sgf4.h

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#ifndef PCL_FEATURES_SGF4_H_
#define PCL_FEATURES_SGF4_H_

#include <pcl17/features/feature.h>

namespace pcl17
{
  const int SGF4_SIZE = 6;

  template <typename PointInT, typename PointOutT>
  class SGF4Estimation : public Feature<PointInT, PointOutT>
  {

    public:

      using Feature<PointInT, PointOutT>::feature_name_;
      using Feature<PointInT, PointOutT>::input_;
      using Feature<PointInT, PointOutT>::indices_;
      using Feature<PointInT, PointOutT>::k_;

      typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
      typedef typename Feature<PointInT, PointOutT>::PointCloudIn  PointCloudIn;

      SGF4Estimation ()
      {
        feature_name_ = "SGF4Estimation";
        k_ = 1;
      };


      void
      computeFeature (PointCloudOut &output)
      {
        // Copy the points specified by the index vector into a new cloud
        typename PointCloud<PointInT>::Ptr cloud (new PointCloud<PointInT> ());
        cloud->width = indices_->size ();
        cloud->height = 1;
        cloud->points.resize (cloud->width * cloud->height);
        for (size_t idx = 0; idx < indices_->size (); ++idx)
        {
          cloud->points[idx] = input_->points[(*indices_)[idx]];
        }


        // Compute eigenvectors and eigenvalues
        EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
        Eigen::Vector4f centroid3;
        compute3DCentroid (*cloud, centroid3);
        computeCovarianceMatrix (*cloud, centroid3, covariance_matrix);
        EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
        EIGEN_ALIGN16 Eigen::Matrix3f eigen_vectors;
        pcl17::eigen33 (covariance_matrix, eigen_vectors, eigen_values);


        // Compute the feature vector
        float eigen_sum = eigen_values.sum ();
        output.points[0].histogram[0] = eigen_values[1] != 0 ? eigen_values[0] / eigen_values[1] : 0;
        output.points[0].histogram[1] = eigen_values[2] != 0 ? eigen_values[1] / eigen_values[2] : 0;
        output.points[0].histogram[2] = eigen_values[2] != 0 ? eigen_values[0] / eigen_values[2] : 0;
        output.points[0].histogram[3] = eigen_sum != 0 ? eigen_values[0] / eigen_sum : 0;
        output.points[0].histogram[4] = eigen_sum != 0 ? eigen_values[1] / eigen_sum : 0;
        output.points[0].histogram[5] = eigen_sum != 0 ? eigen_values[2] / eigen_sum : 0;
      }


    private:

      void
      computeFeatureEigen (pcl17::PointCloud<Eigen::MatrixXf> &) {}
  };
}

#endif  //#ifndef PCL_FEATURES_SGF4_H_