statistical_outlier_removal.hpp

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#ifndef PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_
#define PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_
 
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/search/auto.h> // for autoSelectMethod
 
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::StatisticalOutlierRemoval<PointT>::applyFilterIndices (Indices &indices)
{
  // Initialize the search class
  if (!searcher_)
  {
    searcher_.reset (pcl::search::autoSelectMethod<PointT>(input_, false, pcl::search::Purpose::many_knn_search));
  }
  else 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
  const int searcher_k = mean_k_ + 1;  // Find one more, since results include the query point.
  Indices nn_indices (searcher_k);
  std::vector<float> nn_dists (searcher_k);
  std::vector<float> distances (indices_->size ());
  indices.resize (indices_->size ());
  removed_indices_->resize (indices_->size ());
  int oii = 0, rii = 0;  // oii = output indices iterator, rii = removed indices iterator
 
  // First pass: Compute the mean distances for all points with respect to their k nearest neighbors
  int valid_distances = 0;
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    if (!std::isfinite ((*input_)[(*indices_)[iii]].x) ||
        !std::isfinite ((*input_)[(*indices_)[iii]].y) ||
        !std::isfinite ((*input_)[(*indices_)[iii]].z))
    {
      distances[iii] = 0.0;
      continue;
    }
 
    // Perform the nearest k search
    if (searcher_->nearestKSearch ((*indices_)[iii], searcher_k, nn_indices, nn_dists) == 0)
    {
      distances[iii] = 0.0;
      PCL_WARN ("[pcl::%s::applyFilter] Searching for the closest %d neighbors failed.\n", getClassName ().c_str (), mean_k_);
      continue;
    }
 
    // Calculate the mean distance to its neighbors.
    double dist_sum = 0.0;
    for (std::size_t k = 1; k < nn_dists.size(); ++k) // k = 0 is the query point
      dist_sum += sqrt(nn_dists[k]);
    distances[iii] = static_cast<float>(dist_sum / (nn_dists.size() - 1));
    valid_distances++;
  }
 
  // Estimate the mean and the standard deviation of the distance vector
  double sum = 0, sq_sum = 0;
  for (const float &distance : distances)
  {
    sum += distance;
    sq_sum += distance * distance;
  }
  double mean = sum / static_cast<double>(valid_distances);
  double variance = (sq_sum - sum * sum / static_cast<double>(valid_distances)) / (static_cast<double>(valid_distances) - 1);
  double stddev = sqrt (variance);
  //getMeanStd (distances, mean, stddev);
 
  double distance_threshold = mean + std_mul_ * stddev;
 
  // Second pass: Classify the points on the computed distance threshold
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    // Points having a too high average distance are outliers and are passed to removed indices
    // Unless negative was set, then it's the opposite condition
    if ((!negative_ && distances[iii] > distance_threshold) || (negative_ && distances[iii] <= distance_threshold))
    {
      if (extract_removed_indices_)
        (*removed_indices_)[rii++] = (*indices_)[iii];
      continue;
    }
 
    // Otherwise it was a normal point for output (inlier)
    indices[oii++] = (*indices_)[iii];
  }
 
  // Resize the output arrays
  indices.resize (oii);
  removed_indices_->resize (rii);
}
 
#define PCL_INSTANTIATE_StatisticalOutlierRemoval(T) template class PCL_EXPORTS pcl::StatisticalOutlierRemoval<T>;
 
#endif  // PCL_FILTERS_IMPL_STATISTICAL_OUTLIER_REMOVAL_H_