extract_labeled_clusters.hpp

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#ifndef PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_
#define PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_
 
#include <pcl/segmentation/extract_labeled_clusters.h>
#include <pcl/search/auto.h>
 
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT>
void
pcl::extractLabeledEuclideanClusters(
    const PointCloud<PointT>& cloud,
    const typename search::Search<PointT>::Ptr& tree,
    float tolerance,
    std::vector<std::vector<PointIndices>>& labeled_clusters,
    unsigned int min_pts_per_cluster,
    unsigned int max_pts_per_cluster)
{
  if (tree->getInputCloud()->size() != cloud.size()) {
    PCL_ERROR("[pcl::extractLabeledEuclideanClusters] Tree built for a different point "
              "cloud dataset (%lu) than the input cloud (%lu)!\n",
              tree->getInputCloud()->size(),
              cloud.size());
    return;
  }
  // If tree gives sorted results, we can skip the first one because it is the query point itself
  const std::size_t nn_start_idx = tree->getSortedResults () ? 1 : 0;
  // Create a bool vector of processed point indices, and initialize it to false
  std::vector<bool> processed(cloud.size(), false);
 
  Indices nn_indices;
  std::vector<float> nn_distances;
 
  // Process all points in the indices vector
  for (index_t i = 0; i < static_cast<index_t>(cloud.size()); ++i) {
    if (processed[i])
      continue;
 
    Indices seed_queue;
    int sq_idx = 0;
    seed_queue.push_back(i);
 
    processed[i] = true;
 
    while (sq_idx < static_cast<int>(seed_queue.size())) {
      // Search for sq_idx
      int ret = tree->radiusSearch(seed_queue[sq_idx],
                                   tolerance,
                                   nn_indices,
                                   nn_distances,
                                   std::numeric_limits<int>::max());
      if (ret == -1)
        PCL_ERROR("radiusSearch on tree came back with error -1");
      if (!ret) {
        sq_idx++;
        continue;
      }
 
      for (std::size_t j = nn_start_idx; j < nn_indices.size(); ++j)
      {
        if (processed[nn_indices[j]]) // Has this point been processed before ?
          continue;
        if (cloud[i].label == cloud[nn_indices[j]].label) {
          // Perform a simple Euclidean clustering
          seed_queue.push_back(nn_indices[j]);
          processed[nn_indices[j]] = true;
        }
      }
 
      sq_idx++;
    }
 
    // If this queue is satisfactory, add to the clusters
    if (seed_queue.size() >= min_pts_per_cluster &&
        seed_queue.size() <= max_pts_per_cluster) {
      pcl::PointIndices r;
      r.indices.resize(seed_queue.size());
      for (std::size_t j = 0; j < seed_queue.size(); ++j)
        r.indices[j] = seed_queue[j];
      // After clustering, indices are out of order, so sort them
      std::sort(r.indices.begin(), r.indices.end());
 
      r.header = cloud.header;
      labeled_clusters[cloud[i].label].push_back(
          r); // We could avoid a copy by working directly in the vector
    }
  }
}
//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////
 
template <typename PointT>
void
pcl::LabeledEuclideanClusterExtraction<PointT>::extract(
    std::vector<std::vector<PointIndices>>& labeled_clusters)
{
  if (!initCompute() || (input_ && input_->empty()) ||
      (indices_ && indices_->empty())) {
    labeled_clusters.clear();
    return;
  }
 
  // Initialize the spatial locator
  if (!tree_)
    tree_.reset(pcl::search::autoSelectMethod<PointT>(input_, false, pcl::search::Purpose::radius_search));
  else
    // Send the input dataset to the spatial locator
    tree_->setInputCloud(input_);
  extractLabeledEuclideanClusters(*input_,
                                  tree_,
                                  static_cast<float>(cluster_tolerance_),
                                  labeled_clusters,
                                  min_pts_per_cluster_,
                                  max_pts_per_cluster_);
 
  // Sort the clusters based on their size (largest one first)
  for (auto& labeled_cluster : labeled_clusters)
    std::sort(labeled_cluster.rbegin(), labeled_cluster.rend(), comparePointClusters);
 
  deinitCompute();
}
 
#define PCL_INSTANTIATE_LabeledEuclideanClusterExtraction(T)                           \
  template class PCL_EXPORTS pcl::LabeledEuclideanClusterExtraction<T>;
#define PCL_INSTANTIATE_extractLabeledEuclideanClusters(T)                             \
  template void PCL_EXPORTS pcl::extractLabeledEuclideanClusters<T>(                   \
      const pcl::PointCloud<T>&,                                                       \
      const typename pcl::search::Search<T>::Ptr&,                                     \
      float,                                                                           \
      std::vector<std::vector<pcl::PointIndices>>&,                                    \
      unsigned int,                                                                    \
      unsigned int);
 
#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_