harris_3d.h

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#pragma once
 
#include <pcl/keypoints/keypoint.h>
 
namespace pcl
{
  /** \brief HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses
    * surface normals.
    *
    * \author Suat Gedikli
    * \ingroup keypoints
    */
  template <typename PointInT, typename PointOutT, typename NormalT = pcl::Normal>
  class HarrisKeypoint3D : public Keypoint<PointInT, PointOutT>
  {
    public:
      using Ptr = shared_ptr<HarrisKeypoint3D<PointInT, PointOutT, NormalT> >;
      using ConstPtr = shared_ptr<const HarrisKeypoint3D<PointInT, PointOutT, NormalT> >;
 
      using PointCloudIn = typename Keypoint<PointInT, PointOutT>::PointCloudIn;
      using PointCloudOut = typename Keypoint<PointInT, PointOutT>::PointCloudOut;
      using KdTree = typename Keypoint<PointInT, PointOutT>::KdTree;
      using PointCloudInConstPtr = typename PointCloudIn::ConstPtr;
 
      using PointCloudN = pcl::PointCloud<NormalT>;
      using PointCloudNPtr = typename PointCloudN::Ptr;
      using PointCloudNConstPtr = typename PointCloudN::ConstPtr;
 
      using Keypoint<PointInT, PointOutT>::name_;
      using Keypoint<PointInT, PointOutT>::input_;
      using Keypoint<PointInT, PointOutT>::indices_;
      using Keypoint<PointInT, PointOutT>::surface_;
      using Keypoint<PointInT, PointOutT>::tree_;
      using Keypoint<PointInT, PointOutT>::k_;
      using Keypoint<PointInT, PointOutT>::search_radius_;
      using Keypoint<PointInT, PointOutT>::search_parameter_;
      using Keypoint<PointInT, PointOutT>::keypoints_indices_;
      using Keypoint<PointInT, PointOutT>::initCompute;
      using PCLBase<PointInT>::setInputCloud;
 
      enum ResponseMethod {HARRIS = 1, NOBLE, LOWE, TOMASI, CURVATURE};
 
      /** \brief Constructor
        * \param[in] method the method to be used to determine the corner responses
        * \param[in] radius the radius for normal estimation as well as for non maxima suppression
        * \param[in] threshold the threshold to filter out weak corners
        */
      HarrisKeypoint3D (ResponseMethod method = HARRIS, float radius = 0.01f, float threshold = 0.0f)
      : threshold_ (threshold)
      , refine_ (true)
      , nonmax_ (true)
      , method_ (method)
      , threads_ (0)
      {
        name_ = "HarrisKeypoint3D";
        search_radius_ = radius;
      }
      
      /** \brief Empty destructor */
      ~HarrisKeypoint3D () {}
 
      /** \brief Provide a pointer to the input dataset
        * \param[in] cloud the const boost shared pointer to a PointCloud message
        */
      void
      setInputCloud (const PointCloudInConstPtr &cloud) override;
 
      /** \brief Set the method of the response to be calculated.
        * \param[in] type
        */
      void 
      setMethod (ResponseMethod type);
 
      /** \brief Set the radius for normal estimation and non maxima supression.
        * \param[in] radius
        */
      void 
      setRadius (float radius);
 
      /** \brief Set the threshold value for detecting corners. This is only evaluated if non maxima suppression is turned on.
        * \brief note non maxima suppression needs to be activated in order to use this feature.
        * \param[in] threshold
        */
      void 
      setThreshold (float threshold);
 
      /** \brief Whether non maxima suppression should be applied or the response for each point should be returned
        * \note this value needs to be turned on in order to apply thresholding and refinement
        * \param[in] nonmax default is false
        */
      void 
      setNonMaxSupression (bool = false);
 
      /** \brief Whether the detected key points should be refined or not. If turned of, the key points are a subset of the original point cloud. Otherwise the key points may be arbitrary.
        * \brief note non maxima supression needs to be on in order to use this feature.
        * \param[in] do_refine
        */
      void 
      setRefine (bool do_refine);
 
      /** \brief Set normals if precalculated normals are available.
        * \param normals
        */
      void 
      setNormals (const PointCloudNConstPtr &normals);
 
      /** \brief Provide a pointer to a dataset to add additional information
        * to estimate the features for every point in the input dataset.  This
        * is optional, if this is not set, it will only use the data in the
        * input cloud to estimate the features.  This is useful when you only
        * need to compute the features for a downsampled cloud.
        * \param[in] cloud a pointer to a PointCloud message
        */
      void
      setSearchSurface (const PointCloudInConstPtr &cloud) override { surface_ = cloud; normals_.reset(); }
 
      /** \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 back to automatic)
        */
      inline void
      setNumberOfThreads (unsigned int nr_threads = 0) { threads_ = nr_threads; }
    protected:
      bool
      initCompute () override;
      void detectKeypoints (PointCloudOut &output) override;
      /** \brief gets the corner response for valid input points*/
      void responseHarris (PointCloudOut &output) const;
      void responseNoble (PointCloudOut &output) const;
      void responseLowe (PointCloudOut &output) const;
      void responseTomasi (PointCloudOut &output) const;
      void responseCurvature (PointCloudOut &output) const;
      void refineCorners (PointCloudOut &corners) const;
      /** \brief calculates the upper triangular part of unnormalized covariance matrix over the normals given by the indices.*/
      void calculateNormalCovar (const std::vector<int>& neighbors, float* coefficients) const;
    private:
      float threshold_;
      bool refine_;
      bool nonmax_;
      ResponseMethod method_;
      PointCloudNConstPtr normals_;
      unsigned int threads_;
  };
}
 
#include <pcl/keypoints/impl/harris_3d.hpp>