// Finds a child of the node. Returns NULL if it's not found.
Val_T * getChild(int ID)
{
typename std::map<Id_T, Val_T*>::iterator it = m_children.find(ID);
if(it != m_children.end())
return it->second;
return NULL;
}
void do_scan(typename betree<Key, Value>::iterator &betit,
typename std::map<Key, Value>::iterator &refit,
betree<Key, Value> &b,
typename std::map<Key, Value> &reference)
{
while (refit != reference.end()) {
assert(betit != b.end());
assert(betit.first == refit->first);
assert(betit.second == refit->second);
++refit;
if (refit == reference.end()) {
debug(std::cout << "Almost done" << std::endl);
}
++betit;
}
assert(betit == b.end());
}
/** Returns the id of the given name, throws on lookup failure */
int get(const T& name) const
{
typename std::map<T, int>::const_iterator it = m_name2int.find(name);
if (it == m_name2int.end())
{
raise_exception(std::runtime_error, "lookup failure for: '" << name << "'");
}
else
{
return it->second;
}
}
/** Returns the id of the given name, on lookup failure the name is
put into the table */
int getput(const T& name)
{
typename std::map<T, int>::const_iterator it = m_name2int.find(name);
if (it == m_name2int.end())
{
return put(name);
}
else
{
return it->second;
}
}
// Prints the node's sub-tree in a stream, using indentation
// to represent parents and children.
// d < 0 : display at real depth
// d = 0 : display as root node
// d > 0 : display at specified depth
virtual void printTree(std::ostream & os, int d = -1) const
{
if(d < 0)
d = getDepth();
// Indentation
for(int i = 0; i < d; i++)
std::cout << " ";
// Printing the node
print(os);
// Printing children
typename std::map<Id_T, Val_T*>::iterator it;
for(it = m_children.begin(); it != m_children.end(); it++)
{
it->second->printTree(os, d + 1);
}
}
// Destroys the tree node and its children.
virtual ~TreeNode()
{
typename std::map<Id_T, Val_T*>::iterator it;
for(it = m_children.begin(); it != m_children.end(); it++)
delete it->second;
}
bool has(const T& name) const
{
return m_name2int.find(name) != m_name2int.end();
}
void
pcl::rec_3d_framework::GlobalNNCVFHRecognizer<Distance, PointInT, FeatureT>::recognize ()
{
models_.reset (new std::vector<ModelT>);
transforms_.reset (new std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> >);
PointInTPtr processed (new pcl::PointCloud<PointInT>);
PointInTPtr in (new pcl::PointCloud<PointInT>);
std::vector<pcl::PointCloud<FeatureT>, Eigen::aligned_allocator<pcl::PointCloud<FeatureT> > > signatures;
std::vector < Eigen::Vector3d > centroids;
if (indices_.size ())
pcl::copyPointCloud (*input_, indices_, *in);
else
in = input_;
{
pcl::ScopeTime t ("Estimate feature");
micvfh_estimator_->estimate (in, processed, signatures, centroids);
}
std::vector<index_score> indices_scores;
descriptor_distances_.clear ();
if (signatures.size () > 0)
{
{
pcl::ScopeTime t_matching ("Matching and roll...");
if (use_single_categories_ && (categories_to_be_searched_.size () > 0))
{
//perform search of the different signatures in the categories_to_be_searched_
for (size_t c = 0; c < categories_to_be_searched_.size (); c++)
{
std::cout << "Using category:" << categories_to_be_searched_[c] << std::endl;
for (size_t idx = 0; idx < signatures.size (); idx++)
{
/*double* hist = signatures[idx].points[0].histogram;
std::vector<double> std_hist (hist, hist + getHistogramLength (dummy));
flann_model histogram ("", std_hist);
flann::Matrix<int> indices;
flann::Matrix<double> distances;
std::map<std::string, int>::iterator it;
it = category_to_vectors_indices_.find (categories_to_be_searched_[c]);
assert (it != category_to_vectors_indices_.end ());
nearestKSearch (single_categories_index_[it->second], histogram, nmodels_, indices, distances);*/
double* hist = signatures[idx].points[0].histogram;
int size_feat = sizeof(signatures[idx].points[0].histogram) / sizeof(double);
std::vector<double> std_hist (hist, hist + size_feat);
//ModelT empty;
flann_model histogram;
histogram.descr = std_hist;
flann::Matrix<int> indices;
flann::Matrix<double> distances;
std::map<std::string, int>::iterator it;
it = category_to_vectors_indices_.find (categories_to_be_searched_[c]);
assert (it != category_to_vectors_indices_.end ());
nearestKSearch (single_categories_index_[it->second], histogram, NN_, indices, distances);
//gather NN-search results
double score = 0;
for (size_t i = 0; i < NN_; ++i)
{
score = distances[0][i];
index_score is;
is.idx_models_ = single_categories_pointers_to_models_[it->second]->at (indices[0][i]);
is.idx_input_ = static_cast<int> (idx);
is.score_ = score;
indices_scores.push_back (is);
}
}
//we cannot add more than nmodels per category, so sort here and remove offending ones...
std::sort (indices_scores.begin (), indices_scores.end (), sortIndexScoresOp);
indices_scores.resize ((c + 1) * NN_);
}
}
else
{
for (size_t idx = 0; idx < signatures.size (); idx++)
{
double* hist = signatures[idx].points[0].histogram;
int size_feat = sizeof(signatures[idx].points[0].histogram) / sizeof(double);
std::vector<double> std_hist (hist, hist + size_feat);
//ModelT empty;
flann_model histogram;
histogram.descr = std_hist;
flann::Matrix<int> indices;
flann::Matrix<double> distances;
nearestKSearch (flann_index_, histogram, NN_, indices, distances);
//gather NN-search results
double score = 0;
for (int i = 0; i < NN_; ++i)
{
score = distances[0][i];
index_score is;
is.idx_models_ = indices[0][i];
is.idx_input_ = static_cast<int> (idx);
is.score_ = score;
indices_scores.push_back (is);
//ModelT m = flann_models_[indices[0][i]].model;
}
}
}
std::sort (indices_scores.begin (), indices_scores.end (), sortIndexScoresOp);
/*
* There might be duplicated candidates, in those cases it makes sense to take
* the closer one in descriptor space
*/
typename std::map<flann_model, bool> found;
typename std::map<flann_model, bool>::iterator it_map;
for (size_t i = 0; i < indices_scores.size (); i++)
{
flann_model m = flann_models_[indices_scores[i].idx_models_];
it_map = found.find (m);
if (it_map == found.end ())
{
indices_scores[found.size ()] = indices_scores[i];
found[m] = true;
}
}
indices_scores.resize (found.size ());
int num_n = std::min (NN_, static_cast<int> (indices_scores.size ()));
/*
* Filter some hypothesis regarding to their distance to the first neighbour
*/
/*std::vector<index_score> indices_scores_filtered;
indices_scores_filtered.resize (num_n);
indices_scores_filtered[0] = indices_scores[0];
double best_score = indices_scores[0].score_;
int kept = 1;
for (int i = 1; i < num_n; ++i)
{
//std::cout << best_score << indices_scores[i].score_ << (best_score / indices_scores[i].score_) << std::endl;
if ((best_score / indices_scores[i].score_) > 0.65)
{
indices_scores_filtered[i] = indices_scores[i];
kept++;
}
//best_score = indices_scores[i].score_;
}
indices_scores_filtered.resize (kept);
//std::cout << indices_scores_filtered.size () << " § " << num_n << std::endl;
indices_scores = indices_scores_filtered;
num_n = indices_scores.size ();*/
std::cout << "Number of object hypotheses... " << num_n << std::endl;
std::vector<bool> valid_trans;
std::vector < Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > transformations;
micvfh_estimator_->getValidTransformsVec (valid_trans);
micvfh_estimator_->getTransformsVec (transformations);
for (int i = 0; i < num_n; ++i)
{
ModelT m = flann_models_[indices_scores[i].idx_models_].model;
int view_id = flann_models_[indices_scores[i].idx_models_].view_id;
int desc_id = flann_models_[indices_scores[i].idx_models_].descriptor_id;
int idx_input = indices_scores[i].idx_input_;
std::cout << m.class_ << "/" << m.id_ << " ==> " << indices_scores[i].score_ << std::endl;
Eigen::Matrix4d roll_view_pose;
bool roll_pose_found = getRollPose (m, view_id, desc_id, roll_view_pose);
if (roll_pose_found && valid_trans[idx_input])
{
Eigen::Matrix4d transposed = roll_view_pose.transpose ();
//std::cout << transposed << std::endl;
PointInTPtr view;
getView (m, view_id, view);
Eigen::Matrix4d model_view_pose;
getPose (m, view_id, model_view_pose);
Eigen::Matrix4d scale_mat;
scale_mat.setIdentity (4, 4);
if (compute_scale_)
{
//compute scale using the whole view
PointInTPtr view_transformed (new pcl::PointCloud<PointInT>);
Eigen::Matrix4d hom_from_OVC_to_CC;
hom_from_OVC_to_CC = transformations[idx_input].inverse () * transposed;
pcl::transformPointCloud (*view, *view_transformed, hom_from_OVC_to_CC);
Eigen::Vector3d input_centroid = centroids[indices_scores[i].idx_input_];
Eigen::Vector3d view_centroid;
getCentroid (m, view_id, desc_id, view_centroid);
Eigen::Vector4d cmatch4f (view_centroid[0], view_centroid[1], view_centroid[2], 0);
Eigen::Vector4d cinput4f (input_centroid[0], input_centroid[1], input_centroid[2], 0);
Eigen::Vector4d max_pt_input;
pcl::getMaxDistance (*processed, cinput4f, max_pt_input);
max_pt_input[3] = 0;
double max_dist_input = (cinput4f - max_pt_input).norm ();
//compute max dist for transformed model_view
pcl::getMaxDistance (*view, cmatch4f, max_pt_input);
max_pt_input[3] = 0;
double max_dist_view = (cmatch4f - max_pt_input).norm ();
cmatch4f = hom_from_OVC_to_CC * cmatch4f;
std::cout << max_dist_view << " " << max_dist_input << std::endl;
double scale_factor_view = max_dist_input / max_dist_view;
std::cout << "Scale factor:" << scale_factor_view << std::endl;
Eigen::Matrix4d center, center_inv;
center.setIdentity (4, 4);
center (0, 3) = -cinput4f[0];
center (1, 3) = -cinput4f[1];
center (2, 3) = -cinput4f[2];
center_inv.setIdentity (4, 4);
center_inv (0, 3) = cinput4f[0];
center_inv (1, 3) = cinput4f[1];
center_inv (2, 3) = cinput4f[2];
scale_mat (0, 0) = scale_factor_view;
scale_mat (1, 1) = scale_factor_view;
scale_mat (2, 2) = scale_factor_view;
scale_mat = center_inv * scale_mat * center;
}
Eigen::Matrix4d hom_from_OC_to_CC;
hom_from_OC_to_CC = scale_mat * transformations[idx_input].inverse () * transposed * model_view_pose;
models_->push_back (m);
transforms_->push_back (hom_from_OC_to_CC);
descriptor_distances_.push_back (static_cast<double> (indices_scores[i].score_));
}
else
{
PCL_WARN("The roll pose was not found, should use CRH here... \n");
}
}
}
std::cout << "Number of object hypotheses:" << models_->size () << std::endl;
/**
* POSE REFINEMENT
**/
if (ICP_iterations_ > 0)
{
pcl::ScopeTime t ("Pose refinement");
//Prepare scene and model clouds for the pose refinement step
double VOXEL_SIZE_ICP_ = 0.005;
PointInTPtr cloud_voxelized_icp (new pcl::PointCloud<PointInT> ());
{
pcl::ScopeTime t ("Voxelize stuff...");
pcl::VoxelGrid<PointInT> voxel_grid_icp;
voxel_grid_icp.setInputCloud (processed);
voxel_grid_icp.setLeafSize (VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_);
voxel_grid_icp.filter (*cloud_voxelized_icp);
source_->voxelizeAllModels (VOXEL_SIZE_ICP_);
}
#pragma omp parallel for num_threads(omp_get_num_procs())
for (int i = 0; i < static_cast<int> (models_->size ()); i++)
{
ConstPointInTPtr model_cloud;
PointInTPtr model_aligned (new pcl::PointCloud<PointInT>);
if (compute_scale_)
{
model_cloud = models_->at (i).getAssembled (-1);
PointInTPtr model_aligned_m (new pcl::PointCloud<PointInT>);
pcl::transformPointCloud (*model_cloud, *model_aligned_m, transforms_->at (i));
pcl::VoxelGrid<PointInT> voxel_grid_icp;
voxel_grid_icp.setInputCloud (model_aligned_m);
voxel_grid_icp.setLeafSize (VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_);
voxel_grid_icp.filter (*model_aligned);
}
else
{
model_cloud = models_->at (i).getAssembled (VOXEL_SIZE_ICP_);
pcl::transformPointCloud (*model_cloud, *model_aligned, transforms_->at (i));
}
pcl::IterativeClosestPoint<PointInT, PointInT> reg;
reg.setInputCloud (model_aligned); //model
reg.setInputTarget (cloud_voxelized_icp); //scene
reg.setMaximumIterations (ICP_iterations_);
reg.setMaxCorrespondenceDistance (VOXEL_SIZE_ICP_ * 3.);
reg.setTransformationEpsilon (1e-6);
typename pcl::PointCloud<PointInT>::Ptr output_ (new pcl::PointCloud<PointInT> ());
reg.align (*output_);
Eigen::Matrix4d icp_trans = reg.getFinalTransformation ();
transforms_->at (i) = icp_trans * transforms_->at (i);
}
}
/**
* HYPOTHESES VERIFICATION
**/
if (hv_algorithm_)
{
pcl::ScopeTime t ("HYPOTHESES VERIFICATION");
std::vector<typename pcl::PointCloud<PointInT>::ConstPtr> aligned_models;
aligned_models.resize (models_->size ());
for (size_t i = 0; i < models_->size (); i++)
{
ConstPointInTPtr model_cloud;
PointInTPtr model_aligned (new pcl::PointCloud<PointInT>);
if (compute_scale_)
{
model_cloud = models_->at (i).getAssembled (-1);
PointInTPtr model_aligned_m (new pcl::PointCloud<PointInT>);
pcl::transformPointCloud (*model_cloud, *model_aligned_m, transforms_->at (i));
pcl::VoxelGrid<PointInT> voxel_grid_icp;
voxel_grid_icp.setInputCloud (model_aligned_m);
voxel_grid_icp.setLeafSize (0.005, 0.005, 0.005);
voxel_grid_icp.filter (*model_aligned);
}
else
{
model_cloud = models_->at (i).getAssembled (0.005);
pcl::transformPointCloud (*model_cloud, *model_aligned, transforms_->at (i));
}
//ConstPointInTPtr model_cloud = models_->at (i).getAssembled (0.005);
//PointInTPtr model_aligned (new pcl::PointCloud<PointInT>);
//pcl::transformPointCloud (*model_cloud, *model_aligned, transforms_->at (i));
aligned_models[i] = model_aligned;
}
std::vector<bool> mask_hv;
hv_algorithm_->setSceneCloud (input_);
hv_algorithm_->addModels (aligned_models, true);
hv_algorithm_->verify ();
hv_algorithm_->getMask (mask_hv);
boost::shared_ptr < std::vector<ModelT> > models_temp;
boost::shared_ptr < std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > > transforms_temp;
models_temp.reset (new std::vector<ModelT>);
transforms_temp.reset (new std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> >);
for (size_t i = 0; i < models_->size (); i++)
{
if (!mask_hv[i])
continue;
models_temp->push_back (models_->at (i));
transforms_temp->push_back (transforms_->at (i));
}
models_ = models_temp;
transforms_ = transforms_temp;
}
}
}
