opencv/3rdparty/include/flann/flann.hpp

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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 FLANN_HPP_
#define FLANN_HPP_

#include <vector>
#include <string>

#include "constants.h"
#include "common.h"
#include "matrix.h"

#include "flann.h"

namespace cvflann
{

class NNIndex;

class IndexFactory
{
public:
    virtual ~IndexFactory() {}
	virtual NNIndex* createIndex(const Matrix<float>& dataset) const = 0;
};

struct IndexParams : public IndexFactory {
protected:
	IndexParams() {};
public:

	static IndexParams* createFromParameters(const FLANNParameters& p);

	void fromParameters(const FLANNParameters&) {};
	void toParameters(FLANNParameters&) { };
};

struct LinearIndexParams : public IndexParams {
	LinearIndexParams() {};

	NNIndex* createIndex(const Matrix<float>& dataset) const;
};



struct KDTreeIndexParams : public IndexParams {
	KDTreeIndexParams(int trees_ = 4) : trees(trees_) {};

	int trees;                 // number of randomized trees to use (for kdtree)

	NNIndex* createIndex(const Matrix<float>& dataset) const;

	void fromParameters(const FLANNParameters& p)
	{
		trees = p.trees;
	}

	void toParameters(FLANNParameters& p)
	{
		p.algorithm = KDTREE;
		p.trees = trees;
	};

};

struct KMeansIndexParams : public IndexParams {
	KMeansIndexParams(int branching_ = 32, int iterations_ = 11,
			flann_centers_init_t centers_init_ = CENTERS_RANDOM, float cb_index_ = 0.2 ) :
		branching(branching_),
		iterations(iterations_),
		centers_init(centers_init_),
		cb_index(cb_index_) {};

	int branching;             // branching factor (for kmeans tree)
	int iterations;            // max iterations to perform in one kmeans clustering (kmeans tree)
	flann_centers_init_t centers_init;          // algorithm used for picking the initial cluster centers for kmeans tree
    float cb_index;            // cluster boundary index. Used when searching the kmeans tree


    NNIndex* createIndex(const Matrix<float>& dataset) const;

	void fromParameters(const FLANNParameters& p)
	{
		branching = p.branching;
		iterations = p.iterations;
		centers_init = p.centers_init;
		cb_index = p.cb_index;
	}

	void toParameters(FLANNParameters& p)
	{
		p.algorithm = KMEANS;
		p.branching = branching;
		p.iterations = iterations;
		p.centers_init = centers_init;
		p.cb_index = cb_index;
	};

};


struct CompositeIndexParams : public IndexParams {
	CompositeIndexParams(int trees_ = 4, int branching_ = 32, int iterations_ = 11,
			flann_centers_init_t centers_init_ = CENTERS_RANDOM, float cb_index_ = 0.2 ) :
		trees(trees_),
		branching(branching_),
		iterations(iterations_),
		centers_init(centers_init_),
		cb_index(cb_index_) {};

	int trees;                 // number of randomized trees to use (for kdtree)
	int branching;             // branching factor (for kmeans tree)
	int iterations;            // max iterations to perform in one kmeans clustering (kmeans tree)
	flann_centers_init_t centers_init;          // algorithm used for picking the initial cluster centers for kmeans tree
    float cb_index;            // cluster boundary index. Used when searching the kmeans tree

    NNIndex* createIndex(const Matrix<float>& dataset) const;

	void fromParameters(const FLANNParameters& p)
	{
		trees = p.trees;
		branching = p.branching;
		iterations = p.iterations;
		centers_init = p.centers_init;
		cb_index = p.cb_index;
	}

	void toParameters(FLANNParameters& p)
	{
		p.algorithm = COMPOSITE;
		p.trees = trees;
		p.branching = branching;
		p.iterations = iterations;
		p.centers_init = centers_init;
		p.cb_index = cb_index;
	};
};


struct AutotunedIndexParams : public IndexParams {
	AutotunedIndexParams( float target_precision_ = 0.9, float build_weight_ = 0.01,
			float memory_weight_ = 0, float sample_fraction_ = 0.1) :
		target_precision(target_precision_),
		build_weight(build_weight_),
		memory_weight(memory_weight_),
		sample_fraction(sample_fraction_) {};

	float target_precision;    // precision desired (used for autotuning, -1 otherwise)
	float build_weight;        // build tree time weighting factor
	float memory_weight;       // index memory weighting factor
    float sample_fraction;     // what fraction of the dataset to use for autotuning

    NNIndex* createIndex(const Matrix<float>& dataset) const;

	void fromParameters(const FLANNParameters& p)
	{
		target_precision = p.target_precision;
		build_weight = p.build_weight;
		memory_weight = p.memory_weight;
		sample_fraction = p.sample_fraction;
	}

	void toParameters(FLANNParameters& p)
	{
		p.algorithm = AUTOTUNED;
		p.target_precision = target_precision;
		p.build_weight = build_weight;
		p.memory_weight = memory_weight;
		p.sample_fraction = sample_fraction;
	};
};


struct SavedIndexParams : public IndexParams {
	SavedIndexParams() {
		throw FLANNException("I don't know which index to load");
	}
	SavedIndexParams(std::string filename_) : filename(filename_) {}

	std::string filename;		// filename of the stored index

	NNIndex* createIndex(const Matrix<float>& dataset) const;
};


struct SearchParams {
	SearchParams(int checks_ = 32) :
		checks(checks_) {};

	int checks;
};


class Index {
	NNIndex* nnIndex;

public:
	Index(const Matrix<float>& features, const IndexParams& params);

	~Index();

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

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

	void save(std::string filename);

	int veclen() const;

	int size() const;
};


int hierarchicalClustering(const Matrix<float>& features, Matrix<float>& centers, const KMeansIndexParams& params);


}
#endif /* FLANN_HPP_ */