densecrf.h
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* Author : Christian Potthast
* Email : potthast@usc.edu
*
*/
#ifndef PCL_DENSE_CRF_H_
#define PCL_DENSE_CRF_H_
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/ml/pairwise_potential.h>
namespace pcl
{
/** \brief
*
*/
class PCL_EXPORTS DenseCrf
{
public:
/** \brief Constructor for DenseCrf class */
DenseCrf (int N, int m);
/** \brief Deconstructor for DenseCrf class */
~DenseCrf ();
/** \brief 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);
/** \brief 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);
/** \brief */
void
addPairwiseEnergy (const std::vector<float> &feature, const int feature_dimension, const float w);
/** \brief Add a pairwise gaussian kernel
*
*/
void
addPairwiseGaussian (float sx, float sy, float sz, float w);
/** \brief 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);
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:
/** \brief Number of variables and labels */
int N_, M_;
/** \brief Data vector */
std::vector<Eigen::Vector3i, Eigen::aligned_allocator<Eigen::Vector3i> > data_;
/** \brief Color vector */
std::vector<Eigen::Vector3i, Eigen::aligned_allocator<Eigen::Vector3i> > color_;
/** TODO: double might use to much memory */
/** \brief CRF unary potentials */
std::vector<float> unary_;
std::vector<float> current_;
std::vector<float> next_;
std::vector<float> tmp_;
/** \brief pairwise potentials */
std::vector<PairwisePotential*> pairwise_potential_;
/** \brief input types */
bool xyz_, rgb_, normal_;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
}
#endif
pcl::DenseCrf::current_
std::vector< float > current_
Definition: densecrf.h:151
pcl::DenseCrf::pairwise_potential_
std::vector< PairwisePotential * > pairwise_potential_
pairwise potentials
Definition: densecrf.h:156
pcl
Definition: convolution.h:45
pcl::DenseCrf::N_
int N_
Number of variables and labels.
Definition: densecrf.h:139
pcl::DenseCrf::tmp_
std::vector< float > tmp_
Definition: densecrf.h:153
pcl::DenseCrf::color_
std::vector< Eigen::Vector3i, Eigen::aligned_allocator< Eigen::Vector3i > > color_
Color vector.
Definition: densecrf.h:145
point_types.h
Defines all the PCL implemented PointT point type structures.
pcl::DenseCrf::xyz_
bool xyz_
input types
Definition: densecrf.h:159
pcl::DenseCrf::unary_
std::vector< float > unary_
TODO: double might use to much memory.
Definition: densecrf.h:149
pcl::DenseCrf
Definition: densecrf.h:53
pcl::DenseCrf::data_
std::vector< Eigen::Vector3i, Eigen::aligned_allocator< Eigen::Vector3i > > data_
Data vector.
Definition: densecrf.h:142
pcl::DenseCrf::next_
std::vector< float > next_
Definition: densecrf.h:152