Abstract base class for matching keypoint descriptors. More...

#include <opencv2/features2d.hpp>

Inheritance diagram for cv::DescriptorMatcher:

Classes
class	DescriptorCollection

Public Types
enum	{ FLANNBASED = 1, BRUTEFORCE = 2, BRUTEFORCE_L1 = 3, BRUTEFORCE_HAMMING = 4, BRUTEFORCE_HAMMINGLUT = 5, BRUTEFORCE_SL2 = 6 }

Public Member Functions
virtual	~DescriptorMatcher ()

virtual void	add (InputArrayOfArrays descriptors)
	Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor collection. More...

virtual void	clear () CV_OVERRIDE
	Clears the train descriptor collections. More...

virtual Ptr< DescriptorMatcher >	clone (bool emptyTrainData=false) const =0
	Clones the matcher. More...

virtual bool	empty () const CV_OVERRIDE
	Returns true if there are no train descriptors in the both collections. More...

const std::vector< Mat > &	getTrainDescriptors () const
	Returns a constant link to the train descriptor collection trainDescCollection . More...

virtual bool	isMaskSupported () const =0
	Returns true if the descriptor matcher supports masking permissible matches. More...

void	knnMatch (InputArray queryDescriptors, InputArray trainDescriptors, std::vector< std::vector< DMatch > > &matches, int k, InputArray mask=noArray(), bool compactResult=false) const
	Finds the k best matches for each descriptor from a query set. More...

void	knnMatch (InputArray queryDescriptors, std::vector< std::vector< DMatch > > &matches, int k, InputArrayOfArrays masks=noArray(), bool compactResult=false)

void	match (InputArray queryDescriptors, InputArray trainDescriptors, std::vector< DMatch > &matches, InputArray mask=noArray()) const
	Finds the best match for each descriptor from a query set. More...

void	match (InputArray queryDescriptors, std::vector< DMatch > &matches, InputArrayOfArrays masks=noArray())

void	radiusMatch (InputArray queryDescriptors, InputArray trainDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, InputArray mask=noArray(), bool compactResult=false) const
	For each query descriptor, finds the training descriptors not farther than the specified distance. More...

void	radiusMatch (InputArray queryDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, InputArrayOfArrays masks=noArray(), bool compactResult=false)

void	read (const String &fileName)

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

virtual void	train ()
	Trains a descriptor matcher. 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 String	getDefaultName () const

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< DescriptorMatcher >	create (const String &descriptorMatcherType)
	Creates a descriptor matcher of a given type with the default parameters (using default constructor). More...

static Ptr< DescriptorMatcher >	create (int matcherType)

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...

Protected Member Functions
void	checkMasks (InputArrayOfArrays masks, int queryDescriptorsCount) const

virtual void	knnMatchImpl (InputArray queryDescriptors, std::vector< std::vector< DMatch > > &matches, int k, InputArrayOfArrays masks=noArray(), bool compactResult=false)=0

virtual void	radiusMatchImpl (InputArray queryDescriptors, std::vector< std::vector< DMatch > > &matches, float maxDistance, InputArrayOfArrays masks=noArray(), bool compactResult=false)=0

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

Static Protected Member Functions
static Mat	clone_op (Mat m)

static bool	isMaskedOut (InputArrayOfArrays masks, int queryIdx)

static bool	isPossibleMatch (InputArray mask, int queryIdx, int trainIdx)

Protected Attributes
std::vector< Mat >	trainDescCollection
	Collection of descriptors from train images. More...

std::vector< UMat >	utrainDescCollection

Detailed Description

Abstract base class for matching keypoint descriptors.

It has two groups of match methods: for matching descriptors of an image with another image or with an image set.

Member Enumeration Documentation

◆ anonymous enum

anonymous enum

Enumerator
FLANNBASED
BRUTEFORCE
BRUTEFORCE_L1
BRUTEFORCE_HAMMING
BRUTEFORCE_HAMMINGLUT
BRUTEFORCE_SL2

Constructor & Destructor Documentation

◆ ~DescriptorMatcher()

virtual cv::DescriptorMatcher::~DescriptorMatcher ( )

virtual

Member Function Documentation

◆ add()

virtual void cv::DescriptorMatcher::add ( InputArrayOfArrays descriptors )

virtual

Python:
	None	=	cv.DescriptorMatcher.add(	descriptors	)

Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor collection.

If the collection is not empty, the new descriptors are added to existing train descriptors.

Parameters

descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same train image.

Reimplemented in cv::FlannBasedMatcher.

◆ checkMasks()

void cv::DescriptorMatcher::checkMasks	(	InputArrayOfArrays	masks,
		int	queryDescriptorsCount
	)		const

protected

◆ clear()

virtual void cv::DescriptorMatcher::clear ( )

virtual

Python:
	None	=	cv.DescriptorMatcher.clear(		)

Clears the train descriptor collections.

Reimplemented from cv::Algorithm.

Reimplemented in cv::FlannBasedMatcher.

◆ clone()

virtual Ptr<DescriptorMatcher> cv::DescriptorMatcher::clone ( bool emptyTrainData = false ) const

pure virtual

Python:
	retval	=	cv.DescriptorMatcher.clone(	[, emptyTrainData]	)

Clones the matcher.

Parameters

emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object, that is, copies both parameters and train data. If emptyTrainData is true, the method creates an object copy with the current parameters but with empty train data.

Implemented in cv::FlannBasedMatcher, and cv::BFMatcher.

◆ clone_op()

static Mat cv::DescriptorMatcher::clone_op ( Mat m )

inlinestaticprotected

◆ create() [1/2]

static Ptr<DescriptorMatcher> cv::DescriptorMatcher::create ( const String & descriptorMatcherType )

static

Python:
	retval	=	cv.DescriptorMatcher_create(	descriptorMatcherType	)
	retval	=	cv.DescriptorMatcher_create(	matcherType	)

Creates a descriptor matcher of a given type with the default parameters (using default constructor).

Parameters

descriptorMatcherType

Descriptor matcher type. Now the following matcher types are supported:

BruteForce (it uses L2 )
BruteForce-L1
BruteForce-Hamming
BruteForce-Hamming(2)
FlannBased

◆ create() [2/2]

static Ptr<DescriptorMatcher> cv::DescriptorMatcher::create ( int matcherType )

static

Python:
	retval	=	cv.DescriptorMatcher_create(	descriptorMatcherType	)
	retval	=	cv.DescriptorMatcher_create(	matcherType	)

◆ empty()

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

virtual

Python:
	retval	=	cv.DescriptorMatcher.empty(		)

Returns true if there are no train descriptors in the both collections.

Reimplemented from cv::Algorithm.

◆ getTrainDescriptors()

const std::vector<Mat>& cv::DescriptorMatcher::getTrainDescriptors ( ) const

Python:
	retval	=	cv.DescriptorMatcher.getTrainDescriptors(		)

Returns a constant link to the train descriptor collection trainDescCollection .

◆ isMaskedOut()

static bool cv::DescriptorMatcher::isMaskedOut	(	InputArrayOfArrays	masks,
		int	queryIdx
	)

staticprotected

◆ isMaskSupported()

virtual bool cv::DescriptorMatcher::isMaskSupported ( ) const

pure virtual

Python:
	retval	=	cv.DescriptorMatcher.isMaskSupported(		)

Returns true if the descriptor matcher supports masking permissible matches.

Implemented in cv::FlannBasedMatcher, and cv::BFMatcher.

◆ isPossibleMatch()

static bool cv::DescriptorMatcher::isPossibleMatch	(	InputArray	mask,
		int	queryIdx,
		int	trainIdx
	)

staticprotected

◆ knnMatch() [1/2]

void cv::DescriptorMatcher::knnMatch	(	InputArray	queryDescriptors,
		InputArray	trainDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		int	k,
		InputArray	mask = `noArray()`,
		bool	compactResult = `false`
	)		const

Python:
	matches	=	cv.DescriptorMatcher.knnMatch(	queryDescriptors, trainDescriptors, k[, mask[, compactResult]]	)
	matches	=	cv.DescriptorMatcher.knnMatch(	queryDescriptors, k[, masks[, compactResult]]	)

Finds the k best matches for each descriptor from a query set.

Parameters

queryDescriptors	Query set of descriptors.
trainDescriptors	Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.
mask	Mask specifying permissible matches between an input query and train matrices of descriptors.
matches	Matches. Each matches[i] is k or less matches for the same query descriptor.
k	Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.
compactResult	Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

These extended variants of DescriptorMatcher::match methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match for the details about query and train descriptors.

◆ knnMatch() [2/2]

void cv::DescriptorMatcher::knnMatch	(	InputArray	queryDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		int	k,
		InputArrayOfArrays	masks = `noArray()`,
		bool	compactResult = `false`
	)

Python:
	matches	=	cv.DescriptorMatcher.knnMatch(	queryDescriptors, trainDescriptors, k[, mask[, compactResult]]	)
	matches	=	cv.DescriptorMatcher.knnMatch(	queryDescriptors, k[, masks[, compactResult]]	)

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

Parameters

queryDescriptors	Query set of descriptors.
matches	Matches. Each matches[i] is k or less matches for the same query descriptor.
k	Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total.
masks	Set of masks. Each masks[i] specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection[i].
compactResult	Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

◆ knnMatchImpl()

virtual void cv::DescriptorMatcher::knnMatchImpl	(	InputArray	queryDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		int	k,
		InputArrayOfArrays	masks = `noArray()`,
		bool	compactResult = `false`
	)

protectedpure virtual

In fact the matching is implemented only by the following two methods. These methods suppose that the class object has been trained already. Public match methods call these methods after calling train().

Implemented in cv::FlannBasedMatcher, and cv::BFMatcher.

◆ match() [1/2]

void cv::DescriptorMatcher::match	(	InputArray	queryDescriptors,
		InputArray	trainDescriptors,
		std::vector< DMatch > &	matches,
		InputArray	mask = `noArray()`
	)		const

Python:
	matches	=	cv.DescriptorMatcher.match(	queryDescriptors, trainDescriptors[, mask]	)
	matches	=	cv.DescriptorMatcher.match(	queryDescriptors[, masks]	)

Finds the best match for each descriptor from a query set.

Parameters

queryDescriptors	Query set of descriptors.
trainDescriptors	Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.
matches	Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count.
mask	Mask specifying permissible matches between an input query and train matrices of descriptors.

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if mask.at<uchar>(i,j) is non-zero.

◆ match() [2/2]

void cv::DescriptorMatcher::match	(	InputArray	queryDescriptors,
		std::vector< DMatch > &	matches,
		InputArrayOfArrays	masks = `noArray()`
	)

Python:
	matches	=	cv.DescriptorMatcher.match(	queryDescriptors, trainDescriptors[, mask]	)
	matches	=	cv.DescriptorMatcher.match(	queryDescriptors[, masks]	)

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

Parameters

queryDescriptors	Query set of descriptors.
matches	Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count.
masks	Set of masks. Each masks[i] specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection[i].

◆ radiusMatch() [1/2]

void cv::DescriptorMatcher::radiusMatch	(	InputArray	queryDescriptors,
		InputArray	trainDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		float	maxDistance,
		InputArray	mask = `noArray()`,
		bool	compactResult = `false`
	)		const

Python:
	matches	=	cv.DescriptorMatcher.radiusMatch(	queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]	)
	matches	=	cv.DescriptorMatcher.radiusMatch(	queryDescriptors, maxDistance[, masks[, compactResult]]	)

For each query descriptor, finds the training descriptors not farther than the specified distance.

Parameters

queryDescriptors	Query set of descriptors.
trainDescriptors	Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.
matches	Found matches.
compactResult	Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.
maxDistance	Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!
mask	Mask specifying permissible matches between an input query and train matrices of descriptors.

For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are returned in the distance increasing order.

◆ radiusMatch() [2/2]

void cv::DescriptorMatcher::radiusMatch	(	InputArray	queryDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		float	maxDistance,
		InputArrayOfArrays	masks = `noArray()`,
		bool	compactResult = `false`
	)

Python:
	matches	=	cv.DescriptorMatcher.radiusMatch(	queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]	)
	matches	=	cv.DescriptorMatcher.radiusMatch(	queryDescriptors, maxDistance[, masks[, compactResult]]	)

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

Parameters

queryDescriptors	Query set of descriptors.
matches	Found matches.
maxDistance	Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)!
masks	Set of masks. Each masks[i] specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection[i].
compactResult	Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

◆ radiusMatchImpl()

virtual void cv::DescriptorMatcher::radiusMatchImpl	(	InputArray	queryDescriptors,
		std::vector< std::vector< DMatch > > &	matches,
		float	maxDistance,
		InputArrayOfArrays	masks = `noArray()`,
		bool	compactResult = `false`
	)

protectedpure virtual

Implemented in cv::FlannBasedMatcher, and cv::BFMatcher.

◆ read() [1/2]

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

inline

Python:
	None	=	cv.DescriptorMatcher.read(	fileName	)
	None	=	cv.DescriptorMatcher.read(	arg1	)

◆ read() [2/2]

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

virtual

Python:
	None	=	cv.DescriptorMatcher.read(	fileName	)
	None	=	cv.DescriptorMatcher.read(	arg1	)

Reads algorithm parameters from a file storage.

Reimplemented from cv::Algorithm.

Reimplemented in cv::FlannBasedMatcher.

◆ train()

virtual void cv::DescriptorMatcher::train ( )

virtual

Python:
	None	=	cv.DescriptorMatcher.train(		)

Trains a descriptor matcher.

Trains a descriptor matcher (for example, the flann index). In all methods to match, the method train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher) have an empty implementation of this method. Other matchers really train their inner structures (for example, FlannBasedMatcher trains flann::Index ).

Reimplemented in cv::FlannBasedMatcher.

◆ write() [1/3]

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

inline

Python:
	None	=	cv.DescriptorMatcher.write(	fileName	)
	None	=	cv.DescriptorMatcher.write(	fs[, name]	)

◆ write() [2/3]

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

virtual

Python:
	None	=	cv.DescriptorMatcher.write(	fileName	)
	None	=	cv.DescriptorMatcher.write(	fs[, name]	)

Stores algorithm parameters in a file storage.

Reimplemented from cv::Algorithm.

Reimplemented in cv::FlannBasedMatcher.

◆ write() [3/3]

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

inline

Python:
	None	=	cv.DescriptorMatcher.write(	fileName	)
	None	=	cv.DescriptorMatcher.write(	fs[, name]	)

Member Data Documentation

◆ trainDescCollection

std::vector<Mat> cv::DescriptorMatcher::trainDescCollection

protected

Collection of descriptors from train images.

◆ utrainDescCollection

std::vector<UMat> cv::DescriptorMatcher::utrainDescCollection

protected

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

opencv2/features2d.hpp

Classes

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Enumeration Documentation

◆ anonymous enum

Constructor & Destructor Documentation

◆ ~DescriptorMatcher()

Member Function Documentation

◆ add()

◆ checkMasks()

◆ clear()

◆ clone()

◆ clone_op()

◆ create() [1/2]

◆ create() [2/2]

◆ empty()

◆ getTrainDescriptors()

◆ isMaskedOut()

◆ isMaskSupported()

◆ isPossibleMatch()

◆ knnMatch() [1/2]

◆ knnMatch() [2/2]

◆ knnMatchImpl()

◆ match() [1/2]

◆ match() [2/2]

◆ radiusMatch() [1/2]

◆ radiusMatch() [2/2]

◆ radiusMatchImpl()

◆ read() [1/2]

◆ read() [2/2]

◆ train()

◆ write() [1/3]

◆ write() [2/3]

◆ write() [3/3]

Member Data Documentation

◆ trainDescCollection

◆ utrainDescCollection