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#ifndef IA_RANSAC_H_
#define IA_RANSAC_H_

#include <pcl/registration/registration.h>
#include <pcl/registration/transformation_estimation_svd.h>

namespace pcl
{
  /** \brief @b SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in
    *  section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al.
    * \author Michael Dixon, Radu B. Rusu
    * \ingroup registration
    */
  template <typename PointSource, typename PointTarget, typename FeatureT>
  class SampleConsensusInitialAlignment : public Registration<PointSource, PointTarget>
  {
    public:
      using Registration<PointSource, PointTarget>::reg_name_;
      using Registration<PointSource, PointTarget>::input_;
      using Registration<PointSource, PointTarget>::indices_;
      using Registration<PointSource, PointTarget>::target_;
      using Registration<PointSource, PointTarget>::final_transformation_;
      using Registration<PointSource, PointTarget>::transformation_;
      using Registration<PointSource, PointTarget>::corr_dist_threshold_;
      using Registration<PointSource, PointTarget>::min_number_correspondences_;
      using Registration<PointSource, PointTarget>::max_iterations_;
      using Registration<PointSource, PointTarget>::tree_;
      using Registration<PointSource, PointTarget>::transformation_estimation_;
      using Registration<PointSource, PointTarget>::getClassName;

      typedef typename Registration<PointSource, PointTarget>::PointCloudSource PointCloudSource;
      typedef typename PointCloudSource::Ptr PointCloudSourcePtr;
      typedef typename PointCloudSource::ConstPtr PointCloudSourceConstPtr;

      typedef typename Registration<PointSource, PointTarget>::PointCloudTarget PointCloudTarget;

      typedef PointIndices::Ptr PointIndicesPtr;
      typedef PointIndices::ConstPtr PointIndicesConstPtr;

      typedef pcl::PointCloud<FeatureT> FeatureCloud;
      typedef typename FeatureCloud::Ptr FeatureCloudPtr;
      typedef typename FeatureCloud::ConstPtr FeatureCloudConstPtr;

      typedef boost::shared_ptr<SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT> > Ptr;
      typedef boost::shared_ptr<const SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT> > ConstPtr;


      class ErrorFunctor
      {
        public:
          virtual ~ErrorFunctor () {}
          virtual float operator () (float d) const = 0;
      };

      class HuberPenalty : public ErrorFunctor
      {
        private:
          HuberPenalty () {}
        public:
          HuberPenalty (float threshold)  : threshold_ (threshold) {}
          virtual float operator () (float e) const
          { 
            if (e <= threshold_)
              return (0.5 * e*e); 
            else
              return (0.5 * threshold_ * (2.0 * fabs (e) - threshold_));
          }
        protected:
          float threshold_;
      };

      class TruncatedError : public ErrorFunctor
      {
        private:
          TruncatedError () {}
        public:
          virtual ~TruncatedError () {}

          TruncatedError (float threshold) : threshold_ (threshold) {}
          virtual float operator () (float e) const
          { 
            if (e <= threshold_)
              return (e / threshold_);
            else
              return (1.0);
          }
        protected:
          float threshold_;
      };

      typedef typename KdTreeFLANN<FeatureT>::Ptr FeatureKdTreePtr; 
      /** \brief Constructor. */
      SampleConsensusInitialAlignment () : 
        input_features_ (), target_features_ (), 
        nr_samples_(3), min_sample_distance_ (0.0f), k_correspondences_ (10), 
        feature_tree_ (new pcl::KdTreeFLANN<FeatureT>),
        error_functor_ ()
      {
        reg_name_ = "SampleConsensusInitialAlignment";
        max_iterations_ = 1000;

        // Setting a non-std::numeric_limits<double>::max () value to corr_dist_threshold_ to make it play nicely with TruncatedError
        corr_dist_threshold_ = 100.0f;
        transformation_estimation_.reset (new pcl::registration::TransformationEstimationSVD<PointSource, PointTarget>);
      };

      /** \brief Provide a boost shared pointer to the source point cloud's feature descriptors
        * \param features the source point cloud's features
        */
      void 
      setSourceFeatures (const FeatureCloudConstPtr &features);

      /** \brief Get a pointer to the source point cloud's features */
      inline FeatureCloudConstPtr const 
      getSourceFeatures () { return (input_features_); }

      /** \brief Provide a boost shared pointer to the target point cloud's feature descriptors
        * \param features the target point cloud's features
        */
      void 
      setTargetFeatures (const FeatureCloudConstPtr &features);

      /** \brief Get a pointer to the target point cloud's features */
      inline FeatureCloudConstPtr const 
      getTargetFeatures () { return (target_features_); }

      /** \brief Set the minimum distances between samples
        * \param min_sample_distance the minimum distances between samples
        */
      void 
      setMinSampleDistance (float min_sample_distance) { min_sample_distance_ = min_sample_distance; }

      /** \brief Get the minimum distances between samples, as set by the user */
      float 
      getMinSampleDistance () { return (min_sample_distance_); }

      /** \brief Set the number of samples to use during each iteration
        * \param nr_samples the number of samples to use during each iteration
        */
      void 
      setNumberOfSamples (int nr_samples) { nr_samples_ = nr_samples; }

      /** \brief Get the number of samples to use during each iteration, as set by the user */
      int 
      getNumberOfSamples () { return (nr_samples_); }

      /** \brief Set the number of neighbors to use when selecting a random feature correspondence.  A higher value will
        * add more randomness to the feature matching.
        * \param k the number of neighbors to use when selecting a random feature correspondence.
        */
      void
      setCorrespondenceRandomness (int k) { k_correspondences_ = k; }

      /** \brief Get the number of neighbors used when selecting a random feature correspondence, as set by the user */
      int
      getCorrespondenceRandomness () { return (k_correspondences_); }

      /** \brief Specify the error function to minimize
       * \note This call is optional.  TruncatedError will be used by default
       * \param[in] error_functor a shared pointer to a subclass of SampleConsensusInitialAlignment::ErrorFunctor
       */
      void
      setErrorFunction (const boost::shared_ptr<ErrorFunctor> & error_functor) { error_functor_ = error_functor; }

      /** \brief Get a shared pointer to the ErrorFunctor that is to be minimized  
       * \return A shared pointer to a subclass of SampleConsensusInitialAlignment::ErrorFunctor
       */
      boost::shared_ptr<ErrorFunctor>
      getErrorFunction () { return (error_functor_); }

    protected:
      /** \brief Choose a random index between 0 and n-1
        * \param n the number of possible indices to choose from
        */
      inline int 
      getRandomIndex (int n) { return (static_cast<int> (n * (rand () / (RAND_MAX + 1.0)))); };
      
      /** \brief Select \a nr_samples sample points from cloud while making sure that their pairwise distances are 
        * greater than a user-defined minimum distance, \a min_sample_distance.
        * \param cloud the input point cloud
        * \param nr_samples the number of samples to select
        * \param min_sample_distance the minimum distance between any two samples
        * \param sample_indices the resulting sample indices
        */
      void 
      selectSamples (const PointCloudSource &cloud, int nr_samples, float min_sample_distance, 
                     std::vector<int> &sample_indices);

      /** \brief For each of the sample points, find a list of points in the target cloud whose features are similar to 
        * the sample points' features. From these, select one randomly which will be considered that sample point's 
        * correspondence. 
        * \param input_features a cloud of feature descriptors
        * \param sample_indices the indices of each sample point
        * \param corresponding_indices the resulting indices of each sample's corresponding point in the target cloud
        */
      void 
      findSimilarFeatures (const FeatureCloud &input_features, const std::vector<int> &sample_indices, 
                           std::vector<int> &corresponding_indices);

      /** \brief An error metric for that computes the quality of the alignment between the given cloud and the target.
        * \param cloud the input cloud
        * \param threshold distances greater than this value are capped
        */
      float 
      computeErrorMetric (const PointCloudSource &cloud, float threshold);

      /** \brief Rigid transformation computation method.
        * \param output the transformed input point cloud dataset using the rigid transformation found
        */
      virtual void 
      computeTransformation (PointCloudSource &output, const Eigen::Matrix4f& guess);

      /** \brief The source point cloud's feature descriptors. */
      FeatureCloudConstPtr input_features_;

      /** \brief The target point cloud's feature descriptors. */
      FeatureCloudConstPtr target_features_;  

      /** \brief The number of samples to use during each iteration. */
      int nr_samples_;

      /** \brief The minimum distances between samples. */
      float min_sample_distance_;

      /** \brief The number of neighbors to use when selecting a random feature correspondence. */
      int k_correspondences_;
     
      /** \brief The KdTree used to compare feature descriptors. */
      FeatureKdTreePtr feature_tree_;               

      /** */
      boost::shared_ptr<ErrorFunctor> error_functor_;
    public:
      EIGEN_MAKE_ALIGNED_OPERATOR_NEW
  };
}

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

#endif  //#ifndef IA_RANSAC_H_
