cv::xfeatures2d::BoostDesc Class Reference

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [262] and [263]. More...

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::BoostDesc:

Public Types
enum	{   BGM = 100 ,   BGM_HARD = 101 ,   BGM_BILINEAR = 102 ,   LBGM = 200 ,   BINBOOST_64 = 300 ,   BINBOOST_128 = 301 ,   BINBOOST_256 = 302 }

Public Member Functions
String	getDefaultName () const CV_OVERRIDE
virtual float	getScaleFactor () const =0
virtual bool	getUseScaleOrientation () const =0
virtual void	setScaleFactor (const float scale_factor)=0
virtual void	setUseScaleOrientation (const bool use_scale_orientation)=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 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 (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< BoostDesc >	create (int desc=BoostDesc::BINBOOST_256, bool use_scale_orientation=true, float scale_factor=6.25f)
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.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

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

Detailed Description

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [262] and [263].

Parameters

desc	type of descriptor to use, BoostDesc::BINBOOST_256 is default (256 bit long dimension) Available types are: BoostDesc::BGM, BoostDesc::BGM_HARD, BoostDesc::BGM_BILINEAR, BoostDesc::LBGM, BoostDesc::BINBOOST_64, BoostDesc::BINBOOST_128, BoostDesc::BINBOOST_256
use_orientation	sample patterns using keypoints orientation, enabled by default
scale_factor	adjust the sampling window of detected keypoints 6.25f is default and fits for KAZE, SURF detected keypoints window ratio 6.75f should be the scale for SIFT detected keypoints window ratio 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints window ratio 0.75f should be the scale for ORB keypoints ratio 1.50f was the default in original implementation

Note

BGM is the base descriptor where each binary dimension is computed as the output of a single weak learner. BGM_HARD and BGM_BILINEAR refers to same BGM but use different type of gradient binning. In the BGM_HARD that use ASSIGN_HARD binning type the gradient is assigned to the nearest orientation bin. In the BGM_BILINEAR that use ASSIGN_BILINEAR binning type the gradient is assigned to the two neighbouring bins. In the BGM and all other modes that use ASSIGN_SOFT binning type the gradient is assigned to 8 nearest bins according to the cosine value between the gradient angle and the bin center. LBGM (alias FP-Boost) is the floating point extension where each dimension is computed as a linear combination of the weak learner responses. BINBOOST and subvariants are the binary extensions of LBGM where each bit is computed as a thresholded linear combination of a set of weak learners. BoostDesc header files (boostdesc_*.i) was exported from original binaries with export-boostdesc.py script from samples subfolder.

Member Enumeration Documentation

anonymous enum

anonymous enum

Enumerator
BGM
BGM_HARD
BGM_BILINEAR
LBGM
BINBOOST_64
BINBOOST_128
BINBOOST_256

Member Function Documentation

create()

static Ptr< BoostDesc > cv::xfeatures2d::BoostDesc::create ( int desc = BoostDesc::BINBOOST_256, bool use_scale_orientation = true, float scale_factor = 6.25f )

static

Python:
	cv.xfeatures2d.BoostDesc.create(	[, desc[, use_scale_orientation[, scale_factor]]]	) ->	retval
	cv.xfeatures2d.BoostDesc_create(	[, desc[, use_scale_orientation[, scale_factor]]]	) ->	retval

getDefaultName()

String cv::xfeatures2d::BoostDesc::getDefaultName ( ) const

virtual

Python:
	cv.xfeatures2d.BoostDesc.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.

getScaleFactor()

virtual float cv::xfeatures2d::BoostDesc::getScaleFactor ( ) const

pure virtual

Python:
	cv.xfeatures2d.BoostDesc.getScaleFactor(		) ->	retval

getUseScaleOrientation()

virtual bool cv::xfeatures2d::BoostDesc::getUseScaleOrientation ( ) const

pure virtual

Python:
	cv.xfeatures2d.BoostDesc.getUseScaleOrientation(		) ->	retval

setScaleFactor()

virtual void cv::xfeatures2d::BoostDesc::setScaleFactor ( const float scale_factor )

pure virtual

Python:
	cv.xfeatures2d.BoostDesc.setScaleFactor(	scale_factor	) ->	None

setUseScaleOrientation()

virtual void cv::xfeatures2d::BoostDesc::setUseScaleOrientation ( const bool use_scale_orientation )

pure virtual

Python:
	cv.xfeatures2d.BoostDesc.setUseScaleOrientation(	use_scale_orientation	) ->	None

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

• opencv2/xfeatures2d.hpp