gpu_extract_labeled_clusters.h

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#pragma once
 
#include <pcl/gpu/octree/octree.hpp>
#include <pcl/pcl_macros.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/PointIndices.h>
 
namespace pcl {
namespace gpu {
template <typename PointT>
void
extractLabeledEuclideanClusters(
    const typename pcl::PointCloud<PointT>::Ptr& host_cloud_,
    const pcl::gpu::Octree::Ptr& tree,
    float tolerance,
    std::vector<PointIndices>& clusters,
    unsigned int min_pts_per_cluster,
    unsigned int max_pts_per_cluster);
 
/** \brief @b EuclideanLabeledClusterExtraction represents a segmentation class for
 * cluster extraction in an Euclidean sense, depending on pcl::gpu::octree
 * \author Koen Buys, Radu Bogdan Rusu
 * \ingroup segmentation
 */
template <typename PointT>
class EuclideanLabeledClusterExtraction {
public:
  using PointType = pcl::PointXYZ;
  using PointCloudHost = pcl::PointCloud<PointT>;
  using PointCloudHostPtr = typename PointCloudHost::Ptr;
  using PointCloudHostConstPtr = typename PointCloudHost::ConstPtr;
 
  using PointIndicesPtr = PointIndices::Ptr;
  using PointIndicesConstPtr = PointIndices::ConstPtr;
 
  using GPUTree = pcl::gpu::Octree;
  using GPUTreePtr = pcl::gpu::Octree::Ptr;
 
  using CloudDevice = pcl::gpu::Octree::PointCloud;
 
  //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  /** \brief Empty constructor. */
  EuclideanLabeledClusterExtraction() = default;
 
  /** \brief Default virtual destructor. */
  virtual ~EuclideanLabeledClusterExtraction() = default;
 
  /** \brief Provide a pointer to the search object.
   * \param tree a pointer to the spatial search object.
   */
  inline void
  setSearchMethod(const GPUTreePtr& tree)
  {
    tree_ = tree;
  }
 
  /** \brief Get a pointer to the search method used.
   *  @todo fix this for a generic search tree
   */
  inline GPUTreePtr
  getSearchMethod()
  {
    return (tree_);
  }
 
  /** \brief Set the spatial cluster tolerance as a measure in the L2 Euclidean space
   * \param tolerance the spatial cluster tolerance measured by L2 distance
   */
  inline void
  setClusterTolerance(double tolerance)
  {
    cluster_tolerance_ = tolerance;
  }
 
  /** \brief Get the spatial cluster tolerance as a measure in the L2 Euclidean space.
   */
  inline double
  getClusterTolerance()
  {
    return (cluster_tolerance_);
  }
 
  /** \brief Set the minimum number of points that a cluster needs to contain in order
   * to be considered valid.
   * \param min_cluster_size the minimum cluster size
   */
  inline void
  setMinClusterSize(int min_cluster_size)
  {
    min_pts_per_cluster_ = min_cluster_size;
  }
 
  /** \brief Get the minimum number of points that a cluster needs to contain in order
   * to be considered valid. */
  inline int
  getMinClusterSize()
  {
    return (min_pts_per_cluster_);
  }
 
  /** \brief Set the maximum number of points that a cluster needs to contain in order
   * to be considered valid.
   * \param max_cluster_size the maximum cluster size
   */
  inline void
  setMaxClusterSize(int max_cluster_size)
  {
    max_pts_per_cluster_ = max_cluster_size;
  }
 
  /** \brief Get the maximum number of points that a cluster needs to contain in order
   * to be considered valid. */
  inline int
  getMaxClusterSize()
  {
    return (max_pts_per_cluster_);
  }
 
  inline void
  setInput(CloudDevice input)
  {
    input_ = input;
  }
 
  inline void
  setHostCloud(PointCloudHostPtr host_cloud)
  {
    host_cloud_ = host_cloud;
  }
 
  /** \brief extract clusters of a PointCloud given by <setInputCloud(), setIndices()>
   * \param clusters the resultant point clusters
   */
  void
  extract(std::vector<PointIndices>& clusters);
 
protected:
  /** \brief the input cloud on the GPU */
  CloudDevice input_;
 
  /** \brief the original cloud the Host */
  PointCloudHostPtr host_cloud_;
 
  /** \brief A pointer to the spatial search object. */
  GPUTreePtr tree_;
 
  /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
  double cluster_tolerance_{0.0};
 
  /** \brief The minimum number of points that a cluster needs to contain in order to be
   * considered valid (default = 1). */
  int min_pts_per_cluster_{1};
 
  /** \brief The maximum number of points that a cluster needs to contain in order to be
   * considered valid (default = MAXINT). */
  int max_pts_per_cluster_{std::numeric_limits<int>::max()};
 
  /** \brief Class getName method. */
  virtual std::string
  getClassName() const
  {
    return ("gpu::EuclideanLabeledClusterExtraction");
  }
};
/** \brief Sort clusters method (for std::sort).
 * \ingroup segmentation
 */
inline bool
compareLabeledPointClusters(const pcl::PointIndices& a, const pcl::PointIndices& b)
{
  return (a.indices.size() < b.indices.size());
}
} // namespace gpu
} // namespace pcl