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hv_papazov.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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#pragma once
#include <pcl/recognition/hv/hv_papazov.h>
///////////////////////////////////////////////////////////////////////////////////////////////////
template<typename ModelT, typename SceneT>
void
pcl::PapazovHV<ModelT, SceneT>::initialize ()
{
//clear stuff
recognition_models_.clear ();
graph_id_model_map_.clear ();
conflict_graph_.clear ();
explained_by_RM_.clear ();
points_explained_by_rm_.clear ();
// initialize mask...
mask_.resize (complete_models_.size ());
for (std::size_t i = 0; i < complete_models_.size (); i++)
mask_[i] = true;
// initialize explained_by_RM
explained_by_RM_.resize (scene_cloud_downsampled_->size ());
points_explained_by_rm_.resize (scene_cloud_downsampled_->size ());
// initialize model
for (std::size_t m = 0; m < complete_models_.size (); m++)
{
RecognitionModelPtr recog_model (new RecognitionModel);
// voxelize model cloud
recog_model->cloud_.reset (new pcl::PointCloud<ModelT>);
recog_model->complete_cloud_.reset (new pcl::PointCloud<ModelT>);
recog_model->id_ = static_cast<int> (m);
pcl::VoxelGrid<ModelT> voxel_grid;
voxel_grid.setInputCloud (visible_models_[m]);
voxel_grid.setLeafSize (resolution_, resolution_, resolution_);
voxel_grid.filter (*(recog_model->cloud_));
pcl::VoxelGrid<ModelT> voxel_grid_complete;
voxel_grid_complete.setInputCloud (complete_models_[m]);
voxel_grid_complete.setLeafSize (resolution_, resolution_, resolution_);
voxel_grid_complete.filter (*(recog_model->complete_cloud_));
std::vector<int> explained_indices;
std::vector<int> outliers;
pcl::Indices nn_indices;
std::vector<float> nn_distances;
for (std::size_t i = 0; i < recog_model->cloud_->size (); i++)
{
if (!scene_downsampled_tree_->radiusSearch ((*recog_model->cloud_)[i], inliers_threshold_, nn_indices, nn_distances,
std::numeric_limits<int>::max ()))
{
outliers.push_back (static_cast<int> (i));
}
else
{
for (std::size_t k = 0; k < nn_distances.size (); k++)
{
explained_indices.push_back (nn_indices[k]); //nn_indices[k] points to the scene
}
}
}
std::sort (explained_indices.begin (), explained_indices.end ());
explained_indices.erase (std::unique (explained_indices.begin (), explained_indices.end ()), explained_indices.end ());
recog_model->bad_information_ = static_cast<int> (outliers.size ());
if ((static_cast<float> (recog_model->bad_information_) / static_cast<float> (recog_model->complete_cloud_->size ()))
<= penalty_threshold_ && (static_cast<float> (explained_indices.size ())
/ static_cast<float> (recog_model->complete_cloud_->size ())) >= support_threshold_)
{
recog_model->explained_ = explained_indices;
recognition_models_.push_back (recog_model);
// update explained_by_RM_, add 1
for (const int &explained_index : explained_indices)
{
explained_by_RM_[explained_index]++;
points_explained_by_rm_[explained_index].push_back (recog_model);
}
}
else
{
mask_[m] = false; // the model didn't survive the sequential check...
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////
template<typename ModelT, typename SceneT>
void
pcl::PapazovHV<ModelT, SceneT>::nonMaximaSuppresion ()
{
// iterate over all vertices of the graph and check if they have a better neighbour, then remove that vertex
using VertexIterator = typename boost::graph_traits<Graph>::vertex_iterator;
VertexIterator vi, vi_end;
boost::tie (vi, vi_end) = boost::vertices (conflict_graph_);
for (auto next = vi; next != vi_end; next++)
{
const typename Graph::vertex_descriptor v = boost::vertex (*next, conflict_graph_);
typename boost::graph_traits<Graph>::adjacency_iterator ai;
typename boost::graph_traits<Graph>::adjacency_iterator ai_end;
auto current = std::static_pointer_cast<RecognitionModel> (graph_id_model_map_[static_cast<int>(v)]);
bool a_better_one = false;
for (boost::tie (ai, ai_end) = boost::adjacent_vertices (v, conflict_graph_); (ai != ai_end) && !a_better_one; ++ai)
{
auto neighbour = std::static_pointer_cast<RecognitionModel> (graph_id_model_map_[static_cast<int>(*ai)]);
if ((neighbour->explained_.size () >= current->explained_.size ()) && mask_[neighbour->id_])
{
a_better_one = true;
}
}
if (a_better_one)
{
mask_[current->id_] = false;
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////
template<typename ModelT, typename SceneT>
void
pcl::PapazovHV<ModelT, SceneT>::buildConflictGraph ()
{
// create vertices for the graph
for (std::size_t i = 0; i < (recognition_models_.size ()); i++)
{
const typename Graph::vertex_descriptor v = boost::add_vertex (recognition_models_[i], conflict_graph_);
graph_id_model_map_[static_cast<int>(v)] = std::static_pointer_cast<RecognitionModel> (recognition_models_[i]);
}
// iterate over the remaining models and check for each one if there is a conflict with another one
for (std::size_t i = 0; i < recognition_models_.size (); i++)
{
for (std::size_t j = i; j < recognition_models_.size (); j++)
{
if (i != j)
{
float n_conflicts = 0.f;
// count scene points explained by both models
for (std::size_t k = 0; k < explained_by_RM_.size (); k++)
{
if (explained_by_RM_[k] > 1)
{
// this point could be a conflict
bool i_found = false;
bool j_found = false;
bool both_found = false;
for (std::size_t kk = 0; (kk < points_explained_by_rm_[k].size ()) && !both_found; kk++)
{
if (points_explained_by_rm_[k][kk]->id_ == recognition_models_[i]->id_)
i_found = true;
if (points_explained_by_rm_[k][kk]->id_ == recognition_models_[j]->id_)
j_found = true;
if (i_found && j_found)
both_found = true;
}
if (both_found)
n_conflicts += 1.f;
}
}
// check if number of points is big enough to create a conflict
bool add_conflict = false;
add_conflict = ((n_conflicts / static_cast<float> (recognition_models_[i]->complete_cloud_->size ())) > conflict_threshold_size_)
|| ((n_conflicts / static_cast<float> (recognition_models_[j]->complete_cloud_->size ())) > conflict_threshold_size_);
if (add_conflict)
{
boost::add_edge (i, j, conflict_graph_);
}
}
}
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////
template<typename ModelT, typename SceneT>
void
pcl::PapazovHV<ModelT, SceneT>::verify ()
{
initialize ();
buildConflictGraph ();
nonMaximaSuppresion ();
}
#define PCL_INSTANTIATE_PapazovHV(T1,T2) template class PCL_EXPORTS pcl::PapazovHV<T1,T2>;
pcl::Filter::filter
void filter(PointCloud &output)
Calls the filtering method and returns the filtered dataset in output.
Definition filter.h:121
pcl::PCLBase::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition pcl_base.hpp:65
pcl::PapazovHV::verify
void verify() override
Definition hv_papazov.hpp:226
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:174
pcl::VoxelGrid
VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.
Definition voxel_grid.h:210
pcl::VoxelGrid::setLeafSize
void setLeafSize(const Eigen::Vector4f &leaf_size)
Set the voxel grid leaf size.
Definition voxel_grid.h:247
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133