cv::ANNIndex Class Reference

#include <opencv2/features.hpp>

Collaboration diagram for cv::ANNIndex:

Public Types
enum	Distance {   DIST_EUCLIDEAN ,   DIST_MANHATTAN ,   DIST_ANGULAR ,   DIST_HAMMING ,   DIST_DOTPRODUCT }
	Metrics used to calculate the distance between two feature vectors. More...

Public Member Functions
virtual	~ANNIndex ()=default
virtual void	addItems (InputArray features)=0
	Add feature vectors to index.
virtual void	build (int trees=-1)=0
	Build the index.
virtual int	getItemNumber ()=0
	Return the number of feature vectors in the index.
virtual int	getTreeNumber ()=0
	Return the number of trees in the index.
virtual void	knnSearch (InputArray query, OutputArray indices, OutputArray dists, int knn, int search_k=-1)=0
	Performs a K-nearest neighbor search for given query vector(s) using the index.
virtual void	load (const String &filename, bool prefault=false)=0
	Loads (mmaps) an index from disk.
virtual void	save (const String &filename, bool prefault=false)=0
	Save the index to disk and loads it. After saving, no more vectors can be added.
virtual bool	setOnDiskBuild (const String &filename)=0
	Prepare to build the index in the specified file instead of RAM (execute before adding items, no need to save after build)
virtual void	setSeed (int seed)=0
	Initialize the random number generator with the given seed. Only necessary to pass this before adding the items. Will have no effect after calling build() or load().

Static Public Member Functions
static Ptr< ANNIndex >	create (int dim, ANNIndex::Distance distType=ANNIndex::DIST_EUCLIDEAN)
	Creates an instance of annoy index class with given parameters.

Member Enumeration Documentation

Distance

enum cv::ANNIndex::Distance

Metrics used to calculate the distance between two feature vectors.

Enumerator
DIST_EUCLIDEAN
DIST_MANHATTAN
DIST_ANGULAR
DIST_HAMMING
DIST_DOTPRODUCT

Constructor & Destructor Documentation

~ANNIndex()

virtual cv::ANNIndex::~ANNIndex ( )

virtualdefault

Member Function Documentation

addItems()

virtual void cv::ANNIndex::addItems ( InputArray  features )

pure virtual

Python:
	cv.ANNIndex.addItems(	features	) ->	None

Add feature vectors to index.

Parameters

features

Matrix containing the feature vectors to index. The size of the matrix is num_features x feature_dimension.

build()

virtual void cv::ANNIndex::build ( int  trees = -1 )

pure virtual

Python:
	cv.ANNIndex.build(	[, trees]	) ->	None

Build the index.

Parameters

trees

Number of trees in the index. If not provided, the number is determined automatically in a way that at most 2x as much memory as the features vectors take is used.

create()

static Ptr< ANNIndex > cv::ANNIndex::create ( int  dim, ANNIndex::Distance  distType = ANNIndex::DIST_EUCLIDEAN  )

static

Python:
	cv.ANNIndex.create(	dim[, distType]	) ->	retval
	cv.ANNIndex_create(	dim[, distType]	) ->	retval

Creates an instance of annoy index class with given parameters.

Parameters

dim	The dimension of the feature vector.
distType	Metric to calculate the distance between two feature vectors, can be DIST_EUCLIDEAN, DIST_MANHATTAN, DIST_ANGULAR, DIST_HAMMING, or DIST_DOTPRODUCT.

getItemNumber()

virtual int cv::ANNIndex::getItemNumber ( )

pure virtual

Python:
	cv.ANNIndex.getItemNumber(		) ->	retval

Return the number of feature vectors in the index.

getTreeNumber()

virtual int cv::ANNIndex::getTreeNumber ( )

pure virtual

Python:
	cv.ANNIndex.getTreeNumber(		) ->	retval

Return the number of trees in the index.

knnSearch()

virtual void cv::ANNIndex::knnSearch ( InputArray  query, OutputArray  indices, OutputArray  dists, int  knn, int  search_k = -1  )

pure virtual

Python:
	cv.ANNIndex.knnSearch(	query, knn[, indices[, dists[, search_k]]]	) ->	indices, dists

Performs a K-nearest neighbor search for given query vector(s) using the index.

Parameters

query	The query vector(s).
indices	Matrix that will contain the indices of the K-nearest neighbors found, optional.
dists	Matrix that will contain the distances to the K-nearest neighbors found, optional.
knn	Number of nearest neighbors to search for.
search_k	The maximum number of nodes to inspect, which defaults to trees x knn if not provided.

load()

virtual void cv::ANNIndex::load ( const String &  filename, bool  prefault = false  )

pure virtual

Python:
	cv.ANNIndex.load(	filename[, prefault]	) ->	None

Loads (mmaps) an index from disk.

Parameters

filename	Filename of the index to be loaded.
prefault	If prefault is set to true, it will pre-read the entire file into memory (using mmap with MAP_POPULATE). Default is false.

save()

virtual void cv::ANNIndex::save ( const String &  filename, bool  prefault = false  )

pure virtual

Python:
	cv.ANNIndex.save(	filename[, prefault]	) ->	None

Save the index to disk and loads it. After saving, no more vectors can be added.

Parameters

filename	Filename of the index to be saved.
prefault	If prefault is set to true, it will pre-read the entire file into memory (using mmap with MAP_POPULATE). Default is false.

setOnDiskBuild()

virtual bool cv::ANNIndex::setOnDiskBuild ( const String &  filename )

pure virtual

Python:
	cv.ANNIndex.setOnDiskBuild(	filename	) ->	retval

Prepare to build the index in the specified file instead of RAM (execute before adding items, no need to save after build)

Parameters

filename

Filename of the index to be built.

setSeed()

virtual void cv::ANNIndex::setSeed ( int  seed )

pure virtual

Python:
	cv.ANNIndex.setSeed(	seed	) ->	None

Initialize the random number generator with the given seed. Only necessary to pass this before adding the items. Will have no effect after calling build() or load().

Parameters

seed	The given seed of the random number generator. Its value should be within the range of uint32_t.

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

• opencv2/features.hpp