Class cv::DescriptorMatcher

Abstract base class for matching keypoint descriptors. View details

#include <opencv2/features.hpp>

Collaboration diagram for cv::DescriptorMatcher:

Public Types

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

Public Member Functions

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	Algorithm()
	~Algorithm()
void	clear()	Clears the algorithm state.
bool	empty()	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
String	getDefaultName()
void	read(const FileNode & fn)	Reads algorithm parameters from a file storage.
void	save(const String & filename)
void	write(     const Ptr< FileStorage > & fs,     const String & name = String() )
void	write(FileStorage & fs)	Stores algorithm parameters in a file storage.
void	write(     FileStorage & fs,     const String & name )

Static Public Member Functions

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
static Ptr< _Tp >	load(     const String & filename,     const String & objname = String() )	Loads algorithm from the file.
static Ptr< _Tp >	loadFromString(     const String & strModel,     const String & objname = String() )	Loads algorithm from a String.
static Ptr< _Tp >	read(const FileNode & fn)	Reads algorithm from the file node.

Additional Inherited Members

Protected Member Functions inherited from cv::Algorithm

Return	Name	Description
void	writeFormat(FileStorage & fs)

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

enum MatcherType

FLANNBASED Python: cv.DescriptorMatcher_FLANNBASED

BRUTEFORCE Python: cv.DescriptorMatcher_BRUTEFORCE

BRUTEFORCE_L1 Python: cv.DescriptorMatcher_BRUTEFORCE_L1

BRUTEFORCE_HAMMING Python: cv.DescriptorMatcher_BRUTEFORCE_HAMMING

BRUTEFORCE_HAMMINGLUT Python: cv.DescriptorMatcher_BRUTEFORCE_HAMMINGLUT

BRUTEFORCE_SL2 Python: cv.DescriptorMatcher_BRUTEFORCE_SL2

Constructor & Destructor Documentation

~DescriptorMatcher()

cv::DescriptorMatcher::~DescriptorMatcher()

Member Function Documentation

add()

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

Python:

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.

clear()

void cv::DescriptorMatcher::clear()

Python:

cv.DescriptorMatcher.clear()

Clears the train descriptor collections.

clone()

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

Python:

cv.DescriptorMatcher.clone([, emptyTrainData]) -> retval

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.

empty()

bool cv::DescriptorMatcher::empty()

Python:

cv.DescriptorMatcher.empty() -> retval

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

getTrainDescriptors()

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

Python:

cv.DescriptorMatcher.getTrainDescriptors() -> retval

Returns a constant link to the train descriptor collection trainDescCollection .

isMaskSupported()

bool cv::DescriptorMatcher::isMaskSupported()

Python:

cv.DescriptorMatcher.isMaskSupported() -> retval

Returns true if the descriptor matcher supports masking permissible matches.

knnMatch()

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

Python:

cv.DescriptorMatcher.knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) -> matches

cv.DescriptorMatcher.knnMatch(queryDescriptors, k[, masks[, compactResult]]) -> matches

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

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.

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.

knnMatch()

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

Python:

cv.DescriptorMatcher.knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) -> matches

cv.DescriptorMatcher.knnMatch(queryDescriptors, k[, masks[, compactResult]]) -> matches

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.

match()

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

Python:

cv.DescriptorMatcher.match(queryDescriptors, trainDescriptors[, mask]) -> matches

cv.DescriptorMatcher.match(queryDescriptors[, masks]) -> matches

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

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(i,j) is non-zero.

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.

match()

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

Python:

cv.DescriptorMatcher.match(queryDescriptors, trainDescriptors[, mask]) -> matches

cv.DescriptorMatcher.match(queryDescriptors[, masks]) -> matches

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()

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

Python:

cv.DescriptorMatcher.radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) -> matches

cv.DescriptorMatcher.radiusMatch(queryDescriptors, maxDistance[, masks[, compactResult]]) -> matches

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

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.

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.

radiusMatch()

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

Python:

cv.DescriptorMatcher.radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) -> matches

cv.DescriptorMatcher.radiusMatch(queryDescriptors, maxDistance[, masks[, compactResult]]) -> matches

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.

read()

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

Python:

cv.DescriptorMatcher.read(fileName)

cv.DescriptorMatcher.read(arg1)

Reads algorithm parameters from a file storage.

read()

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

Python:

cv.DescriptorMatcher.read(fileName)

cv.DescriptorMatcher.read(arg1)

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train()

void cv::DescriptorMatcher::train()

Python:

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 ).

write()

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

Python:

cv.DescriptorMatcher.write(fileName)

cv.DescriptorMatcher.write(fs, name)

write()

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

Python:

cv.DescriptorMatcher.write(fileName)

cv.DescriptorMatcher.write(fs, name)

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write()

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

Python:

cv.DescriptorMatcher.write(fileName)

cv.DescriptorMatcher.write(fs, name)

Stores algorithm parameters in a file storage.

write()

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

Python:

cv.DescriptorMatcher.write(fileName)

cv.DescriptorMatcher.write(fs, name)

create()

static Ptr< DescriptorMatcher > cv::DescriptorMatcher::create(const DescriptorMatcher::MatcherType & matcherType)

Python:

cv.DescriptorMatcher.create(descriptorMatcherType) -> retval

cv.DescriptorMatcher.create(matcherType) -> retval

cv.DescriptorMatcher_create(descriptorMatcherType) -> retval

cv.DescriptorMatcher_create(matcherType) -> retval

create()

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

Python:

cv.DescriptorMatcher.create(descriptorMatcherType) -> retval

cv.DescriptorMatcher.create(matcherType) -> retval

cv.DescriptorMatcher_create(descriptorMatcherType) -> retval

cv.DescriptorMatcher_create(matcherType) -> retval

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

checkMasks()

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

knnMatchImpl()

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

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().

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radiusMatchImpl()

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

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clone_op()

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

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isMaskedOut()

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

isPossibleMatch()

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

Member Data Documentation

trainDescCollection

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

Collection of descriptors from train images.

utrainDescCollection

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

Source file

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

• opencv2/features.hpp