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;
  }
 
  // Note: k includes the query point, so is always at least 1
  const int mean_k = min_pts_radius_ + 1;
  const double nn_dists_max = search_radius_ * search_radius_;
 
  // The arrays to be used
  Indices nn_indices (mean_k);
  std::vector<float> nn_dists(mean_k);
  // Set to keep all points and in the filtering set those we don't want to keep, assuming
  // we want to keep the majority of the points.
  // 0 = remove, 1 = keep
  std::vector<std::uint8_t> to_keep(indices_->size(), 1);
  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)
  {
    #pragma omp parallel for \
    schedule(dynamic,64) \
    firstprivate(nn_indices, nn_dists)                                                 \
    shared(to_keep) \
    num_threads(num_threads_)
    for (ptrdiff_t i = 0; i < static_cast<ptrdiff_t>(indices_->size()); i++)
    {
      const auto& index = (*indices_)[i];
      // Perform the nearest-k search
      const 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
      if (k == mean_k)
      {
          // if negative_ is false and a neighbor is further away than max distance, remove the point
          // or
          // if negative is true and a neighbor is closer than max distance, remove the point
          if ((!negative_ && nn_dists_max < nn_dists[k-1]) || (negative_ && nn_dists_max >= nn_dists[k - 1]))
          {
            to_keep[i] = 0;
            continue;
          }
      }
      else
      {
        if (!negative_)
        {
          // if too few neighbors, remove the point
          to_keep[i] = 0;
          continue;
        }
      }
    }
  }
  // NaN or Inf values could exist => use radius search
  else
  {
    #pragma omp parallel for \
    schedule(dynamic, 64) \
    firstprivate(nn_indices, nn_dists) \
    shared(to_keep) \
    num_threads(num_threads_)
    for (ptrdiff_t i = 0; i < static_cast<ptrdiff_t>(indices_->size()); i++)
    {
      const auto& index = (*indices_)[i];
      if (!pcl::isXYZFinite((*input_)[index]))
      {
        to_keep[i] = 0;
        continue;
      }
        
      // 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.
      const int k = searcher_->radiusSearch (index, search_radius_, nn_indices, nn_dists, min_pts_radius_ + 1);
 
      // Points having too few neighbors are removed
      // or if negative_ is true, then if it has too many neighbors
      if ((!negative_ && k <= min_pts_radius_) || (negative_ && k > min_pts_radius_))
      {
        to_keep[i] = 0;
        continue;
      }
    }
  }
 
    for (index_t i=0; i < static_cast<index_t>(to_keep.size()); i++)
    {
      if (to_keep[i] == 0)
      {
        if (extract_removed_indices_)
          (*removed_indices_)[rii++] = (*indices_)[i];
        continue;
      }
 
      // Otherwise it was a normal point for output (inlier)
      indices[oii++] = (*indices_)[i];
    }
 
  // 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_