OpenCV  4.1.2-dev
Open Source Computer Vision
Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::xfeatures2d::VGG Class Referenceabstract

Class implementing VGG (Oxford Visual Geometry Group) descriptor trained end to end using "Descriptor Learning Using Convex Optimisation" (DLCO) aparatus described in [197]. More...

#include <opencv2/xfeatures2d.hpp>

Inheritance diagram for cv::xfeatures2d::VGG:
cv::Feature2D cv::Algorithm

Public Types

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

Public Member Functions

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). 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 CV_OVERRIDE
 Return true if detector object is empty. More...
 
virtual String getDefaultName () const CV_OVERRIDE
 
void read (const String &fileName)
 
virtual void read (const FileNode &) CV_OVERRIDE
 Reads algorithm parameters from a file storage. More...
 
void write (const String &fileName) const
 
virtual void write (FileStorage &) const CV_OVERRIDE
 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...
 

Static Public Member Functions

static Ptr< VGGcreate (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. 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

- 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 [197].

Parameters
desctype 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
isigmagaussian kernel value for image blur (default is 1.4f)
img_normalizeuse image sample intensity normalization (enabled by default)
use_orientationsample patterns using keypoints orientation, enabled by default
scale_factoradjust 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_normalizeclamp 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:
retval=cv.xfeatures2d.VGG_create([, desc[, isigma[, img_normalize[, use_scale_orientation[, scale_factor[, dsc_normalize]]]]]])

§ getScaleFactor()

virtual float cv::xfeatures2d::VGG::getScaleFactor ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_VGG.getScaleFactor()

§ getSigma()

virtual float cv::xfeatures2d::VGG::getSigma ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_VGG.getSigma()

§ getUseNormalizeDescriptor()

virtual bool cv::xfeatures2d::VGG::getUseNormalizeDescriptor ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_VGG.getUseNormalizeDescriptor()

§ getUseNormalizeImage()

virtual bool cv::xfeatures2d::VGG::getUseNormalizeImage ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_VGG.getUseNormalizeImage()

§ getUseScaleOrientation()

virtual bool cv::xfeatures2d::VGG::getUseScaleOrientation ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_VGG.getUseScaleOrientation()

§ setScaleFactor()

virtual void cv::xfeatures2d::VGG::setScaleFactor ( const float  scale_factor)
pure virtual
Python:
None=cv.xfeatures2d_VGG.setScaleFactor(scale_factor)

§ setSigma()

virtual void cv::xfeatures2d::VGG::setSigma ( const float  isigma)
pure virtual
Python:
None=cv.xfeatures2d_VGG.setSigma(isigma)

§ setUseNormalizeDescriptor()

virtual void cv::xfeatures2d::VGG::setUseNormalizeDescriptor ( const bool  dsc_normalize)
pure virtual
Python:
None=cv.xfeatures2d_VGG.setUseNormalizeDescriptor(dsc_normalize)

§ setUseNormalizeImage()

virtual void cv::xfeatures2d::VGG::setUseNormalizeImage ( const bool  img_normalize)
pure virtual
Python:
None=cv.xfeatures2d_VGG.setUseNormalizeImage(img_normalize)

§ setUseScaleOrientation()

virtual void cv::xfeatures2d::VGG::setUseScaleOrientation ( const bool  use_scale_orientation)
pure virtual
Python:
None=cv.xfeatures2d_VGG.setUseScaleOrientation(use_scale_orientation)

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