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#ifndef PCL_REGISTRATION_IMPL_PPF_REGISTRATION_H_
#define PCL_REGISTRATION_IMPL_PPF_REGISTRATION_H_

#include <pcl/registration/ppf_registration.h>
#include <pcl/features/ppf.h>
#include <pcl/common/transforms.h>

#include <pcl/features/pfh.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget> void
pcl::PPFRegistration<PointSource, PointTarget>::setInputTarget (const PointCloudTargetConstPtr &cloud)
{
  Registration<PointSource, PointTarget>::setInputTarget (cloud);

  scene_search_tree_ = typename pcl::KdTreeFLANN<PointTarget>::Ptr (new pcl::KdTreeFLANN<PointTarget>);
  scene_search_tree_->setInputCloud (target_);
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget> void
pcl::PPFRegistration<PointSource, PointTarget>::computeTransformation (PointCloudSource &output, const Eigen::Matrix4f& guess)
{
  if (!search_method_)
  {
    PCL_ERROR("[pcl::PPFRegistration::computeTransformation] Search method not set - skipping computeTransformation!\n");
    return;
  }

  if (guess != Eigen::Matrix4f::Identity ())
  {
    PCL_ERROR("[pcl::PPFRegistration::computeTransformation] setting initial transform (guess) not implemented!\n");
  }

  PoseWithVotesList voted_poses;
  std::vector <std::vector <unsigned int> > accumulator_array;
  accumulator_array.resize (input_->points.size ());

  std::size_t aux_size = static_cast<std::size_t> (std::floor (2 * M_PI / search_method_->getAngleDiscretizationStep ()));
  for (std::size_t i = 0; i < input_->points.size (); ++i)
  {
    std::vector<unsigned int> aux (aux_size);
    accumulator_array[i] = aux;
  }
  PCL_INFO ("Accumulator array size: %u x %u.\n", accumulator_array.size (), accumulator_array.back ().size ());

  // Consider every <scene_reference_point_sampling_rate>-th point as the reference point => fix s_r
  float f1, f2, f3, f4;
  for (std::size_t scene_reference_index = 0; scene_reference_index < target_->points.size (); scene_reference_index += scene_reference_point_sampling_rate_)
  {
    Eigen::Vector3f scene_reference_point = target_->points[scene_reference_index].getVector3fMap (),
        scene_reference_normal = target_->points[scene_reference_index].getNormalVector3fMap ();

    float rotation_angle_sg = std::acos (scene_reference_normal.dot (Eigen::Vector3f::UnitX ()));
    bool parallel_to_x_sg = (scene_reference_normal.y() == 0.0f && scene_reference_normal.z() == 0.0f);
    Eigen::Vector3f rotation_axis_sg = (parallel_to_x_sg)?(Eigen::Vector3f::UnitY ()):(scene_reference_normal.cross (Eigen::Vector3f::UnitX ()). normalized());
    Eigen::AngleAxisf rotation_sg (rotation_angle_sg, rotation_axis_sg);
    Eigen::Affine3f transform_sg (Eigen::Translation3f ( rotation_sg * ((-1) * scene_reference_point)) * rotation_sg);

    // For every other point in the scene => now have pair (s_r, s_i) fixed
    std::vector<int> indices;
    std::vector<float> distances;
    scene_search_tree_->radiusSearch (target_->points[scene_reference_index],
                                     search_method_->getModelDiameter () /2,
                                     indices,
                                     distances);
    for(const std::size_t &scene_point_index : indices)
//    for(std::size_t i = 0; i < target_->points.size (); ++i)
    {
      //size_t scene_point_index = i;
      if (scene_reference_index != scene_point_index)
      {
        if (/*pcl::computePPFPairFeature*/pcl::computePairFeatures (target_->points[scene_reference_index].getVector4fMap (),
                                        target_->points[scene_reference_index].getNormalVector4fMap (),
                                        target_->points[scene_point_index].getVector4fMap (),
                                        target_->points[scene_point_index].getNormalVector4fMap (),
                                        f1, f2, f3, f4))
        {
          std::vector<std::pair<std::size_t, std::size_t> > nearest_indices;
          search_method_->nearestNeighborSearch (f1, f2, f3, f4, nearest_indices);

          // Compute alpha_s angle
          Eigen::Vector3f scene_point = target_->points[scene_point_index].getVector3fMap ();

          Eigen::Vector3f scene_point_transformed = transform_sg * scene_point;
          float alpha_s = std::atan2 ( -scene_point_transformed(2), scene_point_transformed(1));
          if (std::sin (alpha_s) * scene_point_transformed(2) < 0.0f)
            alpha_s *= (-1);
          alpha_s *= (-1);

          // Go through point pairs in the model with the same discretized feature
          for (const auto &nearest_index : nearest_indices)
          {
            std::size_t model_reference_index = nearest_index.first;
            std::size_t model_point_index = nearest_index.second;
            // Calculate angle alpha = alpha_m - alpha_s
            float alpha = search_method_->alpha_m_[model_reference_index][model_point_index] - alpha_s;
            unsigned int alpha_discretized = static_cast<unsigned int> (std::floor (alpha) + std::floor (M_PI / search_method_->getAngleDiscretizationStep ()));
            accumulator_array[model_reference_index][alpha_discretized] ++;
          }
        }
        else PCL_ERROR ("[pcl::PPFRegistration::computeTransformation] Computing pair feature vector between points %u and %u went wrong.\n", scene_reference_index, scene_point_index);
      }
    }

    std::size_t max_votes_i = 0, max_votes_j = 0;
    unsigned int max_votes = 0;

    for (std::size_t i = 0; i < accumulator_array.size (); ++i)
      for (std::size_t j = 0; j < accumulator_array.back ().size (); ++j)
      {
        if (accumulator_array[i][j] > max_votes)
        {
          max_votes = accumulator_array[i][j];
          max_votes_i = i;
          max_votes_j = j;
        }
        // Reset accumulator_array for the next set of iterations with a new scene reference point
        accumulator_array[i][j] = 0;
      }

    Eigen::Vector3f model_reference_point = input_->points[max_votes_i].getVector3fMap (),
        model_reference_normal = input_->points[max_votes_i].getNormalVector3fMap ();
    float rotation_angle_mg = std::acos (model_reference_normal.dot (Eigen::Vector3f::UnitX ()));
    bool parallel_to_x_mg = (model_reference_normal.y() == 0.0f && model_reference_normal.z() == 0.0f);
    Eigen::Vector3f rotation_axis_mg = (parallel_to_x_mg)?(Eigen::Vector3f::UnitY ()):(model_reference_normal.cross (Eigen::Vector3f::UnitX ()). normalized());
    Eigen::AngleAxisf rotation_mg (rotation_angle_mg, rotation_axis_mg);
    Eigen::Affine3f transform_mg (Eigen::Translation3f ( rotation_mg * ((-1) * model_reference_point)) * rotation_mg);
    Eigen::Affine3f max_transform = 
      transform_sg.inverse () * 
      Eigen::AngleAxisf ((static_cast<float> (max_votes_j) - std::floor (static_cast<float> (M_PI) / search_method_->getAngleDiscretizationStep ())) * search_method_->getAngleDiscretizationStep (), Eigen::Vector3f::UnitX ()) * 
      transform_mg;

    voted_poses.push_back (PoseWithVotes (max_transform, max_votes));
  }
  PCL_DEBUG ("Done with the Hough Transform ...\n");

  // Cluster poses for filtering out outliers and obtaining more precise results
  PoseWithVotesList results;
  clusterPoses (voted_poses, results);

  pcl::transformPointCloud (*input_, output, results.front ().pose);

  transformation_ = final_transformation_ = results.front ().pose.matrix ();
  converged_ = true;
}


//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget> void
pcl::PPFRegistration<PointSource, PointTarget>::clusterPoses (typename pcl::PPFRegistration<PointSource, PointTarget>::PoseWithVotesList &poses,
                                                              typename pcl::PPFRegistration<PointSource, PointTarget>::PoseWithVotesList &result)
{
  PCL_INFO ("Clustering poses ...\n");
  // Start off by sorting the poses by the number of votes
  sort(poses.begin (), poses.end (), poseWithVotesCompareFunction);

  std::vector<PoseWithVotesList> clusters;
  std::vector<std::pair<std::size_t, unsigned int> > cluster_votes;
  for (std::size_t poses_i = 0; poses_i < poses.size(); ++ poses_i)
  {
    bool found_cluster = false;
    for (std::size_t clusters_i = 0; clusters_i < clusters.size(); ++ clusters_i)
    {
      if (posesWithinErrorBounds (poses[poses_i].pose, clusters[clusters_i].front ().pose))
      {
        found_cluster = true;
        clusters[clusters_i].push_back (poses[poses_i]);
        cluster_votes[clusters_i].second += poses[poses_i].votes;
        break;
      }
    }

    if (!found_cluster)
    {
      // Create a new cluster with the current pose
      PoseWithVotesList new_cluster;
      new_cluster.push_back (poses[poses_i]);
      clusters.push_back (new_cluster);
      cluster_votes.push_back (std::pair<std::size_t, unsigned int> (clusters.size () - 1, poses[poses_i].votes));
    }
 }

  // Sort clusters by total number of votes
  std::sort (cluster_votes.begin (), cluster_votes.end (), clusterVotesCompareFunction);
  // Compute pose average and put them in result vector
  /// @todo some kind of threshold for determining whether a cluster has enough votes or not...
  /// now just taking the first three clusters
  result.clear ();
  std::size_t max_clusters = (clusters.size () < 3) ? clusters.size () : 3;
  for (std::size_t cluster_i = 0; cluster_i < max_clusters; ++ cluster_i)
  {
    PCL_INFO ("Winning cluster has #votes: %d and #poses voted: %d.\n", cluster_votes[cluster_i].second, clusters[cluster_votes[cluster_i].first].size ());
    Eigen::Vector3f translation_average (0.0, 0.0, 0.0);
    Eigen::Vector4f rotation_average (0.0, 0.0, 0.0, 0.0);
    for (typename PoseWithVotesList::iterator v_it = clusters[cluster_votes[cluster_i].first].begin (); v_it != clusters[cluster_votes[cluster_i].first].end (); ++ v_it)
    {
      translation_average += v_it->pose.translation ();
      /// averaging rotations by just averaging the quaternions in 4D space - reference "On Averaging Rotations" by CLAUS GRAMKOW
      rotation_average += Eigen::Quaternionf (v_it->pose.rotation ()).coeffs ();
    }

    translation_average /= static_cast<float> (clusters[cluster_votes[cluster_i].first].size ());
    rotation_average /= static_cast<float> (clusters[cluster_votes[cluster_i].first].size ());

    Eigen::Affine3f transform_average;
    transform_average.translation ().matrix () = translation_average;
    transform_average.linear ().matrix () = Eigen::Quaternionf (rotation_average).normalized().toRotationMatrix ();

    result.push_back (PoseWithVotes (transform_average, cluster_votes[cluster_i].second));
  }
}


//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget> bool
pcl::PPFRegistration<PointSource, PointTarget>::posesWithinErrorBounds (Eigen::Affine3f &pose1,
                                                                        Eigen::Affine3f &pose2)
{
  float position_diff = (pose1.translation () - pose2.translation ()).norm ();
  Eigen::AngleAxisf rotation_diff_mat ((pose1.rotation ().inverse ().lazyProduct (pose2.rotation ()).eval()));

  float rotation_diff_angle = std::abs (rotation_diff_mat.angle ());

  return (position_diff < clustering_position_diff_threshold_ && rotation_diff_angle < clustering_rotation_diff_threshold_);
}


//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget> bool
pcl::PPFRegistration<PointSource, PointTarget>::poseWithVotesCompareFunction (const typename pcl::PPFRegistration<PointSource, PointTarget>::PoseWithVotes &a,
                                                                              const typename pcl::PPFRegistration<PointSource, PointTarget>::PoseWithVotes &b )
{
  return (a.votes > b.votes);
}


//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget> bool
pcl::PPFRegistration<PointSource, PointTarget>::clusterVotesCompareFunction (const std::pair<std::size_t, unsigned int> &a,
                                                                             const std::pair<std::size_t, unsigned int> &b)
{
  return (a.second > b.second);
}

//#define PCL_INSTANTIATE_PPFRegistration(PointSource,PointTarget) template class PCL_EXPORTS pcl::PPFRegistration<PointSource, PointTarget>;

#endif // PCL_REGISTRATION_IMPL_PPF_REGISTRATION_H_
