sac_model_normal_sphere.hpp

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 * $Id: sac_model_normal_sphere.hpp schrandt $
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_SPHERE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_SPHERE_H_
 
#include <pcl/sample_consensus/sac_model_normal_sphere.h>
#include <pcl/common/common.h> // for getAngle3D
 
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename PointNT> void
pcl::SampleConsensusModelNormalSphere<PointT, PointNT>::selectWithinDistance (
      const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
  if (!normals_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelNormalSphere::selectWithinDistance] No input dataset containing normals was given! Use setInputNormals\n");
    inliers.clear ();
    return;
  }
 
  // Check if the model is valid given the user constraints
  if (!isModelValid (model_coefficients))
  {
    inliers.clear ();
    return;
  }
 
  // Obtain the sphere center
  Eigen::Vector4f center = model_coefficients;
  center[3] = 0.0f;
 
  inliers.clear ();
  error_sqr_dists_.clear ();
  inliers.reserve (indices_->size ());
  error_sqr_dists_.reserve (indices_->size ());
 
  // Iterate through the 3d points and calculate the distances from them to the sphere
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the sphere center as the difference between
    // dist(point,sphere_origin) and sphere_radius
    Eigen::Vector4f p ((*input_)[(*indices_)[i]].x, 
                       (*input_)[(*indices_)[i]].y,
                       (*input_)[(*indices_)[i]].z, 
                       0.0f);
 
    Eigen::Vector4f n_dir = p - center;
    const double weighted_euclid_dist = (1.0 - normal_distance_weight_) * std::abs (n_dir.norm () - model_coefficients[3]);
    if (weighted_euclid_dist > threshold) // Early termination: cannot be an inlier
      continue;
 
    // Calculate the angular distance between the point normal and the sphere normal
    Eigen::Vector4f n ((*normals_)[(*indices_)[i]].normal[0], 
                       (*normals_)[(*indices_)[i]].normal[1], 
                       (*normals_)[(*indices_)[i]].normal[2], 
                       0.0f);
    double d_normal = std::abs (getAngle3D (n, n_dir));
    d_normal = (std::min) (d_normal, M_PI - d_normal);
 
    double distance = std::abs (normal_distance_weight_ * d_normal + weighted_euclid_dist);
    if (distance < threshold)
    {
      // Returns the indices of the points whose distances are smaller than the threshold
      inliers.push_back ((*indices_)[i]);
      error_sqr_dists_.push_back (static_cast<double> (distance));
    }
  }
}
 
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename PointNT> std::size_t
pcl::SampleConsensusModelNormalSphere<PointT, PointNT>::countWithinDistance (
      const Eigen::VectorXf &model_coefficients,  const double threshold) const
{
  if (!normals_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelNormalSphere::getDistancesToModel] No input dataset containing normals was given! Use setInputNormals\n");
    return (0);
  }
 
  // Check if the model is valid given the user constraints
  if (!isModelValid (model_coefficients))
    return(0);
 
 
  // Obtain the sphere centroid
  Eigen::Vector4f center = model_coefficients;
  center[3] = 0.0f;
 
  std::size_t nr_p = 0;
 
  // Iterate through the 3d points and calculate the distances from them to the sphere
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the sphere centroid as the difference between
    // dist(point,sphere_origin) and sphere_radius
    Eigen::Vector4f p ((*input_)[(*indices_)[i]].x, 
                       (*input_)[(*indices_)[i]].y, 
                       (*input_)[(*indices_)[i]].z, 
                       0.0f);
 
    Eigen::Vector4f n_dir = (p-center);
    const double weighted_euclid_dist = (1.0 - normal_distance_weight_) * std::abs (n_dir.norm () - model_coefficients[3]);
    if (weighted_euclid_dist > threshold) // Early termination: cannot be an inlier
      continue;
 
    // Calculate the angular distance between the point normal and the sphere normal
    Eigen::Vector4f n ((*normals_)[(*indices_)[i]].normal[0], 
                       (*normals_)[(*indices_)[i]].normal[1], 
                       (*normals_)[(*indices_)[i]].normal[2], 
                       0.0f);
    double d_normal = std::abs (getAngle3D (n, n_dir));
    d_normal = (std::min) (d_normal, M_PI - d_normal);
 
    if (std::abs (normal_distance_weight_ * d_normal + weighted_euclid_dist) < threshold)
      nr_p++;
  }
  return (nr_p);
}
 
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename PointNT> void
pcl::SampleConsensusModelNormalSphere<PointT, PointNT>::getDistancesToModel (
      const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
  if (!normals_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelNormalSphere::getDistancesToModel] No input dataset containing normals was given! Use setInputNormals\n");
    return;
  }
 
  // Check if the model is valid given the user constraints
  if (!isModelValid (model_coefficients))
  {
    distances.clear ();
    return;
  }
 
  // Obtain the sphere centroid
  Eigen::Vector4f center = model_coefficients;
  center[3] = 0.0f;
 
  distances.resize (indices_->size ());
 
  // Iterate through the 3d points and calculate the distances from them to the sphere
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the sphere as the difference between
    // dist(point,sphere_origin) and sphere_radius
    Eigen::Vector4f p ((*input_)[(*indices_)[i]].x, 
                       (*input_)[(*indices_)[i]].y, 
                       (*input_)[(*indices_)[i]].z, 
                       0.0f);
 
    Eigen::Vector4f n_dir = (p-center);
    const double weighted_euclid_dist = (1.0 - normal_distance_weight_) * std::abs (n_dir.norm () - model_coefficients[3]);
 
    // Calculate the angular distance between the point normal and the sphere normal
    Eigen::Vector4f n ((*normals_)[(*indices_)[i]].normal[0], 
                       (*normals_)[(*indices_)[i]].normal[1], 
                       (*normals_)[(*indices_)[i]].normal[2], 
                       0.0f);
    double d_normal = std::abs (getAngle3D (n, n_dir));
    d_normal = (std::min) (d_normal, M_PI - d_normal);
 
    distances[i] = std::abs (normal_distance_weight_ * d_normal + weighted_euclid_dist);
  }
}
 
#define PCL_INSTANTIATE_SampleConsensusModelNormalSphere(PointT, PointNT) template class PCL_EXPORTS pcl::SampleConsensusModelNormalSphere<PointT, PointNT>;
 
#endif    // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_SPHERE_H_