seeded_hue_segmentation.hpp

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#ifndef PCL_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
#define PCL_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
 
#include <pcl/segmentation/seeded_hue_segmentation.h>
#include <pcl/console/print.h> // for PCL_ERROR
#include <pcl/search/organized.h> // for OrganizedNeighbor
#include <pcl/search/kdtree.h> // for KdTree
 
//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::seededHueSegmentation (const PointCloud<PointXYZRGB>          &cloud,
                            const search::Search<PointXYZRGB>::Ptr &tree,
                            float                                  tolerance,
                            PointIndices                           &indices_in,
                            PointIndices                           &indices_out,
                            float                                  delta_hue)
{
  if (tree->getInputCloud ()->size () != cloud.size ())
  {
    PCL_ERROR("[pcl::seededHueSegmentation] Tree built for a different point cloud "
              "dataset (%zu) than the input cloud (%zu)!\n",
              static_cast<std::size_t>(tree->getInputCloud()->size()),
              static_cast<std::size_t>(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 (const auto &i : indices_in.indices)
  {
    if (processed[i])
      continue;
 
    processed[i] = true;
 
    Indices seed_queue;
    int sq_idx = 0;
    seed_queue.push_back (i);
 
    PointXYZRGB  p;
    p = cloud[i];
    PointXYZHSV h;
    PointXYZRGBtoXYZHSV(p, h);
 
    while (sq_idx < static_cast<int> (seed_queue.size ()))
    {
      int ret = tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances, std::numeric_limits<int>::max());
      if(ret == -1)
        PCL_ERROR("[pcl::seededHueSegmentation] radiusSearch returned error code -1\n");
      // Search for sq_idx
      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;
 
        PointXYZRGB  p_l;
        p_l = cloud[nn_indices[j]];
        PointXYZHSV h_l;
        PointXYZRGBtoXYZHSV(p_l, h_l);
 
        if (std::fabs(h_l.h - h.h) < delta_hue)
        {
          seed_queue.push_back (nn_indices[j]);
          processed[nn_indices[j]] = true;
        }
      }
 
      sq_idx++;
    }
    // Copy the seed queue into the output indices
    for (const auto &l : seed_queue)
      indices_out.indices.push_back(l);
  }
  // This is purely esthetical, can be removed for speed purposes
  std::sort (indices_out.indices.begin (), indices_out.indices.end ());
}
//////////////////////////////////////////////////////////////////////////////////////////////
void
pcl::seededHueSegmentation (const PointCloud<PointXYZRGB>            &cloud,
                            const search::Search<PointXYZRGBL>::Ptr  &tree,
                            float                                    tolerance,
                            PointIndices                             &indices_in,
                            PointIndices                             &indices_out,
                            float                                    delta_hue)
{
  if (tree->getInputCloud ()->size () != cloud.size ())
  {
    PCL_ERROR("[pcl::seededHueSegmentation] Tree built for a different point cloud "
              "dataset (%zu) than the input cloud (%zu)!\n",
              static_cast<std::size_t>(tree->getInputCloud()->size()),
              static_cast<std::size_t>(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 (const auto &i : indices_in.indices)
  {
    if (processed[i])
      continue;
 
    processed[i] = true;
 
    Indices seed_queue;
    int sq_idx = 0;
    seed_queue.push_back (i);
 
    PointXYZRGB  p;
    p = cloud[i];
    PointXYZHSV h;
    PointXYZRGBtoXYZHSV(p, h);
 
    while (sq_idx < static_cast<int> (seed_queue.size ()))
    {
      int ret = tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances, std::numeric_limits<int>::max());
      if(ret == -1)
        PCL_ERROR("[pcl::seededHueSegmentation] radiusSearch returned error code -1\n");
      // Search for sq_idx
      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;
 
        PointXYZRGB  p_l;
        p_l = cloud[nn_indices[j]];
        PointXYZHSV h_l;
        PointXYZRGBtoXYZHSV(p_l, h_l);
 
        if (std::fabs(h_l.h - h.h) < delta_hue)
        {
          seed_queue.push_back (nn_indices[j]);
          processed[nn_indices[j]] = true;
        }
      }
 
      sq_idx++;
    }
    // Copy the seed queue into the output indices
    for (const auto &l : seed_queue)
      indices_out.indices.push_back(l);
  }
  // This is purely esthetical, can be removed for speed purposes
  std::sort (indices_out.indices.begin (), indices_out.indices.end ());
}
//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////
 
void 
pcl::SeededHueSegmentation::segment (PointIndices &indices_in, PointIndices &indices_out)
{
  if (!initCompute () || 
      (input_   && input_->points.empty ()) ||
      (indices_ && indices_->empty ()))
  {
    indices_out.indices.clear ();
    return;
  }
 
  // Initialize the spatial locator
  if (!tree_)
  {
    if (input_->isOrganized ())
      tree_.reset (new pcl::search::OrganizedNeighbor<PointXYZRGB> ());
    else
      tree_.reset (new pcl::search::KdTree<PointXYZRGB> (false));
  }
 
  // Send the input dataset to the spatial locator
  tree_->setInputCloud (input_);
  seededHueSegmentation (*input_, tree_, static_cast<float> (cluster_tolerance_), indices_in, indices_out, delta_hue_);
  deinitCompute ();
}
 
#endif        // PCL_EXTRACT_CLUSTERS_IMPL_H_