Method extractDescriptors

apps/src/feature_matching.cpp:272–309

template <typename FeatureType>
void
ICCVTutorial<FeatureType>::extractDescriptors(
    typename pcl::PointCloud<pcl::PointXYZRGB>::ConstPtr input,
    const typename pcl::PointCloud<pcl::PointXYZI>::Ptr& keypoints,
    typename pcl::PointCloud<FeatureType>::Ptr features)
{
  typename pcl::PointCloud<pcl::PointXYZRGB>::Ptr kpts(
      new pcl::PointCloud<pcl::PointXYZRGB>);
  kpts->points.resize(keypoints->size());

  pcl::copyPointCloud(*keypoints, *kpts);

  typename pcl::FeatureFromNormals<pcl::PointXYZRGB, pcl::Normal, FeatureType>::Ptr
      feature_from_normals = pcl::dynamic_pointer_cast<
          pcl::FeatureFromNormals<pcl::PointXYZRGB, pcl::Normal, FeatureType>>(
          feature_extractor_);

  feature_extractor_->setSearchSurface(input);
  feature_extractor_->setInputCloud(kpts);

  if (feature_from_normals) {
    std::cout << "normal estimation..." << std::flush;
    typename pcl::PointCloud<pcl::Normal>::Ptr normals(
        new pcl::PointCloud<pcl::Normal>);
    pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimation;
    normal_estimation.setSearchMethod(pcl::search::Search<pcl::PointXYZRGB>::Ptr(
        new pcl::search::KdTree<pcl::PointXYZRGB>));
    normal_estimation.setRadiusSearch(0.01);
    normal_estimation.setInputCloud(input);
    normal_estimation.compute(*normals);
    feature_from_normals->setInputNormals(normals);
    std::cout << "OK" << std::endl;
  }

  std::cout << "descriptor extraction..." << std::flush;
  feature_extractor_->compute(*features);
  std::cout << "OK" << std::endl;
}

template <typename FeatureType>
void