correspondence_estimation_organized_projection.h

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#pragma once
 
#include <pcl/registration/correspondence_estimation.h>
#include <pcl/memory.h>
#include <pcl/pcl_macros.h>
 
namespace pcl {
namespace registration {
/** \brief CorrespondenceEstimationOrganizedProjection computes correspondences by
 * projecting the source point cloud onto the target point cloud using the camera
 * intrinsic and extrinsic parameters. The correspondences can be trimmed by a depth
 * threshold and by a distance threshold. It is not as precise as a nearest neighbor
 * search, but it is much faster, as it avoids the usage of any additional structures
 * (i.e., kd-trees). \note The target point cloud must be organized (no restrictions on
 * the source) and the target point cloud must be given in the camera coordinate frame.
 * Any other transformation is specified by the src_to_tgt_transformation_ variable.
 * \author Alex Ichim
 * \ingroup registration
 */
template <typename PointSource, typename PointTarget, typename Scalar = float>
class CorrespondenceEstimationOrganizedProjection
: public CorrespondenceEstimationBase<PointSource, PointTarget, Scalar> {
public:
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::initCompute;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      input_transformed_;
  using PCLBase<PointSource>::deinitCompute;
  using PCLBase<PointSource>::input_;
  using PCLBase<PointSource>::indices_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::getClassName;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      point_representation_;
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::
      target_cloud_updated_;
 
  using PointCloudSource = pcl::PointCloud<PointSource>;
  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
  using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
 
  using PointCloudTarget = pcl::PointCloud<PointTarget>;
  using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
  using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
 
  using Ptr = shared_ptr<
      CorrespondenceEstimationOrganizedProjection<PointSource, PointTarget, Scalar>>;
  using ConstPtr =
      shared_ptr<const CorrespondenceEstimationOrganizedProjection<PointSource,
                                                                   PointTarget,
                                                                   Scalar>>;
 
  /** \brief Empty constructor that sets all the intrinsic calibration to the default
   * Kinect values. */
  CorrespondenceEstimationOrganizedProjection()
  : src_to_tgt_transformation_(Eigen::Matrix4f::Identity())
  , depth_threshold_(std::numeric_limits<float>::max())
  , projection_matrix_(Eigen::Matrix3f::Identity())
  {}
 
  /** \brief Sets the focal length parameters of the target camera.
   * \param[in] fx the focal length in pixels along the x-axis of the image
   * \param[in] fy the focal length in pixels along the y-axis of the image
   */
  inline void
  setFocalLengths(const float fx, const float fy)
  {
    fx_ = fx;
    fy_ = fy;
  }
 
  /** \brief Reads back the focal length parameters of the target camera.
   * \param[out] fx the focal length in pixels along the x-axis of the image
   * \param[out] fy the focal length in pixels along the y-axis of the image
   */
  inline void
  getFocalLengths(float& fx, float& fy) const
  {
    fx = fx_;
    fy = fy_;
  }
 
  /** \brief Sets the camera center parameters of the target camera.
   * \param[in] cx the x-coordinate of the camera center
   * \param[in] cy the y-coordinate of the camera center
   */
  inline void
  setCameraCenters(const float cx, const float cy)
  {
    cx_ = cx;
    cy_ = cy;
  }
 
  /** \brief Reads back the camera center parameters of the target camera.
   * \param[out] cx the x-coordinate of the camera center
   * \param[out] cy the y-coordinate of the camera center
   */
  inline void
  getCameraCenters(float& cx, float& cy) const
  {
    cx = cx_;
    cy = cy_;
  }
 
  /** \brief Sets the transformation from the source point cloud to the target point
   * cloud. \note The target point cloud must be in its local camera coordinates, so use
   * this transformation to correct for that. \param[in] src_to_tgt_transformation the
   * transformation
   */
  inline void
  setSourceTransformation(const Eigen::Matrix4f& src_to_tgt_transformation)
  {
    src_to_tgt_transformation_ = src_to_tgt_transformation;
  }
 
  /** \brief Reads back the transformation from the source point cloud to the target
   * point cloud. \note The target point cloud must be in its local camera coordinates,
   * so use this transformation to correct for that. \return the transformation
   */
  inline Eigen::Matrix4f
  getSourceTransformation() const
  {
    return (src_to_tgt_transformation_);
  }
 
  /** \brief Sets the depth threshold; after projecting the source points in the image
   * space of the target camera, this threshold is applied on the depths of
   * corresponding dexels to eliminate the ones that are too far from each other.
   * \param[in] depth_threshold the depth threshold
   */
  inline void
  setDepthThreshold(const float depth_threshold)
  {
    depth_threshold_ = depth_threshold;
  }
 
  /** \brief Reads back the depth threshold; after projecting the source points in the
   * image space of the target camera, this threshold is applied on the depths of
   * corresponding dexels to eliminate the ones that are too far from each other.
   * \return the depth threshold
   */
  inline float
  getDepthThreshold() const
  {
    return (depth_threshold_);
  }
 
  /** \brief Computes the correspondences, applying a maximum Euclidean distance
   * threshold.
   * \param[out] correspondences the found correspondences (index of query point, index
   * of target point, distance)
   * \param[in] max_distance Euclidean distance threshold above which correspondences
   * will be rejected
   */
  void
  determineCorrespondences(Correspondences& correspondences, double max_distance);
 
  /** \brief Computes the correspondences, applying a maximum Euclidean distance
   * threshold.
   * \param[out] correspondences the found correspondences (index of query and target
   * point, distance)
   * \param[in] max_distance Euclidean distance threshold above which correspondences
   * will be rejected
   */
  void
  determineReciprocalCorrespondences(Correspondences& correspondences,
                                     double max_distance);
 
  /** \brief Clone and cast to CorrespondenceEstimationBase */
  virtual typename CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::Ptr
  clone() const
  {
    Ptr copy(new CorrespondenceEstimationOrganizedProjection<PointSource,
                                                             PointTarget,
                                                             Scalar>(*this));
    return (copy);
  }
 
protected:
  using CorrespondenceEstimationBase<PointSource, PointTarget, Scalar>::target_;
 
  bool
  initCompute();
 
  float fx_{525.f}, fy_{525.f};
  float cx_{319.5f}, cy_{239.5f};
  Eigen::Matrix4f src_to_tgt_transformation_;
  float depth_threshold_;
 
  Eigen::Matrix3f projection_matrix_;
 
public:
  PCL_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace registration
} // namespace pcl
 
#include <pcl/registration/impl/correspondence_estimation_organized_projection.hpp>