neighborhood_feature.cpp

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#include <ias_descriptors_3d/generic/neighborhood_feature.h>

using namespace std;

// --------------------------------------------------------------
/* See function definition */
// --------------------------------------------------------------
NeighborhoodFeature::NeighborhoodFeature()
{
  neighborhood_radius_ = -1.0;
  neighborhood_radius_defined_ = false;
}

// --------------------------------------------------------------
/* See function definition */
// --------------------------------------------------------------
NeighborhoodFeature::~NeighborhoodFeature()
{
}

// --------------------------------------------------------------
/* See function definition */
// --------------------------------------------------------------
void NeighborhoodFeature::doComputation(const sensor_msgs::PointCloud& data,
                                        cloud_kdtree::KdTree& data_kdtree,
                                        const std::vector<const geometry_msgs::Point32*>& interest_pts,
                                        std::vector<std::vector<float> >& results)
{
  // ----------------------------------------
  ROS_INFO("myComputation");
  if (neighborhood_radius_defined_ == false)
  {
    ROS_ERROR("NeighborhoodFeature::doComputation() radius not defined yet");
    return;
  }
  if (neighborhood_radius_ < 1e-6)
  {
    ROS_ERROR("NeighborhoodFeature::doComputation() radius is negative: %f", neighborhood_radius_);
  }

  // ----------------------------------------
  // Iterate over each interest point, compute local neighborhood, compute feature
  int nbr_interest_pts = interest_pts.size();
#pragma omp parallel for
  for (int i = 0 ; i < nbr_interest_pts ; i++)
  {
    // Retrieve interest point
    const geometry_msgs::Point32* curr_interest_pt = interest_pts[static_cast<size_t> (i)];
    if (curr_interest_pt == NULL)
    {
      ROS_WARN("NeighborhoodFeature::doComputation() passed NULL interest point");
    }
    else
    {
      // Grab neighbors around interest point
      vector<int> neighbor_indices;
      vector<float> neighbor_distances; // unused
      // radiusSearch returning false (0 points) is handled in inherited class
      data_kdtree.radiusSearch(*curr_interest_pt, neighborhood_radius_, neighbor_indices, neighbor_distances);

      computeNeighborhoodFeature(data, neighbor_indices, i, results[static_cast<size_t> (i)]);
    }
  }
}

// --------------------------------------------------------------
/* See function definition */
// --------------------------------------------------------------
void NeighborhoodFeature::doComputation(const sensor_msgs::PointCloud& data,
                                        cloud_kdtree::KdTree& data_kdtree,
                                        const std::vector<const vector<int>*>& interest_region_indices,
                                        std::vector<std::vector<float> >& results)
{
  // ----------------------------------------
  if (neighborhood_radius_defined_ == false)
  {
    ROS_ERROR("NeighborhoodFeature::doComputation() radius not defined yet");
    return;
  }

  // ----------------------------------------
  // Iterate over each interest region, compute local neighborhood, compute feature
  int nbr_interest_regions = interest_region_indices.size();
#pragma omp parallel for
  for (int i = 0 ; i < nbr_interest_regions ; i++)
  {
    // Retrieve interest region
    const vector<int>* curr_interest_region = interest_region_indices[static_cast<size_t> (i)];
    if (curr_interest_region == NULL)
    {
      ROS_WARN("NeighborhoodFeature::doComputation() passed NULL interest region");
    }
    else
    {
      // Find the neighborhood around the region's centroid if indicated to.
      vector<int> neighbor_indices;
      if (neighborhood_radius_ > 1e-6)
      {
        // Compute centroid of interest region
        geometry_msgs::Point32 region_centroid;
        cloud_geometry::nearest::computeCentroid(data, *curr_interest_region, region_centroid);

        vector<float> neighbor_distances; // unused
        // radiusSearch returning false (0 points) is handled in inherited class
        data_kdtree.radiusSearch(region_centroid, neighborhood_radius_, neighbor_indices, neighbor_distances);

        // Now point to the neighboring points from radiusSearch
        curr_interest_region = &neighbor_indices;
      }

      computeNeighborhoodFeature(data, *curr_interest_region, i, results[static_cast<size_t> (i)]);
    }
  }
}