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

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [207] and [208]. More...

#include <opencv2/xfeatures2d.hpp>

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

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

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). 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< BoostDesccreate (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

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

Detailed Description

Class implementing BoostDesc (Learning Image Descriptors with Boosting), described in [207] and [208].

Parameters
desctype 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_orientationsample patterns using keypoints orientation, enabled by default
scale_factoradjust 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]]])

§ getScaleFactor()

virtual float cv::xfeatures2d::BoostDesc::getScaleFactor ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_BoostDesc.getScaleFactor()

§ getUseScaleOrientation()

virtual bool cv::xfeatures2d::BoostDesc::getUseScaleOrientation ( ) const
pure virtual
Python:
retval=cv.xfeatures2d_BoostDesc.getUseScaleOrientation()

§ setScaleFactor()

virtual void cv::xfeatures2d::BoostDesc::setScaleFactor ( const float  scale_factor)
pure virtual
Python:
None=cv.xfeatures2d_BoostDesc.setScaleFactor(scale_factor)

§ setUseScaleOrientation()

virtual void cv::xfeatures2d::BoostDesc::setUseScaleOrientation ( const bool  use_scale_orientation)
pure virtual
Python:
None=cv.xfeatures2d_BoostDesc.setUseScaleOrientation(use_scale_orientation)

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