sac_model_line.hpp

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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
 
#include <pcl/sample_consensus/sac_model_line.h>
#include <pcl/common/centroid.h>
#include <pcl/common/concatenate.h>
#include <pcl/common/eigen.h> // for eigen33
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelLine<PointT>::isSampleGood (const Indices &samples) const
{
  if (samples.size () != sample_size_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelLine::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
    return (false);
  }
 
  // Make sure that the two sample points are not identical
  if (
      std::abs ((*input_)[samples[0]].x - (*input_)[samples[1]].x) <= std::numeric_limits<float>::epsilon ()
    &&
      std::abs ((*input_)[samples[0]].y - (*input_)[samples[1]].y) <= std::numeric_limits<float>::epsilon ()
    &&
      std::abs ((*input_)[samples[0]].z - (*input_)[samples[1]].z) <= std::numeric_limits<float>::epsilon ())
  {
    PCL_ERROR ("[pcl::SampleConsensusModelLine::isSampleGood] The two sample points are (almost) identical!\n");
    return (false);
  }
 
  return (true);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelLine<PointT>::computeModelCoefficients (
      const Indices &samples, Eigen::VectorXf &model_coefficients) const
{
  // Make sure that the samples are valid
  if (!isSampleGood (samples))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelLine::computeModelCoefficients] Invalid set of samples given!\n");
    return (false);
  }
 
  model_coefficients.resize (model_size_);
  model_coefficients[0] = (*input_)[samples[0]].x;
  model_coefficients[1] = (*input_)[samples[0]].y;
  model_coefficients[2] = (*input_)[samples[0]].z;
 
  model_coefficients[3] = (*input_)[samples[1]].x - model_coefficients[0];
  model_coefficients[4] = (*input_)[samples[1]].y - model_coefficients[1];
  model_coefficients[5] = (*input_)[samples[1]].z - model_coefficients[2];
 
  // This precondition should hold if the samples have been found to be good
  assert (model_coefficients.template tail<3> ().squaredNorm () > 0.0f);
 
  model_coefficients.template tail<3> ().normalize ();
  PCL_DEBUG ("[pcl::SampleConsensusModelLine::computeModelCoefficients] Model is (%g,%g,%g,%g,%g,%g).\n",
             model_coefficients[0], model_coefficients[1], model_coefficients[2],
             model_coefficients[3], model_coefficients[4], model_coefficients[5]);
  return (true);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::getDistancesToModel (
      const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    return;
  }
 
  distances.resize (indices_->size ());
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
  line_dir.normalize ();
 
  // Iterate through the 3d points and calculate the distances from them to the line
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    // Need to estimate sqrt here to keep MSAC and friends general
    distances[i] = sqrt ((line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ());
  }
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::selectWithinDistance (
      const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
    return;
 
  double sqr_threshold = threshold * threshold;
 
  inliers.clear ();
  error_sqr_dists_.clear ();
  inliers.reserve (indices_->size ());
  error_sqr_dists_.reserve (indices_->size ());
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
  line_dir.normalize ();
 
  // Iterate through the 3d points and calculate the distances from them to the line
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
 
    if (sqr_distance < sqr_threshold)
    {
      // Returns the indices of the points whose squared distances are smaller than the threshold
      inliers.push_back ((*indices_)[i]);
      error_sqr_dists_.push_back (sqr_distance);
    }
  }
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> std::size_t
pcl::SampleConsensusModelLine<PointT>::countWithinDistance (
      const Eigen::VectorXf &model_coefficients, const double threshold) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
    return (0);
 
  double sqr_threshold = threshold * threshold;
 
  std::size_t nr_p = 0;
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
  line_dir.normalize ();
 
  // Iterate through the 3d points and calculate the distances from them to the line
  for (std::size_t i = 0; i < indices_->size (); ++i)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
 
    if (sqr_distance < sqr_threshold)
      nr_p++;
  }
  return (nr_p);
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::optimizeModelCoefficients (
      const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
  {
    optimized_coefficients = model_coefficients;
    return;
  }
 
  // Need more than the minimum sample size to make a difference
  if (inliers.size () <= sample_size_)
  {
    PCL_ERROR ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
    optimized_coefficients = model_coefficients;
    return;
  }
 
  optimized_coefficients.resize (model_size_);
 
  // Compute the 3x3 covariance matrix
  Eigen::Vector4f centroid;
  if (0 == compute3DCentroid (*input_, inliers, centroid))
  {
    PCL_WARN ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] compute3DCentroid failed (returned 0) because there are no valid inliers.\n");
    optimized_coefficients = model_coefficients;
    return;
  }
  Eigen::Matrix3f covariance_matrix;
  computeCovarianceMatrix (*input_, inliers, centroid, covariance_matrix);
  optimized_coefficients[0] = centroid[0];
  optimized_coefficients[1] = centroid[1];
  optimized_coefficients[2] = centroid[2];
 
  // Extract the eigenvalues and eigenvectors
  EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
  EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
  pcl::eigen33 (covariance_matrix, eigen_values);
  pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
  //pcl::eigen33 (covariance_matrix, eigen_vectors, eigen_values);
 
  optimized_coefficients.template tail<3> ().matrix () = eigen_vector;
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::projectPoints (
      const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
{
  // Needs a valid model coefficients
  if (!isModelValid (model_coefficients))
  {
    PCL_ERROR ("[pcl::SampleConsensusModelLine::projectPoints] Given model is invalid!\n");
    return;
  }
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
 
  projected_points.header = input_->header;
  projected_points.is_dense = input_->is_dense;
 
  // Copy all the data fields from the input cloud to the projected one?
  if (copy_data_fields)
  {
    // Allocate enough space and copy the basics
    projected_points.resize (input_->size ());
    projected_points.width    = input_->width;
    projected_points.height   = input_->height;
 
    using FieldList = typename pcl::traits::fieldList<PointT>::type;
    // Iterate over each point
    for (std::size_t i = 0; i < projected_points.size (); ++i)
      // Iterate over each dimension
      pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
 
    // Iterate through the 3d points and calculate the distances from them to the line
    for (const auto &inlier : inliers)
    {
      Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
      // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
      float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
 
      Eigen::Vector4f pp = line_pt + k * line_dir;
      // Calculate the projection of the point on the line (pointProj = A + k * B)
      projected_points[inlier].x = pp[0];
      projected_points[inlier].y = pp[1];
      projected_points[inlier].z = pp[2];
    }
  }
  else
  {
    // Allocate enough space and copy the basics
    projected_points.resize (inliers.size ());
    projected_points.width    = inliers.size ();
    projected_points.height   = 1;
 
    using FieldList = typename pcl::traits::fieldList<PointT>::type;
    // Iterate over each point
    for (std::size_t i = 0; i < inliers.size (); ++i)
      // Iterate over each dimension
      pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
 
    // Iterate through the 3d points and calculate the distances from them to the line
    for (std::size_t i = 0; i < inliers.size (); ++i)
    {
      Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
      // double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
      float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
 
      Eigen::Vector4f pp = line_pt + k * line_dir;
      // Calculate the projection of the point on the line (pointProj = A + k * B)
      projected_points[i].x = pp[0];
      projected_points[i].y = pp[1];
      projected_points[i].z = pp[2];
    }
  }
}
 
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelLine<PointT>::doSamplesVerifyModel (
      const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
{
  // Needs a valid set of model coefficients
  if (!isModelValid (model_coefficients))
    return (false);
 
  // Obtain the line point and direction
  Eigen::Vector4f line_pt  (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
  Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
  line_dir.normalize ();
 
  double sqr_threshold = threshold * threshold;
  // Iterate through the 3d points and calculate the distances from them to the line
  for (const auto &index : indices)
  {
    // Calculate the distance from the point to the line
    // D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
    if ((line_pt - (*input_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
      return (false);
  }
 
  return (true);
}
 
#define PCL_INSTANTIATE_SampleConsensusModelLine(T) template class PCL_EXPORTS pcl::SampleConsensusModelLine<T>;
 
#endif    // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_