greedy_verification.hpp
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#pragma once
#include <pcl/recognition/hv/greedy_verification.h>
template<typename ModelT, typename SceneT>
void
pcl::GreedyVerification<ModelT, SceneT>::initialize ()
{
//clear stuff
recognition_models_.clear ();
points_explained_by_rm_.clear ();
// initialize mask...
mask_.resize (visible_models_.size ());
for (std::size_t i = 0; i < visible_models_.size (); i++)
mask_[i] = false;
// initialize explained_by_RM
points_explained_by_rm_.resize (scene_cloud_downsampled_->size ());
// initialize model
for (std::size_t m = 0; m < visible_models_.size (); m++)
{
RecognitionModelPtr recog_model (new RecognitionModel);
// voxelize model cloud
recog_model->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_));
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 ());
recog_model->explained_ = explained_indices;
recog_model->good_information_ = static_cast<int> (explained_indices.size ());
recog_model->regularizer_ = regularizer_;
recognition_models_.push_back (recog_model);
for (const int &explained_index : explained_indices)
{
points_explained_by_rm_[explained_index].push_back (recog_model);
}
}
sortModels ();
}
template<typename ModelT, typename SceneT>
void
pcl::GreedyVerification<ModelT, SceneT>::verify ()
{
initialize ();
std::vector<bool> best_solution_;
best_solution_.resize (recognition_models_.size ());
for (std::size_t i = 0; i < recognition_models_.size (); i++)
{
if (static_cast<float> (recognition_models_[i]->good_information_) > (regularizer_
* static_cast<float> (recognition_models_[i]->bad_information_)))
{
best_solution_[i] = true;
updateGoodInformation (static_cast<int> (i));
}
else
best_solution_[i] = false;
}
for (std::size_t i = 0; i < best_solution_.size (); i++)
{
if (best_solution_[i])
{
mask_[indices_models_[i].index_] = true;
}
else
{
mask_[indices_models_[i].index_] = false;
}
}
}
#define PCL_INSTANTIATE_GreedyVerification(T1,T2) template class PCL_EXPORTS pcl::GreedyVerification<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::GreedyVerification
A greedy hypothesis verification method.
Definition: greedy_verification.h:54
pcl::GreedyVerification::verify
void verify() override
Starts verification.
Definition: greedy_verification.hpp:111
pcl::PCLBase::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:65
pcl::PointCloud< ModelT >
pcl::VoxelGrid
VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.
Definition: voxel_grid.h:177
pcl::VoxelGrid::setLeafSize
void setLeafSize(const Eigen::Vector4f &leaf_size)
Set the voxel grid leaf size.
Definition: voxel_grid.h:221
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133