Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [247]. More...

#include <opencv2/xfeatures2d.hpp>

Collaboration diagram for cv::xfeatures2d::VGG:

Public Types
enum	{ VGG_120 = 100 , VGG_80 = 101 , VGG_64 = 102 , VGG_48 = 103 }

Public Member Functions
String	getDefaultName () const CV_OVERRIDE

virtual float	getScaleFactor () const =0

virtual float	getSigma () const =0

virtual bool	getUseNormalizeDescriptor () const =0

virtual bool	getUseNormalizeImage () const =0

virtual bool	getUseScaleOrientation () const =0

virtual void	setScaleFactor (const float scale_factor)=0

virtual void	setSigma (const float isigma)=0

virtual void	setUseNormalizeDescriptor (const bool dsc_normalize)=0

virtual void	setUseNormalizeImage (const bool img_normalize)=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 (const Ptr< FileStorage > &fs, const String &name=String()) const

void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< VGG >	create (int desc=VGG::VGG_120, float isigma=1.4f, bool img_normalize=true, bool use_scale_orientation=true, float scale_factor=6.25f, bool dsc_normalize=false)

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 VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [247].

Parameters

desc	type of descriptor to use, VGG::VGG_120 is default (120 dimensions float) Available types are VGG::VGG_120, VGG::VGG_80, VGG::VGG_64, VGG::VGG_48
isigma	gaussian kernel value for image blur (default is 1.4f)
img_normalize	use image sample intensity normalization (enabled by default)
use_orientation	sample patterns using keypoints orientation, enabled by default
scale_factor	adjust the sampling window of detected keypoints to 64.0f (VGG sampling window) 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
dsc_normalize	clamp descriptors to 255 and convert to uchar CV_8UC1 (disabled by default)

Member Enumeration Documentation

◆ anonymous enum

anonymous enum

Enumerator
VGG_120
VGG_80
VGG_64
VGG_48

Member Function Documentation

◆ create()

static Ptr< VGG > cv::xfeatures2d::VGG::create	(	int	desc = `VGG::VGG_120`,
		float	isigma = `1.4f`,
		bool	img_normalize = `true`,
		bool	use_scale_orientation = `true`,
		float	scale_factor = `6.25f`,
		bool	dsc_normalize = `false`
	)

static

Python:
	cv.xfeatures2d.VGG.create(	[, desc[, isigma[, img_normalize[, use_scale_orientation[, scale_factor[, dsc_normalize]]]]]]	) ->	retval
	cv.xfeatures2d.VGG_create(	[, desc[, isigma[, img_normalize[, use_scale_orientation[, scale_factor[, dsc_normalize]]]]]]	) ->	retval

◆ getDefaultName()

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

virtual

Python:
	cv.xfeatures2d.VGG.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::VGG::getScaleFactor ( ) const

pure virtual

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

◆ getSigma()

virtual float cv::xfeatures2d::VGG::getSigma ( ) const

pure virtual

Python:
	cv.xfeatures2d.VGG.getSigma(		) ->	retval

◆ getUseNormalizeDescriptor()

virtual bool cv::xfeatures2d::VGG::getUseNormalizeDescriptor ( ) const

pure virtual

Python:
	cv.xfeatures2d.VGG.getUseNormalizeDescriptor(		) ->	retval

◆ getUseNormalizeImage()

virtual bool cv::xfeatures2d::VGG::getUseNormalizeImage ( ) const

pure virtual

Python:
	cv.xfeatures2d.VGG.getUseNormalizeImage(		) ->	retval

◆ getUseScaleOrientation()

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

pure virtual

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

◆ setScaleFactor()

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

pure virtual

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

◆ setSigma()

virtual void cv::xfeatures2d::VGG::setSigma ( const float isigma )

pure virtual

Python:
	cv.xfeatures2d.VGG.setSigma(	isigma	) ->	None

◆ setUseNormalizeDescriptor()

virtual void cv::xfeatures2d::VGG::setUseNormalizeDescriptor ( const bool dsc_normalize )

pure virtual

Python:
	cv.xfeatures2d.VGG.setUseNormalizeDescriptor(	dsc_normalize	) ->	None

◆ setUseNormalizeImage()

virtual void cv::xfeatures2d::VGG::setUseNormalizeImage ( const bool img_normalize )

pure virtual

Python:
	cv.xfeatures2d.VGG.setUseNormalizeImage(	img_normalize	) ->	None

◆ setUseScaleOrientation()

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

pure virtual

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

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

opencv2/xfeatures2d.hpp

Public Types

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

◆ anonymous enum

Member Function Documentation

◆ create()

◆ getDefaultName()

◆ getScaleFactor()

◆ getSigma()

◆ getUseNormalizeDescriptor()

◆ getUseNormalizeImage()

◆ getUseScaleOrientation()

◆ setScaleFactor()

◆ setSigma()

◆ setUseNormalizeDescriptor()

◆ setUseNormalizeImage()

◆ setUseScaleOrientation()