cv::ORB Class Reference

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

#include "features2d.hpp"

Inheritance diagram for cv::ORB:

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

Public Member Functions
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
	Return true if detector object is empty. More...
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state. 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...

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...

Detailed Description

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

described in [120] . 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 Function Documentation

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

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).
edgeThreshold	This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel	It should be 0 in the current implementation.
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

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

pure virtual

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

• features2d/include/opencv2/features2d.hpp