Class implementing the AKAZE keypoint detector and descriptor extractor, described in [1] . More...

#include "features2d.hpp"

Inheritance diagram for cv::AKAZE:

Collaboration diagram for cv::AKAZE:

Public Types
enum	{ DESCRIPTOR_KAZE_UPRIGHT = 2, DESCRIPTOR_KAZE = 3, DESCRIPTOR_MLDB_UPRIGHT = 4, DESCRIPTOR_MLDB = 5 }

Public Member Functions
virtual void	clear ()
	Clears the algorithm state. More...

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

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

virtual void	detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
	Detects keypoints and computes the descriptors. More...

virtual bool	empty () const
	Return true if detector object is empty. More...

virtual String	getDefaultName () const
	Returns the algorithm string identifier. More...

virtual int	getDescriptorChannels () const =0

virtual int	getDescriptorSize () const =0

virtual int	getDescriptorType () const =0

virtual int	getDiffusivity () const =0

virtual int	getNOctaveLayers () const =0

virtual int	getNOctaves () const =0

virtual double	getThreshold () const =0

void	read (const String &fileName)

virtual void	read (const FileNode &)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const
	Saves the algorithm to a file. More...

virtual void	setDescriptorChannels (int dch)=0

virtual void	setDescriptorSize (int dsize)=0

virtual void	setDescriptorType (int dtype)=0

virtual void	setDiffusivity (int diff)=0

virtual void	setNOctaveLayers (int octaveLayers)=0

virtual void	setNOctaves (int octaves)=0

virtual void	setThreshold (double threshold)=0

void	write (const String &fileName) const

virtual void	write (FileStorage &) const
	Stores algorithm parameters in a file storage. More...

Static Public Member Functions
static Ptr< AKAZE >	create (int descriptor_type=AKAZE::DESCRIPTOR_MLDB, int descriptor_size=0, int descriptor_channels=3, float threshold=0.001f, int nOctaves=4, int nOctaveLayers=4, int diffusivity=KAZE::DIFF_PM_G2)
	The AKAZE constructor. More...

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...

Protected Member Functions
void	writeFormat (FileStorage &fs) const

Detailed Description

Class implementing the AKAZE keypoint detector and descriptor extractor, described in [1] .

:

Note: AKAZE descriptors can only be used with KAZE or AKAZE keypoints. Try to avoid using extract and detect instead of operator() due to performance reasons. .. [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.

Member Enumeration Documentation

anonymous enum

Enumerator
DESCRIPTOR_KAZE_UPRIGHT	Upright descriptors, not invariant to rotation.
DESCRIPTOR_KAZE
DESCRIPTOR_MLDB_UPRIGHT	Upright descriptors, not invariant to rotation.
DESCRIPTOR_MLDB

Member Function Documentation

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::cuda::DescriptorMatcher.

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

virtualinherited

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.

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

virtualinherited

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.

static Ptr<AKAZE> cv::AKAZE::create	(	int	descriptor_type = `AKAZE::DESCRIPTOR_MLDB`,
		int	descriptor_size = `0`,
		int	descriptor_channels = `3`,
		float	threshold = `0.001f`,
		int	nOctaves = `4`,
		int	nOctaveLayers = `4`,
		int	diffusivity = `KAZE::DIFF_PM_G2`
	)

static

The AKAZE constructor.

Parameters

descriptor_type	Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
descriptor_size	Size of the descriptor in bits. 0 -> Full size
descriptor_channels	Number of channels in the descriptor (1, 2, 3)
threshold	Detector response threshold to accept point
nOctaves	Maximum octave evolution of the image
nOctaveLayers	Default number of sublevels per scale level
diffusivity	Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER

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

virtualinherited

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

virtualinherited

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

virtualinherited

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

virtualinherited

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::AgastFeatureDetector_Impl.

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

virtualinherited

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].

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

virtualinherited

Detects keypoints and computes the descriptors.

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

virtualinherited

Return true if detector object is empty.

Reimplemented from cv::Algorithm.

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

virtualinherited

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.

virtual int cv::AKAZE::getDescriptorChannels ( ) const

pure virtual

virtual int cv::AKAZE::getDescriptorSize ( ) const

pure virtual

virtual int cv::AKAZE::getDescriptorType ( ) const

pure virtual

virtual int cv::AKAZE::getDiffusivity ( ) const

pure virtual

virtual int cv::AKAZE::getNOctaveLayers ( ) const

pure virtual

virtual int cv::AKAZE::getNOctaves ( ) const

pure virtual

virtual double cv::AKAZE::getThreshold ( ) const

pure virtual

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::load	(	const String &	filename,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from the file.

Parameters

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

References cv::Ptr< T >::empty(), cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), and cv::FileStorage::READ.

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template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::loadFromString	(	const String &	strModel,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from a String.

Parameters

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

References CV_WRAP, cv::Ptr< T >::empty(), cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.

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void cv::Feature2D::read ( const String & fileName )

inherited

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

virtualinherited

Reads algorithm parameters from a file storage.

Reimplemented from cv::Algorithm.

virtual void cv::Algorithm::save ( const String & filename ) const

virtualinherited

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

virtual void cv::AKAZE::setDescriptorChannels ( int dch )

pure virtual

virtual void cv::AKAZE::setDescriptorSize ( int dsize )

pure virtual

virtual void cv::AKAZE::setDescriptorType ( int dtype )

pure virtual

virtual void cv::AKAZE::setDiffusivity ( int diff )

pure virtual

virtual void cv::AKAZE::setNOctaveLayers ( int octaveLayers )

pure virtual

virtual void cv::AKAZE::setNOctaves ( int octaves )

pure virtual

virtual void cv::AKAZE::setThreshold ( double threshold )

pure virtual

void cv::Feature2D::write ( const String & fileName ) const

inherited

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

virtualinherited

Stores algorithm parameters in a file storage.

Reimplemented from cv::Algorithm.

void cv::Algorithm::writeFormat ( FileStorage & fs ) const

protectedinherited

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

features2d/include/opencv2/features2d.hpp

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Detailed Description

Member Enumeration Documentation

Member Function Documentation