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#ifndef PCL_HARRIS_KEYPOINT_3D_H_
#define PCL_HARRIS_KEYPOINT_3D_H_

#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:
      typedef boost::shared_ptr<HarrisKeypoint3D<PointInT, PointOutT, NormalT> > Ptr;
      typedef boost::shared_ptr<const HarrisKeypoint3D<PointInT, PointOutT, NormalT> > ConstPtr;

      typedef typename Keypoint<PointInT, PointOutT>::PointCloudIn PointCloudIn;
      typedef typename Keypoint<PointInT, PointOutT>::PointCloudOut PointCloudOut;
      typedef typename Keypoint<PointInT, PointOutT>::KdTree KdTree;
      typedef typename PointCloudIn::ConstPtr PointCloudInConstPtr;

      typedef typename pcl::PointCloud<NormalT> PointCloudN;
      typedef typename PointCloudN::Ptr PointCloudNPtr;
      typedef typename PointCloudN::ConstPtr PointCloudNConstPtr;

      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;

      typedef enum {HARRIS = 1, NOBLE, LOWE, TOMASI, CURVATURE} ResponseMethod;

      /** \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 */
      virtual ~HarrisKeypoint3D () {}

      /** \brief Provide a pointer to the input dataset
        * \param[in] cloud the const boost shared pointer to a PointCloud message
        */
      virtual void
      setInputCloud (const PointCloudInConstPtr &cloud);

      /** \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
        */
      virtual void
      setSearchSurface (const PointCloudInConstPtr &cloud) { 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 ();
      void detectKeypoints (PointCloudOut &output);
      /** \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>

#endif // #ifndef PCL_HARRIS_KEYPOINT_3D_H_

