radius_outlier_removal.hpp

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#ifndef PCL_FILTERS_IMPL_RADIUS_OUTLIER_REMOVAL_H_
#define PCL_FILTERS_IMPL_RADIUS_OUTLIER_REMOVAL_H_
 
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/search/organized.h> // for OrganizedNeighbor
#include <pcl/search/kdtree.h> // for KdTree
 
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::RadiusOutlierRemoval<PointT>::applyFilterIndices (Indices &indices)
{
  if (search_radius_ == 0.0)
  {
    PCL_ERROR ("[pcl::%s::applyFilter] No radius defined!\n", getClassName ().c_str ());
    indices.clear ();
    removed_indices_->clear ();
    return;
  }
 
  // Initialize the search class
  if (!searcher_)
  {
    if (input_->isOrganized ())
      searcher_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
    else
      searcher_.reset (new pcl::search::KdTree<PointT> (false));
  }
  if (!searcher_->setInputCloud (input_))
  {
    PCL_ERROR ("[pcl::%s::applyFilter] Error when initializing search method!\n", getClassName ().c_str ());
    indices.clear ();
    removed_indices_->clear ();
    return;
  }
 
  // The arrays to be used
  Indices nn_indices (indices_->size ());
  std::vector<float> nn_dists (indices_->size ());
  indices.resize (indices_->size ());
  removed_indices_->resize (indices_->size ());
  int oii = 0, rii = 0;  // oii = output indices iterator, rii = removed indices iterator
 
  // If the data is dense => use nearest-k search
  if (input_->is_dense)
  {
    // Note: k includes the query point, so is always at least 1
    int mean_k = min_pts_radius_ + 1;
    double nn_dists_max = search_radius_ * search_radius_;
 
    for (const auto& index : (*indices_))
    {
      // Perform the nearest-k search
      int k = searcher_->nearestKSearch (index, mean_k, nn_indices, nn_dists);
 
      // Check the number of neighbors
      // Note: nn_dists is sorted, so check the last item
      bool chk_neighbors = true;
      if (k == mean_k)
      {
        if (negative_)
        {
          chk_neighbors = false;
          if (nn_dists_max < nn_dists[k-1])
          {
            chk_neighbors = true;
          }
        }
        else
        {
          chk_neighbors = true;
          if (nn_dists_max < nn_dists[k-1])
          {
            chk_neighbors = false;
          }
        }
      }
      else
      {
        chk_neighbors = negative_;
      }
 
      // Points having too few neighbors are outliers and are passed to removed indices
      // Unless negative was set, then it's the opposite condition
      if (!chk_neighbors)
      {
        if (extract_removed_indices_)
          (*removed_indices_)[rii++] = index;
        continue;
      }
 
      // Otherwise it was a normal point for output (inlier)
      indices[oii++] = index;
    }
  }
  // NaN or Inf values could exist => use radius search
  else
  {
    for (const auto& index : (*indices_))
    {
      // Perform the radius search
      // Note: k includes the query point, so is always at least 1
      // last parameter (max_nn) is the maximum number of neighbors returned. If enough neighbors are found so that point can not be an outlier, we stop searching.
      int k = searcher_->radiusSearch (index, search_radius_, nn_indices, nn_dists, min_pts_radius_ + 1);
 
      // Points having too few neighbors are outliers and are passed to removed indices
      // Unless negative was set, then it's the opposite condition
      if ((!negative_ && k <= min_pts_radius_) || (negative_ && k > min_pts_radius_))
      {
        if (extract_removed_indices_)
          (*removed_indices_)[rii++] = index;
        continue;
      }
 
      // Otherwise it was a normal point for output (inlier)
      indices[oii++] = index;
    }
  }
 
  // Resize the output arrays
  indices.resize (oii);
  removed_indices_->resize (rii);
}
 
#define PCL_INSTANTIATE_RadiusOutlierRemoval(T) template class PCL_EXPORTS pcl::RadiusOutlierRemoval<T>;
 
#endif  // PCL_FILTERS_IMPL_RADIUS_OUTLIER_REMOVAL_H_