text: /***********************************************************************
   * 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_
  