transforms.hpp

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template <typename PointT> void
pcl::demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                       const Eigen::Vector4f &centroid,
                       pcl::PointCloud<PointT> &cloud_out)
{
  cloud_out = cloud_in;

  // Subtract the centroid from cloud_in
  for (size_t i = 0; i < cloud_in.points.size (); ++i)
    cloud_out.points[i].getVector4fMap () -= centroid;
}

template <typename PointT> void
pcl::demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                       const std::vector<int> &indices,
                       const Eigen::Vector4f &centroid, 
                       pcl::PointCloud<PointT> &cloud_out)
{
  cloud_out.header = cloud_in.header;
  cloud_out.is_dense = cloud_in.is_dense;
  if (indices.size () == cloud_in.points.size ())
  {
    cloud_out.width    = cloud_in.width;
    cloud_out.height   = cloud_in.height;
  }
  else
  {
    cloud_out.width    = indices.size ();
    cloud_out.height   = 1;
  }
  cloud_out.points.resize (indices.size ());

  // Subtract the centroid from cloud_in
  for (size_t i = 0; i < indices.size (); ++i)
    cloud_out.points[i].getVector4fMap () = cloud_in.points[indices[i]].getVector4fMap () - 
                                            centroid;
}

template <typename PointT> void
pcl::demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                       const Eigen::Vector4f &centroid,
                       Eigen::MatrixXf &cloud_out)
{
  size_t npts = cloud_in.points.size ();

  cloud_out = Eigen::MatrixXf::Zero (4, npts);        // keep the data aligned

  for (size_t i = 0; i < npts; ++i)
    // One column at a time
    cloud_out.block<4, 1> (0, i) = cloud_in.points[i].getVector4fMap () - centroid;
  
  // Make sure we zero the 4th dimension out (1 row, N columns)
  cloud_out.block (3, 0, 1, npts).setZero ();
}

template <typename PointT> void
pcl::demeanPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                       const std::vector<int> &indices,
                       const Eigen::Vector4f &centroid, 
                       Eigen::MatrixXf &cloud_out)
{
  size_t npts = indices.size ();

  cloud_out = Eigen::MatrixXf::Zero (4, npts);        // keep the data aligned

  for (size_t i = 0; i < npts; ++i)
    // One column at a time
    cloud_out.block<4, 1> (0, i) = cloud_in.points[indices[i]].getVector4fMap () - centroid;

  // Make sure we zero the 4th dimension out (1 row, N columns)
  cloud_out.block (3, 0, 1, npts).setZero ();
}

template <typename PointT> void
pcl::transformPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                          pcl::PointCloud<PointT> &cloud_out,
                          const Eigen::Affine3f &transform)
{
  // In order to transform the data, we need to remove NaNs
  cloud_out.is_dense = true;
  if (&cloud_in != &cloud_out)
  {
    // Note: could be replaced by cloud_out = cloud_in
    cloud_out.header   = cloud_in.header;
    cloud_out.width    = cloud_in.width;
    cloud_out.height   = cloud_in.height;
    cloud_out.points.reserve (cloud_out.points.size ());
    cloud_out.points.assign (cloud_in.points.begin (), cloud_in.points.end ());
  }

  if (cloud_in.is_dense)
  {
    // If the dataset is dense, simply transform it!
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
      cloud_out.points[i].getVector3fMap () = transform * 
                                              cloud_in.points[i].getVector3fMap ();
  }
  else
  {
    // Dataset might contain NaNs and Infs, so check for them first,
    // otherwise we get errors during the multiplication (?)
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
    {
      if (!pcl_isfinite (cloud_in.points[i].x) || 
          !pcl_isfinite (cloud_in.points[i].y) || 
          !pcl_isfinite (cloud_in.points[i].z))
        continue;
      cloud_out.points[i].getVector3fMap () = transform * 
                                              cloud_in.points[i].getVector3fMap ();
    }
  }
}

template <typename PointT> void
pcl::transformPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                          const std::vector<int> &indices, 
                          pcl::PointCloud<PointT> &cloud_out,
                          const Eigen::Affine3f &transform)
{
  size_t npts = indices.size ();
  // In order to transform the data, we need to remove NaNs
  cloud_out.is_dense = true;
  cloud_out.header   = cloud_in.header;
  cloud_out.width    = npts;
  cloud_out.height   = 1;
  cloud_out.points.resize (npts);

  if (cloud_in.is_dense)
  {
    // If the dataset is dense, simply transform it!
    for (size_t i = 0; i < npts; ++i)
      cloud_out.points[i].getVector3fMap () = transform *
                                              cloud_in.points[indices[i]].getVector3fMap ();
  }
  else
  {
    // Dataset might contain NaNs and Infs, so check for them first,
    // otherwise we get errors during the multiplication (?)
    for (size_t i = 0; i < npts; ++i)
    {
      if (!pcl_isfinite (cloud_in.points[indices[i]].x) || 
          !pcl_isfinite (cloud_in.points[indices[i]].y) || 
          !pcl_isfinite (cloud_in.points[indices[i]].z))
        continue;
      cloud_out.points[i].getVector3fMap () = transform *
                                              cloud_in.points[indices[i]].getVector3fMap ();
    }
  }
}

template <typename PointT> void
pcl::transformPointCloudWithNormals (const pcl::PointCloud<PointT> &cloud_in, 
                                     pcl::PointCloud<PointT> &cloud_out,
                                     const Eigen::Affine3f &transform)
{
  if (&cloud_in != &cloud_out)
  {
    // Note: could be replaced by cloud_out = cloud_in
    cloud_out.header   = cloud_in.header;
    cloud_out.width    = cloud_in.width;
    cloud_out.height   = cloud_in.height;
    cloud_out.is_dense = cloud_in.is_dense;
    cloud_out.points.reserve (cloud_out.points.size ());
    cloud_out.points.assign (cloud_in.points.begin (), cloud_in.points.end ());
  }

  // If the data is dense, we don't need to check for NaN
  if (cloud_in.is_dense)
  {
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
    {
      cloud_out.points[i].getVector3fMap() = transform * 
                                             cloud_in.points[i].getVector3fMap ();

      // Rotate normals
      cloud_out.points[i].getNormalVector3fMap() = transform.rotation () * 
                                                   cloud_in.points[i].getNormalVector3fMap ();
    }
  }
  // Dataset might contain NaNs and Infs, so check for them first.
  else
  {
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
    {
      if (!pcl_isfinite (cloud_in.points[i].x) || 
          !pcl_isfinite (cloud_in.points[i].y) || 
          !pcl_isfinite (cloud_in.points[i].z))
        continue;
      cloud_out.points[i].getVector3fMap() = transform * 
                                             cloud_in.points[i].getVector3fMap ();

      // Rotate normals
      cloud_out.points[i].getNormalVector3fMap() = transform.rotation () * 
                                                   cloud_in.points[i].getNormalVector3fMap ();
    }
  }
}

template <typename PointT> void
pcl::transformPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                          pcl::PointCloud<PointT> &cloud_out,
                          const Eigen::Matrix4f &transform)
{
  if (&cloud_in != &cloud_out)
  {
    // Note: could be replaced by cloud_out = cloud_in
    cloud_out.header   = cloud_in.header;
    cloud_out.width    = cloud_in.width;
    cloud_out.height   = cloud_in.height;
    cloud_out.is_dense = cloud_in.is_dense;
    cloud_out.points.reserve (cloud_out.points.size ());
    cloud_out.points.assign (cloud_in.points.begin (), cloud_in.points.end ());
  }

  Eigen::Matrix3f rot   = transform.block<3, 3> (0, 0);
  Eigen::Vector3f trans = transform.block<3, 1> (0, 3);
  // If the data is dense, we don't need to check for NaN
  if (cloud_in.is_dense)
  {
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
      cloud_out.points[i].getVector3fMap () = rot * 
                                              cloud_in.points[i].getVector3fMap () + trans;
  }
  // Dataset might contain NaNs and Infs, so check for them first.
  else
  {
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
    {
      if (!pcl_isfinite (cloud_in.points[i].x) || 
          !pcl_isfinite (cloud_in.points[i].y) || 
          !pcl_isfinite (cloud_in.points[i].z))
        continue;
      cloud_out.points[i].getVector3fMap () = rot * 
                                              cloud_in.points[i].getVector3fMap () + trans;
    }
  }
}

template <typename PointT> void
pcl::transformPointCloudWithNormals (const pcl::PointCloud<PointT> &cloud_in, 
                                     pcl::PointCloud<PointT> &cloud_out,
                                     const Eigen::Matrix4f &transform)
{
  if (&cloud_in != &cloud_out)
  {
    // Note: could be replaced by cloud_out = cloud_in
    cloud_out.header   = cloud_in.header;
    cloud_out.width    = cloud_in.width;
    cloud_out.height   = cloud_in.height;
    cloud_out.is_dense = cloud_in.is_dense;
    cloud_out.points.reserve (cloud_out.points.size ());
    cloud_out.points.assign (cloud_in.points.begin (), cloud_in.points.end ());
  }

  Eigen::Matrix3f rot   = transform.block<3, 3> (0, 0);
  Eigen::Vector3f trans = transform.block<3, 1> (0, 3);

  // If the data is dense, we don't need to check for NaN
  if (cloud_in.is_dense)
  {
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
    {
      cloud_out.points[i].getVector3fMap () = rot * 
                                              cloud_in.points[i].getVector3fMap () + trans;

      // Rotate normals
      cloud_out.points[i].getNormalVector3fMap() = rot * 
                                                   cloud_in.points[i].getNormalVector3fMap ();
    }
  }
  // Dataset might contain NaNs and Infs, so check for them first.
  else
  {
    for (size_t i = 0; i < cloud_out.points.size (); ++i)
    {
      if (!pcl_isfinite (cloud_in.points[i].x) || 
          !pcl_isfinite (cloud_in.points[i].y) || 
          !pcl_isfinite (cloud_in.points[i].z))
        continue;
      cloud_out.points[i].getVector3fMap () = rot * 
                                              cloud_in.points[i].getVector3fMap () + trans;

      // Rotate normals
      cloud_out.points[i].getNormalVector3fMap() = rot * 
                                                   cloud_in.points[i].getNormalVector3fMap ();
    }
  }
}

template <typename PointT> inline void
pcl::transformPointCloud (const pcl::PointCloud<PointT> &cloud_in, 
                          pcl::PointCloud<PointT> &cloud_out,
                          const Eigen::Vector3f &offset, 
                          const Eigen::Quaternionf &rotation)
{
  Eigen::Translation3f translation (offset);
  // Assemble an Eigen Transform
  Eigen::Affine3f t;
  t = translation * rotation;
  transformPointCloud (cloud_in, cloud_out, t);
}

template <typename PointT> inline void
pcl::transformPointCloudWithNormals (const pcl::PointCloud<PointT> &cloud_in, 
                                     pcl::PointCloud<PointT> &cloud_out,
                                     const Eigen::Vector3f &offset, 
                                     const Eigen::Quaternionf &rotation)
{
  Eigen::Translation3f translation (offset);
  // Assemble an Eigen Transform
  Eigen::Affine3f t;
  t = translation * rotation;
  transformPointCloudWithNormals (cloud_in, cloud_out, t);
}

---

pcl
Author(s): See http://pcl.ros.org/authors for the complete list of authors.
autogenerated on Fri Jan 11 09:55:24 2013