rift.hpp

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 * $Id: rift.hpp 4702 2012-02-23 09:39:33Z gedikli $
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#ifndef PCL_FEATURES_IMPL_RIFT_H_
#define PCL_FEATURES_IMPL_RIFT_H_

#include "pcl/features/rift.h"

template <typename PointInT, typename GradientT, typename PointOutT> void
pcl::RIFTEstimation<PointInT, GradientT, PointOutT>::computeRIFT (
      const PointCloudIn &cloud, const PointCloudGradient &gradient, 
      int p_idx, float radius, const std::vector<int> &indices, 
      const std::vector<float> &sqr_distances, Eigen::MatrixXf &rift_descriptor)
{
  if (indices.empty ())
  {
    PCL_ERROR ("[pcl::RIFTEstimation] Null indices points passed!\n");
    return;
  }

  // Determine the number of bins to use based on the size of rift_descriptor
  int nr_distance_bins = rift_descriptor.rows ();
  int nr_gradient_bins = rift_descriptor.cols ();

  // Get the center point
  pcl::Vector3fMapConst p0 = cloud.points[p_idx].getVector3fMap ();

  // Compute the RIFT descriptor
  rift_descriptor.setZero ();
  for (size_t idx = 0; idx < indices.size (); ++idx)
  {
    // Compute the gradient magnitude and orientation (relative to the center point)
    pcl::Vector3fMapConst point = cloud.points[indices[idx]].getVector3fMap ();
    Eigen::Map<const Eigen::Vector3f> gradient_vector (& (gradient.points[indices[idx]].gradient[0]));

    float gradient_magnitude = gradient_vector.norm ();
    float gradient_angle_from_center = acos (gradient_vector.dot ((point - p0).normalized ()) / gradient_magnitude);
    if (!pcl_isfinite (gradient_angle_from_center))
    {
      gradient_angle_from_center = 0.0;
    }

    // Normalize distance and angle values to: 0.0 <= d,g < nr_distances_bins,nr_gradient_bins
    const float eps = std::numeric_limits<float>::epsilon ();
    float d = nr_distance_bins * sqrt (sqr_distances[idx]) / (radius + eps);
    float g = nr_gradient_bins * gradient_angle_from_center / (M_PI + eps);

    // Compute the bin indices that need to be updated
    int d_idx_min = (std::max)((int) ceil (d - 1), 0);
    int d_idx_max = (std::min)((int) floor (d + 1), nr_distance_bins - 1);
    int g_idx_min = (int) ceil (g - 1);
    int g_idx_max = (int) floor (g + 1);

    // Update the appropriate bins of the histogram 
    for (int g_idx = g_idx_min; g_idx <= g_idx_max; ++g_idx)  
    {
      // Because gradient orientation is cyclical, out-of-bounds values must wrap around
      int g_idx_wrapped = ((g_idx + nr_gradient_bins) % nr_gradient_bins); 

      for (int d_idx = d_idx_min; d_idx <= d_idx_max; ++d_idx)
      {
        // To avoid boundary effects, use linear interpolation when updating each bin 
        float w = (1 - fabs (d - d_idx)) * (1 - fabs (g - g_idx));

        rift_descriptor (d_idx, g_idx_wrapped) += w * gradient_magnitude;
      }
    }
  }

  // Normalize the RIFT descriptor to unit magnitude
  rift_descriptor.normalize ();
}


template <typename PointInT, typename GradientT, typename PointOutT> void
pcl::RIFTEstimation<PointInT, GradientT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Make sure a search radius is set
  if (search_radius_ == 0.0)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] The search radius must be set before computing the feature!\n",
               getClassName ().c_str ());
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }

  // Make sure the RIFT descriptor has valid dimensions
  if (nr_gradient_bins_ <= 0)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] The number of gradient bins must be greater than zero!\n",
               getClassName ().c_str ());
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }
  if (nr_distance_bins_ <= 0)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] The number of distance bins must be greater than zero!\n",
               getClassName ().c_str ());
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }

  // Check for valid input gradient
  if (!gradient_)
  {
    PCL_ERROR ("[pcl::%s::computeFeature] No input gradient was given!\n", getClassName ().c_str ());
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }
  if (gradient_->points.size () != surface_->points.size ())
  {
    PCL_ERROR ("[pcl::%s::computeFeature] ", getClassName ().c_str ());
    PCL_ERROR ("The number of points in the input dataset differs from the number of points in the gradient!\n");
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }

  Eigen::MatrixXf rift_descriptor (nr_distance_bins_, nr_gradient_bins_);
  std::vector<int> nn_indices;
  std::vector<float> nn_dist_sqr;
 
  // Iterating over the entire index vector
  for (size_t idx = 0; idx < indices_->size (); ++idx)
  {
    // Find neighbors within the search radius
    tree_->radiusSearch ((*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr);

    // Compute the RIFT descriptor
    computeRIFT (*surface_, *gradient_, (*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr, rift_descriptor);

    // Copy into the resultant cloud
    int bin = 0;
    for (int g_bin = 0; g_bin < rift_descriptor.cols (); ++g_bin)
      for (int d_bin = 0; d_bin < rift_descriptor.rows (); ++d_bin)
        output.points[idx].histogram[bin++] = rift_descriptor (d_bin, g_bin);
  }
}

template <typename PointInT, typename GradientT> void
pcl::RIFTEstimation<PointInT, GradientT, Eigen::MatrixXf>::computeFeatureEigen (pcl::PointCloud<Eigen::MatrixXf> &output)
{
  // These should be moved into initCompute ()
  {
    // Make sure a search radius is set
    if (search_radius_ == 0.0)
    {
      PCL_ERROR ("[pcl::%s::computeFeature] The search radius must be set before computing the feature!\n",
                 getClassName ().c_str ());
      output.width = output.height = 0;
      output.points.resize (0, 0);
      return;
    }

    // Make sure the RIFT descriptor has valid dimensions
    if (nr_gradient_bins_ <= 0)
    {
      PCL_ERROR ("[pcl::%s::computeFeature] The number of gradient bins must be greater than zero!\n",
                 getClassName ().c_str ());
      output.width = output.height = 0;
      output.points.resize (0, 0);
      return;
    }
    if (nr_distance_bins_ <= 0)
    {
      PCL_ERROR ("[pcl::%s::computeFeature] The number of distance bins must be greater than zero!\n",
                 getClassName ().c_str ());
      output.width = output.height = 0;
      output.points.resize (0, 0);
      return;
    }

    // Check for valid input gradient
    if (!gradient_)
    {
      PCL_ERROR ("[pcl::%s::computeFeature] No input gradient was given!\n", getClassName ().c_str ());
      output.width = output.height = 0;
      output.points.resize (0, 0);
      return;
    }
    if (gradient_->points.size () != surface_->points.size ())
    {
      PCL_ERROR ("[pcl::%s::computeFeature] ", getClassName ().c_str ());
      PCL_ERROR ("The number of points in the input dataset differs from the number of points in the gradient!\n");
      output.width = output.height = 0;
      output.points.resize (0, 0);
      return;
    }
  }
  
  output.points.resize (indices_->size (), nr_gradient_bins_ * nr_distance_bins_);
  Eigen::MatrixXf rift_descriptor (nr_distance_bins_, nr_gradient_bins_);
  std::vector<int> nn_indices;
  std::vector<float> nn_dist_sqr;
 
  output.is_dense = true;
  // Iterating over the entire index vector
  for (size_t idx = 0; idx < indices_->size (); ++idx)
  {
    // Find neighbors within the search radius
    if (tree_->radiusSearch ((*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr) == 0)
    {
      output.points.row (idx).setConstant (std::numeric_limits<float>::quiet_NaN ());
      output.is_dense = false;
      continue;
    }

    // Compute the RIFT descriptor
    this->computeRIFT (*surface_, *gradient_, (*indices_)[idx], search_radius_, nn_indices, nn_dist_sqr, 
                       rift_descriptor);

    // Copy into the resultant cloud
    int bin = 0;
    for (int g_bin = 0; g_bin < rift_descriptor.cols (); ++g_bin)
      for (int d_bin = 0; d_bin < rift_descriptor.rows (); ++d_bin)
        output.points (idx, bin++) = rift_descriptor (d_bin, g_bin);

  }
}

#define PCL_INSTANTIATE_RIFTEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::RIFTEstimation<T,NT,OutT>;

#endif    // PCL_FEATURES_IMPL_RIFT_H_