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#ifndef PCL_FILTERS_COVARIANCE_SAMPLING_H_
#define PCL_FILTERS_COVARIANCE_SAMPLING_H_

#include <pcl/filters/filter_indices.h>

namespace pcl
{
  /** \brief Point Cloud sampling based on the 6D covariances. It selects the points such that the resulting cloud is
    * as stable as possible for being registered (against a copy of itself) with ICP. The algorithm adds points to the
    * resulting cloud incrementally, while trying to keep all the 6 eigenvalues of the covariance matrix as close to each
    * other as possible.
    * This class also comes with the \a computeConditionNumber method that returns a number which shows how stable a point
    * cloud will be when used as input for ICP (the closer the value it is to 1.0, the better).
    *
    * Based on the following publication:
    *    * "Geometrically Stable Sampling for the ICP Algorithm" - N. Gelfand, L. Ikemoto, S. Rusinkiewicz, M. Levoy
    *
    * \author Alexandru E. Ichim, alex.e.ichim@gmail.com
    */
  template <typename PointT, typename PointNT>
  class CovarianceSampling : public FilterIndices<PointT>
  {
      using FilterIndices<PointT>::filter_name_;
      using FilterIndices<PointT>::getClassName;
      using FilterIndices<PointT>::indices_;
      using FilterIndices<PointT>::input_;
      using FilterIndices<PointT>::initCompute;

      typedef typename FilterIndices<PointT>::PointCloud Cloud;
      typedef typename Cloud::Ptr CloudPtr;
      typedef typename Cloud::ConstPtr CloudConstPtr;
      typedef typename pcl::PointCloud<PointNT>::ConstPtr NormalsConstPtr;

    public:
      typedef boost::shared_ptr< CovarianceSampling<PointT, PointNT> > Ptr;
      typedef boost::shared_ptr< const CovarianceSampling<PointT, PointNT> > ConstPtr;
 
      /** \brief Empty constructor. */
      CovarianceSampling ()
      { filter_name_ = "CovarianceSampling"; }

      /** \brief Set number of indices to be sampled.
        * \param[in] samples the number of sample indices
        */
      inline void
      setNumberOfSamples (unsigned int samples)
      { num_samples_ = samples; }

      /** \brief Get the value of the internal \a num_samples_ parameter. */
      inline unsigned int
      getNumberOfSamples () const
      { return (num_samples_); }

      /** \brief Set the normals computed on the input point cloud
        * \param[in] normals the normals computed for the input cloud
        */
      inline void
      setNormals (const NormalsConstPtr &normals)
      { input_normals_ = normals; }

      /** \brief Get the normals computed on the input point cloud */
      inline NormalsConstPtr
      getNormals () const
      { return (input_normals_); }



      /** \brief Compute the condition number of the input point cloud. The condition number is the ratio between the
        * largest and smallest eigenvalues of the 6x6 covariance matrix of the cloud. The closer this number is to 1.0,
        * the more stable the cloud is for ICP registration.
        * \return the condition number
        */
      double
      computeConditionNumber ();

      /** \brief Compute the condition number of the input point cloud. The condition number is the ratio between the
        * largest and smallest eigenvalues of the 6x6 covariance matrix of the cloud. The closer this number is to 1.0,
        * the more stable the cloud is for ICP registration.
        * \param[in] covariance_matrix user given covariance matrix
        * \return the condition number
        */
      static double
      computeConditionNumber (const Eigen::Matrix<double, 6, 6> &covariance_matrix);

      /** \brief Computes the covariance matrix of the input cloud.
        * \param[out] covariance_matrix the computed covariance matrix.
        * \return whether the computation succeeded or not
        */
      bool
      computeCovarianceMatrix (Eigen::Matrix<double, 6, 6> &covariance_matrix);

    protected:
      /** \brief Number of indices that will be returned. */
      unsigned int num_samples_;

      /** \brief The normals computed at each point in the input cloud */
      NormalsConstPtr input_normals_;

      std::vector<Eigen::Vector3f> scaled_points_;

      bool
      initCompute ();

      /** \brief Sample of point indices into a separate PointCloud
        * \param[out] output the resultant point cloud
        */
      void
      applyFilter (Cloud &output);

      /** \brief Sample of point indices
        * \param[out] indices the resultant point cloud indices
        */
      void
      applyFilter (std::vector<int> &indices);

      static bool
      sort_dot_list_function (std::pair<int, double> a,
                              std::pair<int, double> b)
      { return (a.second > b.second); }

    public:
      EIGEN_MAKE_ALIGNED_OPERATOR_NEW
  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/filters/impl/covariance_sampling.hpp>
#endif


#endif /* PCL_FILTERS_COVARIANCE_SAMPLING_H_ */
