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#pragma once

#include <random>

#include <pcl/point_types.h>
#include <pcl/features/feature.h>

namespace pcl
{
  /** \brief ShapeContext3DEstimation implements the 3D shape context descriptor as
    * described in:
    *   - Andrea Frome, Daniel Huber, Ravi Kolluri and Thomas Bülow, Jitendra Malik
    *     Recognizing Objects in Range Data Using Regional Point Descriptors,
    *     In proceedings of the 8th European Conference on Computer Vision (ECCV),
    *     Prague, May 11-14, 2004
    *
    * The suggested PointOutT is pcl::ShapeContext1980
    *
    * \attention
    * The convention for a 3D shape context descriptor is:
    *   - if a query point's nearest neighbors cannot be estimated, the feature descriptor will be set to NaN (not a number), and the RF to 0
    *   - it is impossible to estimate a 3D shape context descriptor for a
    *     point that doesn't have finite 3D coordinates. Therefore, any point
    *     that contains NaN data on x, y, or z, will have its boundary feature
    *     property set to NaN.
    *
    * \author Alessandro Franchi, Samuele Salti, Federico Tombari (original code)
    * \author Nizar Sallem (port to PCL)
    * \ingroup features
    */
  template <typename PointInT, typename PointNT, typename PointOutT = pcl::ShapeContext1980>
  class ShapeContext3DEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
  {
    public:
      using Ptr = shared_ptr<ShapeContext3DEstimation<PointInT, PointNT, PointOutT> >;
      using ConstPtr = shared_ptr<const ShapeContext3DEstimation<PointInT, PointNT, PointOutT> >;

      using Feature<PointInT, PointOutT>::feature_name_;
      using Feature<PointInT, PointOutT>::getClassName;
      using Feature<PointInT, PointOutT>::indices_;
      using Feature<PointInT, PointOutT>::search_parameter_;
      using Feature<PointInT, PointOutT>::search_radius_;
      using Feature<PointInT, PointOutT>::surface_;
      using Feature<PointInT, PointOutT>::input_;
      using Feature<PointInT, PointOutT>::searchForNeighbors;
      using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;

      using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
      using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;

      /** \brief Constructor.
        * \param[in] random If true the random seed is set to current time, else it is
        * set to 12345 prior to computing the descriptor (used to select X axis)
        */
      ShapeContext3DEstimation (bool random = false) :
        radii_interval_(0),
        theta_divisions_(0),
        phi_divisions_(0),
        volume_lut_(0),
        azimuth_bins_(12),
        elevation_bins_(11),
        radius_bins_(15),
        min_radius_(0.1),
        point_density_radius_(0.2),
        descriptor_length_ (),
        rng_dist_ (0.0f, 1.0f)
      {
        feature_name_ = "ShapeContext3DEstimation";
        search_radius_ = 2.5;

        // Create a random number generator object
        if (random)
        {
          std::random_device rd;
          rng_.seed (rd());
        }
        else
          rng_.seed (12345u);
      }

      ~ShapeContext3DEstimation() {}

      //inline void
      //setAzimuthBins (std::size_t bins) { azimuth_bins_ = bins; }

      /** \return the number of bins along the azimuth */
      inline std::size_t
      getAzimuthBins () { return (azimuth_bins_); }

      //inline void
      //setElevationBins (std::size_t bins) { elevation_bins_ = bins; }

      /** \return The number of bins along the elevation */
      inline std::size_t
      getElevationBins () { return (elevation_bins_); }

      //inline void
      //setRadiusBins (std::size_t bins) { radius_bins_ = bins; }

      /** \return The number of bins along the radii direction */
      inline std::size_t
      getRadiusBins () { return (radius_bins_); }

      /** \brief The minimal radius value for the search sphere (rmin) in the original paper
        * \param[in] radius the desired minimal radius
        */
      inline void
      setMinimalRadius (double radius) { min_radius_ = radius; }

      /** \return The minimal sphere radius */
      inline double
      getMinimalRadius () { return (min_radius_); }

      /** \brief This radius is used to compute local point density
        * density = number of points within this radius
        * \param[in] radius value of the point density search radius
        */
      inline void
      setPointDensityRadius (double radius) { point_density_radius_ = radius; }

      /** \return The point density search radius */
      inline double
      getPointDensityRadius () { return (point_density_radius_); }

    protected:
      /** \brief Initialize computation by allocating all the intervals and the volume lookup table. */
      bool
      initCompute () override;

      /** \brief Estimate a descriptor for a given point.
        * \param[in] index the index of the point to estimate a descriptor for
        * \param[in] normals a pointer to the set of normals
        * \param[in] rf the reference frame
        * \param[out] desc the resultant estimated descriptor
        * \return true if the descriptor was computed successfully, false if there was an error
        * (e.g. the nearest neighbor didn't return any neighbors)
        */
      bool
      computePoint (std::size_t index, const pcl::PointCloud<PointNT> &normals, float rf[9], std::vector<float> &desc);

      /** \brief Estimate the actual feature.
        * \param[out] output the resultant feature
        */
      void
      computeFeature (PointCloudOut &output) override;

      /** \brief Values of the radii interval */
      std::vector<float> radii_interval_;

      /** \brief Theta divisions interval */
      std::vector<float> theta_divisions_;

      /** \brief Phi divisions interval */
      std::vector<float> phi_divisions_;

      /** \brief Volumes look up table */
      std::vector<float> volume_lut_;

      /** \brief Bins along the azimuth dimension */
      std::size_t azimuth_bins_;

      /** \brief Bins along the elevation dimension */
      std::size_t elevation_bins_;

      /** \brief Bins along the radius dimension */
      std::size_t radius_bins_;

      /** \brief Minimal radius value */
      double min_radius_;

      /** \brief Point density radius */
      double point_density_radius_;

      /** \brief Descriptor length */
      std::size_t descriptor_length_;

      /** \brief Random number generator algorithm. */
      std::mt19937 rng_;

      /** \brief Random number generator distribution. */
      std::uniform_real_distribution<float> rng_dist_;

     /*  \brief Shift computed descriptor "L" times along the azimuthal direction
       * \param[in] block_size the size of each azimuthal block
       * \param[in] desc at input desc == original descriptor and on output it contains
       *  shifted descriptor resized descriptor_length_ * azimuth_bins_
       */
      //void
      //shiftAlongAzimuth (std::size_t block_size, std::vector<float>& desc);

      /** \brief Boost-based random number generator. */
      inline float
      rnd ()
      {
        return (rng_dist_ (rng_));
      }
  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/3dsc.hpp>
#endif