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#pragma once

#include <pcl/memory.h>
#include <pcl/pcl_macros.h>
#include <pcl/point_representation.h>
#include <pcl/search/kdtree.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/registration/transformation_validation.h>

namespace pcl
{
  namespace registration
  {
    /** \brief TransformationValidationEuclidean computes an L2SQR norm between a source and target
      * dataset.
      * 
      * To prevent points with bad correspondences to contribute to the overall score, the class also 
      * accepts a maximum_range parameter given via \ref setMaxRange that is used as a cutoff value for
      * nearest neighbor distance comparisons.
      * 
      * The output score is normalized with respect to the number of valid correspondences found.
      *
      * Usage example:
      * \code
      * pcl::TransformationValidationEuclidean<pcl::PointXYZ, pcl::PointXYZ> tve;
      * tve.setMaxRange (0.01);  // 1cm
      * double score = tve.validateTransformation (source, target, transformation);
      * \endcode
      *
      * \note The class is templated on the source and target point types as well as on the output scalar of the transformation matrix (i.e., float or double). Default: float.
      * \author Radu B. Rusu
      * \ingroup registration
      */
    template <typename PointSource, typename PointTarget, typename Scalar = float>
    class TransformationValidationEuclidean
    {
      public:
        using Matrix4 = typename TransformationValidation<PointSource, PointTarget, Scalar>::Matrix4;
        
        using Ptr = shared_ptr<TransformationValidation<PointSource, PointTarget, Scalar> >;
        using ConstPtr = shared_ptr<const TransformationValidation<PointSource, PointTarget, Scalar> >;

        using KdTree = pcl::search::KdTree<PointTarget>;
        using KdTreePtr = typename KdTree::Ptr;

        using PointRepresentationConstPtr = typename KdTree::PointRepresentationConstPtr;

        using PointCloudSourceConstPtr = typename TransformationValidation<PointSource, PointTarget>::PointCloudSourceConstPtr;
        using PointCloudTargetConstPtr = typename TransformationValidation<PointSource, PointTarget>::PointCloudTargetConstPtr;

        /** \brief Constructor.
          * Sets the \a max_range parameter to double::max, \a threshold_ to NaN
          * and initializes the internal search \a tree to a FLANN kd-tree.
          */
        TransformationValidationEuclidean () : 
          max_range_ (std::numeric_limits<double>::max ()),
          threshold_ (std::numeric_limits<double>::quiet_NaN ()),
          tree_ (new pcl::search::KdTree<PointTarget>),
          force_no_recompute_ (false)
        {
        }

        virtual ~TransformationValidationEuclidean () {};

        /** \brief Set the maximum allowable distance between a point and its correspondence in the 
          * target in order for a correspondence to be considered \a valid. Default: double::max.
          * \param[in] max_range the new maximum allowable distance
          */
        inline void
        setMaxRange (double max_range)
        {
          max_range_ = max_range;
        }

        /** \brief Get the maximum allowable distance between a point and its 
          * correspondence, as set by the user.
          */
        inline double
        getMaxRange ()
        {
          return (max_range_);
        }


        /** \brief Provide a pointer to the search object used to find correspondences in
          * the target cloud.
          * \param[in] tree a pointer to the spatial search object.
          * \param[in] force_no_recompute If set to true, this tree will NEVER be 
          * recomputed, regardless of calls to setInputTarget. Only use if you are 
          * confident that the tree will be set correctly.
          */
        inline void
        setSearchMethodTarget (const KdTreePtr &tree, 
                               bool force_no_recompute = false) 
        { 
          tree_ = tree; 
          if (force_no_recompute)
          {
            force_no_recompute_ = true;
          }
        }

        /** \brief Set a threshold for which a specific transformation is considered valid.
          *
          * \note Since we're using MSE (Mean Squared Error) as a metric, the threshold
          * represents the mean Euclidean distance threshold over all nearest neighbors
          * up to max_range.
          *
          * \param[in] threshold the threshold for which a transformation is vali
          */
        inline void
        setThreshold (double threshold)
        {
          threshold_ = threshold;
        }

        /** \brief Get the threshold for which a specific transformation is valid. */
        inline double
        getThreshold ()
        {
          return (threshold_);
        }

        /** \brief Validate the given transformation with respect to the input cloud data, and return a score.
          *
          * \param[in] cloud_src the source point cloud dataset
          * \param[in] cloud_tgt the target point cloud dataset
          * \param[out] transformation_matrix the resultant transformation matrix
          *
          * \return the score or confidence measure for the given
          * transformation_matrix with respect to the input data
          */
        double
        validateTransformation (
            const PointCloudSourceConstPtr &cloud_src,
            const PointCloudTargetConstPtr &cloud_tgt,
            const Matrix4 &transformation_matrix) const;

        /** \brief Comparator function for deciding which score is better after running the 
          * validation on multiple transforms.
          *
          * \param[in] score1 the first value
          * \param[in] score2 the second value
          *
          * \return true if score1 is better than score2
          */
        virtual bool
        operator() (const double &score1, const double &score2) const
        {
          return (score1 < score2);
        }

        /** \brief Check if the score is valid for a specific transformation.
          *
          * \param[in] cloud_src the source point cloud dataset
          * \param[in] cloud_tgt the target point cloud dataset
          * \param[out] transformation_matrix the transformation matrix
          *
          * \return true if the transformation is valid, false otherwise.
          */
        virtual bool
        isValid (
            const PointCloudSourceConstPtr &cloud_src,
            const PointCloudTargetConstPtr &cloud_tgt,
            const Matrix4 &transformation_matrix) const
        {
          if (std::isnan (threshold_))
          {
            PCL_ERROR ("[pcl::TransformationValidationEuclidean::isValid] Threshold not set! Please use setThreshold () before continuing.");
            return (false);
          }

          return (validateTransformation (cloud_src, cloud_tgt, transformation_matrix) < threshold_);
        }

      protected:
        /** \brief The maximum allowable distance between a point and its correspondence in the target 
          * in order for a correspondence to be considered \a valid. Default: double::max.
          */
        double max_range_;

        /** \brief The threshold for which a specific transformation is valid. 
          * Set to NaN by default, as we must require the user to set it.
          */
        double threshold_;

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

        /** \brief A flag which, if set, means the tree operating on the target cloud 
         * will never be recomputed*/
        bool force_no_recompute_;


        /** \brief Internal point representation uses only 3D coordinates for L2 */
        class MyPointRepresentation: public pcl::PointRepresentation<PointTarget>
        {
          using pcl::PointRepresentation<PointTarget>::nr_dimensions_;
          using pcl::PointRepresentation<PointTarget>::trivial_;
          public:
            using Ptr = shared_ptr<MyPointRepresentation>;
            using ConstPtr = shared_ptr<const MyPointRepresentation>;
            
            MyPointRepresentation ()
            {
              nr_dimensions_ = 3;
              trivial_ = true;
            }
      
            /** \brief Empty destructor */
            virtual ~MyPointRepresentation () {}

            virtual void
            copyToFloatArray (const PointTarget &p, float * out) const
            {
              out[0] = p.x;
              out[1] = p.y;
              out[2] = p.z;
            }
        };

      public:
        PCL_MAKE_ALIGNED_OPERATOR_NEW
    };
  }
}

#include <pcl/registration/impl/transformation_validation_euclidean.hpp>
