nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType > Class Template Reference

#include <nanoflann.hpp>

Classes
struct	BranchStruct
struct	Interval
struct	Node

Public Types
typedef Distance::DistanceType	DistanceType
typedef Distance::ElementType	ElementType

Public Member Functions
void	buildIndex ()
	KDTreeSingleIndexAdaptor (const int dimensionality, const DatasetAdaptor &inputData, const KDTreeSingleIndexAdaptorParams &params=KDTreeSingleIndexAdaptorParams())
size_t	size () const
size_t	usedMemory () const
size_t	veclen () const
	~KDTreeSingleIndexAdaptor ()
Query methods
template<typename RESULTSET >
void	findNeighbors (RESULTSET &result, const ElementType *vec, const SearchParams &searchParams) const
void	knnSearch (const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int nChecks_IGNORED=10) const
size_t	radiusSearch (const ElementType *query_point, const DistanceType radius, std::vector< std::pair< IndexType, DistanceType > > &IndicesDists, const SearchParams &searchParams) const

Public Attributes
Distance	distance

Protected Types
typedef std::vector< Interval >	BoundingBox
typedef BranchSt *	Branch
typedef BranchStruct< NodePtr, DistanceType >	BranchSt
typedef Node *	NodePtr

Protected Attributes
const DatasetAdaptor &	dataset
	The source of our data. More...
int	dim
	Dimensionality of each data point. More...
const KDTreeSingleIndexAdaptorParams	index_params
size_t	m_leaf_max_size
size_t	m_size
PooledAllocator	pool
BoundingBox	root_bbox
NodePtr	root_node
std::vector< IndexType >	vind

Private Member Functions
void	computeBoundingBox (BoundingBox &bbox)
DistanceType	computeInitialDistances (const ElementType *vec, std::vector< DistanceType > &dists) const
void	computeMinMax (IndexType *ind, IndexType count, int element, ElementType &min_elem, ElementType &max_elem)
ElementType	dataset_get (size_t idx, int component) const
	Helper accessor to the dataset points: More...
NodePtr	divideTree (const IndexType left, const IndexType right, BoundingBox &bbox)
void	init_vind ()
void	load_tree (FILE *stream, NodePtr &tree)
void	loadIndex (FILE *stream)
void	middleSplit (IndexType *ind, IndexType count, IndexType &index, int &cutfeat, DistanceType &cutval, const BoundingBox &bbox)
void	middleSplit_ (IndexType *ind, IndexType count, IndexType &index, int &cutfeat, DistanceType &cutval, const BoundingBox &bbox)
void	planeSplit (IndexType *ind, const IndexType count, int cutfeat, DistanceType cutval, IndexType &lim1, IndexType &lim2)
void	save_tree (FILE *stream, NodePtr tree)
void	saveIndex (FILE *stream)
template<class RESULTSET >
void	searchLevel (RESULTSET &result_set, const ElementType *vec, const NodePtr node, DistanceType mindistsq, std::vector< DistanceType > &dists, const float epsError) const

Detailed Description

template<typename Distance, class DatasetAdaptor, int DIM = -1, typename IndexType = size_t>
class nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >

kd-tree index

Contains the k-d trees and other information for indexing a set of points for nearest-neighbor matching.

The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):

// Must return the number of data points
inline size_t kdtree_get_point_count() const { ... }
 
// Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
 
// Must return the dim'th component of the idx'th point in the class:
inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
 
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX &bb) const
{
   bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
   bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
   ...
   return true;
}

Template Parameters

IndexType

Will be typically size_t or int

Definition at line 634 of file nanoflann.hpp.

Member Typedef Documentation

BoundingBox

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef std::vector<Interval> nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::BoundingBox

protected

Definition at line 696 of file nanoflann.hpp.

Branch

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef BranchSt* nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::Branch

protected

Definition at line 723 of file nanoflann.hpp.

BranchSt

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef BranchStruct<NodePtr, DistanceType> nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::BranchSt

protected

Definition at line 722 of file nanoflann.hpp.

DistanceType

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef Distance::DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::DistanceType

Definition at line 638 of file nanoflann.hpp.

ElementType

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef Distance::ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::ElementType

Definition at line 637 of file nanoflann.hpp.

NodePtr

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

typedef Node* nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::NodePtr

protected

Definition at line 688 of file nanoflann.hpp.

Constructor & Destructor Documentation

KDTreeSingleIndexAdaptor()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::KDTreeSingleIndexAdaptor ( const int  dimensionality, const DatasetAdaptor &  inputData, const KDTreeSingleIndexAdaptorParams &  params = KDTreeSingleIndexAdaptorParams()  )

inline

KDTree constructor

Params: inputData = dataset with the input features params = parameters passed to the kdtree algorithm (see http://code.google.com/p/nanoflann/ for help choosing the parameters)

Definition at line 747 of file nanoflann.hpp.

~KDTreeSingleIndexAdaptor()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::~KDTreeSingleIndexAdaptor ( )

inline

Standard destructor

Definition at line 765 of file nanoflann.hpp.

Member Function Documentation

buildIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::buildIndex ( )

inline

Builds the index

Definition at line 772 of file nanoflann.hpp.

computeBoundingBox()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeBoundingBox ( BoundingBox &  bbox )

inlineprivate

Definition at line 911 of file nanoflann.hpp.

computeInitialDistances()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

DistanceType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeInitialDistances ( const ElementType *  vec, std::vector< DistanceType > &  dists  ) const

inlineprivate

Definition at line 1130 of file nanoflann.hpp.

computeMinMax()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::computeMinMax ( IndexType *  ind, IndexType  count, int  element, ElementType &  min_elem, ElementType &  max_elem  )

inlineprivate

Definition at line 994 of file nanoflann.hpp.

dataset_get()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

ElementType nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dataset_get ( size_t  idx, int  component  ) const

inlineprivate

Helper accessor to the dataset points:

Definition at line 881 of file nanoflann.hpp.

divideTree()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

NodePtr nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::divideTree ( const IndexType  left, const IndexType  right, BoundingBox &  bbox  )

inlineprivate

Create a tree node that subdivides the list of vecs from vind[first] to vind[last]. The routine is called recursively on each sublist. Place a pointer to this new tree node in the location pTree.

Params: pTree = the new node to create first = index of the first vector last = index of the last vector

Definition at line 944 of file nanoflann.hpp.

findNeighbors()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

template<typename RESULTSET >

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::findNeighbors ( RESULTSET &  result, const ElementType *  vec, const SearchParams &  searchParams  ) const

inline

Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored inside the result object.

Params: result = the result object in which the indices of the nearest-neighbors are stored vec = the vector for which to search the nearest neighbors

Template Parameters

RESULTSET

Should be any ResultSet<DistanceType>

See also

knnSearch, radiusSearch

Definition at line 819 of file nanoflann.hpp.

init_vind()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::init_vind ( )

inlineprivate

Make sure the auxiliary list vind has the same size than the current dataset, and re-generate if size has changed.

Definition at line 869 of file nanoflann.hpp.

knnSearch()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::knnSearch ( const ElementType *  query_point, const size_t  num_closest, IndexType *  out_indices, DistanceType *  out_distances_sq, const int  nChecks_IGNORED = 10  ) const

inline

Find the "num_closest" nearest neighbors to the query_point[0:dim-1]. Their indices are stored inside the result object.

See also

radiusSearch, findNeighbors

Note

nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.

Definition at line 835 of file nanoflann.hpp.

load_tree()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::load_tree ( FILE *  stream, NodePtr &  tree  )

inlineprivate

Definition at line 898 of file nanoflann.hpp.

loadIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::loadIndex ( FILE *  stream )

inlineprivate

Definition at line 1214 of file nanoflann.hpp.

middleSplit()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::middleSplit ( IndexType *  ind, IndexType  count, IndexType &  index, int &  cutfeat, DistanceType &  cutval, const BoundingBox &  bbox  )

inlineprivate

Definition at line 1005 of file nanoflann.hpp.

middleSplit_()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::middleSplit_ ( IndexType *  ind, IndexType  count, IndexType &  index, int &  cutfeat, DistanceType &  cutval, const BoundingBox &  bbox  )

inlineprivate

Definition at line 1050 of file nanoflann.hpp.

planeSplit()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::planeSplit ( IndexType *  ind, const IndexType  count, int  cutfeat, DistanceType  cutval, IndexType &  lim1, IndexType &  lim2  )

inlineprivate

Subdivide the list of points by a plane perpendicular on axe corresponding to the 'cutfeat' dimension at 'cutval' position.

On return: dataset[ind[0..lim1-1]][cutfeat]<cutval dataset[ind[lim1..lim2-1]][cutfeat]==cutval dataset[ind[lim2..count]][cutfeat]>cutval

Definition at line 1101 of file nanoflann.hpp.

radiusSearch()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::radiusSearch ( const ElementType *  query_point, const DistanceType  radius, std::vector< std::pair< IndexType, DistanceType > > &  IndicesDists, const SearchParams &  searchParams  ) const

inline

Find all the neighbors to query_point[0:dim-1] within a maximum radius. The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. Previous contents of IndicesDists are cleared.

If searchParams.sorted==true, the output list is sorted by ascending distances.

For a better performance, it is advisable to do a .reserve() on the vector if you have any wild guess about the number of expected matches.

See also

knnSearch, findNeighbors

Returns

The number of points within the given radius (i.e. indices.size() or dists.size() )

Definition at line 854 of file nanoflann.hpp.

save_tree()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::save_tree ( FILE *  stream, NodePtr  tree  )

inlineprivate

Definition at line 886 of file nanoflann.hpp.

saveIndex()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::saveIndex ( FILE *  stream )

inlineprivate

Definition at line 1204 of file nanoflann.hpp.

searchLevel()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

template<class RESULTSET >

void nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::searchLevel ( RESULTSET &  result_set, const ElementType *  vec, const NodePtr  node, DistanceType  mindistsq, std::vector< DistanceType > &  dists, const float  epsError  ) const

inlineprivate

Performs an exact search in the tree starting from a node.

Template Parameters

RESULTSET

Should be any ResultSet<DistanceType>

Definition at line 1154 of file nanoflann.hpp.

size()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::size ( ) const

inline

Returns size of index.

Definition at line 782 of file nanoflann.hpp.

usedMemory()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::usedMemory ( ) const

inline

Computes the inde memory usage Returns: memory used by the index

Definition at line 799 of file nanoflann.hpp.

veclen()

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::veclen ( ) const

inline

Returns the length of an index feature.

Definition at line 790 of file nanoflann.hpp.

Member Data Documentation

dataset

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

const DatasetAdaptor& nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dataset

protected

The source of our data.

The dataset used by this index

Definition at line 652 of file nanoflann.hpp.

dim

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

int nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::dim

protected

Dimensionality of each data point.

Definition at line 657 of file nanoflann.hpp.

distance

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

Distance nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::distance

Definition at line 738 of file nanoflann.hpp.

index_params

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

const KDTreeSingleIndexAdaptorParams nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::index_params

protected

Definition at line 654 of file nanoflann.hpp.

m_leaf_max_size

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::m_leaf_max_size

protected

Definition at line 646 of file nanoflann.hpp.

m_size

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

size_t nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::m_size

protected

Definition at line 656 of file nanoflann.hpp.

pool

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

PooledAllocator nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::pool

protected

Pooled memory allocator.

Using a pooled memory allocator is more efficient than allocating memory directly when there is a large number small of memory allocations.

Definition at line 734 of file nanoflann.hpp.

root_bbox

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

BoundingBox nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::root_bbox

protected

Definition at line 725 of file nanoflann.hpp.

root_node

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

NodePtr nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::root_node

protected

Array of k-d trees used to find neighbours.

Definition at line 721 of file nanoflann.hpp.

vind

template<typename Distance , class DatasetAdaptor , int DIM = -1, typename IndexType = size_t>

std::vector<IndexType> nanoflann::KDTreeSingleIndexAdaptor< Distance, DatasetAdaptor, DIM, IndexType >::vind

protected

Array of indices to vectors in the dataset.

Definition at line 644 of file nanoflann.hpp.

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

• nanoflann.hpp