std::vector<poseT> RefinePoses(const pcl::PointCloud<myPointXYZ>::Ptr scene, const std::vector<ModelT> &mesh_set, const std::vector<poseT> &all_poses)
{
int pose_num = all_poses.size();
std::vector<ModelT> est_models(pose_num);
pcl::PointCloud<myPointXYZ>::Ptr down_scene(new pcl::PointCloud<myPointXYZ>());
pcl::VoxelGrid<myPointXYZ> sor;
sor.setInputCloud(scene);
sor.setLeafSize(0.005, 0.005, 0.005);
sor.filter(*down_scene);
#pragma omp parallel for schedule(dynamic, 1)
for(int i = 0 ; i < pose_num ; i++ ){
for( int j = 0 ; j < mesh_set.size() ; j++ ){
if( mesh_set[j].model_label == all_poses[i].model_name )
{
est_models[i].model_label = all_poses[i].model_name;
est_models[i].model_cloud = pcl::PointCloud<myPointXYZ>::Ptr (new pcl::PointCloud<myPointXYZ>());
pcl::transformPointCloud(*mesh_set[j].model_cloud, *est_models[i].model_cloud, all_poses[i].shift, all_poses[i].rotation);
break;
}
}
}
std::vector< pcl::search::KdTree<myPointXYZ>::Ptr > tree_set(est_models.size());
#pragma omp parallel for schedule(dynamic, 1)
for( int i = 0 ; i < pose_num ; i++ )
{
tree_set[i] = pcl::search::KdTree<myPointXYZ>::Ptr (new pcl::search::KdTree<myPointXYZ>());
tree_set[i]->setInputCloud(est_models[i].model_cloud);
}
std::vector<int> votes(pose_num, 0);
std::vector< std::vector<int> > adj_graph(pose_num);
for( int i = 0 ; i < pose_num ; i++ )
adj_graph[i].resize(pose_num, 0);
float sqrT = 0.01*0.01;
int down_num = down_scene->size();
std::vector< std::vector<int> > bin_vec(down_num);
#pragma omp parallel for
for(int i = 0 ; i < pose_num ; i++ )
{
int count = 0;
for( pcl::PointCloud<myPointXYZ>::const_iterator it = down_scene->begin() ; it < down_scene->end() ; it++, count++ )
{
std::vector<int> idx (1);
std::vector<float> sqrDist (1);
int nres = tree_set[i]->nearestKSearch(*it, 1, idx, sqrDist);
if ( nres >= 1 && sqrDist[0] <= sqrT )
{
#pragma omp critical
{
bin_vec[count].push_back(i);
}
votes[i]++;
}
}
}
for( int it = 0 ; it < down_num ; it++ )
for( std::vector<int>::iterator ii = bin_vec[it].begin() ; ii < bin_vec[it].end() ; ii++ )
for( std::vector<int>::iterator jj = ii+1 ; jj < bin_vec[it].end() ; jj++ )
{
adj_graph[*ii][*jj]++;
adj_graph[*jj][*ii]++;
}
std::vector<bool> dead_flag(pose_num, 0);
for( int i = 0 ; i < pose_num ; i++ ){
if( dead_flag[i] == true )
continue;
for( int j = i+1 ; j < pose_num ; j++ )
{
if( dead_flag[j] == true )
continue;
int min_tmp = std::min(votes[i], votes[j]);
if( (adj_graph[i][j]+0.0) / min_tmp >= 0.75 )
{
if( votes[i] > votes[j] )
dead_flag[j] = true;
else
{
dead_flag[i] = true;
break;
}
}
}
}
std::vector<poseT> refined_poses;
for( int i = 0 ; i < pose_num ; i++ )
if( dead_flag[i] == false )
refined_poses.push_back(all_poses[i]);
return refined_poses;
}
/**
* @brief Add a new position vector to the histogram.
*
* Create the integer bin coordinates from the given position vector, then
* increment the count of this bin.
*
* @parameter r Vector3D to add to the histogram.
*/
void Hist3D::update(const Vector3D& r) {
std::vector<int> bin_vec(3, 0);
for (int i = 0; i < 3; ++i)
bin_vec[i] = static_cast<int>(std::floor(r[i]/bin_size_));
++hist_[bin_vec];
}
