OpenCV  4.10.0-dev
Open Source Computer Vision
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Public Types | Public Member Functions | Static Public Member Functions | Static Public Attributes | List of all members

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

#include <opencv2/features2d.hpp>

Collaboration diagram for cv::ORB:

Public Types

enum  ScoreType {

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 ORB::ScoreType 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 (ORB::ScoreType 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).
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).
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.
virtual void read (const FileNode &) CV_OVERRIDE
 Reads algorithm parameters from a file storage.
void read (const String &fileName)
void write (const Ptr< FileStorage > &fs, const String &name) const
void write (const String &fileName) const
virtual void write (FileStorage &) const CV_OVERRIDE
 Stores algorithm parameters in a file storage.
void write (FileStorage &fs, const String &name) const
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
virtual ~Algorithm ()
virtual void clear ()
 Clears the algorithm state.
virtual void save (const String &filename) const
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
void write (FileStorage &fs, const String &name) const

Static Public Member Functions

static Ptr< ORBcreate (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.
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tpload (const String &filename, const String &objname=String())
 Loads algorithm from the file.
template<typename _Tp >
static Ptr< _TploadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tpread (const FileNode &fn)
 Reads algorithm from the file node.

Static Public Attributes

static const int kBytes = 32

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 [229] . 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


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,
ORB::ScoreType  scoreType = ORB::HARRIS_SCORE,
int  patchSize = 31,
int  fastThreshold = 20 
cv.ORB.create([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) -> retval
cv.ORB_create([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) -> retval

The ORB constructor.

nfeaturesThe maximum number of features to retain.
scaleFactorPyramid 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.
nlevelsThe number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
edgeThresholdThis is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevelThe level of pyramid to put source image to. Previous layers are filled with upscaled source image.
WTA_KThe 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).
scoreTypeThe 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.
patchSizesize 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.
fastThresholdthe fast threshold

◆ getDefaultName()

virtual String cv::ORB::getDefaultName ( ) const
cv.ORB.getDefaultName() -> retval

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
cv.ORB.getEdgeThreshold() -> retval

◆ getFastThreshold()

virtual int cv::ORB::getFastThreshold ( ) const
pure virtual
cv.ORB.getFastThreshold() -> retval

◆ getFirstLevel()

virtual int cv::ORB::getFirstLevel ( ) const
pure virtual
cv.ORB.getFirstLevel() -> retval

◆ getMaxFeatures()

virtual int cv::ORB::getMaxFeatures ( ) const
pure virtual
cv.ORB.getMaxFeatures() -> retval

◆ getNLevels()

virtual int cv::ORB::getNLevels ( ) const
pure virtual
cv.ORB.getNLevels() -> retval

◆ getPatchSize()

virtual int cv::ORB::getPatchSize ( ) const
pure virtual
cv.ORB.getPatchSize() -> retval

◆ getScaleFactor()

virtual double cv::ORB::getScaleFactor ( ) const
pure virtual
cv.ORB.getScaleFactor() -> retval

◆ getScoreType()

virtual ORB::ScoreType cv::ORB::getScoreType ( ) const
pure virtual
cv.ORB.getScoreType() -> retval

◆ getWTA_K()

virtual int cv::ORB::getWTA_K ( ) const
pure virtual
cv.ORB.getWTA_K() -> retval

◆ setEdgeThreshold()

virtual void cv::ORB::setEdgeThreshold ( int  edgeThreshold)
pure virtual
cv.ORB.setEdgeThreshold(edgeThreshold) -> None

◆ setFastThreshold()

virtual void cv::ORB::setFastThreshold ( int  fastThreshold)
pure virtual
cv.ORB.setFastThreshold(fastThreshold) -> None

◆ setFirstLevel()

virtual void cv::ORB::setFirstLevel ( int  firstLevel)
pure virtual
cv.ORB.setFirstLevel(firstLevel) -> None

◆ setMaxFeatures()

virtual void cv::ORB::setMaxFeatures ( int  maxFeatures)
pure virtual
cv.ORB.setMaxFeatures(maxFeatures) -> None

◆ setNLevels()

virtual void cv::ORB::setNLevels ( int  nlevels)
pure virtual
cv.ORB.setNLevels(nlevels) -> None

◆ setPatchSize()

virtual void cv::ORB::setPatchSize ( int  patchSize)
pure virtual
cv.ORB.setPatchSize(patchSize) -> None

◆ setScaleFactor()

virtual void cv::ORB::setScaleFactor ( double  scaleFactor)
pure virtual
cv.ORB.setScaleFactor(scaleFactor) -> None

◆ setScoreType()

virtual void cv::ORB::setScoreType ( ORB::ScoreType  scoreType)
pure virtual
cv.ORB.setScoreType(scoreType) -> None

◆ setWTA_K()

virtual void cv::ORB::setWTA_K ( int  wta_k)
pure virtual
cv.ORB.setWTA_K(wta_k) -> None

Member Data Documentation

◆ kBytes

const int cv::ORB::kBytes = 32

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