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#pragma once

#include <pcl/pcl_base.h>
#include <pcl/point_types_conversion.h>
#include <pcl/search/search.h> // for Search

namespace pcl
{
  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  /** \brief Decompose a region of space into clusters based on the Euclidean distance between points
    * \param[in] cloud the point cloud message
    * \param[in] tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
    * \note the tree has to be created as a spatial locator on \a cloud
    * \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
    * \param[in] indices_in the cluster containing the seed point indices (as a vector of PointIndices)
    * \param[out] indices_out 
    * \param[in] delta_hue
    * \todo look how to make this templated!
    * \ingroup segmentation
    */
  void 
  seededHueSegmentation (const PointCloud<PointXYZRGB>            &cloud,
                         const search::Search<PointXYZRGB>::Ptr   &tree,
                         float                                    tolerance,
                         PointIndices                             &indices_in,
                         PointIndices                             &indices_out,
                         float                                    delta_hue = 0.0);

  /** \brief Decompose a region of space into clusters based on the Euclidean distance between points
    * \param[in] cloud the point cloud message
    * \param[in] tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
    * \note the tree has to be created as a spatial locator on \a cloud
    * \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
    * \param[in] indices_in the cluster containing the seed point indices (as a vector of PointIndices)
    * \param[out] indices_out 
    * \param[in] delta_hue
    * \todo look how to make this templated!
    * \ingroup segmentation
    */
  void 
  seededHueSegmentation (const PointCloud<PointXYZRGB>            &cloud,
                         const search::Search<PointXYZRGBL>::Ptr  &tree,
                         float                                    tolerance,
                         PointIndices                             &indices_in,
                         PointIndices                             &indices_out,
                         float                                    delta_hue = 0.0);

  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  /** \brief SeededHueSegmentation 
    * \author Koen Buys
    * \ingroup segmentation
    */
  class SeededHueSegmentation: public PCLBase<PointXYZRGB>
  {
    using BasePCLBase = PCLBase<PointXYZRGB>;

    public:
      using PointCloud = pcl::PointCloud<PointXYZRGB>;
      using PointCloudPtr = PointCloud::Ptr;
      using PointCloudConstPtr = PointCloud::ConstPtr;

      using KdTree = pcl::search::Search<PointXYZRGB>;
      using KdTreePtr = pcl::search::Search<PointXYZRGB>::Ptr;

      using PointIndicesPtr = PointIndices::Ptr;
      using PointIndicesConstPtr = PointIndices::ConstPtr;

      //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
      /** \brief Empty constructor. */
      SeededHueSegmentation () = default;

      /** \brief Provide a pointer to the search object.
        * \param[in] tree a pointer to the spatial search object.
        */
      inline void 
      setSearchMethod (const KdTreePtr &tree) { tree_ = tree; }

      /** \brief Get a pointer to the search method used. */
      inline KdTreePtr 
      getSearchMethod () const { return (tree_); }

      /** \brief Set the spatial cluster tolerance as a measure in the L2 Euclidean space
        * \param[in] tolerance the spatial cluster tolerance as a measure in the L2 Euclidean space
        */
      inline void 
      setClusterTolerance (double tolerance) { cluster_tolerance_ = tolerance; }

      /** \brief Get the spatial cluster tolerance as a measure in the L2 Euclidean space. */
      inline double 
      getClusterTolerance () const { return (cluster_tolerance_); }

      /** \brief Set the tolerance on the hue
        * \param[in] delta_hue the new delta hue
        */
      inline void 
      setDeltaHue (float delta_hue) { delta_hue_ = delta_hue; }

      /** \brief Get the tolerance on the hue */
      inline float 
      getDeltaHue () const { return (delta_hue_); }

      /** \brief Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
        * \param[in] indices_in
        * \param[out] indices_out
        */
      void 
      segment (PointIndices &indices_in, PointIndices &indices_out);

    protected:
      // Members derived from the base class
      using BasePCLBase::input_;
      using BasePCLBase::indices_;
      using BasePCLBase::initCompute;
      using BasePCLBase::deinitCompute;

      /** \brief A pointer to the spatial search object. */
      KdTreePtr tree_{nullptr};

      /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
      double cluster_tolerance_{0.0};

      /** \brief The allowed difference on the hue*/
      float delta_hue_{0.0f};

      /** \brief Class getName method. */
      virtual std::string getClassName () const { return ("seededHueSegmentation"); }
  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/segmentation/impl/seeded_hue_segmentation.hpp>
#endif
