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#ifndef PCL_NORMAL_3D_H_
#define PCL_NORMAL_3D_H_

#include <pcl/features/feature.h>
#include <pcl/common/centroid.h>

namespace pcl
{
  /** \brief Compute the Least-Squares plane fit for a given set of points, and return the estimated plane
    * parameters together with the surface curvature.
    * \param cloud the input point cloud
    * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
    * \param curvature the estimated surface curvature as a measure of
    * \f[
    * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
    * \f]
    * \ingroup features
    */
  template <typename PointT> inline bool
  computePointNormal (const pcl::PointCloud<PointT> &cloud,
                      Eigen::Vector4f &plane_parameters, float &curvature)
  {
    // Placeholder for the 3x3 covariance matrix at each surface patch
    EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
    // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
    Eigen::Vector4f xyz_centroid;

    if (cloud.size () < 3 ||
        computeMeanAndCovarianceMatrix (cloud, covariance_matrix, xyz_centroid) == 0)
    {
      plane_parameters.setConstant (std::numeric_limits<float>::quiet_NaN ());
      curvature = std::numeric_limits<float>::quiet_NaN ();
      return false;
    }

    // Get the plane normal and surface curvature
    solvePlaneParameters (covariance_matrix, xyz_centroid, plane_parameters, curvature);
    return true;
  }

  /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
    * and return the estimated plane parameters together with the surface curvature.
    * \param cloud the input point cloud
    * \param indices the point cloud indices that need to be used
    * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
    * \param curvature the estimated surface curvature as a measure of
    * \f[
    * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
    * \f]
    * \ingroup features
    */
  template <typename PointT> inline bool
  computePointNormal (const pcl::PointCloud<PointT> &cloud, const std::vector<int> &indices,
                      Eigen::Vector4f &plane_parameters, float &curvature)
  {
    // Placeholder for the 3x3 covariance matrix at each surface patch
    EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
    // 16-bytes aligned placeholder for the XYZ centroid of a surface patch
    Eigen::Vector4f xyz_centroid;
    if (indices.size () < 3 ||
        computeMeanAndCovarianceMatrix (cloud, indices, covariance_matrix, xyz_centroid) == 0)
    {
      plane_parameters.setConstant (std::numeric_limits<float>::quiet_NaN ());
      curvature = std::numeric_limits<float>::quiet_NaN ();
      return false;
    }
    // Get the plane normal and surface curvature
    solvePlaneParameters (covariance_matrix, xyz_centroid, plane_parameters, curvature);
    return true;
  }

  /** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
    * \param point a given point
    * \param vp_x the X coordinate of the viewpoint
    * \param vp_y the X coordinate of the viewpoint
    * \param vp_z the X coordinate of the viewpoint
    * \param normal the plane normal to be flipped
    * \ingroup features
    */
  template <typename PointT, typename Scalar> inline void
  flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
                              Eigen::Matrix<Scalar, 4, 1>& normal)
  {
    Eigen::Matrix <Scalar, 4, 1> vp (vp_x - point.x, vp_y - point.y, vp_z - point.z, 0);

    // Dot product between the (viewpoint - point) and the plane normal
    float cos_theta = vp.dot (normal);

    // Flip the plane normal
    if (cos_theta < 0)
    {
      normal *= -1;
      normal[3] = 0.0f;
      // Hessian form (D = nc . p_plane (centroid here) + p)
      normal[3] = -1 * normal.dot (point.getVector4fMap ());
    }
  }

  /** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
    * \param point a given point
    * \param vp_x the X coordinate of the viewpoint
    * \param vp_y the X coordinate of the viewpoint
    * \param vp_z the X coordinate of the viewpoint
    * \param normal the plane normal to be flipped
    * \ingroup features
    */
  template <typename PointT, typename Scalar> inline void
  flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
                              Eigen::Matrix<Scalar, 3, 1>& normal)
  {
    Eigen::Matrix <Scalar, 3, 1> vp (vp_x - point.x, vp_y - point.y, vp_z - point.z);

    // Flip the plane normal
    if (vp.dot (normal) < 0)
      normal *= -1;
  }
  
  /** \brief Flip (in place) the estimated normal of a point towards a given viewpoint
    * \param point a given point
    * \param vp_x the X coordinate of the viewpoint
    * \param vp_y the X coordinate of the viewpoint
    * \param vp_z the X coordinate of the viewpoint
    * \param nx the resultant X component of the plane normal
    * \param ny the resultant Y component of the plane normal
    * \param nz the resultant Z component of the plane normal
    * \ingroup features
    */
  template <typename PointT> inline void
  flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
                              float &nx, float &ny, float &nz)
  {
    // See if we need to flip any plane normals
    vp_x -= point.x;
    vp_y -= point.y;
    vp_z -= point.z;

    // Dot product between the (viewpoint - point) and the plane normal
    float cos_theta = (vp_x * nx + vp_y * ny + vp_z * nz);

    // Flip the plane normal
    if (cos_theta < 0)
    {
      nx *= -1;
      ny *= -1;
      nz *= -1;
    }
  }

  /** \brief Flip (in place) normal to get the same sign of the mean of the normals specified by normal_indices.
    * 
    * The method is described in:
    * A. Petrelli, L. Di Stefano, "A repeatable and efficient canonical reference for surface matching", 3DimPVT, 2012
    * A. Petrelli, L. Di Stefano, "On the repeatability of the local reference frame for partial shape matching", 13th International Conference on Computer Vision (ICCV), 2011
    *
    * Normals should be unit vectors. Otherwise the resulting mean would be weighted by the normal norms.
    * \param[in] normal_cloud Cloud of normals used to compute the mean
    * \param[in] normal_indices Indices of normals used to compute the mean 
    * \param[in] normal input Normal to flip. Normal is modified by the function.
    * \return false if normal_indices does not contain any valid normal.
    * \ingroup features
    */
  template<typename PointNT> inline bool
  flipNormalTowardsNormalsMean ( pcl::PointCloud<PointNT> const &normal_cloud,
                                 std::vector<int> const &normal_indices,
                                 Eigen::Vector3f &normal)
  {
    Eigen::Vector3f normal_mean = Eigen::Vector3f::Zero ();

    for (size_t i = 0; i < normal_indices.size (); ++i)
    {
      const PointNT& cur_pt = normal_cloud[normal_indices[i]];

      if (pcl::isFinite (cur_pt))
      {
        normal_mean += cur_pt.getNormalVector3fMap ();
      }
    }

    if (normal_mean.isZero ())
      return false;

    normal_mean.normalize ();

    if (normal.dot (normal_mean) < 0)
    {
      normal = -normal;
    }

    return true;
  }

  /** \brief NormalEstimation estimates local surface properties (surface normals and curvatures)at each
    * 3D point. If PointOutT is specified as pcl::Normal, the normal is stored in the first 3 components (0-2),
    * and the curvature is stored in component 3.
    *
    * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
    * \ref NormalEstimationOMP for a parallel implementation.
    * \author Radu B. Rusu
    * \ingroup features
    */
  template <typename PointInT, typename PointOutT>
  class NormalEstimation: public Feature<PointInT, PointOutT>
  {
    public:
      typedef boost::shared_ptr<NormalEstimation<PointInT, PointOutT> > Ptr;
      typedef boost::shared_ptr<const NormalEstimation<PointInT, PointOutT> > ConstPtr;
      using Feature<PointInT, PointOutT>::feature_name_;
      using Feature<PointInT, PointOutT>::getClassName;
      using Feature<PointInT, PointOutT>::indices_;
      using Feature<PointInT, PointOutT>::input_;
      using Feature<PointInT, PointOutT>::surface_;
      using Feature<PointInT, PointOutT>::k_;
      using Feature<PointInT, PointOutT>::search_radius_;
      using Feature<PointInT, PointOutT>::search_parameter_;
      
      typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
      typedef typename Feature<PointInT, PointOutT>::PointCloudConstPtr PointCloudConstPtr;
      
      /** \brief Empty constructor. */
      NormalEstimation () 
      : vpx_ (0)
      , vpy_ (0)
      , vpz_ (0)
      , covariance_matrix_ ()
      , xyz_centroid_ ()
      , use_sensor_origin_ (true)
      {
        feature_name_ = "NormalEstimation";
      };
      
      /** \brief Empty destructor */
      virtual ~NormalEstimation () {}

      /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
        * and return the estimated plane parameters together with the surface curvature.
        * \param cloud the input point cloud
        * \param indices the point cloud indices that need to be used
        * \param plane_parameters the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
        * \param curvature the estimated surface curvature as a measure of
        * \f[
        * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
        * \f]
        */
      inline bool
      computePointNormal (const pcl::PointCloud<PointInT> &cloud, const std::vector<int> &indices,
                          Eigen::Vector4f &plane_parameters, float &curvature)
      {
        if (indices.size () < 3 ||
            computeMeanAndCovarianceMatrix (cloud, indices, covariance_matrix_, xyz_centroid_) == 0)
        {
          plane_parameters.setConstant (std::numeric_limits<float>::quiet_NaN ());
          curvature = std::numeric_limits<float>::quiet_NaN ();
          return false;
        }

        // Get the plane normal and surface curvature
        solvePlaneParameters (covariance_matrix_, xyz_centroid_, plane_parameters, curvature);
        return true;
      }

      /** \brief Compute the Least-Squares plane fit for a given set of points, using their indices,
        * and return the estimated plane parameters together with the surface curvature.
        * \param cloud the input point cloud
        * \param indices the point cloud indices that need to be used
        * \param nx the resultant X component of the plane normal
        * \param ny the resultant Y component of the plane normal
        * \param nz the resultant Z component of the plane normal
        * \param curvature the estimated surface curvature as a measure of
        * \f[
        * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
        * \f]
        */
      inline bool
      computePointNormal (const pcl::PointCloud<PointInT> &cloud, const std::vector<int> &indices,
                          float &nx, float &ny, float &nz, float &curvature)
      {
        if (indices.size () < 3 ||
            computeMeanAndCovarianceMatrix (cloud, indices, covariance_matrix_, xyz_centroid_) == 0)
        {
          nx = ny = nz = curvature = std::numeric_limits<float>::quiet_NaN ();
          return false;
        }

        // Get the plane normal and surface curvature
        solvePlaneParameters (covariance_matrix_, nx, ny, nz, curvature);
        return true;
      }

      /** \brief Provide a pointer to the input dataset
        * \param cloud the const boost shared pointer to a PointCloud message
        */
      virtual inline void 
      setInputCloud (const PointCloudConstPtr &cloud)
      {
        input_ = cloud;
        if (use_sensor_origin_)
        {
          vpx_ = input_->sensor_origin_.coeff (0);
          vpy_ = input_->sensor_origin_.coeff (1);
          vpz_ = input_->sensor_origin_.coeff (2);
        }
      }
      
      /** \brief Set the viewpoint.
        * \param vpx the X coordinate of the viewpoint
        * \param vpy the Y coordinate of the viewpoint
        * \param vpz the Z coordinate of the viewpoint
        */
      inline void
      setViewPoint (float vpx, float vpy, float vpz)
      {
        vpx_ = vpx;
        vpy_ = vpy;
        vpz_ = vpz;
        use_sensor_origin_ = false;
      }

      /** \brief Get the viewpoint.
        * \param [out] vpx x-coordinate of the view point
        * \param [out] vpy y-coordinate of the view point
        * \param [out] vpz z-coordinate of the view point
        * \note this method returns the currently used viewpoint for normal flipping.
        * If the viewpoint is set manually using the setViewPoint method, this method will return the set view point coordinates.
        * If an input cloud is set, it will return the sensor origin otherwise it will return the origin (0, 0, 0)
        */
      inline void
      getViewPoint (float &vpx, float &vpy, float &vpz)
      {
        vpx = vpx_;
        vpy = vpy_;
        vpz = vpz_;
      }

      /** \brief sets whether the sensor origin or a user given viewpoint should be used. After this method, the 
        * normal estimation method uses the sensor origin of the input cloud.
        * to use a user defined view point, use the method setViewPoint
        */
      inline void
      useSensorOriginAsViewPoint ()
      {
        use_sensor_origin_ = true;
        if (input_)
        {
          vpx_ = input_->sensor_origin_.coeff (0);
          vpy_ = input_->sensor_origin_.coeff (1);
          vpz_ = input_->sensor_origin_.coeff (2);
        }
        else
        {
          vpx_ = 0;
          vpy_ = 0;
          vpz_ = 0;
        }
      }
      
    protected:
      /** \brief Estimate normals for all points given in <setInputCloud (), setIndices ()> using the surface in
        * setSearchSurface () and the spatial locator in setSearchMethod ()
        * \note In situations where not enough neighbors are found, the normal and curvature values are set to NaN.
        * \param output the resultant point cloud model dataset that contains surface normals and curvatures
        */
      void
      computeFeature (PointCloudOut &output);

      /** \brief Values describing the viewpoint ("pinhole" camera model assumed). For per point viewpoints, inherit
        * from NormalEstimation and provide your own computeFeature (). By default, the viewpoint is set to 0,0,0. */
      float vpx_, vpy_, vpz_;

      /** \brief Placeholder for the 3x3 covariance matrix at each surface patch. */
      EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix_;

      /** \brief 16-bytes aligned placeholder for the XYZ centroid of a surface patch. */
      Eigen::Vector4f xyz_centroid_;
      
      /** whether the sensor origin of the input cloud or a user given viewpoint should be used.*/
      bool use_sensor_origin_;

    public:
      EIGEN_MAKE_ALIGNED_OPERATOR_NEW
  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/normal_3d.hpp>
#endif

#endif  //#ifndef PCL_NORMAL_3D_H_

