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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
int k = searcher_->radiusSearch (index, search_radius_, nn_indices, nn_dists);
// 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_
pcl::RadiusOutlierRemoval::applyFilterIndices
void applyFilterIndices(Indices &indices)
Filtered results are indexed by an indices array.
Definition radius_outlier_removal.hpp:49
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neighbor search in organized projectable point clo...
Definition organized.h:65
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133