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