densecrf.h

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 * Author : Christian Potthast
 * Email  : potthast@usc.edu
 *
 */
 
#pragma once
 
#include <pcl/ml/pairwise_potential.h>
#include <pcl/memory.h>
#include <pcl/pcl_macros.h>
 
namespace pcl {
 
class PCL_EXPORTS DenseCrf {
public:
  /** Constructor for DenseCrf class. */
  DenseCrf(int N, int m);
 
  /** Deconstructor for DenseCrf class. */
  ~DenseCrf();
 
  /** Set the input data vector.
   *
   *  The input data vector holds the measurements coordinates as ijk of the voxel grid.
   */
  void
  setDataVector(const std::vector<Eigen::Vector3i,
                                  Eigen::aligned_allocator<Eigen::Vector3i>> data);
 
  /** The associated color of the data. */
  void
  setColorVector(const std::vector<Eigen::Vector3i,
                                   Eigen::aligned_allocator<Eigen::Vector3i>> color);
 
  void
  setUnaryEnergy(const std::vector<float> unary);
 
  void
  addPairwiseEnergy(const std::vector<float>& feature,
                    const int feature_dimension,
                    const float w);
 
  /** Add a pairwise gaussian kernel. */
  void
  addPairwiseGaussian(float sx, float sy, float sz, float w);
 
  /** Add a bilateral gaussian kernel. */
  void
  addPairwiseBilateral(
      float sx, float sy, float sz, float sr, float sg, float sb, float w);
 
  void
  addPairwiseNormals(
      std::vector<Eigen::Vector3i, Eigen::aligned_allocator<Eigen::Vector3i>>& coord,
      std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f>>& normals,
      float sx,
      float sy,
      float sz,
      float snx,
      float sny,
      float snz,
      float w);
 
  void
  inference(int n_iterations, std::vector<float>& result, float relax = 1.0f);
 
  void
  mapInference(int n_iterations, std::vector<int>& result, float relax = 1.0f);
 
  void
  expAndNormalize(std::vector<float>& out,
                  const std::vector<float>& in,
                  float scale,
                  float relax = 1.0f) const;
 
  void
  expAndNormalizeORI(float* out,
                     const float* in,
                     float scale = 1.0f,
                     float relax = 1.0f);
  void
  map(int n_iterations, std::vector<int> result, float relax = 1.0f);
 
  std::vector<float>
  runInference(int n_iterations, float relax);
 
  void
  startInference();
 
  void
  stepInference(float relax);
 
  void
  runInference(float relax);
 
  void
  getBarycentric(int idx, std::vector<float>& bary);
 
  void
  getFeatures(int idx, std::vector<float>& features);
 
protected:
  /** Number of variables and labels */
  int N_, M_;
 
  /** Data vector */
  std::vector<Eigen::Vector3i, Eigen::aligned_allocator<Eigen::Vector3i>> data_;
 
  /** Color vector */
  std::vector<Eigen::Vector3i, Eigen::aligned_allocator<Eigen::Vector3i>> color_;
 
  /** CRF unary potentials */
  /** TODO: double might use to much memory */
  std::vector<float> unary_;
 
  std::vector<float> current_;
  std::vector<float> next_;
  std::vector<float> tmp_;
 
  /** Pairwise potentials */
  std::vector<PairwisePotential*> pairwise_potential_;
 
  /** Input types */
  bool xyz_, rgb_, normal_;
 
public:
  PCL_MAKE_ALIGNED_OPERATOR_NEW
};
 
} // namespace pcl