static Public Attributes

Protected Member Functions

Detailed Description

Member Enumeration Documentation

◆ ScoreType

Member Function Documentation

◆ clear()

◆ compute() [1/2]

◆ compute() [2/2]

◆ create()

◆ defaultNorm()

◆ descriptorSize()

◆ descriptorType()

◆ detect() [1/2]

◆ detect() [2/2]

◆ detectAndCompute()

◆ empty()

◆ getDefaultName()

◆ getEdgeThreshold()

◆ getFastThreshold()

◆ getFirstLevel()

◆ getMaxFeatures()

◆ getNLevels()

◆ getPatchSize()

◆ getScaleFactor()

◆ getScoreType()

◆ getWTA_K()

◆ load()

◆ loadFromString()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setEdgeThreshold()

◆ setFastThreshold()

◆ setFirstLevel()

◆ setMaxFeatures()

◆ setNLevels()

◆ setPatchSize()

◆ setScaleFactor()

◆ setScoreType()

◆ setWTA_K()

◆ write() [1/3]

◆ write() [2/3]

◆ write() [3/3]

◆ writeFormat()

Member Data Documentation

◆ kBytes