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#ifndef PCL_RECOGNITION_GEOMETRIC_CONSISTENCY_IMPL_H_
#define PCL_RECOGNITION_GEOMETRIC_CONSISTENCY_IMPL_H_

#include <pcl/recognition/cg/geometric_consistency.h>
#include <pcl/registration/correspondence_types.h>
#include <pcl/registration/correspondence_rejection_sample_consensus.h>
#include <pcl/common/io.h>

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
bool
gcCorrespSorter (pcl::Correspondence i, pcl::Correspondence j) 
{ 
  return (i.distance < j.distance); 
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointModelT, typename PointSceneT> void
pcl::GeometricConsistencyGrouping<PointModelT, PointSceneT>::clusterCorrespondences (std::vector<Correspondences> &model_instances)
{
  model_instances.clear ();
  found_transformations_.clear ();

  if (!model_scene_corrs_)
  {
    PCL_ERROR(
      "[pcl::GeometricConsistencyGrouping::clusterCorrespondences()] Error! Correspondences not set, please set them before calling again this function.\n");
    return;
  }

  CorrespondencesPtr sorted_corrs (new Correspondences (*model_scene_corrs_));

  std::sort (sorted_corrs->begin (), sorted_corrs->end (), gcCorrespSorter);

  model_scene_corrs_ = sorted_corrs;

  std::vector<int> consensus_set;
  std::vector<bool> taken_corresps (model_scene_corrs_->size (), false);

  Eigen::Vector3f dist_ref, dist_trg;

  //temp copy of scene cloud with the type cast to ModelT in order to use Ransac
  PointCloudPtr temp_scene_cloud_ptr (new PointCloud ());
  pcl::copyPointCloud<PointSceneT, PointModelT> (*scene_, *temp_scene_cloud_ptr);

  pcl::registration::CorrespondenceRejectorSampleConsensus<PointModelT> corr_rejector;
  corr_rejector.setMaximumIterations (10000);
  corr_rejector.setInlierThreshold (gc_size_);
  corr_rejector.setInputSource(input_);
  corr_rejector.setInputTarget (temp_scene_cloud_ptr);

  for (size_t i = 0; i < model_scene_corrs_->size (); ++i)
  {
    if (taken_corresps[i])
      continue;

    consensus_set.clear ();
    consensus_set.push_back (static_cast<int> (i));
    
    for (size_t j = 0; j < model_scene_corrs_->size (); ++j)
    {
      if ( j != i &&  !taken_corresps[j])
      {
        //Let's check if j fits into the current consensus set
        bool is_a_good_candidate = true;
        for (size_t k = 0; k < consensus_set.size (); ++k)
        {
          int scene_index_k = model_scene_corrs_->at (consensus_set[k]).index_match;
          int model_index_k = model_scene_corrs_->at (consensus_set[k]).index_query;
          int scene_index_j = model_scene_corrs_->at (j).index_match;
          int model_index_j = model_scene_corrs_->at (j).index_query;
          
          const Eigen::Vector3f& scene_point_k = scene_->at (scene_index_k).getVector3fMap ();
          const Eigen::Vector3f& model_point_k = input_->at (model_index_k).getVector3fMap ();
          const Eigen::Vector3f& scene_point_j = scene_->at (scene_index_j).getVector3fMap ();
          const Eigen::Vector3f& model_point_j = input_->at (model_index_j).getVector3fMap ();

          dist_ref = scene_point_k - scene_point_j;
          dist_trg = model_point_k - model_point_j;

          double distance = fabs (dist_ref.norm () - dist_trg.norm ());

          if (distance > gc_size_)
          {
            is_a_good_candidate = false;
            break;
          }
        }

        if (is_a_good_candidate)
          consensus_set.push_back (static_cast<int> (j));
      }
    }
    
    if (static_cast<int> (consensus_set.size ()) > gc_threshold_)
    {
      Correspondences temp_corrs, filtered_corrs;
      for (size_t j = 0; j < consensus_set.size (); j++)
      {
        temp_corrs.push_back (model_scene_corrs_->at (consensus_set[j]));
        taken_corresps[ consensus_set[j] ] = true;
      }
      //ransac filtering
      corr_rejector.getRemainingCorrespondences (temp_corrs, filtered_corrs);
      //save transformations for recognize
      found_transformations_.push_back (corr_rejector.getBestTransformation ());

      model_instances.push_back (filtered_corrs);
    }
  }
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointModelT, typename PointSceneT> bool
pcl::GeometricConsistencyGrouping<PointModelT, PointSceneT>::recognize (
    std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > &transformations)
{
  std::vector<pcl::Correspondences> model_instances;
  return (this->recognize (transformations, model_instances));
}

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointModelT, typename PointSceneT> bool
pcl::GeometricConsistencyGrouping<PointModelT, PointSceneT>::recognize (
    std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > &transformations, std::vector<pcl::Correspondences> &clustered_corrs)
{
  transformations.clear ();
  if (!this->initCompute ())
  {
    PCL_ERROR(
      "[pcl::GeometricConsistencyGrouping::recognize()] Error! Model cloud or Scene cloud not set, please set them before calling again this function.\n");
    return (false);
  }

  clusterCorrespondences (clustered_corrs);

  transformations = found_transformations_;

  this->deinitCompute ();
  return (true);
}

#define PCL_INSTANTIATE_GeometricConsistencyGrouping(T,ST) template class PCL_EXPORTS pcl::GeometricConsistencyGrouping<T,ST>;

#endif // PCL_RECOGNITION_GEOMETRIC_CONSISTENCY_IMPL_H_
