cvflann Namespace Reference

Namespaces
	anyimpl
	lsh

Classes
struct	Accumulator
struct	Accumulator< char >
struct	Accumulator< int >
struct	Accumulator< short >
struct	Accumulator< unsigned char >
struct	Accumulator< unsigned int >
struct	Accumulator< unsigned short >
struct	any
class	AutotunedIndex
struct	AutotunedIndexParams
struct	BranchStruct
struct	ChiSquareDistance
class	CompositeIndex
	This index builds a kd-tree index and a k-means index and performs nearest neighbour search both indexes. More...
struct	CompositeIndexParams
	Index parameters for the CompositeIndex. More...
class	CreatorNotFound
struct	Datatype
struct	Datatype< char >
struct	Datatype< double >
struct	Datatype< float >
struct	Datatype< int >
struct	Datatype< short >
struct	Datatype< unsigned char >
struct	Datatype< unsigned int >
struct	Datatype< unsigned short >
class	DynamicBitset
	Class re-implementing the boost version of it This helps not depending on boost, it also does not do the bound checks and has a way to reset a block for speed. More...
class	False
class	FLANNException
struct	Hamming
	Hamming distance functor (pop count between two binary vectors, i.e. More...
struct	Hamming2
struct	HammingLUT
	Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor bit count of A exclusive XOR'ed with B. More...
class	Heap
	Priority Queue Implementation. More...
struct	HellingerDistance
class	HierarchicalClusteringIndex
	Hierarchical index. More...
struct	HierarchicalClusteringIndexParams
struct	HistIntersectionDistance
class	Index
struct	index_creator
struct	index_creator< False, False, Distance >
struct	index_creator< False, VectorSpace, Distance >
struct	IndexHeader
	Structure representing the index header. More...
class	KDTreeIndex
	Randomized kd-tree index. More...
struct	KDTreeIndexParams
class	KDTreeSingleIndex
	Randomized kd-tree index. More...
struct	KDTreeSingleIndexParams
struct	KL_Divergence
class	KMeansIndex
	Hierarchical kmeans index. More...
struct	KMeansIndexParams
class	KNNRadiusUniqueResultSet
	Class that holds the k NN neighbors within a radius distance. More...
class	KNNResultSet
	K-Nearest neighbour result set. More...
class	KNNSimpleResultSet
	KNNSimpleResultSet does not ensure that the element it holds are unique. More...
class	KNNUniqueResultSet
	Class that holds the k NN neighbors Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays. More...
struct	L1
struct	L2
	Squared Euclidean distance functor, optimized version. More...
struct	L2_Simple
	Squared Euclidean distance functor. More...
class	LinearIndex
struct	LinearIndexParams
class	Logger
class	LshIndex
	Randomized kd-tree index. More...
struct	LshIndexParams
class	Matrix
	Class that implements a simple rectangular matrix stored in a memory buffer and provides convenient matrix-like access using the [] operators. More...
struct	MaxDistance
struct	MinkowskiDistance
class	NNIndex
	Nearest-neighbour index base class. More...
class	ObjectFactory
class	PooledAllocator
class	RadiusResultSet
	A result-set class used when performing a radius based search. More...
class	RadiusUniqueResultSet
	Class that holds the radius nearest neighbors It is more accurate than RadiusResult as it is not limited in the number of neighbors. More...
class	ResultSet
struct	SavedIndexParams
	(Deprecated) Index parameters for creating a saved index. More...
struct	SearchParams
struct	simpleDistance
struct	simpleDistance< ChiSquareDistance< ElementType >, ElementType >
struct	simpleDistance< HellingerDistance< ElementType >, ElementType >
struct	simpleDistance< L2< ElementType >, ElementType >
struct	simpleDistance< L2_Simple< ElementType >, ElementType >
struct	simpleDistance< MinkowskiDistance< ElementType >, ElementType >
struct	squareDistance
struct	squareDistance< ChiSquareDistance< ElementType >, ElementType >
struct	squareDistance< HellingerDistance< ElementType >, ElementType >
struct	squareDistance< L2< ElementType >, ElementType >
struct	squareDistance< L2_Simple< ElementType >, ElementType >
struct	squareDistance< MinkowskiDistance< ElementType >, ElementType >
class	StartStopTimer
	A start-stop timer class. More...
class	True
class	UniqueRandom
	Random number generator that returns a distinct number from the [0,n) interval each time. More...
class	UniqueResultSet
	Class that holds the k NN neighbors Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays. More...
class	UntypedMatrix
struct	ZeroIterator

Typedefs
typedef std::map< cv::String, any >	IndexParams

Enumerations
enum	{   FLANN_CHECKS_UNLIMITED = -1,   FLANN_CHECKS_AUTOTUNED = -2 }
enum	flann_algorithm_t {   FLANN_INDEX_LINEAR = 0,   FLANN_INDEX_KDTREE = 1,   FLANN_INDEX_KMEANS = 2,   FLANN_INDEX_COMPOSITE = 3,   FLANN_INDEX_KDTREE_SINGLE = 4,   FLANN_INDEX_HIERARCHICAL = 5,   FLANN_INDEX_LSH = 6,   FLANN_INDEX_SAVED = 254,   FLANN_INDEX_AUTOTUNED = 255,   LINEAR = 0,   KDTREE = 1,   KMEANS = 2,   COMPOSITE = 3,   KDTREE_SINGLE = 4,   SAVED = 254,   AUTOTUNED = 255 }
enum	flann_centers_init_t {   FLANN_CENTERS_RANDOM = 0,   FLANN_CENTERS_GONZALES = 1,   FLANN_CENTERS_KMEANSPP = 2,   FLANN_CENTERS_GROUPWISE = 3,   CENTERS_RANDOM = 0,   CENTERS_GONZALES = 1,   CENTERS_KMEANSPP = 2 }
enum	flann_datatype_t {   FLANN_INT8 = 0,   FLANN_INT16 = 1,   FLANN_INT32 = 2,   FLANN_INT64 = 3,   FLANN_UINT8 = 4,   FLANN_UINT16 = 5,   FLANN_UINT32 = 6,   FLANN_UINT64 = 7,   FLANN_FLOAT32 = 8,   FLANN_FLOAT64 = 9 }
enum	flann_distance_t {   FLANN_DIST_EUCLIDEAN = 1,   FLANN_DIST_L2 = 1,   FLANN_DIST_MANHATTAN = 2,   FLANN_DIST_L1 = 2,   FLANN_DIST_MINKOWSKI = 3,   FLANN_DIST_MAX = 4,   FLANN_DIST_HIST_INTERSECT = 5,   FLANN_DIST_HELLINGER = 6,   FLANN_DIST_CHI_SQUARE = 7,   FLANN_DIST_CS = 7,   FLANN_DIST_KULLBACK_LEIBLER = 8,   FLANN_DIST_KL = 8,   FLANN_DIST_HAMMING = 9,   EUCLIDEAN = 1,   MANHATTAN = 2,   MINKOWSKI = 3,   MAX_DIST = 4,   HIST_INTERSECT = 5,   HELLINGER = 6,   CS = 7,   KL = 8,   KULLBACK_LEIBLER = 8 }
enum	flann_log_level_t {   FLANN_LOG_NONE = 0,   FLANN_LOG_FATAL = 1,   FLANN_LOG_ERROR = 2,   FLANN_LOG_WARN = 3,   FLANN_LOG_INFO = 4 }

Functions
template<typename T >
T	abs (T x)
template<>
double	abs< double > (double x)
template<>
float	abs< float > (float x)
template<typename T >
void	addValue (int pos, float val, float *vals, T *point, T *points, int n)
	Adds val to array vals (and point to array points) and keeping the arrays sorted by vals. More...
template<typename T >
T *	allocate (size_t count=1)
	Allocates (using C's malloc) a generic type T. More...
template<typename Distance >
void	compute_ground_truth (const Matrix< typename Distance::ElementType > &dataset, const Matrix< typename Distance::ElementType > &testset, Matrix< int > &matches, int skip=0, Distance d=Distance())
template<typename Distance >
Distance::ResultType	computeDistanceRaport (const Matrix< typename Distance::ElementType > &inputData, typename Distance::ElementType *target, int *neighbors, int *groundTruth, int veclen, int n, const Distance &distance)
int	countCorrectMatches (int *neighbors, int *groundTruth, int n)
template<typename Distance >
NNIndex< Distance > *	create_index_by_type (const Matrix< typename Distance::ElementType > &dataset, const IndexParams &params, const Distance &distance)
void	dummyfunc ()
template<typename Distance >
Distance::ResultType	ensureSimpleDistance (typename Distance::ResultType dist)
template<typename Distance >
Distance::ResultType	ensureSquareDistance (typename Distance::ResultType dist)
template<typename Distance >
void	find_nearest (const Matrix< typename Distance::ElementType > &dataset, typename Distance::ElementType *query, int *matches, int nn, int skip=0, Distance distance=Distance())
flann_distance_t	flann_distance_type ()
template<typename T >
T	get_param (const IndexParams &params, cv::String name, const T &default_value)
template<typename T >
T	get_param (const IndexParams &params, cv::String name)
template<typename Distance >
int	hierarchicalClustering (const Matrix< typename Distance::ElementType > &points, Matrix< typename Distance::ResultType > &centers, const KMeansIndexParams &params, Distance d=Distance())
	Performs a hierarchical clustering of the points passed as argument and then takes a cut in the the clustering tree to return a flat clustering. More...
IndexHeader	load_header (FILE *stream)
template<typename Distance >
NNIndex< Distance > *	load_saved_index (const Matrix< typename Distance::ElementType > &dataset, const cv::String &filename, Distance distance)
template<typename T >
void	load_value (FILE *stream, T &value, size_t count=1)
template<typename T >
void	load_value (FILE *stream, cvflann::Matrix< T > &value)
template<typename T >
void	load_value (FILE *stream, std::vector< T > &value)
void	log_verbosity (int level)
	Sets the log level used for all flann functions. More...
std::ostream &	operator<< (std::ostream &out, const any &any_val)
template<typename T , typename F >
float	optimizeSimplexDownhill (T *points, int n, F func, float *vals=NULL)
	Simplex downhill optimization function. More...
void	print_params (const IndexParams &params, std::ostream &stream)
void	print_params (const IndexParams &params)
int	rand ()
double	rand_double (double high=1.0, double low=0)
	Generates a random double value. More...
int	rand_int (int high=RAND_MAX, int low=0)
	Generates a random integer value. More...
template<typename T >
Matrix< T >	random_sample (Matrix< T > &srcMatrix, long size, bool remove=false)
template<typename T >
Matrix< T >	random_sample (const Matrix< T > &srcMatrix, size_t size)
template<typename Distance >
void	save_header (FILE *stream, const NNIndex< Distance > &index)
	Saves index header to stream. More...
template<typename T >
void	save_value (FILE *stream, const T &value, size_t count=1)
template<typename T >
void	save_value (FILE *stream, const cvflann::Matrix< T > &value)
template<typename T >
void	save_value (FILE *stream, const std::vector< T > &value)
template<typename Distance >
float	search_with_ground_truth (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, int nn, int checks, float &time, typename Distance::ResultType &dist, const Distance &distance, int skipMatches)
void	seed_random (unsigned int seed)
	Seeds the random number generator. More...
void	set_distance_type (flann_distance_t distance_type, int order)
template<typename Distance >
float	test_index_checks (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, int checks, float &precision, const Distance &distance, int nn=1, int skipMatches=0)
template<typename Distance >
float	test_index_precision (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, float precision, int &checks, const Distance &distance, int nn=1, int skipMatches=0)
template<typename Distance >
void	test_index_precisions (NNIndex< Distance > &index, const Matrix< typename Distance::ElementType > &inputData, const Matrix< typename Distance::ElementType > &testData, const Matrix< int > &matches, float *precisions, int precisions_length, const Distance &distance, int nn=1, int skipMatches=0, float maxTime=0)

Variables
const size_t	BLOCKSIZE =8192
const size_t	WORDSIZE =16
	Pooled storage allocator. More...

Typedef Documentation

IndexParams

typedef std::map<cv::String, any> cvflann::IndexParams

Enumeration Type Documentation

anonymous enum

anonymous enum

Enumerator
FLANN_CHECKS_UNLIMITED
FLANN_CHECKS_AUTOTUNED

flann_algorithm_t

enum cvflann::flann_algorithm_t

Enumerator
FLANN_INDEX_LINEAR
FLANN_INDEX_KDTREE
FLANN_INDEX_KMEANS
FLANN_INDEX_COMPOSITE
FLANN_INDEX_KDTREE_SINGLE
FLANN_INDEX_HIERARCHICAL
FLANN_INDEX_LSH
FLANN_INDEX_SAVED
FLANN_INDEX_AUTOTUNED
LINEAR
KDTREE
KMEANS
COMPOSITE
KDTREE_SINGLE
SAVED
AUTOTUNED

flann_centers_init_t

enum cvflann::flann_centers_init_t

Enumerator
FLANN_CENTERS_RANDOM
FLANN_CENTERS_GONZALES
FLANN_CENTERS_KMEANSPP
FLANN_CENTERS_GROUPWISE
CENTERS_RANDOM
CENTERS_GONZALES
CENTERS_KMEANSPP

flann_datatype_t

enum cvflann::flann_datatype_t

Enumerator
FLANN_INT8
FLANN_INT16
FLANN_INT32
FLANN_INT64
FLANN_UINT8
FLANN_UINT16
FLANN_UINT32
FLANN_UINT64
FLANN_FLOAT32
FLANN_FLOAT64

flann_distance_t

enum cvflann::flann_distance_t

Enumerator
FLANN_DIST_EUCLIDEAN
FLANN_DIST_L2
FLANN_DIST_MANHATTAN
FLANN_DIST_L1
FLANN_DIST_MINKOWSKI
FLANN_DIST_MAX
FLANN_DIST_HIST_INTERSECT
FLANN_DIST_HELLINGER
FLANN_DIST_CHI_SQUARE
FLANN_DIST_CS
FLANN_DIST_KULLBACK_LEIBLER
FLANN_DIST_KL
FLANN_DIST_HAMMING
EUCLIDEAN
MANHATTAN
MINKOWSKI
MAX_DIST
HIST_INTERSECT
HELLINGER
CS
KL
KULLBACK_LEIBLER

flann_log_level_t

enum cvflann::flann_log_level_t

Enumerator
FLANN_LOG_NONE
FLANN_LOG_FATAL
FLANN_LOG_ERROR
FLANN_LOG_WARN
FLANN_LOG_INFO

Function Documentation

abs()

template<typename T >

T cvflann::abs ( T  x )

inline

References cv::abs().

Referenced by cvflann::L1< T >::accum_dist(), cvflann::MinkowskiDistance< T >::accum_dist(), cvflann::L1< T >::operator()(), cvflann::MinkowskiDistance< T >::operator()(), and cvflann::MaxDistance< T >::operator()().

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abs< double >()

template<>

double cvflann::abs< double > ( double  x )

inline

abs< float >()

template<>

float cvflann::abs< float > ( float  x )

inline

addValue()

template<typename T >

void cvflann::addValue	(	int	pos,
		float	val,
		float *	vals,
		T *	point,
		T *	points,
		int	n
	)

Adds val to array vals (and point to array points) and keeping the arrays sorted by vals.

References cv::swap().

Referenced by optimizeSimplexDownhill().

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allocate()

template<typename T >

T* cvflann::allocate

(

size_t

count = 1

)

Allocates (using C's malloc) a generic type T.

Params: count = number of instances to allocate. Returns: pointer (of type T*) to memory buffer

compute_ground_truth()

template<typename Distance >

void cvflann::compute_ground_truth	(	const Matrix< typename Distance::ElementType > &	dataset,
		const Matrix< typename Distance::ElementType > &	testset,
		Matrix< int > &	matches,
		int	skip = 0,
		Distance	d = Distance()
	)

References cvflann::Matrix< T >::rows.

computeDistanceRaport()

template<typename Distance >

Distance::ResultType cvflann::computeDistanceRaport	(	const Matrix< typename Distance::ElementType > &	inputData,
		typename Distance::ElementType *	target,
		int *	neighbors,
		int *	groundTruth,
		int	veclen,
		int	n,
		const Distance &	distance
	)

countCorrectMatches()

int cvflann::countCorrectMatches ( int *  neighbors, int *  groundTruth, int  n  )

inline

Referenced by search_with_ground_truth().

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create_index_by_type()

template<typename Distance >

NNIndex< Distance > * cvflann::create_index_by_type	(	const Matrix< typename Distance::ElementType > &	dataset,
		const IndexParams &	params,
		const Distance &	distance
	)

References cvflann::index_creator< KDTreeCapability, VectorSpace, Distance >::create().

Referenced by cvflann::AutotunedIndex< Distance >::buildIndex().

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dummyfunc()

void cvflann::dummyfunc ( )

inline

ensureSimpleDistance()

template<typename Distance >

Distance::ResultType cvflann::ensureSimpleDistance

(

typename Distance::ResultType

dist

)

ensureSquareDistance()

template<typename Distance >

Distance::ResultType cvflann::ensureSquareDistance

(

typename Distance::ResultType

dist

)

find_nearest()

template<typename Distance >

void cvflann::find_nearest	(	const Matrix< typename Distance::ElementType > &	dataset,
		typename Distance::ElementType *	query,
		int *	matches,
		int	nn,
		int	skip = 0,
		Distance	distance = Distance()
	)

References cvflann::Matrix< T >::cols, cvflann::Matrix< T >::rows, and cv::swap().

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flann_distance_type()

flann_distance_t cvflann::flann_distance_type

(

)

Referenced by cv::flann::hierarchicalClustering(), and cv::flann::Index_< T >::Index_().

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get_param() [1/2]

template<typename T >

T cvflann::get_param	(	const IndexParams &	params,
		cv::String	name,
		const T &	default_value
	)

Referenced by cvflann::AutotunedIndex< Distance >::AutotunedIndex(), cvflann::KDTreeIndex< Distance >::findNeighbors(), cvflann::KDTreeSingleIndex< Distance >::findNeighbors(), cvflann::KMeansIndex< Distance >::findNeighbors(), cvflann::HierarchicalClusteringIndex< Distance >::findNeighbors(), cvflann::HierarchicalClusteringIndex< Distance >::HierarchicalClusteringIndex(), cvflann::KDTreeIndex< Distance >::KDTreeIndex(), cvflann::KDTreeSingleIndex< Distance >::KDTreeSingleIndex(), cvflann::KMeansIndex< Distance >::KMeansIndex(), cvflann::NNIndex< cv::L1< ElementType > >::knnSearch(), cvflann::LshIndex< Distance >::knnSearch(), and cvflann::NNIndex< cv::L1< ElementType > >::radiusSearch().

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get_param() [2/2]

template<typename T >

T cvflann::get_param	(	const IndexParams &	params,
		cv::String	name
	)

hierarchicalClustering()

template<typename Distance >

int cvflann::hierarchicalClustering	(	const Matrix< typename Distance::ElementType > &	points,
		Matrix< typename Distance::ResultType > &	centers,
		const KMeansIndexParams &	params,
		Distance	d = Distance()
	)

Performs a hierarchical clustering of the points passed as argument and then takes a cut in the the clustering tree to return a flat clustering.

Parameters

[in]	points	Points to be clustered
	centers	The computed cluster centres. Matrix should be preallocated and centers.rows is the number of clusters requested.
	params	Clustering parameters (The same as for cvflann::KMeansIndex)
	d	Distance to be used for clustering (eg: cvflann::L2)

Returns

number of clusters computed (can be different than clusters.rows and is the highest number of the form (branching-1)*K+1 smaller than clusters.rows).

References cvflann::KMeansIndex< Distance >::buildIndex(), cvflann::KMeansIndex< Distance >::getClusterCenters(), and cv::kmeans().

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load_header()

IndexHeader cvflann::load_header ( FILE *  stream )

inline

Parameters

stream

- Stream to load from

Returns

Index header

References cvflann::IndexHeader::signature.

Referenced by load_saved_index().

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load_saved_index()

template<typename Distance >

NNIndex<Distance>* cvflann::load_saved_index	(	const Matrix< typename Distance::ElementType > &	dataset,
		const cv::String &	filename,
		Distance	distance
	)

References cvflann::Matrix< T >::cols, cvflann::IndexHeader::cols, cvflann::IndexHeader::data_type, cvflann::IndexHeader::index_type, load_header(), cvflann::NNIndex< Distance >::loadIndex(), cvflann::Matrix< T >::rows, and cvflann::IndexHeader::rows.

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load_value() [1/3]

template<typename T >

void cvflann::load_value	(	FILE *	stream,
		T &	value,
		size_t	count = 1
	)

Referenced by cvflann::KDTreeSingleIndex< Distance >::findNeighbors(), cvflann::KDTreeIndex< Distance >::getParameters(), cvflann::KMeansIndex< Distance >::getParameters(), cvflann::HierarchicalClusteringIndex< Distance >::getParameters(), cvflann::LshIndex< Distance >::loadIndex(), cvflann::AutotunedIndex< Distance >::loadIndex(), cvflann::KDTreeIndex< Distance >::loadIndex(), cvflann::KDTreeSingleIndex< Distance >::loadIndex(), cvflann::KMeansIndex< Distance >::loadIndex(), and cvflann::HierarchicalClusteringIndex< Distance >::loadIndex().

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load_value() [2/3]

template<typename T >

void cvflann::load_value	(	FILE *	stream,
		cvflann::Matrix< T > &	value
	)

load_value() [3/3]

template<typename T >

void cvflann::load_value	(	FILE *	stream,
		std::vector< T > &	value
	)

log_verbosity()

void cvflann::log_verbosity ( int  level )

inline

Sets the log level used for all flann functions.

Parameters

level

Verbosity level

References cvflann::Logger::setLevel().

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operator<<()

std::ostream& cvflann::operator<< ( std::ostream &  out, const any &  any_val  )

inline

References cvflann::anyimpl::base_any_policy::print().

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optimizeSimplexDownhill()

template<typename T , typename F >

float cvflann::optimizeSimplexDownhill	(	T *	points,
		int	n,
		F	func,
		float *	vals = NULL
	)

Simplex downhill optimization function.

Preconditions: points is a 2D mattrix of size (n+1) x n func is the cost function taking n an array of n params and returning float vals is the cost function in the n+1 simplex points, if NULL it will be computed

Postcondition: returns optimum value and points[0..n] are the optimum parameters

References addValue(), and cvflann::Logger::info().

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print_params() [1/2]

void cvflann::print_params ( const IndexParams &  params, std::ostream &  stream  )

inline

Referenced by cvflann::AutotunedIndex< Distance >::buildIndex(), and print_params().

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print_params() [2/2]

void cvflann::print_params ( const IndexParams &  params )

inline

References print_params().

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rand()

int cvflann::rand ( )

inline

References cv::RNG::next(), cv::theRNG(), and cv::RNG::uniform().

Referenced by rand_double(), rand_int(), and random_sample().

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rand_double()

double cvflann::rand_double ( double  high = 1.0, double  low = 0  )

inline

Generates a random double value.

Parameters

high	Upper limit
low	Lower limit

Returns

Random double value

References rand().

Referenced by cvflann::KMeansIndex< Distance >::chooseCentersKMeanspp().

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rand_int()

int cvflann::rand_int ( int  high = RAND_MAX, int  low = 0  )

inline

Generates a random integer value.

Parameters

high	Upper limit
low	Lower limit

Returns

Random integer value

References rand().

Referenced by cvflann::KMeansIndex< Distance >::chooseCentersGonzales(), cvflann::KMeansIndex< Distance >::chooseCentersKMeanspp(), cvflann::KDTreeIndex< Distance >::getParameters(), and random_sample().

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random_sample() [1/2]

template<typename T >

Matrix<T> cvflann::random_sample	(	Matrix< T > &	srcMatrix,
		long	size,
		bool	remove = false
	)

References rand_int().

Referenced by cvflann::AutotunedIndex< Distance >::getType().

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random_sample() [2/2]

template<typename T >

Matrix<T> cvflann::random_sample	(	const Matrix< T > &	srcMatrix,
		size_t	size
	)

References cvflann::UniqueRandom::next(), and rand().

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save_header()

template<typename Distance >

void cvflann::save_header	(	FILE *	stream,
		const NNIndex< Distance > &	index
	)

Saves index header to stream.

Parameters

stream	- Stream to save to
index	- The index to save

References cvflann::IndexHeader::cols, cvflann::IndexHeader::data_type, cvflann::NNIndex< Distance >::getType(), cvflann::IndexHeader::index_type, cvflann::IndexHeader::rows, cvflann::IndexHeader::signature, cvflann::NNIndex< Distance >::size(), cvflann::NNIndex< Distance >::veclen(), and cvflann::IndexHeader::version.

Referenced by cvflann::Index< cv::L1< ElementType > >::save().

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save_value() [1/3]

template<typename T >

void cvflann::save_value	(	FILE *	stream,
		const T &	value,
		size_t	count = 1
	)

Referenced by cvflann::KDTreeSingleIndex< Distance >::findNeighbors(), cvflann::KDTreeIndex< Distance >::getParameters(), cvflann::KMeansIndex< Distance >::getParameters(), cvflann::HierarchicalClusteringIndex< Distance >::getParameters(), cvflann::AutotunedIndex< Distance >::saveIndex(), cvflann::LshIndex< Distance >::saveIndex(), cvflann::KDTreeSingleIndex< Distance >::saveIndex(), cvflann::KDTreeIndex< Distance >::saveIndex(), cvflann::KMeansIndex< Distance >::saveIndex(), and cvflann::HierarchicalClusteringIndex< Distance >::saveIndex().

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save_value() [2/3]

template<typename T >

void cvflann::save_value	(	FILE *	stream,
		const cvflann::Matrix< T > &	value
	)

save_value() [3/3]

template<typename T >

void cvflann::save_value	(	FILE *	stream,
		const std::vector< T > &	value
	)

search_with_ground_truth()

template<typename Distance >

float cvflann::search_with_ground_truth	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		int	nn,
		int	checks,
		float &	time,
		typename Distance::ResultType &	dist,
		const Distance &	distance,
		int	skipMatches
	)

References countCorrectMatches(), cvflann::NNIndex< Distance >::findNeighbors(), cvflann::Logger::info(), cvflann::KNNResultSet< DistanceType >::init(), cvflann::Matrix< T >::rows, cvflann::StartStopTimer::start(), cvflann::StartStopTimer::stop(), and cvflann::StartStopTimer::value.

Referenced by test_index_checks(), test_index_precision(), and test_index_precisions().

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seed_random()

void cvflann::seed_random ( unsigned int  seed )

inline

Seeds the random number generator.

Parameters

seed	Random seed

References cv::theRNG().

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set_distance_type()

void cvflann::set_distance_type	(	flann_distance_t	distance_type,
		int	order
	)

test_index_checks()

template<typename Distance >

float cvflann::test_index_checks	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		int	checks,
		float &	precision,
		const Distance &	distance,
		int	nn = 1,
		int	skipMatches = 0
	)

References cvflann::Logger::info(), and search_with_ground_truth().

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test_index_precision()

template<typename Distance >

float cvflann::test_index_precision	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		float	precision,
		int &	checks,
		const Distance &	distance,
		int	nn = 1,
		int	skipMatches = 0
	)

References cvflann::Logger::info(), and search_with_ground_truth().

Referenced by cvflann::AutotunedIndex< Distance >::getType().

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test_index_precisions()

template<typename Distance >

void cvflann::test_index_precisions	(	NNIndex< Distance > &	index,
		const Matrix< typename Distance::ElementType > &	inputData,
		const Matrix< typename Distance::ElementType > &	testData,
		const Matrix< int > &	matches,
		float *	precisions,
		int	precisions_length,
		const Distance &	distance,
		int	nn = 1,
		int	skipMatches = 0,
		float	maxTime = 0
	)

References cvflann::Logger::info(), search_with_ground_truth(), and cv::sort().

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Variable Documentation

BLOCKSIZE

const size_t cvflann::BLOCKSIZE =8192

Referenced by cvflann::PooledAllocator::allocateMemory().

WORDSIZE

const size_t cvflann::WORDSIZE =16

Pooled storage allocator.

The following routines allow for the efficient allocation of storage in small chunks from a specified pool. Rather than allowing each structure to be freed individually, an entire pool of storage is freed at once. This method has two advantages over just using malloc() and free(). First, it is far more efficient for allocating small objects, as there is no overhead for remembering all the information needed to free each object or consolidating fragmented memory. Second, the decision about how long to keep an object is made at the time of allocation, and there is no need to track down all the objects to free them.