cv::xfeatures2d::SIFT Class Reference

Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [132] . More...

#include <opencv2/xfeatures2d/nonfree.hpp>

Inheritance diagram for cv::xfeatures2d::SIFT:

Static Public Member Functions
static Ptr< SIFT >	create (int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6)
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 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...
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [132] .

Member Function Documentation

create()

static Ptr<SIFT> cv::xfeatures2d::SIFT::create ( int  nfeatures = 0, int  nOctaveLayers = 3, double  contrastThreshold = 0.04, double  edgeThreshold = 10, double  sigma = 1.6  )

static

Python:
	retval	=	cv.xfeatures2d.SIFT_create(	[, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]	)

Parameters

nfeatures	The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
nOctaveLayers	The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
contrastThreshold	The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
edgeThreshold	The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
sigma	The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.

---

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

• opencv2/xfeatures2d/nonfree.hpp