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#pragma once

#include <pcl/features/pfh.h>

#include <pcl/common/point_tests.h> // for pcl::isFinite


//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> bool
pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computePairFeatures (
      const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
      int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
{
  pcl::computePairFeatures (cloud.points[p_idx].getVector4fMap (), normals.points[p_idx].getNormalVector4fMap (),
                            cloud.points[q_idx].getVector4fMap (), normals.points[q_idx].getNormalVector4fMap (),
                            f1, f2, f3, f4);
  return (true);
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computePointPFHSignature (
      const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
      const std::vector<int> &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
{
  int h_index, h_p;

  // Clear the resultant point histogram
  pfh_histogram.setZero ();

  // Factorization constant
  float hist_incr = 100.0f / static_cast<float> (indices.size () * (indices.size () - 1) / 2);

  std::pair<int, int> key;
  bool key_found = false;

  // Iterate over all the points in the neighborhood
  for (std::size_t i_idx = 0; i_idx < indices.size (); ++i_idx)
  {
    for (std::size_t j_idx = 0; j_idx < i_idx; ++j_idx)
    {
      // If the 3D points are invalid, don't bother estimating, just continue
      if (!isFinite (cloud.points[indices[i_idx]]) || !isFinite (cloud.points[indices[j_idx]]))
        continue;

      if (use_cache_)
      {
        // In order to create the key, always use the smaller index as the first key pair member
        int p1, p2;
  //      if (indices[i_idx] >= indices[j_idx])
  //      {
          p1 = indices[i_idx];
          p2 = indices[j_idx];
  //      }
  //      else
  //      {
  //        p1 = indices[j_idx];
  //        p2 = indices[i_idx];
  //      }
        key = std::pair<int, int> (p1, p2);

        // Check to see if we already estimated this pair in the global hashmap
        std::map<std::pair<int, int>, Eigen::Vector4f, std::less<>, Eigen::aligned_allocator<std::pair<const std::pair<int, int>, Eigen::Vector4f> > >::iterator fm_it = feature_map_.find (key);
        if (fm_it != feature_map_.end ())
        {
          pfh_tuple_ = fm_it->second;
          key_found = true;
        }
        else
        {
          // Compute the pair NNi to NNj
          if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
                                    pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
            continue;

          key_found = false;
        }
      }
      else
        if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
                                  pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
          continue;

      // Normalize the f1, f2, f3 features and push them in the histogram
      f_index_[0] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[0] + M_PI) * d_pi_)));
      if (f_index_[0] < 0)         f_index_[0] = 0;
      if (f_index_[0] >= nr_split) f_index_[0] = nr_split - 1;

      f_index_[1] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[1] + 1.0) * 0.5)));
      if (f_index_[1] < 0)         f_index_[1] = 0;
      if (f_index_[1] >= nr_split) f_index_[1] = nr_split - 1;

      f_index_[2] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[2] + 1.0) * 0.5)));
      if (f_index_[2] < 0)         f_index_[2] = 0;
      if (f_index_[2] >= nr_split) f_index_[2] = nr_split - 1;

      // Copy into the histogram
      h_index = 0;
      h_p     = 1;
      for (const int &d : f_index_)
      {
        h_index += h_p * d;
        h_p     *= nr_split;
      }
      pfh_histogram[h_index] += hist_incr;

      if (use_cache_ && !key_found)
      {
        // Save the value in the hashmap
        feature_map_[key] = pfh_tuple_;

        // Use a maximum cache so that we don't go overboard on RAM usage
        key_list_.push (key);
        // Check to see if we need to remove an element due to exceeding max_size
        if (key_list_.size () > max_cache_size_)
        {
          // Remove the oldest element.
          feature_map_.erase (key_list_.front ());
          key_list_.pop ();
        }
      }
    }
  }
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PFHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Clear the feature map
  feature_map_.clear ();
  std::queue<std::pair<int, int> > empty;
  std::swap (key_list_, empty);

  pfh_histogram_.setZero (nr_subdiv_ * nr_subdiv_ * nr_subdiv_);

  // Allocate enough space to hold the results
  // \note This resize is irrelevant for a radiusSearch ().
  std::vector<int> nn_indices (k_);
  std::vector<float> nn_dists (k_);

  output.is_dense = true;
  // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
  if (input_->is_dense)
  {
    // Iterating over the entire index vector
    for (std::size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
          output.points[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();

        output.is_dense = false;
        continue;
      }

      // Estimate the PFH signature at each patch
      computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);

      // Copy into the resultant cloud
      for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
        output.points[idx].histogram[d] = pfh_histogram_[d];
    }
  }
  else
  {
    // Iterating over the entire index vector
    for (std::size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (!isFinite ((*input_)[(*indices_)[idx]]) ||
          this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
          output.points[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();

        output.is_dense = false;
        continue;
      }

      // Estimate the PFH signature at each patch
      computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);

      // Copy into the resultant cloud
      for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
        output.points[idx].histogram[d] = pfh_histogram_[d];
    }
  }
}

#define PCL_INSTANTIATE_PFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PFHEstimation<T,NT,OutT>;

