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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_
#define OPENCV_FLANN_COMPOSITE_INDEX_H_
#include "general.h"
#include "nn_index.h"
#include "kdtree_index.h"
#include "kmeans_index.h"
namespace cvflann
{
/**
* Index parameters for the CompositeIndex.
*/
struct CompositeIndexParams : public IndexParams
{
CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
{
(*this)["algorithm"] = FLANN_INDEX_KMEANS;
// number of randomized trees to use (for kdtree)
(*this)["trees"] = trees;
// branching factor
(*this)["branching"] = branching;
// max iterations to perform in one kmeans clustering (kmeans tree)
(*this)["iterations"] = iterations;
// algorithm used for picking the initial cluster centers for kmeans tree
(*this)["centers_init"] = centers_init;
// cluster boundary index. Used when searching the kmeans tree
(*this)["cb_index"] = cb_index;
}
};
/**
* This index builds a kd-tree index and a k-means index and performs nearest
* neighbour search both indexes. This gives a slight boost in search performance
* as some of the neighbours that are missed by one index are found by the other.
*/
template <typename Distance>
class CompositeIndex : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
/**
* Index constructor
* @param inputData dataset containing the points to index
* @param params Index parameters
* @param d Distance functor
* @return
*/
CompositeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = CompositeIndexParams(),
Distance d = Distance()) : index_params_(params)
{
kdtree_index_ = new KDTreeIndex<Distance>(inputData, params, d);
kmeans_index_ = new KMeansIndex<Distance>(inputData, params, d);
}
CompositeIndex(const CompositeIndex&);
CompositeIndex& operator=(const CompositeIndex&);
virtual ~CompositeIndex()
{
delete kdtree_index_;
delete kmeans_index_;
}
/**
* @return The index type
*/
flann_algorithm_t getType() const
{
return FLANN_INDEX_COMPOSITE;
}
/**
* @return Size of the index
*/
size_t size() const
{
return kdtree_index_->size();
}
/**
* \returns The dimensionality of the features in this index.
*/
size_t veclen() const
{
return kdtree_index_->veclen();
}
/**
* \returns The amount of memory (in bytes) used by the index.
*/
int usedMemory() const
{
return kmeans_index_->usedMemory() + kdtree_index_->usedMemory();
}
/**
* \brief Builds the index
*/
void buildIndex()
{
Logger::info("Building kmeans tree...\n");
kmeans_index_->buildIndex();
Logger::info("Building kdtree tree...\n");
kdtree_index_->buildIndex();
}
/**
* \brief Saves the index to a stream
* \param stream The stream to save the index to
*/
void saveIndex(FILE* stream)
{
kmeans_index_->saveIndex(stream);
kdtree_index_->saveIndex(stream);
}
/**
* \brief Loads the index from a stream
* \param stream The stream from which the index is loaded
*/
void loadIndex(FILE* stream)
{
kmeans_index_->loadIndex(stream);
kdtree_index_->loadIndex(stream);
}
/**
* \returns The index parameters
*/
IndexParams getParameters() const
{
return index_params_;
}
/**
* \brief Method that searches for nearest-neighbours
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
{
kmeans_index_->findNeighbors(result, vec, searchParams);
kdtree_index_->findNeighbors(result, vec, searchParams);
}
private:
/** The k-means index */
KMeansIndex<Distance>* kmeans_index_;
/** The kd-tree index */
KDTreeIndex<Distance>* kdtree_index_;
/** The index parameters */
const IndexParams index_params_;
};
}
#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_