principal_curvatures.h

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#pragma once
 
#include <pcl/features/feature.h>
#include <pcl/point_types.h>       // for pcl::PrincipalCurvatures
 
namespace pcl
{
  /** \brief PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of
    * principal surface curvatures for a given point cloud dataset containing points and normals. The output contains 
    * the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1) and min (pc2) 
    * eigenvalues. Parallel execution is supported through OpenMP.
    *
    * The recommended PointOutT is pcl::PrincipalCurvatures.
    *
    * \author Radu B. Rusu, Jared Glover, Alex Navarro
    * \ingroup features
    */
  template <typename PointInT, typename PointNT, typename PointOutT = pcl::PrincipalCurvatures>
  class PrincipalCurvaturesEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
  {
    public:
      using Ptr = shared_ptr<PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
      using ConstPtr = shared_ptr<const PrincipalCurvaturesEstimation<PointInT, PointNT, PointOutT> >;
      using Feature<PointInT, PointOutT>::feature_name_;
      using Feature<PointInT, PointOutT>::getClassName;
      using Feature<PointInT, PointOutT>::indices_;
      using Feature<PointInT, PointOutT>::k_;
      using Feature<PointInT, PointOutT>::search_parameter_;
      using Feature<PointInT, PointOutT>::surface_;
      using Feature<PointInT, PointOutT>::input_;
      using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
 
      using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
      using PointCloudIn = pcl::PointCloud<PointInT>;
 
      /** \brief Initialize the scheduler and set the number of threads to use.
        * \param nr_threads the number of hardware threads to use (0 sets the value to automatic)
        * \param chunk_size PCL will use dynamic scheduling with this chunk size. Setting it too 
        *                   low will lead to more parallelization overhead. Setting it too high 
        *                   will lead to a worse balancing between the threads.
        */
      PrincipalCurvaturesEstimation (unsigned int nr_threads = 1, int chunk_size = 256) : 
        chunk_size_(chunk_size) 
      {
        feature_name_ = "PrincipalCurvaturesEstimation";
 
        setNumberOfThreads(nr_threads);
      };
 
      /** \brief Perform Principal Components Analysis (PCA) on the point normals of a surface patch in the tangent
       *  plane of the given point normal, and return the principal curvature (eigenvector of the max eigenvalue),
       *  along with both the max (pc1) and min (pc2) eigenvalues
       * \param[in] normals the point cloud normals
       * \param[in] p_idx the query point at which the least-squares plane was estimated
       * \param[in] indices the point cloud indices that need to be used
       * \param[out] pcx the principal curvature X direction
       * \param[out] pcy the principal curvature Y direction
       * \param[out] pcz the principal curvature Z direction
       * \param[out] pc1 the max eigenvalue of curvature
       * \param[out] pc2 the min eigenvalue of curvature
       */
      void
      computePointPrincipalCurvatures (const pcl::PointCloud<PointNT> &normals,
                                       int p_idx, const pcl::Indices &indices,
                                       float &pcx, float &pcy, float &pcz, float &pc1, float &pc2);
 
      /** \brief Initialize the scheduler and set the number of threads to use. The default behavior is
       *         single threaded exectution
       * \param nr_threads the number of hardware threads to use (0 sets the value to automatic)
       */
      void
      setNumberOfThreads (unsigned int nr_threads);
 
    protected:
      /** \brief The number of threads the scheduler should use. */
      unsigned int threads_;
 
      /** \brief Chunk size for (dynamic) scheduling. */
      int chunk_size_;
 
      /** \brief Estimate the principal curvature (eigenvector of the max eigenvalue), along with both the max (pc1)
        * and min (pc2) eigenvalues for all points given in <setInputCloud (), setIndices ()> using the surface in
        * setSearchSurface () and the spatial locator in setSearchMethod ()
        * \param[out] output the resultant point cloud model dataset that contains the principal curvature estimates
        */
      void
      computeFeature (PointCloudOut &output) override;
  };
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/principal_curvatures.hpp>
#endif