correspondence_estimation.hpp

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#ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_H_
#define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_H_
 
#include <pcl/common/copy_point.h>
#include <pcl/common/io.h>
 
namespace pcl {
 
namespace registration {
 
template <typename PointSource, typename PointTarget, typename Scalar>
void
CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::setInputTarget(
    const PointCloudTargetConstPtr& cloud)
{
  if (cloud->points.empty()) {
    PCL_ERROR("[pcl::registration::%s::setInputTarget] Invalid or empty point cloud "
              "dataset given!\n",
              getClassName().c_str());
    return;
  }
  target_ = cloud;
 
  // Set the internal point representation of choice
  if (point_representation_)
    tree_->setPointRepresentation(point_representation_);
 
  target_cloud_updated_ = true;
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
bool
CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute()
{
  if (!target_) {
    PCL_ERROR("[pcl::registration::%s::compute] No input target dataset was given!\n",
              getClassName().c_str());
    return (false);
  }
 
  // Only update target kd-tree if a new target cloud was set
  if (target_cloud_updated_ && !force_no_recompute_) {
    // If the target indices have been given via setIndicesTarget
    if (target_indices_)
      tree_->setInputCloud(target_, target_indices_);
    else
      tree_->setInputCloud(target_);
 
    target_cloud_updated_ = false;
  }
 
  return (PCLBase<PointSource>::initCompute());
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
bool
CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initComputeReciprocal()
{
  // Only update source kd-tree if a new target cloud was set
  if (source_cloud_updated_ && !force_no_recompute_reciprocal_) {
    if (point_representation_)
      tree_reciprocal_->setPointRepresentation(point_representation_);
    // If the target indices have been given via setIndicesTarget
    if (indices_)
      tree_reciprocal_->setInputCloud(getInputSource(), getIndicesSource());
    else
      tree_reciprocal_->setInputCloud(getInputSource());
 
    source_cloud_updated_ = false;
  }
 
  return (true);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
void
CorrespondenceEstimation<PointSource, PointTarget, Scalar>::determineCorrespondences(
    pcl::Correspondences& correspondences, double max_distance)
{
  if (!initCompute())
    return;
 
  double max_dist_sqr = max_distance * max_distance;
 
  correspondences.resize(indices_->size());
 
  pcl::Indices index(1);
  std::vector<float> distance(1);
  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>()) {
    // Iterate over the input set of source indices
    for (const auto& idx : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx], 1, index, distance);
      if (distance[0] > max_dist_sqr)
        continue;
 
      corr.index_query = idx;
      corr.index_match = index[0];
      corr.distance = distance[0];
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  else {
    PointTarget pt;
 
    // Iterate over the input set of source indices
    for (const auto& idx : (*indices_)) {
      // Copy the source data to a target PointTarget format so we can search in the
      // tree
      copyPoint((*input_)[idx], pt);
 
      tree_->nearestKSearch(pt, 1, index, distance);
      if (distance[0] > max_dist_sqr)
        continue;
 
      corr.index_query = idx;
      corr.index_match = index[0];
      corr.distance = distance[0];
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
void
CorrespondenceEstimation<PointSource, PointTarget, Scalar>::
    determineReciprocalCorrespondences(pcl::Correspondences& correspondences,
                                       double max_distance)
{
  if (!initCompute())
    return;
 
  // setup tree for reciprocal search
  // Set the internal point representation of choice
  if (!initComputeReciprocal())
    return;
  double max_dist_sqr = max_distance * max_distance;
 
  correspondences.resize(indices_->size());
  pcl::Indices index(1);
  std::vector<float> distance(1);
  pcl::Indices index_reciprocal(1);
  std::vector<float> distance_reciprocal(1);
  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>()) {
    // Iterate over the input set of source indices
    for (const auto& idx : (*indices_)) {
      tree_->nearestKSearch((*input_)[idx], 1, index, distance);
      if (distance[0] > max_dist_sqr)
        continue;
 
      target_idx = index[0];
 
      tree_reciprocal_->nearestKSearch(
          (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);
      if (distance_reciprocal[0] > max_dist_sqr || idx != index_reciprocal[0])
        continue;
 
      corr.index_query = idx;
      corr.index_match = index[0];
      corr.distance = distance[0];
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  else {
    PointTarget pt_src;
    PointSource pt_tgt;
 
    // Iterate over the input set of source indices
    for (const auto& idx : (*indices_)) {
      // Copy the source data to a target PointTarget format so we can search in the
      // tree
      copyPoint((*input_)[idx], pt_src);
 
      tree_->nearestKSearch(pt_src, 1, index, distance);
      if (distance[0] > max_dist_sqr)
        continue;
 
      target_idx = index[0];
 
      // Copy the target data to a target PointSource format so we can search in the
      // tree_reciprocal
      copyPoint((*target_)[target_idx], pt_tgt);
 
      tree_reciprocal_->nearestKSearch(
          pt_tgt, 1, index_reciprocal, distance_reciprocal);
      if (distance_reciprocal[0] > max_dist_sqr || idx != index_reciprocal[0])
        continue;
 
      corr.index_query = idx;
      corr.index_match = index[0];
      corr.distance = distance[0];
      correspondences[nr_valid_correspondences++] = corr;
    }
  }
  correspondences.resize(nr_valid_correspondences);
  deinitCompute();
}
 
} // namespace registration
} // namespace pcl
 
//#define PCL_INSTANTIATE_CorrespondenceEstimation(T,U) template class PCL_EXPORTS
// pcl::registration::CorrespondenceEstimation<T,U>;
 
#endif /* PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_H_ */