404 lines
15 KiB
C
404 lines
15 KiB
C
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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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/***********************************************************************
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* Author: Vincent Rabaud
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*************************************************************************/
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#ifndef OPENCV_FLANN_LSH_INDEX_H_
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#define OPENCV_FLANN_LSH_INDEX_H_
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//! @cond IGNORED
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#include <algorithm>
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#include <cstring>
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#include <map>
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#include <vector>
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#include "nn_index.h"
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#include "matrix.h"
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#include "result_set.h"
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#include "heap.h"
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#include "lsh_table.h"
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#include "allocator.h"
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#include "random.h"
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#include "saving.h"
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#ifdef _MSC_VER
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#pragma warning(push)
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#pragma warning(disable: 4702) //disable unreachable code
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#endif
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namespace cvflann
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{
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struct LshIndexParams : public IndexParams
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{
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LshIndexParams(int table_number = 12, int key_size = 20, int multi_probe_level = 2)
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{
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(*this)["algorithm"] = FLANN_INDEX_LSH;
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// The number of hash tables to use
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(*this)["table_number"] = table_number;
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// The length of the key in the hash tables
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(*this)["key_size"] = key_size;
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// Number of levels to use in multi-probe (0 for standard LSH)
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(*this)["multi_probe_level"] = multi_probe_level;
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}
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};
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/**
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* Locality-sensitive hashing index
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*
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* Contains the tables and other information for indexing a set of points
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* for nearest-neighbor matching.
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*/
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template<typename Distance>
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class LshIndex : 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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/** Constructor
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* @param input_data dataset with the input features
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* @param params parameters passed to the LSH algorithm
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* @param d the distance used
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*/
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LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(),
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Distance d = Distance()) :
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dataset_(input_data), index_params_(params), distance_(d)
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{
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// cv::flann::IndexParams sets integer params as 'int', so it is used with get_param
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// in place of 'unsigned int'
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table_number_ = get_param(index_params_,"table_number",12);
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key_size_ = get_param(index_params_,"key_size",20);
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multi_probe_level_ = get_param(index_params_,"multi_probe_level",2);
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feature_size_ = (unsigned)dataset_.cols;
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fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
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}
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LshIndex(const LshIndex&);
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LshIndex& operator=(const LshIndex&);
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/**
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* Builds the index
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*/
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void buildIndex() CV_OVERRIDE
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{
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tables_.resize(table_number_);
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for (int i = 0; i < table_number_; ++i) {
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lsh::LshTable<ElementType>& table = tables_[i];
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table = lsh::LshTable<ElementType>(feature_size_, key_size_);
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// Add the features to the table
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table.add(dataset_);
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}
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}
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flann_algorithm_t getType() const CV_OVERRIDE
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{
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return FLANN_INDEX_LSH;
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}
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void saveIndex(FILE* stream) CV_OVERRIDE
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{
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save_value(stream,table_number_);
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save_value(stream,key_size_);
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save_value(stream,multi_probe_level_);
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save_value(stream, dataset_);
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}
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void loadIndex(FILE* stream) CV_OVERRIDE
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{
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load_value(stream, table_number_);
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load_value(stream, key_size_);
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load_value(stream, multi_probe_level_);
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load_value(stream, dataset_);
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// Building the index is so fast we can afford not storing it
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buildIndex();
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index_params_["algorithm"] = getType();
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index_params_["table_number"] = table_number_;
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index_params_["key_size"] = key_size_;
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index_params_["multi_probe_level"] = multi_probe_level_;
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}
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/**
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* Returns size of index.
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*/
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size_t size() const CV_OVERRIDE
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{
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return dataset_.rows;
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}
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/**
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* Returns the length of an index feature.
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*/
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size_t veclen() const CV_OVERRIDE
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{
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return feature_size_;
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}
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/**
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* Computes the index memory usage
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* Returns: memory used by the index
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*/
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int usedMemory() const CV_OVERRIDE
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{
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return (int)(dataset_.rows * sizeof(int));
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}
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IndexParams getParameters() const CV_OVERRIDE
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{
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return index_params_;
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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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virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
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{
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CV_Assert(queries.cols == veclen());
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CV_Assert(indices.rows >= queries.rows);
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CV_Assert(dists.rows >= queries.rows);
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CV_Assert(int(indices.cols) >= knn);
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CV_Assert(int(dists.cols) >= knn);
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KNNUniqueResultSet<DistanceType> resultSet(knn);
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for (size_t i = 0; i < queries.rows; i++) {
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resultSet.clear();
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std::fill_n(indices[i], knn, -1);
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std::fill_n(dists[i], knn, std::numeric_limits<DistanceType>::max());
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findNeighbors(resultSet, queries[i], params);
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if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
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else resultSet.copy(indices[i], dists[i], knn);
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}
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}
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/**
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* Find set of nearest neighbors to vec. Their indices are stored inside
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* the result object.
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*
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* Params:
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* result = the result object in which the indices of the nearest-neighbors are stored
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* vec = the vector for which to search the nearest neighbors
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* maxCheck = the maximum number of restarts (in a best-bin-first manner)
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*/
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void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE
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{
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getNeighbors(vec, result);
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}
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private:
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/** Defines the comparator on score and index
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*/
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typedef std::pair<float, unsigned int> ScoreIndexPair;
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struct SortScoreIndexPairOnSecond
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{
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bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
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{
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return left.second < right.second;
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}
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};
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/** Fills the different xor masks to use when getting the neighbors in multi-probe LSH
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* @param key the key we build neighbors from
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* @param lowest_index the lowest index of the bit set
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* @param level the multi-probe level we are at
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* @param xor_masks all the xor mask
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*/
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void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
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std::vector<lsh::BucketKey>& xor_masks)
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{
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xor_masks.push_back(key);
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if (level == 0) return;
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for (int index = lowest_index - 1; index >= 0; --index) {
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// Create a new key
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lsh::BucketKey new_key = key | (1 << index);
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fill_xor_mask(new_key, index, level - 1, xor_masks);
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}
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}
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/** Performs the approximate nearest-neighbor search.
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* @param vec the feature to analyze
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* @param do_radius flag indicating if we check the radius too
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* @param radius the radius if it is a radius search
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* @param do_k flag indicating if we limit the number of nn
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* @param k_nn the number of nearest neighbors
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* @param checked_average used for debugging
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*/
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void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn,
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float& /*checked_average*/)
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{
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static std::vector<ScoreIndexPair> score_index_heap;
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if (do_k) {
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unsigned int worst_score = std::numeric_limits<unsigned int>::max();
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typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
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typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
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for (; table != table_end; ++table) {
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size_t key = table->getKey(vec);
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std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
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std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
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for (; xor_mask != xor_mask_end; ++xor_mask) {
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size_t sub_key = key ^ (*xor_mask);
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const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
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if (bucket == 0) continue;
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// Go over each descriptor index
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std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
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std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
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DistanceType hamming_distance;
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// Process the rest of the candidates
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for (; training_index < last_training_index; ++training_index) {
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hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
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if (hamming_distance < worst_score) {
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// Insert the new element
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score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
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std::push_heap(score_index_heap.begin(), score_index_heap.end());
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if (score_index_heap.size() > (unsigned int)k_nn) {
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// Remove the highest distance value as we have too many elements
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std::pop_heap(score_index_heap.begin(), score_index_heap.end());
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score_index_heap.pop_back();
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// Keep track of the worst score
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worst_score = score_index_heap.front().first;
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}
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}
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}
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}
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}
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}
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else {
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typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
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typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
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for (; table != table_end; ++table) {
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size_t key = table->getKey(vec);
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std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
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std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
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for (; xor_mask != xor_mask_end; ++xor_mask) {
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size_t sub_key = key ^ (*xor_mask);
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const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
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if (bucket == 0) continue;
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// Go over each descriptor index
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std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
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std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
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DistanceType hamming_distance;
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// Process the rest of the candidates
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for (; training_index < last_training_index; ++training_index) {
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// Compute the Hamming distance
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hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
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if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
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}
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}
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}
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}
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}
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/** Performs the approximate nearest-neighbor search.
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* This is a slower version than the above as it uses the ResultSet
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* @param vec the feature to analyze
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*/
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void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result)
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{
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typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
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typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
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for (; table != table_end; ++table) {
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size_t key = table->getKey(vec);
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std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
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std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
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for (; xor_mask != xor_mask_end; ++xor_mask) {
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size_t sub_key = key ^ (*xor_mask);
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const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key);
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if (bucket == 0) continue;
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// Go over each descriptor index
|
||
|
|
std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
|
||
|
|
std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
|
||
|
|
DistanceType hamming_distance;
|
||
|
|
|
||
|
|
// Process the rest of the candidates
|
||
|
|
for (; training_index < last_training_index; ++training_index) {
|
||
|
|
// Compute the Hamming distance
|
||
|
|
hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols);
|
||
|
|
result.addPoint(hamming_distance, *training_index);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/** The different hash tables */
|
||
|
|
std::vector<lsh::LshTable<ElementType> > tables_;
|
||
|
|
|
||
|
|
/** The data the LSH tables where built from */
|
||
|
|
Matrix<ElementType> dataset_;
|
||
|
|
|
||
|
|
/** The size of the features (as ElementType[]) */
|
||
|
|
unsigned int feature_size_;
|
||
|
|
|
||
|
|
IndexParams index_params_;
|
||
|
|
|
||
|
|
/** table number */
|
||
|
|
int table_number_;
|
||
|
|
/** key size */
|
||
|
|
int key_size_;
|
||
|
|
/** How far should we look for neighbors in multi-probe LSH */
|
||
|
|
int multi_probe_level_;
|
||
|
|
|
||
|
|
/** The XOR masks to apply to a key to get the neighboring buckets */
|
||
|
|
std::vector<lsh::BucketKey> xor_masks_;
|
||
|
|
|
||
|
|
Distance distance_;
|
||
|
|
};
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifdef _MSC_VER
|
||
|
|
#pragma warning(pop)
|
||
|
|
#endif
|
||
|
|
|
||
|
|
//! @endcond
|
||
|
|
|
||
|
|
#endif //OPENCV_FLANN_LSH_INDEX_H_
|