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#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_

#include <pcl/common/copy_point.h>

namespace pcl {

namespace registration {

template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
bool
CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::
    initCompute()
{
  if (!source_normals_ || !target_normals_) {
    PCL_WARN("[pcl::registration::%s::initCompute] Datasets containing normals for "
             "source/target have not been given!\n",
             getClassName().c_str());
    return (false);
  }

  return (
      CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute());
}

///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::
    determineCorrespondences(pcl::Correspondences& correspondences, double max_distance)
{
  if (!initCompute())
    return;

  correspondences.resize(indices_->size());

  pcl::Indices nn_indices(k_);
  std::vector<float> nn_dists(k_);

  int min_index = 0;

  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;

  // Check if the template types are the same. If true, avoid a copy.
  // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
  // macro!
  if (isSamePointType<PointSource, PointTarget>()) {
    PointTarget pt;
    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);

      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      float min_dist = std::numeric_limits<float>::max();

      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        float cos_angle = (*source_normals_)[idx_i].normal_x *
                              (*target_normals_)[nn_indices[j]].normal_x +
                          (*source_normals_)[idx_i].normal_y *
                              (*target_normals_)[nn_indices[j]].normal_y +
                          (*source_normals_)[idx_i].normal_z *
                              (*target_normals_)[nn_indices[j]].normal_z;
        float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);

        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;

      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  else {
    PointTarget pt;

    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);

      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      float min_dist = std::numeric_limits<float>::max();

      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        PointSource pt_src;
        // Copy the source data to a target PointTarget format so we can search in the
        // tree
        copyPoint((*input_)[idx_i], pt_src);

        float cos_angle = (*source_normals_)[idx_i].normal_x *
                              (*target_normals_)[nn_indices[j]].normal_x +
                          (*source_normals_)[idx_i].normal_y *
                              (*target_normals_)[nn_indices[j]].normal_y +
                          (*source_normals_)[idx_i].normal_z *
                              (*target_normals_)[nn_indices[j]].normal_z;
        float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);

        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;

      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}

template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
void
CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT, Scalar>::
    determineReciprocalCorrespondences(pcl::Correspondences& correspondences,
                                       double max_distance)
{
  if (!initCompute())
    return;

  // Set the internal point representation of choice
  if (!initComputeReciprocal())
    return;

  correspondences.resize(indices_->size());

  pcl::Indices nn_indices(k_);
  std::vector<float> nn_dists(k_);
  pcl::Indices index_reciprocal(1);
  std::vector<float> distance_reciprocal(1);

  int min_index = 0;

  pcl::Correspondence corr;
  unsigned int nr_valid_correspondences = 0;
  int target_idx = 0;

  // Check if the template types are the same. If true, avoid a copy.
  // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
  // macro!
  if (isSamePointType<PointSource, PointTarget>()) {
    PointTarget pt;
    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);

      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      float min_dist = std::numeric_limits<float>::max();

      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        float cos_angle = (*source_normals_)[idx_i].normal_x *
                              (*target_normals_)[nn_indices[j]].normal_x +
                          (*source_normals_)[idx_i].normal_y *
                              (*target_normals_)[nn_indices[j]].normal_y +
                          (*source_normals_)[idx_i].normal_z *
                              (*target_normals_)[nn_indices[j]].normal_z;
        float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);

        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;

      // Check if the correspondence is reciprocal
      target_idx = nn_indices[min_index];
      tree_reciprocal_->nearestKSearch(
          (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);

      if (idx_i != index_reciprocal[0])
        continue;

      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  else {
    PointTarget pt;

    // Iterate over the input set of source indices
    for (const auto& idx_i : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);

      // Among the K nearest neighbours find the one with minimum perpendicular distance
      // to the normal
      float min_dist = std::numeric_limits<float>::max();

      // Find the best correspondence
      for (std::size_t j = 0; j < nn_indices.size(); j++) {
        PointSource pt_src;
        // Copy the source data to a target PointTarget format so we can search in the
        // tree
        copyPoint((*input_)[idx_i], pt_src);

        float cos_angle = (*source_normals_)[idx_i].normal_x *
                              (*target_normals_)[nn_indices[j]].normal_x +
                          (*source_normals_)[idx_i].normal_y *
                              (*target_normals_)[nn_indices[j]].normal_y +
                          (*source_normals_)[idx_i].normal_z *
                              (*target_normals_)[nn_indices[j]].normal_z;
        float dist = nn_dists[j] * (2.0f - cos_angle * cos_angle);

        if (dist < min_dist) {
          min_dist = dist;
          min_index = static_cast<int>(j);
        }
      }
      if (min_dist > max_distance)
        continue;

      // Check if the correspondence is reciprocal
      target_idx = nn_indices[min_index];
      tree_reciprocal_->nearestKSearch(
          (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);

      if (idx_i != index_reciprocal[0])
        continue;

      corr.index_query = idx_i;
      corr.index_match = nn_indices[min_index];
      corr.distance = nn_dists[min_index]; // min_dist;
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}

} // namespace registration
} // namespace pcl

#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_BACK_PROJECTION_HPP_
