Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [171] and [172]. More...

#include "xfeatures2d.hpp"

Inheritance 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 }

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. 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
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...

virtual String	getDefaultName () const

void	read (const String &fileName)

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

void	write (const String &fileName) const

virtual void	write (FileStorage &) const
	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...

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

Detailed Description

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [171] and [172].

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:
	retval	=	cv.xfeatures2d.BoostDesc_create(	[, desc[, use_scale_orientation[, scale_factor]]]	)

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

/build/master-contrib_docs-lin64/opencv_contrib/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

Public Types

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

§ anonymous enum

Member Function Documentation

§ create()