thirdParty/PCL 1.12.0/include/pcl-1.12/pcl/features/impl/cppf.hpp

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2011, Alexandru-Eugen Ichim
 *  Copyright (c) 2012-, Open Perception, Inc.
 *  Copyright (c) 2013, Martin Szarski
 *
 *  All rights reserved.
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions
 *  are met:
 *
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.
 *   * Neither the name of the copyright holder(s) nor the names of its
 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
 *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
 *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 *  POSSIBILITY OF SUCH DAMAGE.
 *
 * $Id$
 */

#ifndef PCL_FEATURES_IMPL_CPPF_H_
#define PCL_FEATURES_IMPL_CPPF_H_

#include <pcl/features/cppf.h>

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT>
pcl::CPPFEstimation<PointInT, PointNT, PointOutT>::CPPFEstimation ()
    : FeatureFromNormals <PointInT, PointNT, PointOutT> ()
{
  feature_name_ = "CPPFEstimation";
  // Slight hack in order to pass the check for the presence of a search method in Feature::initCompute ()
  Feature<PointInT, PointOutT>::tree_.reset (new pcl::search::KdTree <PointInT> ());
  Feature<PointInT, PointOutT>::search_radius_ = 1.0f;
}


//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::CPPFEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
  // Initialize output container
  output.points.clear ();
  output.points.reserve (indices_->size () * input_->size ());
  output.is_dense = true;
  // Compute point pair features for every pair of points in the cloud
  for (const auto& i: *indices_)
  {
    for (std::size_t j = 0 ; j < input_->size (); ++j)
    {
      PointOutT p;
      // No need to calculate feature for identity pair (i, j) as they aren't used in future calculations
      // @TODO: resolve issue with comparison in a better manner
      if (static_cast<std::size_t>(i) != j)
      {
        if (
            pcl::computeCPPFPairFeature ((*input_)[i].getVector4fMap (),
                                      (*normals_)[i].getNormalVector4fMap (),
									  (*input_)[i].getRGBVector4i (),
                                      (*input_)[j].getVector4fMap (),
                                      (*normals_)[j].getNormalVector4fMap (),
									  (*input_)[j].getRGBVector4i (),
                                      p.f1, p.f2, p.f3, p.f4, p.f5, p.f6, p.f7, p.f8, p.f9, p.f10))
        {
          // Calculate alpha_m angle
          Eigen::Vector3f model_reference_point = (*input_)[i].getVector3fMap (),
                          model_reference_normal = (*normals_)[i].getNormalVector3fMap (),
                          model_point = (*input_)[j].getVector3fMap ();
          Eigen::AngleAxisf rotation_mg (std::acos (model_reference_normal.dot (Eigen::Vector3f::UnitX ())),
                                         model_reference_normal.cross (Eigen::Vector3f::UnitX ()).normalized ());
          Eigen::Affine3f transform_mg = Eigen::Translation3f ( rotation_mg * ((-1) * model_reference_point)) * rotation_mg;

          Eigen::Vector3f model_point_transformed = transform_mg * model_point;
          float angle = std::atan2 ( -model_point_transformed(2), model_point_transformed(1));
          if (std::sin (angle) * model_point_transformed(2) < 0.0f)
            angle *= (-1);
          p.alpha_m = -angle;
        }
        else
        {
          PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), i, j);
          p.f1 = p.f2 = p.f3 = p.f4 = p.f5 = p.f6 = p.f7 = p.f8 = p.f9 = p.f10 = p.alpha_m = std::numeric_limits<float>::quiet_NaN ();
          output.is_dense = false;
        }
      }
      else
      {
        p.f1 = p.f2 = p.f3 = p.f4 = p.f5 = p.f6 = p.f7 = p.f8 = p.f9 = p.f10 = p.alpha_m = std::numeric_limits<float>::quiet_NaN ();
        output.is_dense = false;
      }

      output.push_back (p);
    }
  }
  // overwrite the sizes done by Feature::initCompute ()
  output.height = 1;
  output.width = output.size ();
}

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


#endif // PCL_FEATURES_IMPL_CPPF_H_