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>
//////////////////////////////////////////////////////////////////////////////////////////////
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,
unsigned int)
{
if (tree->getInputCloud ()->points.size () != cloud.points.size ())
{
PCL_ERROR ("[pcl::extractLabeledEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n", tree->getInputCloud ()->points.size (), cloud.points.size ());
return;
}
// Create a bool vector of processed point indices, and initialize it to false
std::vector<bool> processed (cloud.points.size (), false);
std::vector<int> nn_indices;
std::vector<float> nn_distances;
// Process all points in the indices vector
for (int i = 0; i < static_cast<int> (cloud.points.size ()); ++i)
{
if (processed[i])
continue;
std::vector<int> 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 = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx
{
if (processed[nn_indices[j]]) // Has this point been processed before ?
continue;
if (cloud.points[i].label == cloud.points[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];
std::sort (r.indices.begin (), r.indices.end ());
r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
r.header = cloud.header;
labeled_clusters[cloud.points[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_->points.empty ()) ||
(indices_ && indices_->empty ()))
{
labeled_clusters.clear ();
return;
}
// Initialize the spatial locator
if (!tree_)
{
if (input_->isOrganized ())
tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
else
tree_.reset (new pcl::search::KdTree<PointT> (false));
}
// 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_, max_label_);
// 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, unsigned int);
#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:397
pcl::search::Search::getInputCloud
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition: search.h:124
pcl::LabeledEuclideanClusterExtraction::extract
void extract(std::vector< std::vector< PointIndices > > &labeled_clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
Definition: extract_labeled_clusters.hpp:123
pcl::PointIndices::header
::pcl::PCLHeader header
Definition: PointIndices.h:17
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:52
pcl::gpu::comparePointClusters
bool comparePointClusters(const pcl::PointIndices &a, const pcl::PointIndices &b)
Sort clusters method (for std::sort).
Definition: gpu_extract_clusters.h:161
pcl::search::KdTree< PointT >
pcl::search::Search::Ptr
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition: search.h:80
pcl::PointIndices::indices
std::vector< int > indices
Definition: PointIndices.h:19
pcl::PointIndices
Definition: PointIndices.h:12
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:394
pcl::search::Search::radiusSearch
virtual int radiusSearch(const PointT &point, double radius, std::vector< int > &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.
Definition: organized.h:62
pcl::extractLabeledEuclideanClusters
void 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=1, unsigned int max_pts_per_cluster=std::numeric_limits< unsigned int >::max(), unsigned int max_label=std::numeric_limits< unsigned int >::max())
Decompose a region of space into clusters based on the Euclidean distance between points.
Definition: extract_labeled_clusters.hpp:44