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#ifndef PCL_REGISTRATION_CORRESPONDENCE_REJECTION_POLY_H_
#define PCL_REGISTRATION_CORRESPONDENCE_REJECTION_POLY_H_

#include <pcl/registration/correspondence_rejection.h>
#include <pcl/point_cloud.h>

namespace pcl
{
  namespace registration
  {
    /** \brief CorrespondenceRejectorPoly implements a correspondence rejection method that exploits low-level and
      * pose-invariant geometric constraints between two point sets by forming virtual polygons of a user-specifiable
      * cardinality on each model using the input correspondences.
      * These polygons are then checked in a pose-invariant manner (i.e. the side lengths must be approximately equal),
      * and rejection is performed by thresholding these edge lengths.
      * 
      * If you use this in academic work, please cite:
      * 
      * A. G. Buch, D. Kraft, J.-K. Kämäräinen, H. G. Petersen and N. Krüger.
      * Pose Estimation using Local Structure-Specific Shape and Appearance Context.
      * International Conference on Robotics and Automation (ICRA), 2013. 
      *
      * \author Anders Glent Buch
      * \ingroup registration
      */
    template <typename SourceT, typename TargetT>
    class PCL_EXPORTS CorrespondenceRejectorPoly: public CorrespondenceRejector
    {
      using CorrespondenceRejector::input_correspondences_;
      using CorrespondenceRejector::rejection_name_;
      using CorrespondenceRejector::getClassName;

      public:
        typedef boost::shared_ptr<CorrespondenceRejectorPoly> Ptr;
        typedef boost::shared_ptr<const CorrespondenceRejectorPoly> ConstPtr;
        
        typedef pcl::PointCloud<SourceT> PointCloudSource;
        typedef typename PointCloudSource::Ptr PointCloudSourcePtr;
        typedef typename PointCloudSource::ConstPtr PointCloudSourceConstPtr;
        
        typedef pcl::PointCloud<TargetT> PointCloudTarget;
        typedef typename PointCloudTarget::Ptr PointCloudTargetPtr;
        typedef typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr;

        /** \brief Empty constructor */
        CorrespondenceRejectorPoly ()
          : iterations_ (10000)
          , cardinality_ (3)
          , similarity_threshold_ (0.75f)
          , similarity_threshold_squared_ (0.75f * 0.75f)
        {
          rejection_name_ = "CorrespondenceRejectorPoly";
        }

        /** \brief Get a list of valid correspondences after rejection from the original set of correspondences.
          * \param[in] original_correspondences the set of initial correspondences given
          * \param[out] remaining_correspondences the resultant filtered set of remaining correspondences
          */
        void 
        getRemainingCorrespondences (const pcl::Correspondences& original_correspondences, 
                                     pcl::Correspondences& remaining_correspondences);

        /** \brief Provide a source point cloud dataset (must contain XYZ data!), used to compute the correspondence distance.
          * \param[in] cloud a cloud containing XYZ data
          */
        inline void 
        setInputSource (const PointCloudSourceConstPtr &cloud)
        {
          input_ = cloud;
        }

        /** \brief Provide a source point cloud dataset (must contain XYZ data!), used to compute the correspondence distance.
          * \param[in] cloud a cloud containing XYZ data
          */
        inline void 
        setInputCloud (const PointCloudSourceConstPtr &cloud)
        {
          PCL_WARN ("[pcl::registration::%s::setInputCloud] setInputCloud is deprecated. Please use setInputSource instead.\n",
                    getClassName ().c_str ());
          input_ = cloud;
        }

        /** \brief Provide a target point cloud dataset (must contain XYZ data!), used to compute the correspondence distance.
          * \param[in] target a cloud containing XYZ data
          */
        inline void 
        setInputTarget (const PointCloudTargetConstPtr &target)
        {
          target_ = target;
        }
        
        /** \brief See if this rejector requires source points */
        bool
        requiresSourcePoints () const
        { return (true); }

        /** \brief Blob method for setting the source cloud */
        void
        setSourcePoints (pcl::PCLPointCloud2::ConstPtr cloud2)
        { 
          PointCloudSourcePtr cloud (new PointCloudSource);
          fromPCLPointCloud2 (*cloud2, *cloud);
          setInputSource (cloud);
        }
        
        /** \brief See if this rejector requires a target cloud */
        bool
        requiresTargetPoints () const
        { return (true); }

        /** \brief Method for setting the target cloud */
        void
        setTargetPoints (pcl::PCLPointCloud2::ConstPtr cloud2)
        { 
          PointCloudTargetPtr cloud (new PointCloudTarget);
          fromPCLPointCloud2 (*cloud2, *cloud);
          setInputTarget (cloud);
        }
        
        /** \brief Set the polygon cardinality
          * \param cardinality polygon cardinality
          */
        inline void 
        setCardinality (int cardinality)
        {
          cardinality_ = cardinality;
        }
        
        /** \brief Get the polygon cardinality
          * \return polygon cardinality
          */
        inline int 
        getCardinality ()
        {
          return (cardinality_);
        }
        
        /** \brief Set the similarity threshold in [0,1[ between edge lengths,
          * where 1 is a perfect match
          * \param similarity_threshold similarity threshold
          */
        inline void 
        setSimilarityThreshold (float similarity_threshold)
        {
          similarity_threshold_ = similarity_threshold;
          similarity_threshold_squared_ = similarity_threshold * similarity_threshold;
        }
        
        /** \brief Get the similarity threshold between edge lengths
          * \return similarity threshold
          */
        inline float 
        getSimilarityThreshold ()
        {
          return (similarity_threshold_);
        }
        
        /** \brief Set the number of iterations
          * \param iterations number of iterations
          */
        inline void 
        setIterations (int iterations)
        {
          iterations_ = iterations;
        }
        
        /** \brief Get the number of iterations
          * \return number of iterations
          */
        inline int 
        getIterations ()
        {
          return (iterations_);
        }
        
        /** \brief Polygonal rejection of a single polygon, indexed by a subset of correspondences
          * \param corr all correspondences into \ref input_ and \ref target_
          * \param idx sampled indices into \b correspondences, must have a size equal to \ref cardinality_
          * \return true if all edge length ratios are larger than or equal to \ref similarity_threshold_
          */
        inline bool 
        thresholdPolygon (const pcl::Correspondences& corr, const std::vector<int>& idx)
        {
          if (cardinality_ == 2) // Special case: when two points are considered, we only have one edge
          {
            return (thresholdEdgeLength (corr[ idx[0] ].index_query, corr[ idx[1] ].index_query,
                                         corr[ idx[0] ].index_match, corr[ idx[1] ].index_match,
                                         cardinality_));
          }
          else
          { // Otherwise check all edges
            for (int i = 0; i < cardinality_; ++i)
              if (!thresholdEdgeLength (corr[ idx[i] ].index_query, corr[ idx[(i+1)%cardinality_] ].index_query,
                                        corr[ idx[i] ].index_match, corr[ idx[(i+1)%cardinality_] ].index_match,
                                        similarity_threshold_squared_))
                return (false);
            
            return (true);
          }
        }
        
        /** \brief Polygonal rejection of a single polygon, indexed by two point index vectors
          * \param source_indices indices of polygon points in \ref input_, must have a size equal to \ref cardinality_
          * \param target_indices corresponding indices of polygon points in \ref target_, must have a size equal to \ref cardinality_
          * \return true if all edge length ratios are larger than or equal to \ref similarity_threshold_
          */
        inline bool 
        thresholdPolygon (const std::vector<int>& source_indices, const std::vector<int>& target_indices)
        {
          // Convert indices to correspondences and an index vector pointing to each element
          pcl::Correspondences corr (cardinality_);
          std::vector<int> idx (cardinality_);
          for (int i = 0; i < cardinality_; ++i)
          {
            corr[i].index_query = source_indices[i];
            corr[i].index_match = target_indices[i];
            idx[i] = i;
          }
          
          return (thresholdPolygon (corr, idx));
        }

      protected:
        /** \brief Apply the rejection algorithm.
          * \param[out] correspondences the set of resultant correspondences.
          */
        inline void 
        applyRejection (pcl::Correspondences &correspondences)
        {
          getRemainingCorrespondences (*input_correspondences_, correspondences);
        }
        
        /** \brief Get k unique random indices in range {0,...,n-1} (sampling without replacement)
          * \note No check is made to ensure that k <= n.
          * \param n upper index range, exclusive
          * \param k number of unique indices to sample
          * \return k unique random indices in range {0,...,n-1}
          */
        inline std::vector<int> 
        getUniqueRandomIndices (int n, int k)
        {
          // Marked sampled indices and sample counter
          std::vector<bool> sampled (n, false);
          int samples = 0;
          // Resulting unique indices
          std::vector<int> result;
          result.reserve (k);
          do
          {
            // Pick a random index in the range
            const int idx = (std::rand () % n);
            // If unique
            if (!sampled[idx])
            {
              // Mark as sampled and increment result counter
              sampled[idx] = true;
              ++samples;
              // Store
              result.push_back (idx);
            }
          }
          while (samples < k);
          
          return (result);
        }
        
        /** \brief Squared Euclidean distance between two points using the members x, y and z
          * \param p1 first point
          * \param p2 second point
          * \return squared Euclidean distance
          */
        inline float 
        computeSquaredDistance (const SourceT& p1, const TargetT& p2)
        {
          const float dx = p2.x - p1.x;
          const float dy = p2.y - p1.y;
          const float dz = p2.z - p1.z;
          
          return (dx*dx + dy*dy + dz*dz);
        }
        
        /** \brief Edge length similarity thresholding
          * \param index_query_1 index of first source vertex
          * \param index_query_2 index of second source vertex
          * \param index_match_1 index of first target vertex
          * \param index_match_2 index of second target vertex
          * \param simsq squared similarity threshold in [0,1]
          * \return true if edge length ratio is larger than or equal to threshold
          */
        inline bool 
        thresholdEdgeLength (int index_query_1,
                             int index_query_2,
                             int index_match_1,
                             int index_match_2,
                             float simsq)
        {
          // Distance between source points
          const float dist_src = computeSquaredDistance ((*input_)[index_query_1], (*input_)[index_query_2]);
          // Distance between target points
          const float dist_tgt = computeSquaredDistance ((*target_)[index_match_1], (*target_)[index_match_2]);
          // Edge length similarity [0,1] where 1 is a perfect match
          const float edge_sim = (dist_src < dist_tgt ? dist_src / dist_tgt : dist_tgt / dist_src);
          
          return (edge_sim >= simsq);
        }
        
        /** \brief Compute a linear histogram. This function is equivalent to the MATLAB function \b histc, with the
          * edges set as follows: <b> lower:(upper-lower)/bins:upper </b>
          * \param data input samples
          * \param lower lower bound of input samples
          * \param upper upper bound of input samples
          * \param bins number of bins in output
          * \return linear histogram
          */
        std::vector<int> 
        computeHistogram (const std::vector<float>& data, float lower, float upper, int bins);
        
        /** \brief Find the optimal value for binary histogram thresholding using Otsu's method
          * \param histogram input histogram
          * \return threshold value according to Otsu's criterion
          */
        int 
        findThresholdOtsu (const std::vector<int>& histogram);

        /** \brief The input point cloud dataset */
        PointCloudSourceConstPtr input_;

        /** \brief The input point cloud dataset target */
        PointCloudTargetConstPtr target_;
        
        /** \brief Number of iterations to run */
        int iterations_;
        
        /** \brief The polygon cardinality used during rejection */
        int cardinality_;
        
        /** \brief Lower edge length threshold in [0,1] used for verifying polygon similarities, where 1 is a perfect match */
        float similarity_threshold_;
        
        /** \brief Squared value if \ref similarity_threshold_, only for internal use */
        float similarity_threshold_squared_;
    };
  }
}

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

#endif    // PCL_REGISTRATION_CORRESPONDENCE_REJECTION_POLY_H_
