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/common/io.h>
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::RadiusOutlierRemoval<PointT>::applyFilter (PointCloud &output)
{
std::vector<int> indices;
if (keep_organized_)
{
bool temp = extract_removed_indices_;
extract_removed_indices_ = true;
applyFilterIndices (indices);
extract_removed_indices_ = temp;
output = *input_;
for (int rii = 0; rii < static_cast<int> (removed_indices_->size ()); ++rii) // rii = removed indices iterator
output.points[(*removed_indices_)[rii]].x = output.points[(*removed_indices_)[rii]].y = output.points[(*removed_indices_)[rii]].z = user_filter_value_;
if (!pcl_isfinite (user_filter_value_))
output.is_dense = false;
}
else
{
applyFilterIndices (indices);
copyPointCloud (*input_, indices, output);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::RadiusOutlierRemoval<PointT>::applyFilterIndices (std::vector<int> &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));
}
searcher_->setInputCloud (input_);
// The arrays to be used
std::vector<int> 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
for (std::vector<int>::const_iterator it = indices_->begin (); it != indices_->end (); ++it)
{
// Perform the radius search
// Note: k includes the query point, so is always at least 1
int k = searcher_->radiusSearch (*it, 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++] = *it;
continue;
}
// Otherwise it was a normal point for output (inlier)
indices[oii++] = *it;
}
// 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::search::KdTree< PointT >
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::copyPointCloud
PCL_EXPORTS void copyPointCloud(const pcl::PCLPointCloud2 &cloud_in, const std::vector< int > &indices, pcl::PCLPointCloud2 &cloud_out)
Extract the indices of a given point cloud as a new point cloud.
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points. ...
Definition: projection_matrix.h:53
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values).
Definition: point_cloud.h:418
pcl::RadiusOutlierRemoval::applyFilter
void applyFilter(PointCloud &output)
Filtered results are stored in a separate point cloud.
Definition: radius_outlier_removal.hpp:48
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds...
Definition: organized.h:62
pcl::RadiusOutlierRemoval::applyFilterIndices
void applyFilterIndices(std::vector< int > &indices)
Filtered results are indexed by an indices array.
Definition: radius_outlier_removal.hpp:73