cv::Feature2D Class Reference

Abstract base class for 2D image feature detectors and descriptor extractors. More...

#include <opencv2/features2d.hpp>

Collaboration diagram for cv::Feature2D:

Public Member Functions
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 String	getDefaultName () const CV_OVERRIDE
virtual void	read (const FileNode &) CV_OVERRIDE
	Reads algorithm parameters from a file storage.
void	read (const String &fileName)
void	write (const Ptr< FileStorage > &fs, const String &name) const
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

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

Detailed Description

Abstract base class for 2D image feature detectors and descriptor extractors.

Constructor & Destructor Documentation

~Feature2D()

virtual cv::Feature2D::~Feature2D ( )

virtual

Member Function Documentation

compute() [1/2]

virtual void cv::Feature2D::compute ( InputArray image, std::vector< KeyPoint > & keypoints, OutputArray descriptors )

virtual

Python:
	cv.Feature2D.compute(	image, keypoints[, descriptors]	) ->	keypoints, descriptors
	cv.Feature2D.compute(	images, keypoints[, descriptors]	) ->	keypoints, descriptors

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

Parameters

image	Image.
keypoints	Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors	Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Reimplemented in cv::xfeatures2d::DAISY.

compute() [2/2]

virtual void cv::Feature2D::compute ( InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > & keypoints, OutputArrayOfArrays descriptors )

virtual

Python:
	cv.Feature2D.compute(	image, keypoints[, descriptors]	) ->	keypoints, descriptors
	cv.Feature2D.compute(	images, keypoints[, descriptors]	) ->	keypoints, descriptors

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

images	Image set.
keypoints	Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors	Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Reimplemented in cv::xfeatures2d::DAISY.

defaultNorm()

virtual int cv::Feature2D::defaultNorm ( ) const

virtual

Python:
	cv.Feature2D.defaultNorm(		) ->	retval

descriptorSize()

virtual int cv::Feature2D::descriptorSize ( ) const

virtual

Python:
	cv.Feature2D.descriptorSize(		) ->	retval

descriptorType()

virtual int cv::Feature2D::descriptorType ( ) const

virtual

Python:
	cv.Feature2D.descriptorType(		) ->	retval

detect() [1/2]

virtual void cv::Feature2D::detect ( InputArray image, std::vector< KeyPoint > & keypoints, InputArray mask = noArray() )

virtual

Python:
	cv.Feature2D.detect(	image[, mask]	) ->	keypoints
	cv.Feature2D.detect(	images[, masks]	) ->	keypoints

Detects keypoints in an image (first variant) or image set (second variant).

Parameters

image	Image.
keypoints	The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
mask	Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.

Reimplemented in cv::xfeatures2d::AffineFeature2D.

Here is the call graph for this function:

detect() [2/2]

virtual void cv::Feature2D::detect ( InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > & keypoints, InputArrayOfArrays masks = noArray() )

virtual

Python:
	cv.Feature2D.detect(	image[, mask]	) ->	keypoints
	cv.Feature2D.detect(	images[, masks]	) ->	keypoints

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

images	Image set.
keypoints	The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
masks	Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

Reimplemented in cv::xfeatures2d::AffineFeature2D.

Here is the call graph for this function:

detectAndCompute()

virtual void cv::Feature2D::detectAndCompute ( InputArray image, InputArray mask, std::vector< KeyPoint > & keypoints, OutputArray descriptors, bool useProvidedKeypoints = false )

virtual

Python:
	cv.Feature2D.detectAndCompute(	image, mask[, descriptors[, useProvidedKeypoints]]	) ->	keypoints, descriptors

Detects keypoints and computes the descriptors

Reimplemented in cv::xfeatures2d::AffineFeature2D.

empty()

virtual bool cv::Feature2D::empty ( ) const

virtual

Python:
	cv.Feature2D.empty(		) ->	retval

Return true if detector object is empty.

Reimplemented from cv::Algorithm.

getDefaultName()

virtual String cv::Feature2D::getDefaultName ( ) const

virtual

Python:
	cv.Feature2D.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::Algorithm.

Reimplemented in cv::AffineFeature, cv::AgastFeatureDetector, cv::AKAZE, cv::BRISK, cv::FastFeatureDetector, cv::GFTTDetector, cv::KAZE, cv::MSER, cv::ORB, cv::SIFT, cv::SimpleBlobDetector, cv::xfeatures2d::BEBLID, cv::xfeatures2d::BoostDesc, cv::xfeatures2d::BriefDescriptorExtractor, cv::xfeatures2d::DAISY, cv::xfeatures2d::FREAK, cv::xfeatures2d::HarrisLaplaceFeatureDetector, cv::xfeatures2d::LATCH, cv::xfeatures2d::LUCID, cv::xfeatures2d::MSDDetector, cv::xfeatures2d::StarDetector, cv::xfeatures2d::SURF, cv::xfeatures2d::TBMR, cv::xfeatures2d::TEBLID, and cv::xfeatures2d::VGG.

read() [1/2]

virtual void cv::Feature2D::read ( const FileNode & fn )

virtual

Python:
	cv.Feature2D.read(	fileName	) ->	None
	cv.Feature2D.read(	arg1	) ->	None

Reads algorithm parameters from a file storage.

Reimplemented from cv::Algorithm.

read() [2/2]

void cv::Feature2D::read

(

const String &

fileName

)

Python:
	cv.Feature2D.read(	fileName	) ->	None
	cv.Feature2D.read(	arg1	) ->	None

write() [1/4]

void cv::Feature2D::write ( const Ptr< FileStorage > & fs, const String & name ) const

inline

Python:
	cv.Feature2D.write(	fileName	) ->	None
	cv.Feature2D.write(	fs, name	) ->	None

write() [2/4]

void cv::Feature2D::write

(

const String &

fileName

)

const

Python:
	cv.Feature2D.write(	fileName	) ->	None
	cv.Feature2D.write(	fs, name	) ->	None

write() [3/4]

virtual void cv::Feature2D::write ( FileStorage & fs ) const

virtual

Python:
	cv.Feature2D.write(	fileName	) ->	None
	cv.Feature2D.write(	fs, name	) ->	None

Stores algorithm parameters in a file storage.

Reimplemented from cv::Algorithm.

write() [4/4]

void cv::Feature2D::write ( FileStorage & fs, const String & name ) const

inline

Python:
	cv.Feature2D.write(	fileName	) ->	None
	cv.Feature2D.write(	fs, name	) ->	None

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

• opencv2/features2d.hpp