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#ifndef PCL_SAMPLE_CONSENSUS_MODEL_LINE_H_
#define PCL_SAMPLE_CONSENSUS_MODEL_LINE_H_

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

namespace pcl
{
  /** \brief SampleConsensusModelLine defines a model for 3D line segmentation.
    * The model coefficients are defined as:
    *   - \b point_on_line.x  : the X coordinate of a point on the line
    *   - \b point_on_line.y  : the Y coordinate of a point on the line
    *   - \b point_on_line.z  : the Z coordinate of a point on the line
    *   - \b line_direction.x : the X coordinate of a line's direction
    *   - \b line_direction.y : the Y coordinate of a line's direction
    *   - \b line_direction.z : the Z coordinate of a line's direction
    *
    * \author Radu B. Rusu
    * \ingroup sample_consensus
    */
  template <typename PointT>
  class SampleConsensusModelLine : public SampleConsensusModel<PointT>
  {
    public:
      using SampleConsensusModel<PointT>::model_name_;
      using SampleConsensusModel<PointT>::input_;
      using SampleConsensusModel<PointT>::indices_;
      using SampleConsensusModel<PointT>::error_sqr_dists_;
      using SampleConsensusModel<PointT>::isModelValid;

      typedef typename SampleConsensusModel<PointT>::PointCloud PointCloud;
      typedef typename SampleConsensusModel<PointT>::PointCloudPtr PointCloudPtr;
      typedef typename SampleConsensusModel<PointT>::PointCloudConstPtr PointCloudConstPtr;

      typedef boost::shared_ptr<SampleConsensusModelLine> Ptr;

      /** \brief Constructor for base SampleConsensusModelLine.
        * \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)
        */
      SampleConsensusModelLine (const PointCloudConstPtr &cloud, bool random = false) 
        : SampleConsensusModel<PointT> (cloud, random)
      {
        model_name_ = "SampleConsensusModelLine";
        sample_size_ = 2;
        model_size_ = 6;
      }

      /** \brief Constructor for base SampleConsensusModelLine.
        * \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)
        */
      SampleConsensusModelLine (const PointCloudConstPtr &cloud, 
                                const std::vector<int> &indices,
                                bool random = false) 
        : SampleConsensusModel<PointT> (cloud, indices, random)
      {
        model_name_ = "SampleConsensusModelLine";
        sample_size_ = 2;
        model_size_ = 6;
      }
      
      /** \brief Empty destructor */
      virtual ~SampleConsensusModelLine () {}

      /** \brief Check whether the given index samples can form a valid line model, compute the model coefficients from
        * these samples and store them internally in model_coefficients_. The line coefficients are represented by a
        * point and a line direction
        * \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 std::vector<int> &samples,
                                Eigen::VectorXf &model_coefficients) const;

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

      /** \brief Select all the points which respect the given model coefficients as inliers.
        * \param[in] model_coefficients the coefficients of a line 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, 
                            std::vector<int> &inliers);

      /** \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
        */
      virtual int
      countWithinDistance (const Eigen::VectorXf &model_coefficients,
                           const double threshold) const;

      /** \brief Recompute the line coefficients using the given inlier set and return them to the user.
        * @note: these are the coefficients of the line 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 model coefficients
        * \param[out] optimized_coefficients the resultant recomputed coefficients after optimization
        */
      void
      optimizeModelCoefficients (const std::vector<int> &inliers,
                                 const Eigen::VectorXf &model_coefficients,
                                 Eigen::VectorXf &optimized_coefficients) const;

      /** \brief Create a new point cloud with inliers projected onto the line model.
        * \param[in] inliers the data inliers that we want to project on the line model
        * \param[in] model_coefficients the *normalized* coefficients of a line 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 std::vector<int> &inliers,
                     const Eigen::VectorXf &model_coefficients,
                     PointCloud &projected_points,
                     bool copy_data_fields = true) const;

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

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

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

      /** \brief Check if a sample of indices results in a good sample of points
        * indices.
        * \param[in] samples the resultant index samples
        */
      bool
      isSampleGood (const std::vector<int> &samples) const;
  };
}

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

#endif  //#ifndef PCL_SAMPLE_CONSENSUS_MODEL_LINE_H_
