radius_outlier_removal.h

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#pragma once
 
#include <pcl/filters/filter_indices.h>
#include <pcl/search/search.h>           // for Search, Search<>::Ptr
#include <pcl/point_types.h>             // for pcl::PointXYZ
 
namespace pcl
{
  /** \brief @b RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have.
    * \details Iterates through the entire input once, and for each point, retrieves the number of neighbors within a certain radius.
    * The point will be considered an outlier if it has too few neighbors, as determined by setMinNeighborsInRadius().
    * The radius can be changed using setRadiusSearch().
    * <br>
    * The neighbors found for each query point will be found amongst ALL points of setInputCloud(), not just those indexed by setIndices().
    * The setIndices() method only indexes the points that will be iterated through as search query points.
    * <br><br>
    * Usage example:
    * \code
    * pcl::RadiusOutlierRemoval<PointType> rorfilter (true); // Initializing with true will allow us to extract the removed indices
    * rorfilter.setInputCloud (cloud_in);
    * rorfilter.setRadiusSearch (0.1);
    * rorfilter.setMinNeighborsInRadius (5);
    * rorfilter.setNegative (true);
    * rorfilter.filter (*cloud_out);
    * // The resulting cloud_out contains all points of cloud_in that have 4 or less neighbors within the 0.1 search radius
    * indices_rem = rorfilter.getRemovedIndices ();
    * // The indices_rem array indexes all points of cloud_in that have 5 or more neighbors within the 0.1 search radius
    * \endcode
    * \sa StatisticalOutlierRemoval
    * \author Radu Bogdan Rusu
    * \ingroup filters
    */
  template<typename PointT>
  class RadiusOutlierRemoval : public FilterIndices<PointT>
  {
    protected:
      using PointCloud = typename FilterIndices<PointT>::PointCloud;
      using PointCloudPtr = typename PointCloud::Ptr;
      using PointCloudConstPtr = typename PointCloud::ConstPtr;
      using SearcherPtr = typename pcl::search::Search<PointT>::Ptr;
 
    public:
 
      using Ptr = shared_ptr<RadiusOutlierRemoval<PointT> >;
      using ConstPtr = shared_ptr<const RadiusOutlierRemoval<PointT> >;
 
 
      /** \brief Constructor.
        * \param[in] extract_removed_indices Set to true if you want to be able to extract the indices of points being removed (default = false).
        */
      RadiusOutlierRemoval (bool extract_removed_indices = false) :
        FilterIndices<PointT> (extract_removed_indices),
        searcher_ ()
      {
        filter_name_ = "RadiusOutlierRemoval";
      }
 
      /** \brief Set the radius of the sphere that will determine which points are neighbors.
        * \details The number of points within this distance from the query point will need to be equal or greater
        * than setMinNeighborsInRadius() in order to be classified as an inlier point (i.e. will not be filtered).
        * \param[in] radius The radius of the sphere for nearest neighbor searching.
        */
      inline void
      setRadiusSearch (double radius)
      {
        search_radius_ = radius;
      }
 
      /** \brief Get the radius of the sphere that will determine which points are neighbors.
        * \details The number of points within this distance from the query point will need to be equal or greater
        * than setMinNeighborsInRadius() in order to be classified as an inlier point (i.e. will not be filtered).
        * \return The radius of the sphere for nearest neighbor searching.
        */
      inline double
      getRadiusSearch () const
      {
        return (search_radius_);
      }
 
      /** \brief Set the number of neighbors that need to be present in order to be classified as an inlier.
        * \details The number of points within setRadiusSearch() from the query point will need to be equal or greater
        * than this number in order to be classified as an inlier point (i.e. will not be filtered).
        * \param min_pts The minimum number of neighbors (default = 1).
        */
      inline void
      setMinNeighborsInRadius (int min_pts)
      {
        min_pts_radius_ = min_pts;
      }
 
      /** \brief Get the number of neighbors that need to be present in order to be classified as an inlier.
        * \details The number of points within setRadiusSearch() from the query point will need to be equal or greater
        * than this number in order to be classified as an inlier point (i.e. will not be filtered).
        * \return The minimum number of neighbors (default = 1).
        */
      inline int
      getMinNeighborsInRadius () const
      {
        return (min_pts_radius_);
      }
 
      /** \brief Provide a pointer to the search object.
        * Calling this is optional. If not called, the search method will be chosen automatically.
        * \param[in] searcher a pointer to the spatial search object.
        */
      inline void
      setSearchMethod (const SearcherPtr &searcher) { searcher_ = searcher; }
 
      /** \brief Set the number of threads to use.
       * \param nr_threads the number of hardware threads to use (0 sets the value back
       * to automatic)
       */
      void
      setNumberOfThreads(unsigned int nr_threads = 0)
      {
#ifdef _OPENMP
        num_threads_ = nr_threads != 0 ? nr_threads : omp_get_num_procs();
#else
        if (num_threads_ != 1) {
          PCL_WARN("OpenMP is not available. Keeping number of threads unchanged at 1\n");
        }
        num_threads_ = 1;
#endif
      }
 
    protected:
      using PCLBase<PointT>::input_;
      using PCLBase<PointT>::indices_;
      using Filter<PointT>::filter_name_;
      using Filter<PointT>::getClassName;
      using FilterIndices<PointT>::negative_;
      using FilterIndices<PointT>::keep_organized_;
      using FilterIndices<PointT>::user_filter_value_;
      using FilterIndices<PointT>::extract_removed_indices_;
      using FilterIndices<PointT>::removed_indices_;
 
      /** \brief Filtered results are indexed by an indices array.
        * \param[out] indices The resultant indices.
        */
      void
      applyFilter (Indices &indices) override
      {
        applyFilterIndices (indices);
      }
 
      /** \brief Filtered results are indexed by an indices array.
        * \param[out] indices The resultant indices.
        */
      void
      applyFilterIndices (Indices &indices);
 
    private:
      /** \brief A pointer to the spatial search object. */
      SearcherPtr searcher_;
 
      /** \brief The nearest neighbors search radius for each point. */
      double search_radius_{0.0};
 
      /** \brief The minimum number of neighbors that a point needs to have in the given search radius to be considered an inlier. */
      int min_pts_radius_{1};
 
      /**
       * @brief Number of threads used during filtering
       */
      int num_threads_{1};
  };
 
  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  /** \brief @b RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain
    * search radius is smaller than a given K.
    * \author Radu Bogdan Rusu
    * \ingroup filters
    */
  template<>
  class PCL_EXPORTS RadiusOutlierRemoval<pcl::PCLPointCloud2> : public FilterIndices<pcl::PCLPointCloud2>
  {
    using Filter<pcl::PCLPointCloud2>::filter_name_;
    using Filter<pcl::PCLPointCloud2>::getClassName;
 
    using Filter<pcl::PCLPointCloud2>::removed_indices_;
    using Filter<pcl::PCLPointCloud2>::extract_removed_indices_;
 
    using KdTree = pcl::search::Search<pcl::PointXYZ>;
    using KdTreePtr = pcl::search::Search<pcl::PointXYZ>::Ptr;
 
    using PCLPointCloud2 = pcl::PCLPointCloud2;
    using PCLPointCloud2Ptr = PCLPointCloud2::Ptr;
    using PCLPointCloud2ConstPtr = PCLPointCloud2::ConstPtr;
 
    public:
      /** \brief Empty constructor. */
      RadiusOutlierRemoval (bool extract_removed_indices = false) :
        FilterIndices<pcl::PCLPointCloud2>::FilterIndices (extract_removed_indices)
      {
        filter_name_ = "RadiusOutlierRemoval";
      }
 
      /** \brief Set the sphere radius that is to be used for determining the k-nearest neighbors for filtering.
        * \param radius the sphere radius that is to contain all k-nearest neighbors
        */
      inline void
      setRadiusSearch (double radius)
      {
        search_radius_ = radius;
      }
 
      /** \brief Get the sphere radius used for determining the k-nearest neighbors. */
      inline double
      getRadiusSearch () const
      {
        return (search_radius_);
      }
 
      /** \brief Set the minimum number of neighbors that a point needs to have in the given search radius in order to
        * be considered an inlier (i.e., valid).
        * \param min_pts the minimum number of neighbors
        */
      inline void
      setMinNeighborsInRadius (int min_pts)
      {
        min_pts_radius_ = min_pts;
      }
 
      /** \brief Get the minimum number of neighbors that a point needs to have in the given search radius to be
        * considered an inlier and avoid being filtered.
        */
      inline double
      getMinNeighborsInRadius () const
      {
        return (min_pts_radius_);
      }
 
    protected:
      /** \brief The nearest neighbors search radius for each point. */
      double search_radius_{0.0};
 
      /** \brief The minimum number of neighbors that a point needs to have in the given search radius to be considered
        * an inlier.
        */
      int min_pts_radius_{1};
 
      /** \brief A pointer to the spatial search object. */
      KdTreePtr searcher_;
 
      void
      applyFilter (PCLPointCloud2 &output) override;
 
      void
      applyFilter (Indices &indices) override;
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/filters/impl/radius_outlier_removal.hpp>
#endif