thirdParty/PCL 1.12.0/include/pcl-1.12/pcl/ml/densecrf.h

/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *
 *  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 Willow Garage, Inc. 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.
 *
 * 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