densecrf.h
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* Author : Christian Potthast
* Email : potthast@usc.edu
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*/
#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
pcl::DenseCrf
Definition: densecrf.h:48
pcl::DenseCrf::getFeatures
void getFeatures(int idx, std::vector< float > &features)
pcl::DenseCrf::startInference
void startInference()
pcl::DenseCrf::tmp_
std::vector< float > tmp_
Definition: densecrf.h:152
pcl::DenseCrf::runInference
void runInference(float relax)
pcl::DenseCrf::setColorVector
void setColorVector(const std::vector< Eigen::Vector3i, Eigen::aligned_allocator< Eigen::Vector3i >> color)
The associated color of the data.
pcl::DenseCrf::DenseCrf
DenseCrf(int N, int m)
Constructor for DenseCrf class.
pcl::DenseCrf::~DenseCrf
~DenseCrf()
Deconstructor for DenseCrf class.
pcl::DenseCrf::data_
std::vector< Eigen::Vector3i, Eigen::aligned_allocator< Eigen::Vector3i > > data_
Data vector.
Definition: densecrf.h:141
pcl::DenseCrf::setDataVector
void setDataVector(const std::vector< Eigen::Vector3i, Eigen::aligned_allocator< Eigen::Vector3i >> data)
Set the input data vector.
pcl::DenseCrf::expAndNormalize
void expAndNormalize(std::vector< float > &out, const std::vector< float > &in, float scale, float relax=1.0f) const
pcl::DenseCrf::expAndNormalizeORI
void expAndNormalizeORI(float *out, const float *in, float scale=1.0f, float relax=1.0f)
pcl::DenseCrf::addPairwiseGaussian
void addPairwiseGaussian(float sx, float sy, float sz, float w)
Add a pairwise gaussian kernel.
pcl::DenseCrf::setUnaryEnergy
void setUnaryEnergy(const std::vector< float > unary)
pcl::DenseCrf::current_
std::vector< float > current_
Definition: densecrf.h:150
pcl::DenseCrf::pairwise_potential_
std::vector< PairwisePotential * > pairwise_potential_
Pairwise potentials.
Definition: densecrf.h:155
pcl::DenseCrf::normal_
bool normal_
Definition: densecrf.h:158
pcl::DenseCrf::color_
std::vector< Eigen::Vector3i, Eigen::aligned_allocator< Eigen::Vector3i > > color_
Color vector.
Definition: densecrf.h:144
pcl::DenseCrf::addPairwiseBilateral
void addPairwiseBilateral(float sx, float sy, float sz, float sr, float sg, float sb, float w)
Add a bilateral gaussian kernel.
pcl::DenseCrf::mapInference
void mapInference(int n_iterations, std::vector< int > &result, float relax=1.0f)
pcl::DenseCrf::map
void map(int n_iterations, std::vector< int > result, float relax=1.0f)
pcl::DenseCrf::M_
int M_
Definition: densecrf.h:138
pcl::DenseCrf::unary_
std::vector< float > unary_
CRF unary potentials.
Definition: densecrf.h:148
pcl::DenseCrf::stepInference
void stepInference(float relax)
pcl::DenseCrf::runInference
std::vector< float > runInference(int n_iterations, float relax)
pcl::DenseCrf::inference
void inference(int n_iterations, std::vector< float > &result, float relax=1.0f)
pcl::DenseCrf::addPairwiseNormals
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)
pcl::DenseCrf::next_
std::vector< float > next_
Definition: densecrf.h:151
pcl::DenseCrf::addPairwiseEnergy
void addPairwiseEnergy(const std::vector< float > &feature, const int feature_dimension, const float w)
pcl::DenseCrf::getBarycentric
void getBarycentric(int idx, std::vector< float > &bary)
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
memory.h
Defines functions, macros and traits for allocating and using memory.
pcl
Definition: convolution.h:46
pcl_macros.h
Defines all the PCL and non-PCL macros used.
PCL_EXPORTS
#define PCL_EXPORTS
Definition: pcl_macros.h:323