conditional_euclidean_clustering.hpp

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#ifndef PCL_SEGMENTATION_IMPL_CONDITIONAL_EUCLIDEAN_CLUSTERING_HPP_
#define PCL_SEGMENTATION_IMPL_CONDITIONAL_EUCLIDEAN_CLUSTERING_HPP_

#include <pcl/segmentation/conditional_euclidean_clustering.h>

template<typename PointT> void
pcl::ConditionalEuclideanClustering<PointT>::segment (pcl::IndicesClusters &clusters)
{
  // Prepare output (going to use push_back)
  clusters.clear ();
  if (extract_removed_clusters_)
  {
    small_clusters_->clear ();
    large_clusters_->clear ();
  }

  // Validity checks
  if (!initCompute () || input_->points.empty () || indices_->empty () || !condition_function_)
    return;

  // Initialize the search class
  if (!searcher_)
  {
    if (input_->isOrganized ())
      searcher_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
    else
      searcher_.reset (new pcl::search::KdTree<PointT> ());
  }
  searcher_->setInputCloud (input_, indices_);

  // Temp variables used by search class
  std::vector<int> nn_indices;
  std::vector<float> nn_distances;

  // Create a bool vector of processed point indices, and initialize it to false
  // Need to have it contain all possible points because radius search can not return indices into indices
  std::vector<bool> processed (input_->points.size (), false);

  // Process all points indexed by indices_
  for (int iii = 0; iii < static_cast<int> (indices_->size ()); ++iii)  // iii = input indices iterator
  {
    // Has this point been processed before?
    if ((*indices_)[iii] == -1 || processed[(*indices_)[iii]])
      continue;

    // Set up a new growing cluster
    std::vector<int> current_cluster;
    int cii = 0;  // cii = cluster indices iterator

    // Add the point to the cluster
    current_cluster.push_back ((*indices_)[iii]);
    processed[(*indices_)[iii]] = true;

    // Process the current cluster (it can be growing in size as it is being processed)
    while (cii < static_cast<int> (current_cluster.size ()))
    {
      // Search for neighbors around the current seed point of the current cluster
      if (searcher_->radiusSearch (input_->points[current_cluster[cii]], cluster_tolerance_, nn_indices, nn_distances) < 1)
      {
        cii++;
        continue;
      }

      // Process the neighbors
      for (int nii = 1; nii < static_cast<int> (nn_indices.size ()); ++nii)  // nii = neighbor indices iterator
      {
        // Has this point been processed before?
        if (nn_indices[nii] == -1 || processed[nn_indices[nii]])
          continue;

        // Validate if condition holds
        if (condition_function_ (input_->points[current_cluster[cii]], input_->points[nn_indices[nii]], nn_distances[nii]))
        {
          // Add the point to the cluster
          current_cluster.push_back (nn_indices[nii]);
          processed[nn_indices[nii]] = true;
        }
      }
      cii++;
    }

    // If extracting removed clusters, all clusters need to be saved, otherwise only the ones within the given cluster size range
    if (extract_removed_clusters_ || (current_cluster.size () >= min_cluster_size_ && current_cluster.size () <= max_cluster_size_))
    {
      pcl::PointIndices pi;
      pi.header = input_->header;
      pi.indices.resize (current_cluster.size ());
      for (int ii = 0; ii < static_cast<int> (current_cluster.size ()); ++ii)  // ii = indices iterator
        pi.indices[ii] = current_cluster[ii];

      if (extract_removed_clusters_ && current_cluster.size () < min_cluster_size_)
        small_clusters_->push_back (pi);
      else if (extract_removed_clusters_ && current_cluster.size () > max_cluster_size_)
        large_clusters_->push_back (pi);
      else
        clusters.push_back (pi);
    }
  }

  deinitCompute ();
}

#define PCL_INSTANTIATE_ConditionalEuclideanClustering(T) template class PCL_EXPORTS pcl::ConditionalEuclideanClustering<T>;

#endif  // PCL_SEGMENTATION_IMPL_CONDITIONAL_EUCLIDEAN_CLUSTERING_HPP_