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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.
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  #ifndef OPENCV_FLANN_BASE_HPP_
  #define OPENCV_FLANN_BASE_HPP_
  
  #include <vector>
  #include <string>
  #include <cassert>
  #include <cstdio>
  
  #include "general.h"
  #include "matrix.h"
  #include "params.h"
  #include "saving.h"
  
  #include "all_indices.h"
  
  namespace cvflann
  {
  
  /**
   * Sets the log level used for all flann functions
   * @param level Verbosity level
   */
  inline void log_verbosity(int level)
  {
      if (level >= 0) {
          Logger::setLevel(level);
      }
  }
  
  /**
   * (Deprecated) Index parameters for creating a saved index.
   */
  struct SavedIndexParams : public IndexParams
  {
      SavedIndexParams(std::string filename)
      {
          (* this)["algorithm"] = FLANN_INDEX_SAVED;
          (*this)["filename"] = filename;
      }
  };
  
  
  template<typename Distance>
  NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const std::string& filename, Distance distance)
  {
      typedef typename Distance::ElementType ElementType;
  
      FILE* fin = fopen(filename.c_str(), "rb");
      if (fin == NULL) {
          return NULL;
      }
      IndexHeader header = load_header(fin);
      if (header.data_type != Datatype<ElementType>::type()) {
          throw FLANNException("Datatype of saved index is different than of the one to be created.");
      }
      if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) {
          throw FLANNException("The index saved belongs to a different dataset");
      }
  
      IndexParams params;
      params["algorithm"] = header.index_type;
      NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
      nnIndex->loadIndex(fin);
      fclose(fin);
  
      return nnIndex;
  }
  
  
  template<typename Distance>
  class Index : public NNIndex<Distance>
  {
  public:
      typedef typename Distance::ElementType ElementType;
      typedef typename Distance::ResultType DistanceType;
  
      Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
          : index_params_(params)
      {
          flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
          loaded_ = false;
  
          if (index_type == FLANN_INDEX_SAVED) {
              nnIndex_ = load_saved_index<Distance>(features, get_param<std::string>(params,"filename"), distance);
              loaded_ = true;
          }
          else {
              nnIndex_ = create_index_by_type<Distance>(features, params, distance);
          }
      }
  
      ~Index()
      {
          delete nnIndex_;
      }
  
      /**
       * Builds the index.
       */
      void buildIndex()
      {
          if (!loaded_) {
              nnIndex_->buildIndex();
          }
      }
  
      void save(std::string filename)
      {
          FILE* fout = fopen(filename.c_str(), "wb");
          if (fout == NULL) {
              throw FLANNException("Cannot open file");
          }
          save_header(fout, *nnIndex_);
          saveIndex(fout);
          fclose(fout);
      }
  
      /**
       * \brief Saves the index to a stream
       * \param stream The stream to save the index to
       */
      virtual void saveIndex(FILE* stream)
      {
          nnIndex_->saveIndex(stream);
      }
  
      /**
       * \brief Loads the index from a stream
       * \param stream The stream from which the index is loaded
       */
      virtual void loadIndex(FILE* stream)
      {
          nnIndex_->loadIndex(stream);
      }
  
      /**
       * \returns number of features in this index.
       */
      size_t veclen() const
      {
          return nnIndex_->veclen();
      }
  
      /**
       * \returns The dimensionality of the features in this index.
       */
      size_t size() const
      {
          return nnIndex_->size();
      }
  
      /**
       * \returns The index type (kdtree, kmeans,...)
       */
      flann_algorithm_t getType() const
      {
          return nnIndex_->getType();
      }
  
      /**
       * \returns The amount of memory (in bytes) used by the index.
       */
      virtual int usedMemory() const
      {
          return nnIndex_->usedMemory();
      }
  
  
      /**
       * \returns The index parameters
       */
      IndexParams getParameters() const
      {
          return nnIndex_->getParameters();
      }
  
      /**
       * \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
       */
      void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
      {
          nnIndex_->knnSearch(queries, indices, dists, knn, params);
      }
  
      /**
       * \brief Perform radius search
       * \param[in] query The query point
       * \param[out] indices The indinces of the neighbors found within the given radius
       * \param[out] dists The distances to the nearest neighbors found
       * \param[in] radius The radius used for search
       * \param[in] params Search parameters
       * \returns Number of neighbors found
       */
      int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
      {
          return nnIndex_->radiusSearch(query, indices, dists, radius, params);
      }
  
      /**
       * \brief Method that searches for nearest-neighbours
       */
      void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
      {
          nnIndex_->findNeighbors(result, vec, searchParams);
      }
  
      /**
       * \brief Returns actual index
       */
      FLANN_DEPRECATED NNIndex<Distance>* getIndex()
      {
          return nnIndex_;
      }
  
      /**
       * \brief Returns index parameters.
       * \deprecated use getParameters() instead.
       */
      FLANN_DEPRECATED  const IndexParams* getIndexParameters()
      {
          return &index_params_;
      }
  
  private:
      /** Pointer to actual index class */
      NNIndex<Distance>* nnIndex_;
      /** Indices if the index was loaded from a file */
      bool loaded_;
      /** Parameters passed to the index */
      IndexParams index_params_;
  };
  
  /**
   * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
   * the clustering tree to return a flat clustering.
   * @param[in] points Points to be clustered
   * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
   *  number of clusters requested.
   * @param params Clustering parameters (The same as for cvflann::KMeansIndex)
   * @param d Distance to be used for clustering (eg: cvflann::L2)
   * @return number of clusters computed (can be different than clusters.rows and is the highest number
   * of the form (branching-1)*K+1 smaller than clusters.rows).
   */
  template <typename Distance>
  int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
                             const KMeansIndexParams& params, Distance d = Distance())
  {
      KMeansIndex<Distance> kmeans(points, params, d);
      kmeans.buildIndex();
  
      int clusterNum = kmeans.getClusterCenters(centers);
      return clusterNum;
  }
  
  }
  #endif /* OPENCV_FLANN_BASE_HPP_ */
  