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#pragma once

#include <pcl/sample_consensus/sac_model.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/common/common.h>
#include <pcl/common/distances.h>
#include <climits>

namespace pcl
{
  /** \brief @b SampleConsensusModelCone defines a model for 3D cone segmentation.
    * The model coefficients are defined as:
    * <ul>
    * <li><b>apex.x</b>  : the X coordinate of cone's apex
    * <li><b>apex.y</b>  : the Y coordinate of cone's apex
    * <li><b>apex.z</b>  : the Z coordinate of cone's apex
    * <li><b>axis_direction.x</b> : the X coordinate of the cone's axis direction
    * <li><b>axis_direction.y</b> : the Y coordinate of the cone's axis direction
    * <li><b>axis_direction.z</b> : the Z coordinate of the cone's axis direction
    * <li><b>opening_angle</b>    : the cone's opening angle
    * </ul>
    * \author Stefan Schrandt
    * \ingroup sample_consensus
    */
  template <typename PointT, typename PointNT>
  class SampleConsensusModelCone : public SampleConsensusModel<PointT>, public SampleConsensusModelFromNormals<PointT, PointNT>
  {
    public:
      using SampleConsensusModel<PointT>::model_name_;
      using SampleConsensusModel<PointT>::input_;
      using SampleConsensusModel<PointT>::indices_;
      using SampleConsensusModel<PointT>::radius_min_;
      using SampleConsensusModel<PointT>::radius_max_;
      using SampleConsensusModelFromNormals<PointT, PointNT>::normals_;
      using SampleConsensusModelFromNormals<PointT, PointNT>::normal_distance_weight_;
      using SampleConsensusModel<PointT>::error_sqr_dists_;

      using PointCloud = typename SampleConsensusModel<PointT>::PointCloud;
      using PointCloudPtr = typename SampleConsensusModel<PointT>::PointCloudPtr;
      using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;

      using Ptr = shared_ptr<SampleConsensusModelCone<PointT, PointNT> >;
      using ConstPtr = shared_ptr<const SampleConsensusModelCone<PointT, PointNT>>;

      /** \brief Constructor for base SampleConsensusModelCone.
        * \param[in] cloud the input point cloud dataset
        * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
        */
      SampleConsensusModelCone (const PointCloudConstPtr &cloud, bool random = false) 
        : SampleConsensusModel<PointT> (cloud, random)
        , SampleConsensusModelFromNormals<PointT, PointNT> ()
        , axis_ (Eigen::Vector3f::Zero ())
        , eps_angle_ (0)
        , min_angle_ (-std::numeric_limits<double>::max ())
        , max_angle_ (std::numeric_limits<double>::max ())
      {
        model_name_ = "SampleConsensusModelCone";
        sample_size_ = 3;
        model_size_ = 7;
      }

      /** \brief Constructor for base SampleConsensusModelCone.
        * \param[in] cloud the input point cloud dataset
        * \param[in] indices a vector of point indices to be used from \a cloud
        * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
        */
      SampleConsensusModelCone (const PointCloudConstPtr &cloud, 
                                const Indices &indices,
                                bool random = false) 
        : SampleConsensusModel<PointT> (cloud, indices, random)
        , SampleConsensusModelFromNormals<PointT, PointNT> ()
        , axis_ (Eigen::Vector3f::Zero ())
        , eps_angle_ (0)
        , min_angle_ (-std::numeric_limits<double>::max ())
        , max_angle_ (std::numeric_limits<double>::max ())
      {
        model_name_ = "SampleConsensusModelCone";
        sample_size_ = 3;
        model_size_ = 7;
      }

      /** \brief Copy constructor.
        * \param[in] source the model to copy into this
        */
      SampleConsensusModelCone (const SampleConsensusModelCone &source) :
        SampleConsensusModel<PointT> (), 
        SampleConsensusModelFromNormals<PointT, PointNT> (),
        eps_angle_ (), min_angle_ (), max_angle_ ()
      {
        *this = source;
        model_name_ = "SampleConsensusModelCone";
      }
      
      /** \brief Empty destructor */
      ~SampleConsensusModelCone () {}

      /** \brief Copy constructor.
        * \param[in] source the model to copy into this
        */
      inline SampleConsensusModelCone&
      operator = (const SampleConsensusModelCone &source)
      {
        SampleConsensusModel<PointT>::operator=(source);
        SampleConsensusModelFromNormals<PointT, PointNT>::operator=(source);
        axis_ = source.axis_;
        eps_angle_ = source.eps_angle_;
        min_angle_ = source.min_angle_;
        max_angle_ = source.max_angle_;
        return (*this);
      }

      /** \brief Set the angle epsilon (delta) threshold.
        * \param[in] ea the maximum allowed difference between the cone's axis and the given axis.
        */
      inline void 
      setEpsAngle (double ea) { eps_angle_ = ea; }

      /** \brief Get the angle epsilon (delta) threshold. */
      inline double 
      getEpsAngle () const { return (eps_angle_); }

      /** \brief Set the axis along which we need to search for a cone direction.
        * \param[in] ax the axis along which we need to search for a cone direction
        */
      inline void 
      setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }

      /** \brief Get the axis along which we need to search for a cone direction. */
      inline Eigen::Vector3f 
      getAxis () const { return (axis_); }

      /** \brief Set the minimum and maximum allowable opening angle for a cone model
        * given from a user.
        * \param[in] min_angle the minimum allowable opening angle of a cone model
        * \param[in] max_angle the maximum allowable opening angle of a cone model
        */
      inline void
      setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
      {
        min_angle_ = min_angle;
        max_angle_ = max_angle;
      }

      /** \brief Get the opening angle which we need minimum to validate a cone model.
        * \param[out] min_angle the minimum allowable opening angle of a cone model
        * \param[out] max_angle the maximum allowable opening angle of a cone model
        */
      inline void
      getMinMaxOpeningAngle (double &min_angle, double &max_angle) const
      {
        min_angle = min_angle_;
        max_angle = max_angle_;
      }

      /** \brief Check whether the given index samples can form a valid cone model, compute the model coefficients
        * from these samples and store them in model_coefficients. The cone coefficients are: apex,
        * axis_direction, opening_angle.
        * \param[in] samples the point indices found as possible good candidates for creating a valid model
        * \param[out] model_coefficients the resultant model coefficients
        */
      bool
      computeModelCoefficients (const Indices &samples,
                                Eigen::VectorXf &model_coefficients) const override;

      /** \brief Compute all distances from the cloud data to a given cone model.
        * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
        * \param[out] distances the resultant estimated distances
        */
      void
      getDistancesToModel (const Eigen::VectorXf &model_coefficients,
                           std::vector<double> &distances) const override;

      /** \brief Select all the points which respect the given model coefficients as inliers.
        * \param[in] model_coefficients the coefficients of a cone model that we need to compute distances to
        * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
        * \param[out] inliers the resultant model inliers
        */
      void 
      selectWithinDistance (const Eigen::VectorXf &model_coefficients, 
                            const double threshold, 
                            Indices &inliers) override;

      /** \brief Count all the points which respect the given model coefficients as inliers. 
        * 
        * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
        * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
        * \return the resultant number of inliers
        */
      std::size_t
      countWithinDistance (const Eigen::VectorXf &model_coefficients,
                           const double threshold) const override;


      /** \brief Recompute the cone coefficients using the given inlier set and return them to the user.
        * @note: these are the coefficients of the cone model after refinement (e.g. after SVD)
        * \param[in] inliers the data inliers found as supporting the model
        * \param[in] model_coefficients the initial guess for the optimization
        * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
        */
      void
      optimizeModelCoefficients (const Indices &inliers,
                                 const Eigen::VectorXf &model_coefficients,
                                 Eigen::VectorXf &optimized_coefficients) const override;


      /** \brief Create a new point cloud with inliers projected onto the cone model.
        * \param[in] inliers the data inliers that we want to project on the cone model
        * \param[in] model_coefficients the coefficients of a cone model
        * \param[out] projected_points the resultant projected points
        * \param[in] copy_data_fields set to true if we need to copy the other data fields
        */
      void
      projectPoints (const Indices &inliers,
                     const Eigen::VectorXf &model_coefficients,
                     PointCloud &projected_points,
                     bool copy_data_fields = true) const override;

      /** \brief Verify whether a subset of indices verifies the given cone model coefficients.
        * \param[in] indices the data indices that need to be tested against the cone model
        * \param[in] model_coefficients the cone model coefficients
        * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
        */
      bool
      doSamplesVerifyModel (const std::set<index_t> &indices,
                            const Eigen::VectorXf &model_coefficients,
                            const double threshold) const override;

      /** \brief Return a unique id for this model (SACMODEL_CONE). */
      inline pcl::SacModel 
      getModelType () const override { return (SACMODEL_CONE); }

    protected:
      using SampleConsensusModel<PointT>::sample_size_;
      using SampleConsensusModel<PointT>::model_size_;

      /** \brief Get the distance from a point to a line (represented by a point and a direction)
        * \param[in] pt a point
        * \param[in] model_coefficients the line coefficients (a point on the line, line direction)
        */
      double
      pointToAxisDistance (const Eigen::Vector4f &pt, const Eigen::VectorXf &model_coefficients) const;

      /** \brief Check whether a model is valid given the user constraints.
        * \param[in] model_coefficients the set of model coefficients
        */
      bool
      isModelValid (const Eigen::VectorXf &model_coefficients) const override;

      /** \brief Check if a sample of indices results in a good sample of points
        * indices. Pure virtual.
        * \param[in] samples the resultant index samples
        */
      bool
      isSampleGood (const Indices &samples) const override;

    private:
      /** \brief The axis along which we need to search for a cone direction. */
      Eigen::Vector3f axis_;
    
      /** \brief The maximum allowed difference between the cone direction and the given axis. */
      double eps_angle_;

      /** \brief The minimum and maximum allowed opening angles of valid cone model. */
      double min_angle_;
      double max_angle_;

      /** \brief Functor for the optimization function */
      struct OptimizationFunctor : pcl::Functor<float>
      {
        /** Functor constructor
          * \param[in] indices the indices of data points to evaluate
          * \param[in] estimator pointer to the estimator object
          */
        OptimizationFunctor (const pcl::SampleConsensusModelCone<PointT, PointNT> *model, const Indices& indices) :
          pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}

        /** Cost function to be minimized
          * \param[in] x variables array
          * \param[out] fvec resultant functions evaluations
          * \return 0
          */
        int 
        operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
        {
          Eigen::Vector4f apex  (x[0], x[1], x[2], 0);
          Eigen::Vector4f axis_dir (x[3], x[4], x[5], 0);
          float opening_angle = x[6];

          float apexdotdir = apex.dot (axis_dir);
          float dirdotdir = 1.0f / axis_dir.dot (axis_dir);

          for (int i = 0; i < values (); ++i)
          {
            // dist = f - r
            Eigen::Vector4f pt = (*model_->input_)[indices_[i]].getVector4fMap();
            pt[3] = 0;

            // Calculate the point's projection on the cone axis
            float k = (pt.dot (axis_dir) - apexdotdir) * dirdotdir;
            Eigen::Vector4f pt_proj = apex + k * axis_dir;

            // Calculate the actual radius of the cone at the level of the projected point
            Eigen::Vector4f height = apex-pt_proj;
            float actual_cone_radius = tanf (opening_angle) * height.norm ();

            fvec[i] = static_cast<float> (pcl::sqrPointToLineDistance (pt, apex, axis_dir) - actual_cone_radius * actual_cone_radius);
          }
          return (0);
        }

        const pcl::SampleConsensusModelCone<PointT, PointNT> *model_;
        const Indices &indices_;
      };
  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/sample_consensus/impl/sac_model_cone.hpp>
#endif
