kdtree_flann.h

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 * $Id: kdtree_flann.h 36261 2011-02-26 01:34:42Z mariusm $
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#pragma once
 
#include <pcl/kdtree/kdtree.h>
#include <flann/util/params.h>
 
#include <memory>
 
// Forward declarations
namespace flann
{
  template <typename T> struct L2_Simple;
  template <typename T> class Index;
}
 
namespace pcl
{
  /** \brief KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. The class is making use of
    * the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe.
    *
    * \author Radu B. Rusu, Marius Muja
    * \ingroup kdtree
    */
  template <typename PointT, typename Dist = ::flann::L2_Simple<float> >
  class KdTreeFLANN : public pcl::KdTree<PointT>
  {
    public:
      using KdTree<PointT>::input_;
      using KdTree<PointT>::indices_;
      using KdTree<PointT>::epsilon_;
      using KdTree<PointT>::sorted_;
      using KdTree<PointT>::point_representation_;
      using KdTree<PointT>::nearestKSearch;
      using KdTree<PointT>::radiusSearch;
 
      using PointCloud = typename KdTree<PointT>::PointCloud;
      using PointCloudConstPtr = typename KdTree<PointT>::PointCloudConstPtr;
 
      using IndicesPtr = shared_ptr<std::vector<int> >;
      using IndicesConstPtr = shared_ptr<const std::vector<int> >;
 
      using FLANNIndex = ::flann::Index<Dist>;
 
      // Boost shared pointers
      using Ptr = shared_ptr<KdTreeFLANN<PointT, Dist> >;
      using ConstPtr = shared_ptr<const KdTreeFLANN<PointT, Dist> >;
 
      /** \brief Default Constructor for KdTreeFLANN.
        * \param[in] sorted set to true if the application that the tree will be used for requires sorted nearest neighbor indices (default). False otherwise.
        *
        * By setting sorted to false, the \ref radiusSearch operations will be faster.
        */
      KdTreeFLANN (bool sorted = true);
 
      /** \brief Copy constructor
        * \param[in] k the tree to copy into this
        */
      KdTreeFLANN (const KdTreeFLANN<PointT, Dist> &k);
 
      /** \brief Copy operator
        * \param[in] k the tree to copy into this
        */
      inline KdTreeFLANN<PointT, Dist>&
      operator = (const KdTreeFLANN<PointT, Dist>& k)
      {
        KdTree<PointT>::operator=(k);
        flann_index_ = k.flann_index_;
        cloud_ = k.cloud_;
        index_mapping_ = k.index_mapping_;
        identity_mapping_ = k.identity_mapping_;
        dim_ = k.dim_;
        total_nr_points_ = k.total_nr_points_;
        param_k_ = k.param_k_;
        param_radius_ = k.param_radius_;
        return (*this);
      }
 
      /** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
        * \param[in] eps precision (error bound) for nearest neighbors searches
        */
      void
      setEpsilon (float eps) override;
 
      void
      setSortedResults (bool sorted);
 
      inline Ptr makeShared () { return Ptr (new KdTreeFLANN<PointT, Dist> (*this)); }
 
      /** \brief Destructor for KdTreeFLANN.
        * Deletes all allocated data arrays and destroys the kd-tree structures.
        */
      ~KdTreeFLANN ()
      {
        cleanup ();
      }
 
      /** \brief Provide a pointer to the input dataset.
        * \param[in] cloud the const boost shared pointer to a PointCloud message
        * \param[in] indices the point indices subset that is to be used from \a cloud - if NULL the whole cloud is used
        */
      void
      setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices = IndicesConstPtr ()) override;
 
      /** \brief Search for k-nearest neighbors for the given query point.
        *
        * \attention This method does not do any bounds checking for the input index
        * (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
        *
        * \param[in] point a given \a valid (i.e., finite) query point
        * \param[in] k the number of neighbors to search for
        * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
        * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
        * a priori!)
        * \return number of neighbors found
        *
        * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
        */
      int
      nearestKSearch (const PointT &point, unsigned int k,
                      std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const override;
 
      /** \brief Search for all the nearest neighbors of the query point in a given radius.
        *
        * \attention This method does not do any bounds checking for the input index
        * (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
        *
        * \param[in] point a given \a valid (i.e., finite) query point
        * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
        * \param[out] k_indices the resultant indices of the neighboring points
        * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
        * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
        * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
        * returned.
        * \return number of neighbors found in radius
        *
        * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
        */
      int
      radiusSearch (const PointT &point, double radius, std::vector<int> &k_indices,
                    std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const override;
 
    private:
      /** \brief Internal cleanup method. */
      void
      cleanup ();
 
      /** \brief Converts a PointCloud to the internal FLANN point array representation. Returns the number
        * of points.
        * \param cloud the PointCloud
        */
      void
      convertCloudToArray (const PointCloud &cloud);
 
      /** \brief Converts a PointCloud with a given set of indices to the internal FLANN point array
        * representation. Returns the number of points.
        * \param[in] cloud the PointCloud data
        * \param[in] indices the point cloud indices
       */
      void
      convertCloudToArray (const PointCloud &cloud, const std::vector<int> &indices);
 
    private:
      /** \brief Class getName method. */
      std::string
      getName () const override { return ("KdTreeFLANN"); }
 
      /** \brief A FLANN index object. */
      std::shared_ptr<FLANNIndex> flann_index_;
 
      /** \brief Internal pointer to data. TODO: replace with std::shared_ptr<float[]> with C++17*/
      std::shared_ptr<float> cloud_;
 
      /** \brief mapping between internal and external indices. */
      std::vector<int> index_mapping_;
 
      /** \brief whether the mapping between internal and external indices is identity */
      bool identity_mapping_;
 
      /** \brief Tree dimensionality (i.e. the number of dimensions per point). */
      int dim_;
 
      /** \brief The total size of the data (either equal to the number of points in the input cloud or to the number of indices - if passed). */
      uindex_t total_nr_points_;
 
      /** \brief The KdTree search parameters for K-nearest neighbors. */
      ::flann::SearchParams param_k_;
 
      /** \brief The KdTree search parameters for radius search. */
      ::flann::SearchParams param_radius_;
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/kdtree/impl/kdtree_flann.hpp>
#endif