296 lines
8.8 KiB
C++
296 lines
8.8 KiB
C++
/***********************************************************************
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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 OPENCV_FLANN_BASE_HPP_
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#define OPENCV_FLANN_BASE_HPP_
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#include <vector>
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#include <cassert>
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#include <cstdio>
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#include "general.h"
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#include "matrix.h"
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#include "params.h"
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#include "saving.h"
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#include "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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* @param level Verbosity level
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*/
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inline void log_verbosity(int level)
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{
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if (level >= 0) {
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Logger::setLevel(level);
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}
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}
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/**
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* (Deprecated) Index parameters for creating a saved index.
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*/
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struct SavedIndexParams : public IndexParams
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{
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SavedIndexParams(cv::String filename)
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{
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(* this)["algorithm"] = FLANN_INDEX_SAVED;
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(*this)["filename"] = filename;
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}
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};
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template<typename Distance>
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NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const cv::String& filename, Distance distance)
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{
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typedef typename Distance::ElementType ElementType;
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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<ElementType>::type()) {
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fclose(fin);
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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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fclose(fin);
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throw FLANNException("The index saved belongs to a different dataset");
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}
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IndexParams params;
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params["algorithm"] = header.index_type;
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NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
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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 Distance>
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class Index : public NNIndex<Distance>
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{
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public:
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typedef typename Distance::ElementType ElementType;
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typedef typename Distance::ResultType DistanceType;
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Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
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: index_params_(params)
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{
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flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
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loaded_ = false;
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if (index_type == FLANN_INDEX_SAVED) {
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nnIndex_ = load_saved_index<Distance>(features, get_param<cv::String>(params,"filename"), distance);
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loaded_ = true;
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}
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else {
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nnIndex_ = create_index_by_type<Distance>(features, params, distance);
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}
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}
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~Index()
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{
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delete nnIndex_;
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}
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/**
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* Builds the index.
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*/
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void buildIndex()
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{
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if (!loaded_) {
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nnIndex_->buildIndex();
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}
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}
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void save(cv::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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saveIndex(fout);
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fclose(fout);
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}
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/**
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* \brief Saves the index to a stream
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* \param stream The stream to save the index to
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*/
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virtual void saveIndex(FILE* stream)
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{
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nnIndex_->saveIndex(stream);
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}
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/**
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* \brief Loads the index from a stream
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* \param stream The stream from which the index is loaded
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*/
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virtual void loadIndex(FILE* stream)
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{
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nnIndex_->loadIndex(stream);
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}
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/**
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* \returns number of features in this index.
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*/
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size_t veclen() const
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{
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return nnIndex_->veclen();
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}
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/**
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* \returns The dimensionality of the features in this index.
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*/
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size_t size() const
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{
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return nnIndex_->size();
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}
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/**
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* \returns The index type (kdtree, kmeans,...)
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*/
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flann_algorithm_t getType() const
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{
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return nnIndex_->getType();
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}
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/**
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* \returns The amount of memory (in bytes) used by the index.
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*/
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virtual int usedMemory() const
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{
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return nnIndex_->usedMemory();
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}
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/**
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* \returns The index parameters
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*/
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IndexParams getParameters() const
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{
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return nnIndex_->getParameters();
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}
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/**
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* \brief Perform k-nearest neighbor search
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* \param[in] queries The query points for which to find the nearest neighbors
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* \param[out] indices The indices of the nearest neighbors found
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* \param[out] dists Distances to the nearest neighbors found
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* \param[in] knn Number of nearest neighbors to return
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* \param[in] params Search parameters
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*/
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void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
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{
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nnIndex_->knnSearch(queries, indices, dists, knn, params);
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}
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/**
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* \brief Perform radius search
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* \param[in] query The query point
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* \param[out] indices The indinces of the neighbors found within the given radius
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* \param[out] dists The distances to the nearest neighbors found
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* \param[in] radius The radius used for search
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* \param[in] params Search parameters
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* \returns Number of neighbors found
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*/
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int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
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{
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return nnIndex_->radiusSearch(query, indices, dists, radius, params);
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}
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/**
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* \brief Method that searches for nearest-neighbours
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*/
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void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
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{
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nnIndex_->findNeighbors(result, vec, searchParams);
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}
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/**
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* \brief Returns actual index
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*/
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CV_DEPRECATED NNIndex<Distance>* getIndex()
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{
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return nnIndex_;
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}
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/**
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* \brief Returns index parameters.
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* \deprecated use getParameters() instead.
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*/
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CV_DEPRECATED const IndexParams* getIndexParameters()
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{
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return &index_params_;
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}
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private:
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/** Pointer to actual index class */
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NNIndex<Distance>* nnIndex_;
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/** Indices if the index was loaded from a file */
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bool loaded_;
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/** Parameters passed to the index */
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IndexParams index_params_;
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Index(const Index &); // copy disabled
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Index& operator=(const Index &); // assign disabled
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};
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/**
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* Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
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* the clustering tree to return a flat clustering.
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* @param[in] points Points to be clustered
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* @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
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* number of clusters requested.
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* @param params Clustering parameters (The same as for cvflann::KMeansIndex)
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* @param d Distance to be used for clustering (eg: cvflann::L2)
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* @return number of clusters computed (can be different than clusters.rows and is the highest number
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* of the form (branching-1)*K+1 smaller than clusters.rows).
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*/
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template <typename Distance>
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int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
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const KMeansIndexParams& params, Distance d = Distance())
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{
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KMeansIndex<Distance> kmeans(points, params, d);
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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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}
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#endif /* OPENCV_FLANN_BASE_HPP_ */
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