moment_invariants.hpp

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 * $Id: moment_invariants.hpp 5026 2012-03-12 02:51:44Z rusu $
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#ifndef PCL_FEATURES_IMPL_MOMENT_INVARIANTS_H_
#define PCL_FEATURES_IMPL_MOMENT_INVARIANTS_H_

#include <pcl/features/moment_invariants.h>

template <typename PointInT, typename PointOutT> void
pcl::MomentInvariantsEstimation<PointInT, PointOutT>::computePointMomentInvariants (
      const pcl::PointCloud<PointInT> &cloud, const std::vector<int> &indices,
      float &j1, float &j2, float &j3)
{
  // Estimate the XYZ centroid
  compute3DCentroid (cloud, indices, xyz_centroid_);

  // Initalize the centralized moments
  float mu200 = 0, mu020 = 0, mu002 = 0, mu110 = 0, mu101 = 0, mu011  = 0;

  // Iterate over the nearest neighbors set
  for (size_t nn_idx = 0; nn_idx < indices.size (); ++nn_idx)
  {
    // Demean the points
    temp_pt_[0] = cloud.points[indices[nn_idx]].x - xyz_centroid_[0];
    temp_pt_[1] = cloud.points[indices[nn_idx]].y - xyz_centroid_[1];
    temp_pt_[2] = cloud.points[indices[nn_idx]].z - xyz_centroid_[2];

    mu200 += temp_pt_[0] * temp_pt_[0];
    mu020 += temp_pt_[1] * temp_pt_[1];
    mu002 += temp_pt_[2] * temp_pt_[2];
    mu110 += temp_pt_[0] * temp_pt_[1];
    mu101 += temp_pt_[0] * temp_pt_[2];
    mu011 += temp_pt_[1] * temp_pt_[2];
  }

  // Save the moment invariants
  j1 = mu200             + mu020               + mu002;
  j2 = mu200*mu020       + mu200*mu002         + mu020*mu002       - mu110*mu110       - mu101*mu101       - mu011*mu011;
  j3 = mu200*mu020*mu002 + 2*mu110*mu101*mu011 - mu002*mu110*mu110 - mu020*mu101*mu101 - mu200*mu011*mu011;
}

template <typename PointInT, typename PointOutT> void
pcl::MomentInvariantsEstimation<PointInT, PointOutT>::computePointMomentInvariants (
      const pcl::PointCloud<PointInT> &cloud, float &j1, float &j2, float &j3)
{
  // Estimate the XYZ centroid
  compute3DCentroid (cloud, xyz_centroid_);

  // Initalize the centralized moments
  float mu200 = 0, mu020 = 0, mu002 = 0, mu110 = 0, mu101 = 0, mu011  = 0;

  // Iterate over the nearest neighbors set
  for (size_t nn_idx = 0; nn_idx < cloud.points.size (); ++nn_idx )
  {
    // Demean the points
    temp_pt_[0] = cloud.points[nn_idx].x - xyz_centroid_[0];
    temp_pt_[1] = cloud.points[nn_idx].y - xyz_centroid_[1];
    temp_pt_[2] = cloud.points[nn_idx].z - xyz_centroid_[2];

    mu200 += temp_pt_[0] * temp_pt_[0];
    mu020 += temp_pt_[1] * temp_pt_[1];
    mu002 += temp_pt_[2] * temp_pt_[2];
    mu110 += temp_pt_[0] * temp_pt_[1];
    mu101 += temp_pt_[0] * temp_pt_[2];
    mu011 += temp_pt_[1] * temp_pt_[2];
  }

  // Save the moment invariants
  j1 = mu200             + mu020               + mu002;
  j2 = mu200*mu020       + mu200*mu002         + mu020*mu002       - mu110*mu110       - mu101*mu101       - mu011*mu011;
  j3 = mu200*mu020*mu002 + 2*mu110*mu101*mu011 - mu002*mu110*mu110 - mu020*mu101*mu101 - mu200*mu011*mu011;
}

template <typename PointInT, typename PointOutT> void
pcl::MomentInvariantsEstimation<PointInT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Allocate enough space to hold the results
  // \note This resize is irrelevant for a radiusSearch ().
  std::vector<int> nn_indices (k_);
  std::vector<float> nn_dists (k_);

  output.is_dense = true;
  // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
  if (input_->is_dense)
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points[idx].j1 = output.points[idx].j2 = output.points[idx].j3 = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }
     
      computePointMomentInvariants (*surface_, nn_indices,
                                    output.points[idx].j1, output.points[idx].j2, output.points[idx].j3);
    }
  }
  else
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (!isFinite ((*input_)[(*indices_)[idx]]) ||
          this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points[idx].j1 = output.points[idx].j2 = output.points[idx].j3 = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }

      computePointMomentInvariants (*surface_, nn_indices,
                                    output.points[idx].j1, output.points[idx].j2, output.points[idx].j3);
    }
  }
}

template <typename PointInT> void
pcl::MomentInvariantsEstimation<PointInT, Eigen::MatrixXf>::computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &output)
{
  // Resize the output dataset
  output.points.resize (indices_->size (), 3);

  // Allocate enough space to hold the results
  // \note This resize is irrelevant for a radiusSearch ().
  std::vector<int> nn_indices (k_);
  std::vector<float> nn_dists (k_);

  output.is_dense = true;
  // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
  if (input_->is_dense)
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points (idx, 0) = output.points (idx, 1) = output.points (idx, 2) = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
      }

      this->computePointMomentInvariants (*surface_, nn_indices,
                                    output.points (idx, 0), output.points (idx, 1), output.points (idx, 2));
    }
  }
  else
  {
    // Iterating over the entire index vector
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      if (!isFinite ((*input_)[(*indices_)[idx]]) ||
          this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
      {
        output.points (idx, 0) = output.points (idx, 1) = output.points (idx, 2) = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
        continue;
       }

      this->computePointMomentInvariants (*surface_, nn_indices,
                                    output.points (idx, 0), output.points (idx, 1), output.points (idx, 2));
    }
  }
}


#define PCL_INSTANTIATE_MomentInvariantsEstimation(T,NT) template class PCL_EXPORTS pcl::MomentInvariantsEstimation<T,NT>;

#endif    // PCL_FEATURES_IMPL_MOMENT_INVARIANTS_H_