uniform_sampling.hpp

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#ifndef PCL_FILTERS_UNIFORM_SAMPLING_IMPL_H_
#define PCL_FILTERS_UNIFORM_SAMPLING_IMPL_H_
 
#include <pcl/common/common.h>
#include <pcl/filters/uniform_sampling.h>
 
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::UniformSampling<PointT>::applyFilter (PointCloud &output)
{
  // Has the input dataset been set already?
  if (!input_)
  {
    PCL_WARN ("[pcl::%s::detectKeypoints] No input dataset given!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.clear ();
    return;
  }
 
  output.height       = 1;                    // downsampling breaks the organized structure
  output.is_dense     = true;                 // we filter out invalid points
 
  Eigen::Vector4f min_p, max_p;
  // Get the minimum and maximum dimensions
  pcl::getMinMax3D<PointT>(*input_, min_p, max_p);
 
  // Compute the minimum and maximum bounding box values
  min_b_[0] = static_cast<int> (std::floor (min_p[0] * inverse_leaf_size_[0]));
  max_b_[0] = static_cast<int> (std::floor (max_p[0] * inverse_leaf_size_[0]));
  min_b_[1] = static_cast<int> (std::floor (min_p[1] * inverse_leaf_size_[1]));
  max_b_[1] = static_cast<int> (std::floor (max_p[1] * inverse_leaf_size_[1]));
  min_b_[2] = static_cast<int> (std::floor (min_p[2] * inverse_leaf_size_[2]));
  max_b_[2] = static_cast<int> (std::floor (max_p[2] * inverse_leaf_size_[2]));
 
  // Compute the number of divisions needed along all axis
  div_b_ = max_b_ - min_b_ + Eigen::Vector4i::Ones ();
  div_b_[3] = 0;
 
  // Clear the leaves
  leaves_.clear ();
 
  // Set up the division multiplier
  divb_mul_ = Eigen::Vector4i (1, div_b_[0], div_b_[0] * div_b_[1], 0);
 
  removed_indices_->clear();
  // First pass: build a set of leaves with the point index closest to the leaf center
  for (std::size_t cp = 0; cp < indices_->size (); ++cp)
  {
    if (!input_->is_dense)
    {
      // Check if the point is invalid
      if (!std::isfinite ((*input_)[(*indices_)[cp]].x) || 
          !std::isfinite ((*input_)[(*indices_)[cp]].y) || 
          !std::isfinite ((*input_)[(*indices_)[cp]].z))
      {
        if (extract_removed_indices_)
          removed_indices_->push_back ((*indices_)[cp]);
        continue;
      }
    }
 
    Eigen::Vector4i ijk = Eigen::Vector4i::Zero ();
    ijk[0] = static_cast<int> (std::floor ((*input_)[(*indices_)[cp]].x * inverse_leaf_size_[0]));
    ijk[1] = static_cast<int> (std::floor ((*input_)[(*indices_)[cp]].y * inverse_leaf_size_[1]));
    ijk[2] = static_cast<int> (std::floor ((*input_)[(*indices_)[cp]].z * inverse_leaf_size_[2]));
 
    // Compute the leaf index
    int idx = (ijk - min_b_).dot (divb_mul_);
    Leaf& leaf = leaves_[idx];
    // First time we initialize the index
    if (leaf.idx == -1)
    {
      leaf.idx = (*indices_)[cp];
      continue;
    }
 
    // Compute the voxel center
    Eigen::Vector4f voxel_center = (ijk.cast<float>() + Eigen::Vector4f::Constant(0.5)) * search_radius_;
    voxel_center[3] = 0;
    // Check to see if this point is closer to the leaf center than the previous one we saved
    float diff_cur   = ((*input_)[(*indices_)[cp]].getVector4fMap () - voxel_center).squaredNorm ();
    float diff_prev  = ((*input_)[leaf.idx].getVector4fMap ()        - voxel_center).squaredNorm ();
 
    // If current point is closer, copy its index instead
    if (diff_cur < diff_prev)
    {
      if (extract_removed_indices_)
        removed_indices_->push_back (leaf.idx);
      leaf.idx = (*indices_)[cp];
    }
    else
    {
      if (extract_removed_indices_)
        removed_indices_->push_back ((*indices_)[cp]);
    }
  }
 
  // Second pass: go over all leaves and copy data
  output.resize (leaves_.size ());
  int cp = 0;
 
  for (const auto& leaf : leaves_)
    output[cp++] = (*input_)[leaf.second.idx];
  output.width = output.size ();
}
 
#define PCL_INSTANTIATE_UniformSampling(T) template class PCL_EXPORTS pcl::UniformSampling<T>;
 
#endif    // PCL_FILTERS_UNIFORM_SAMPLING_IMPL_H_