template class cvflann::KDTreeIndex

Overview

Randomized kd-tree index

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

#include <kdtree_index.h>

template <typename Distance>
class KDTreeIndex: public cvflann::NNIndex
{
public:
    // typedefs

    typedef Distance::ResultType DistanceType;
    typedef Distance::ElementType ElementType;

    // structs

    struct Node;

    // construction

    KDTreeIndex(
        const Matrix<ElementType>& inputData,
        const IndexParams& params = KDTreeIndexParams(),
        Distance d = Distance()
        );

    KDTreeIndex(const KDTreeIndex&);

    // methods

    virtual
    void
    buildIndex();

    virtual
    void
    findNeighbors(
        ResultSet<DistanceType>& result,
        const ElementType* vec,
        const SearchParams& searchParams
        );

    virtual
    IndexParams
    getParameters() const;

    virtual
    flann_algorithm_t
    getType() const;

    virtual
    void
    loadIndex(FILE* stream);

    KDTreeIndex&
    operator=(const KDTreeIndex&);

    virtual
    void
    saveIndex(FILE* stream);

    virtual
    size_t
    size() const;

    virtual
    int
    usedMemory() const;

    virtual
    size_t
    veclen() const;
};

Inherited Members

public:
    // methods

    virtual
    void
    buildIndex() = 0;

    virtual
    void
    findNeighbors(
        ResultSet<DistanceType>& result,
        const ElementType* vec,
        const SearchParams& searchParams
        ) = 0;

    virtual
    IndexParams
    getParameters() const = 0;

    virtual
    flann_algorithm_t
    getType() const = 0;

    virtual
    void
    knnSearch(
        const Matrix<ElementType>& queries,
        Matrix<int>& indices,
        Matrix<DistanceType>& dists,
        int knn,
        const SearchParams& params
        );

    virtual
    void
    loadIndex(FILE* stream) = 0;

    virtual
    int
    radiusSearch(
        const Matrix<ElementType>& query,
        Matrix<int>& indices,
        Matrix<DistanceType>& dists,
        float radius,
        const SearchParams& params
        );

    virtual
    void
    saveIndex(FILE* stream) = 0;

    virtual
    size_t
    size() const = 0;

    virtual
    int
    usedMemory() const = 0;

    virtual
    size_t
    veclen() const = 0;

Detailed Documentation

Randomized kd-tree index

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

Construction

KDTreeIndex(
    const Matrix<ElementType>& inputData,
    const IndexParams& params = KDTreeIndexParams(),
    Distance d = Distance()
    )

KDTree constructor

Params: inputData = dataset with the input features params = parameters passed to the kdtree algorithm

Methods

virtual
void
buildIndex()

Builds the index

virtual
void
findNeighbors(
    ResultSet<DistanceType>& result,
    const ElementType* vec,
    const SearchParams& searchParams
    )

Find set of nearest neighbors to vec. 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 maxCheck = the maximum number of restarts (in a best-bin-first manner)

virtual
IndexParams
getParameters() const

Returns:

The index parameters

virtual
flann_algorithm_t
getType() const

Returns:

The index type (kdtree, kmeans,…)

virtual
void
loadIndex(FILE* stream)

Loads the index from a stream.

Parameters:

stream

The stream from which the index is loaded

virtual
void
saveIndex(FILE* stream)

Saves the index to a stream.

Parameters:

stream

The stream to save the index to

virtual
size_t
size() const

Returns size of index.

virtual
int
usedMemory() const

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

virtual
size_t
veclen() const

Returns the length of an index feature.