Updated FLANN to version 1.5
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Marius Muja
261
modules/flann/include/opencv2/flann/flann_base.hpp
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261
modules/flann/include/opencv2/flann/flann_base.hpp
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/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#ifndef FLANN_HPP_
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#define FLANN_HPP_
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#include <vector>
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#include <string>
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#include <cassert>
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#include <cstdio>
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#include "opencv2/flann/general.h"
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#include "opencv2/flann/matrix.h"
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#include "opencv2/flann/result_set.h"
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#include "opencv2/flann/index_testing.h"
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#include "opencv2/flann/object_factory.h"
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#include "opencv2/flann/saving.h"
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#include "opencv2/flann/all_indices.h"
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namespace cvflann
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{
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/**
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Sets the log level used for all flann functions
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Params:
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level = verbosity level
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*/
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void log_verbosity(int level);
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/**
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* Sets the distance type to use throughout FLANN.
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* If distance type specified is MINKOWSKI, the second argument
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* specifies which order the minkowski distance should have.
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*/
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void set_distance_type(flann_distance_t distance_type, int order);
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struct SavedIndexParams : public IndexParams {
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SavedIndexParams(std::string filename_) : IndexParams(SAVED), filename(filename_) {}
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std::string filename; // filename of the stored index
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flann_algorithm_t getIndexType() const { return algorithm; }
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void print() const
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{
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logger.info("Index type: %d\n",(int)algorithm);
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logger.info("Filename: %s\n", filename.c_str());
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}
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};
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template<typename T>
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class Index {
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NNIndex<T>* nnIndex;
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bool built;
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public:
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Index(const Matrix<T>& features, const IndexParams& params);
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~Index();
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void buildIndex();
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void knnSearch(const Matrix<T>& queries, Matrix<int>& indices, Matrix<float>& dists, int knn, const SearchParams& params);
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int radiusSearch(const Matrix<T>& query, Matrix<int>& indices, Matrix<float>& dists, float radius, const SearchParams& params);
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void save(std::string filename);
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int veclen() const;
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int size() const;
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NNIndex<T>* getIndex() { return nnIndex; }
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const IndexParams* getIndexParameters() { return nnIndex->getParameters(); }
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};
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template<typename T>
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NNIndex<T>* load_saved_index(const Matrix<T>& dataset, const string& filename)
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{
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FILE* fin = fopen(filename.c_str(), "rb");
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if (fin==NULL) {
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return NULL;
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}
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IndexHeader header = load_header(fin);
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if (header.data_type!=Datatype<T>::type()) {
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throw FLANNException("Datatype of saved index is different than of the one to be created.");
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}
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if (size_t(header.rows)!=dataset.rows || size_t(header.cols)!=dataset.cols) {
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throw FLANNException("The index saved belongs to a different dataset");
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}
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IndexParams* params = ParamsFactory::instance().create(header.index_type);
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NNIndex<T>* nnIndex = create_index_by_type(dataset, *params);
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nnIndex->loadIndex(fin);
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fclose(fin);
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return nnIndex;
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}
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template<typename T>
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Index<T>::Index(const Matrix<T>& dataset, const IndexParams& params)
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{
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flann_algorithm_t index_type = params.getIndexType();
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built = false;
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if (index_type==SAVED) {
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nnIndex = load_saved_index(dataset, ((const SavedIndexParams&)params).filename);
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built = true;
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}
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else {
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nnIndex = create_index_by_type(dataset, params);
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}
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}
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template<typename T>
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Index<T>::~Index()
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{
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delete nnIndex;
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}
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template<typename T>
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void Index<T>::buildIndex()
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{
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if (!built) {
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nnIndex->buildIndex();
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built = true;
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}
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}
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template<typename T>
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void Index<T>::knnSearch(const Matrix<T>& queries, Matrix<int>& indices, Matrix<float>& dists, int knn, const SearchParams& searchParams)
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{
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if (!built) {
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throw FLANNException("You must build the index before searching.");
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}
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assert(queries.cols==nnIndex->veclen());
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assert(indices.rows>=queries.rows);
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assert(dists.rows>=queries.rows);
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assert(int(indices.cols)>=knn);
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assert(int(dists.cols)>=knn);
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KNNResultSet<T> resultSet(knn);
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for (size_t i = 0; i < queries.rows; i++) {
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T* target = queries[i];
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resultSet.init(target, queries.cols);
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nnIndex->findNeighbors(resultSet, target, searchParams);
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int* neighbors = resultSet.getNeighbors();
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float* distances = resultSet.getDistances();
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memcpy(indices[i], neighbors, knn*sizeof(int));
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memcpy(dists[i], distances, knn*sizeof(float));
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}
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}
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template<typename T>
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int Index<T>::radiusSearch(const Matrix<T>& query, Matrix<int>& indices, Matrix<float>& dists, float radius, const SearchParams& searchParams)
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{
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if (!built) {
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throw FLANNException("You must build the index before searching.");
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}
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if (query.rows!=1) {
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fprintf(stderr, "I can only search one feature at a time for range search\n");
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return -1;
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}
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assert(query.cols==nnIndex->veclen());
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RadiusResultSet<T> resultSet(radius);
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resultSet.init(query.data, query.cols);
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nnIndex->findNeighbors(resultSet,query.data,searchParams);
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// TODO: optimise here
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int* neighbors = resultSet.getNeighbors();
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float* distances = resultSet.getDistances();
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size_t count_nn = min(resultSet.size(), indices.cols);
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assert (dists.cols>=count_nn);
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for (size_t i=0;i<count_nn;++i) {
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indices[0][i] = neighbors[i];
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dists[0][i] = distances[i];
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}
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return count_nn;
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}
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template<typename T>
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void Index<T>::save(string filename)
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{
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FILE* fout = fopen(filename.c_str(), "wb");
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if (fout==NULL) {
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throw FLANNException("Cannot open file");
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}
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save_header(fout, *nnIndex);
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nnIndex->saveIndex(fout);
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fclose(fout);
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}
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template<typename T>
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int Index<T>::size() const
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{
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return nnIndex->size();
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}
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template<typename T>
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int Index<T>::veclen() const
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{
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return nnIndex->veclen();
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}
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template <typename ELEM_TYPE, typename DIST_TYPE>
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int hierarchicalClustering(const Matrix<ELEM_TYPE>& features, Matrix<DIST_TYPE>& centers, const KMeansIndexParams& params)
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{
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KMeansIndex<ELEM_TYPE, DIST_TYPE> kmeans(features, params);
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kmeans.buildIndex();
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int clusterNum = kmeans.getClusterCenters(centers);
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return clusterNum;
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}
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} // namespace cvflann
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#endif /* FLANN_HPP_ */
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