kdtree_flann.hpp

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#ifndef PCL_KDTREE_KDTREE_IMPL_FLANN_H_
#define PCL_KDTREE_KDTREE_IMPL_FLANN_H_
 
#include <flann/flann.hpp>
 
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/console/print.h>
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist>
pcl::KdTreeFLANN<PointT, Dist>::KdTreeFLANN (bool sorted)
  : pcl::KdTree<PointT> (sorted)
  , flann_index_ ()
  , identity_mapping_ (false)
  , dim_ (0), total_nr_points_ (0)
  , param_k_ (::flann::SearchParams (-1 , epsilon_))
  , param_radius_ (::flann::SearchParams (-1, epsilon_, sorted))
{
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist>
pcl::KdTreeFLANN<PointT, Dist>::KdTreeFLANN (const KdTreeFLANN<PointT, Dist> &k)
  : pcl::KdTree<PointT> (false)
  , flann_index_ ()
  , identity_mapping_ (false)
  , dim_ (0), total_nr_points_ (0)
  , param_k_ (::flann::SearchParams (-1 , epsilon_))
  , param_radius_ (::flann::SearchParams (-1, epsilon_, false))
{
  *this = k;
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::setEpsilon (float eps)
{
  epsilon_ = eps;
  param_k_ =  ::flann::SearchParams (-1 , epsilon_);
  param_radius_ = ::flann::SearchParams (-1 , epsilon_, sorted_);
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::setSortedResults (bool sorted)
{
  sorted_ = sorted;
  param_k_ = ::flann::SearchParams (-1, epsilon_);
  param_radius_ = ::flann::SearchParams (-1, epsilon_, sorted_);
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices)
{
  cleanup ();   // Perform an automatic cleanup of structures
 
  epsilon_ = 0.0f;   // default error bound value
  dim_ = point_representation_->getNumberOfDimensions (); // Number of dimensions - default is 3 = xyz
 
  input_   = cloud;
  indices_ = indices;
 
  // Allocate enough data
  if (!input_)
  {
    PCL_ERROR ("[pcl::KdTreeFLANN::setInputCloud] Invalid input!\n");
    return;
  }
  if (indices != nullptr)
  {
    convertCloudToArray (*input_, *indices_);
  }
  else
  {
    convertCloudToArray (*input_);
  }
  total_nr_points_ = static_cast<uindex_t> (index_mapping_.size ());
  if (total_nr_points_ == 0)
  {
    PCL_ERROR ("[pcl::KdTreeFLANN::setInputCloud] Cannot create a KDTree with an empty input cloud!\n");
    return;
  }
 
  flann_index_.reset (new FLANNIndex (::flann::Matrix<float> (cloud_.get (),
                                                              index_mapping_.size (),
                                                              dim_),
                                      ::flann::KDTreeSingleIndexParams (15))); // max 15 points/leaf
  flann_index_->buildIndex ();
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> int
pcl::KdTreeFLANN<PointT, Dist>::nearestKSearch (const PointT &point, unsigned int k,
                                                std::vector<int> &k_indices,
                                                std::vector<float> &k_distances) const
{
  assert (point_representation_->isValid (point) && "Invalid (NaN, Inf) point coordinates given to nearestKSearch!");
 
  if (k > total_nr_points_)
    k = total_nr_points_;
 
  k_indices.resize (k);
  k_distances.resize (k);
 
  if (k==0)
    return 0;
 
  std::vector<float> query (dim_);
  point_representation_->vectorize (static_cast<PointT> (point), query);
 
  ::flann::Matrix<int> k_indices_mat (&k_indices[0], 1, k);
  ::flann::Matrix<float> k_distances_mat (&k_distances[0], 1, k);
  // Wrap the k_indices and k_distances vectors (no data copy)
  flann_index_->knnSearch (::flann::Matrix<float> (&query[0], 1, dim_),
                           k_indices_mat, k_distances_mat,
                           k, param_k_);
 
  // Do mapping to original point cloud
  if (!identity_mapping_)
  {
    for (std::size_t i = 0; i < static_cast<std::size_t> (k); ++i)
    {
      int& neighbor_index = k_indices[i];
      neighbor_index = index_mapping_[neighbor_index];
    }
  }
 
  return (k);
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> int
pcl::KdTreeFLANN<PointT, Dist>::radiusSearch (const PointT &point, double radius, std::vector<int> &k_indices,
                                              std::vector<float> &k_sqr_dists, unsigned int max_nn) const
{
  assert (point_representation_->isValid (point) && "Invalid (NaN, Inf) point coordinates given to radiusSearch!");
 
  std::vector<float> query (dim_);
  point_representation_->vectorize (static_cast<PointT> (point), query);
 
  // Has max_nn been set properly?
  if (max_nn == 0 || max_nn > total_nr_points_)
    max_nn = total_nr_points_;
 
  std::vector<std::vector<int> > indices(1);
  std::vector<std::vector<float> > dists(1);
 
  ::flann::SearchParams params (param_radius_);
  if (max_nn == total_nr_points_)
    params.max_neighbors = -1;  // return all neighbors in radius
  else
    params.max_neighbors = max_nn;
 
  int neighbors_in_radius = flann_index_->radiusSearch (::flann::Matrix<float> (&query[0], 1, dim_),
      indices,
      dists,
      static_cast<float> (radius * radius),
      params);
 
  k_indices = indices[0];
  k_sqr_dists = dists[0];
 
  // Do mapping to original point cloud
  if (!identity_mapping_)
  {
    for (int i = 0; i < neighbors_in_radius; ++i)
    {
      int& neighbor_index = k_indices[i];
      neighbor_index = index_mapping_[neighbor_index];
    }
  }
 
  return (neighbors_in_radius);
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::cleanup ()
{
  // Data array cleanup
  index_mapping_.clear ();
 
  if (indices_)
    indices_.reset ();
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::convertCloudToArray (const PointCloud &cloud)
{
  // No point in doing anything if the array is empty
  if (cloud.empty ())
  {
    cloud_.reset ();
    return;
  }
 
  const auto original_no_of_points = cloud.size ();
 
  cloud_.reset (new float[original_no_of_points * dim_], std::default_delete<float[]> ());
  float* cloud_ptr = cloud_.get ();
  index_mapping_.reserve (original_no_of_points);
  identity_mapping_ = true;
 
  for (std::size_t cloud_index = 0; cloud_index < original_no_of_points; ++cloud_index)
  {
    // Check if the point is invalid
    if (!point_representation_->isValid (cloud[cloud_index]))
    {
      identity_mapping_ = false;
      continue;
    }
 
    index_mapping_.push_back (cloud_index);
 
    point_representation_->vectorize (cloud[cloud_index], cloud_ptr);
    cloud_ptr += dim_;
  }
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename Dist> void
pcl::KdTreeFLANN<PointT, Dist>::convertCloudToArray (const PointCloud &cloud, const std::vector<int> &indices)
{
  // No point in doing anything if the array is empty
  if (cloud.empty ())
  {
    cloud_.reset ();
    return;
  }
 
  int original_no_of_points = static_cast<int> (indices.size ());
 
  cloud_.reset (new float[original_no_of_points * dim_], std::default_delete<float[]> ());
  float* cloud_ptr = cloud_.get ();
  index_mapping_.reserve (original_no_of_points);
  // its a subcloud -> false
  // true only identity:
  //     - indices size equals cloud size
  //     - indices only contain values between 0 and cloud.size - 1
  //     - no index is multiple times in the list
  //     => index is complete
  // But we can not guarantee that => identity_mapping_ = false
  identity_mapping_ = false;
 
  for (const auto &index : indices)
  {
    // Check if the point is invalid
    if (!point_representation_->isValid (cloud[index]))
      continue;
 
    // map from 0 - N -> indices [0] - indices [N]
    index_mapping_.push_back (index);  // If the returned index should be for the indices vector
 
    point_representation_->vectorize (cloud[index], cloud_ptr);
    cloud_ptr += dim_;
  }
}
 
#define PCL_INSTANTIATE_KdTreeFLANN(T) template class PCL_EXPORTS pcl::KdTreeFLANN<T>;
 
#endif  //#ifndef _PCL_KDTREE_KDTREE_IMPL_FLANN_H_