/*********************************************************************** * Software License Agreement (BSD License) * * 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. * * THE BSD LICENSE * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. *************************************************************************/ #ifndef FLANN_HPP_ #define FLANN_HPP_ #include #include #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& dataset) const = 0; }; struct IndexParams : public IndexFactory { protected: IndexParams() {}; public: static IndexParams* createFromParameters(const FLANNParameters& p); virtual void fromParameters(const FLANNParameters&) {}; virtual void toParameters(FLANNParameters&) { }; }; struct LinearIndexParams : public IndexParams { LinearIndexParams() {}; NNIndex* createIndex(const Matrix& 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& 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& 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& 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& 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& dataset) const; }; struct SearchParams { SearchParams(int checks_ = 32) : checks(checks_) {}; int checks; }; class Index { NNIndex* nnIndex; public: Index(const Matrix& features, const IndexParams& params); ~Index(); void knnSearch(const Matrix& queries, Matrix& indices, Matrix& dists, int knn, const SearchParams& params); int radiusSearch(const Matrix& query, Matrix indices, Matrix dists, float radius, const SearchParams& params); void save(std::string filename); int veclen() const; int size() const; }; int hierarchicalClustering(const Matrix& features, Matrix& centers, const KMeansIndexParams& params); } #endif /* FLANN_HPP_ */