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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_NNINDEX_H
  #define OPENCV_FLANN_NNINDEX_H
  
  #include <string>
  
  #include "general.h"
  #include "matrix.h"
  #include "result_set.h"
  #include "params.h"
  
  namespace cvflann
  {
  
  /**
   * Nearest-neighbour index base class
   */
  template <typename Distance>
  class NNIndex
  {
      typedef typename Distance::ElementType ElementType;
      typedef typename Distance::ResultType DistanceType;
  
  public:
  
      virtual ~NNIndex() {}
  
      /**
       * \brief Builds the index
       */
      virtual void buildIndex() = 0;
  
      /**
       * \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);
  
  #if 0
          KNNResultSet<DistanceType> resultSet(knn);
          for (size_t i = 0; i < queries.rows; i++) {
              resultSet.init(indices[i], dists[i]);
              findNeighbors(resultSet, queries[i], params);
          }
  #else
          KNNUniqueResultSet<DistanceType> resultSet(knn);
          for (size_t i = 0; i < queries.rows; i++) {
              resultSet.clear();
              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);
          }
  #endif
      }
  
      /**
       * \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
       */
      virtual int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
      {
          if (query.rows != 1) {
              fprintf(stderr, "I can only search one feature at a time for range search\n");
              return -1;
          }
          assert(query.cols == veclen());
          assert(indices.cols == dists.cols);
  
          int n = 0;
          int* indices_ptr = NULL;
          DistanceType* dists_ptr = NULL;
          if (indices.cols > 0) {
              n = (int)indices.cols;
              indices_ptr = indices[0];
              dists_ptr = dists[0];
          }
  
          RadiusUniqueResultSet<DistanceType> resultSet((DistanceType)radius);
          resultSet.clear();
          findNeighbors(resultSet, query[0], params);
          if (n>0) {
              if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n);
              else resultSet.copy(indices_ptr, dists_ptr, n);
          }
  
          return (int)resultSet.size();
      }
  
      /**
       * \brief Saves the index to a stream
       * \param stream The stream to save the index to
       */
      virtual void saveIndex(FILE* stream) = 0;
  
      /**
       * \brief Loads the index from a stream
       * \param stream The stream from which the index is loaded
       */
      virtual void loadIndex(FILE* stream) = 0;
  
      /**
       * \returns number of features in this index.
       */
      virtual size_t size() const = 0;
  
      /**
       * \returns The dimensionality of the features in this index.
       */
      virtual size_t veclen() const = 0;
  
      /**
       * \returns The amount of memory (in bytes) used by the index.
       */
      virtual int usedMemory() const = 0;
  
      /**
       * \returns The index type (kdtree, kmeans,...)
       */
      virtual flann_algorithm_t getType() const = 0;
  
      /**
       * \returns The index parameters
       */
      virtual IndexParams getParameters() const = 0;
  
  
      /**
       * \brief Method that searches for nearest-neighbours
       */
      virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) = 0;
  };
  
  }
  
  #endif //OPENCV_FLANN_NNINDEX_H
  