shot_lrf_omp.hpp

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#ifndef PCL_FEATURES_IMPL_SHOT_LRF_OMP_H_
#define PCL_FEATURES_IMPL_SHOT_LRF_OMP_H_

#include <utility>
#include <pcl/features/shot_lrf_omp.h>
#include <pcl/features/shot_lrf.h>

template<typename PointInT, typename PointOutT>
void
pcl::SHOTLocalReferenceFrameEstimationOMP<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
  if (threads_ < 0)
    threads_ = 1;

  //check whether used with search radius or search k-neighbors
  if (this->getKSearch () != 0)
  {
    PCL_ERROR(
        "[pcl::%s::computeFeature] Error! Search method set to k-neighborhood. Call setKSearch(0) and setRadiusSearch( radius ) to use this class.\n",
        getClassName().c_str ());
    return;
  }
  tree_->setSortedResults (true);

  int data_size = static_cast<int> (indices_->size ());
#pragma omp parallel for num_threads(threads_)
  for (int i = 0; i < data_size; ++i)
  {
    // point result
    Eigen::Matrix3f rf;
    PointOutT& output_rf = output[i];

    //output_rf.confidence = getLocalRF ((*indices_)[i], rf);
    //if (output_rf.confidence == std::numeric_limits<float>::max ())

    std::vector<int> n_indices;
    std::vector<float> n_sqr_distances;
    this->searchForNeighbors ((*indices_)[i], search_parameter_, n_indices, n_sqr_distances);
    if (getLocalRF ((*indices_)[i], rf) == std::numeric_limits<float>::max ())
    {
      output.is_dense = false;
    }

    output_rf.x_axis.getNormalVector3fMap () = rf.row (0);
    output_rf.y_axis.getNormalVector3fMap () = rf.row (1);
    output_rf.z_axis.getNormalVector3fMap () = rf.row (2);
  }

}

template<typename PointInT, typename PointOutT>
void
pcl::SHOTLocalReferenceFrameEstimationOMP<PointInT, PointOutT>::computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &output)
{
  if (threads_ < 0)
     threads_ = 1;

  //check whether used with search radius or search k-neighbors
  if (this->getKSearch () != 0)
  {
    PCL_ERROR(
        "[pcl::%s::computeFeatureEigen] Error! Search method set to k-neighborhood. Call setKSearch(0) and setRadiusSearch( radius ) to use this class.\n",
        getClassName().c_str ());
    return;
  }
  tree_->setSortedResults (true);

  int data_size = static_cast<int> (indices_->size ());

  // Set up the output channels
  output.channels["shot_lrf"].name = "shot_lrf";
  output.channels["shot_lrf"].offset = 0;
  output.channels["shot_lrf"].size = 4;
  output.channels["shot_lrf"].count = 9;
  output.channels["shot_lrf"].datatype = sensor_msgs::PointField::FLOAT32;

  //output.points.resize (indices_->size (), 10);
  output.points.resize (data_size, 9);

#pragma omp parallel for num_threads(threads_)
  for (int i = 0; i < data_size; ++i)
  {
    // point result
    Eigen::Matrix3f rf;

    //output.points (i, 9) = getLocalRF ((*indices_)[i], rf);
    //if (output.points (i, 9) == std::numeric_limits<float>::max ())
    if (getLocalRF ((*indices_)[i], rf) == std::numeric_limits<float>::max ())
    {
      output.is_dense = false;
    }

    output.points.block<1, 3> (i, 0) = rf.row (0);
    output.points.block<1, 3> (i, 3) = rf.row (1);
    output.points.block<1, 3> (i, 6) = rf.row (2);
  }

}

#define PCL_INSTANTIATE_SHOTLocalReferenceFrameEstimationOMP(T,OutT) template class PCL_EXPORTS pcl::SHOTLocalReferenceFrameEstimationOMP<T,OutT>;

#endif    // PCL_FEATURES_IMPL_SHOT_LRF_H_