normal_3d_omp.hpp

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#ifndef PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_
#define PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_
 
#include <pcl/features/normal_3d_omp.h>
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::NormalEstimationOMP<PointInT, PointOutT>::setNumberOfThreads (unsigned int nr_threads)
{
#ifdef _OPENMP
  if (nr_threads == 0)
    threads_ = omp_get_num_procs();
  else
    threads_ = nr_threads;
  PCL_DEBUG ("[pcl::NormalEstimationOMP::setNumberOfThreads] Setting number of threads to %u.\n", threads_);
#else
  threads_ = 1;
  if (nr_threads != 1)
    PCL_WARN ("[pcl::NormalEstimationOMP::setNumberOfThreads] Parallelization is requested, but OpenMP is not available! Continuing without parallelization.\n");
#endif // _OPENMP
}
 
///////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::NormalEstimationOMP<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Allocate enough space to hold the results
  // \note This resize is irrelevant for a radiusSearch ().
  pcl::Indices 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)
  {
#pragma omp parallel for \
  default(none) \
  shared(output) \
  firstprivate(nn_indices, nn_dists) \
  num_threads(threads_) \
  schedule(dynamic, chunk_size_)
    // Iterating over the entire index vector
    for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
    {
      Eigen::Vector4f n;
      if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0 ||
          !pcl::computePointNormal (*surface_, nn_indices, n, output[idx].curvature))
      {
        output[idx].normal[0] = output[idx].normal[1] = output[idx].normal[2] = output[idx].curvature = std::numeric_limits<float>::quiet_NaN ();
 
        output.is_dense = false;
        continue;
      }
 
      output[idx].normal_x = n[0];
      output[idx].normal_y = n[1];
      output[idx].normal_z = n[2];
 
      flipNormalTowardsViewpoint ((*input_)[(*indices_)[idx]], vpx_, vpy_, vpz_,
                                  output[idx].normal[0], output[idx].normal[1], output[idx].normal[2]);
 
    }
  }
  else
  {
#pragma omp parallel for \
  default(none) \
  shared(output) \
  firstprivate(nn_indices, nn_dists) \
  num_threads(threads_) \
  schedule(dynamic, chunk_size_)
    // Iterating over the entire index vector
    for (std::ptrdiff_t idx = 0; idx < static_cast<std::ptrdiff_t> (indices_->size ()); ++idx)
    {
      Eigen::Vector4f n;
      if (!isFinite ((*input_)[(*indices_)[idx]]) ||
          this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0 ||
          !pcl::computePointNormal (*surface_, nn_indices, n, output[idx].curvature))
      {
        output[idx].normal[0] = output[idx].normal[1] = output[idx].normal[2] = output[idx].curvature = std::numeric_limits<float>::quiet_NaN ();
 
        output.is_dense = false;
        continue;
      }
 
      output[idx].normal_x = n[0];
      output[idx].normal_y = n[1];
      output[idx].normal_z = n[2];
 
      flipNormalTowardsViewpoint ((*input_)[(*indices_)[idx]], vpx_, vpy_, vpz_,
                                  output[idx].normal[0], output[idx].normal[1], output[idx].normal[2]);
 
    }
  }
}
 
#define PCL_INSTANTIATE_NormalEstimationOMP(T,NT) template class PCL_EXPORTS pcl::NormalEstimationOMP<T,NT>;
 
#endif    // PCL_FEATURES_IMPL_NORMAL_3D_OMP_H_