//if a point in a plane
bool isInPlane(Point p, pcl::KdTreeFLANN<Point> tree){
// Neighbors containers
vector<int> k_indices;
vector<float> k_distances;
tree.radiusSearch (p, 0.005, k_indices, k_distances);
if(k_indices.size()>0){
return true;
}
return false;
}
std::vector<Quadric> HandSearch::findQuadrics(const PointCloud::Ptr cloud,
const Eigen::VectorXi& pts_cam_source, const pcl::KdTreeFLANN<pcl::PointXYZ>& kdtree,
const std::vector<int>& indices)
{
double t1 = omp_get_wtime();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > T_cams;
T_cams.push_back(cam_tf_left_);
T_cams.push_back(cam_tf_right_);
std::vector<Quadric> quadric_list(indices.size());
#ifdef _OPENMP // parallelization using OpenMP
#pragma omp parallel for private(nn_indices, nn_dists) num_threads(num_threads_)
#endif
for (int i = 0; i < indices.size(); i++)
{
const pcl::PointXYZ& sample = cloud->points[indices[i]];
// std::cout << "i: " << i << ", index: " << indices[i] << ", sample: " << sample << std::endl;
if (kdtree.radiusSearch(sample, nn_radius_taubin_, nn_indices, nn_dists) > 0)
{
Eigen::VectorXi nn_cam_source(nn_indices.size());
// std::cout << " Found " << nn_indices.size() << " neighbors.\n";
for (int j = 0; j < nn_cam_source.size(); j++)
{
nn_cam_source(j) = pts_cam_source(nn_indices[j]);
}
Eigen::Vector3d sample_eigen = sample.getVector3fMap().cast<double>();
Quadric q(T_cams, cloud, sample_eigen, uses_determinstic_normal_estimation_);
q.fitQuadric(nn_indices);
// std::cout << " Fitted quadric\n";
q.findTaubinNormalAxis(nn_indices, nn_cam_source);
// std::cout << " Found local axes\n";
quadric_list[i] = q;
cloud_normals_.col(indices[i]) = q.getNormal();
}
}
double t2 = omp_get_wtime();
//std::cout << "Fitted " << quadric_list.size() << " quadrics in " << t2 - t1 << " sec.\n";
// quadric_list[0].print(); // debugging
// plot_.plotLocalAxes(quadric_list, cloud);
return quadric_list;
}
std::vector<GraspHypothesis> HandSearch::findHands(const PointCloud::Ptr cloud,
const Eigen::VectorXi& pts_cam_source, const std::vector<Quadric>& quadric_list,
const Eigen::VectorXi& hands_cam_source, const pcl::KdTreeFLANN<pcl::PointXYZ>& kdtree)
{
double t1 = omp_get_wtime();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
Eigen::Matrix3Xd nn_normals(3, nn_indices.size());
Eigen::VectorXi nn_cam_source(nn_indices.size());
Eigen::Matrix3Xd centered_neighborhood(3, nn_indices.size());
std::vector<RotatingHand> hand_list(quadric_list.size());
// std::vector<RotatingHand> hand_list;
double time_eval_hand = 0.0;
double time_iter = 0.0;
double time_nn = 0.0;
double time_tf = 0.0;
std::vector< std::vector<GraspHypothesis> > grasp_lists(quadric_list.size(), std::vector<GraspHypothesis>(0));
#ifdef _OPENMP // parallelization using OpenMP
#pragma omp parallel for private(nn_indices, nn_dists, nn_normals, nn_cam_source, centered_neighborhood) num_threads(num_threads_)
#endif
for (std::size_t i = 0; i < quadric_list.size(); i++)
{
double timei = omp_get_wtime();
pcl::PointXYZ sample;
sample.x = quadric_list[i].getSample()(0);
sample.y = quadric_list[i].getSample()(1);
sample.z = quadric_list[i].getSample()(2);
// std::cout << "i: " << i << ", sample: " << sample << std::endl;
if (kdtree.radiusSearch(sample, nn_radius_hands_, nn_indices, nn_dists) > 0)
{
time_nn += omp_get_wtime() - timei;
nn_normals.setZero(3, nn_indices.size());
nn_cam_source.setZero(nn_indices.size());
centered_neighborhood.setZero(3, nn_indices.size());
for (int j = 0; j < nn_indices.size(); j++)
{
nn_cam_source(j) = pts_cam_source(nn_indices[j]);
centered_neighborhood.col(j) = (cloud->points[nn_indices[j]].getVector3fMap()
- sample.getVector3fMap()).cast<double>();
nn_normals.col(j) = cloud_normals_.col(nn_indices[j]);
}
FingerHand finger_hand(finger_width_, hand_outer_diameter_, hand_depth_);
Eigen::Vector3d sample_eig = sample.getVector3fMap().cast<double>();
RotatingHand rotating_hand(cam_tf_left_.block<3, 1>(0, 3) - sample_eig,
cam_tf_right_.block<3, 1>(0, 3) - sample_eig, finger_hand, tolerant_antipodal_, hands_cam_source(i));
const Quadric& q = quadric_list[i];
double time_tf1 = omp_get_wtime();
rotating_hand.transformPoints(centered_neighborhood, q.getNormal(), q.getCurvatureAxis(), nn_normals,
nn_cam_source, hand_height_);
time_tf += omp_get_wtime() - time_tf1;
double time_eval1 = omp_get_wtime();
std::vector<GraspHypothesis> grasps = rotating_hand.evaluateHand(init_bite_, sample_eig, true);
time_eval_hand += omp_get_wtime() - time_eval1;
if (grasps.size() > 0)
{
// grasp_list.insert(grasp_list.end(), grasps.begin(), grasps.end());
grasp_lists[i] = grasps;
}
}
time_iter += omp_get_wtime() - timei;
}
time_eval_hand /= quadric_list.size();
time_nn /= quadric_list.size();
time_iter /= quadric_list.size();
time_tf /= quadric_list.size();
//std::cout << " avg time for transforming point neighborhood: " << time_tf << " sec.\n";
//std::cout << " avg time for NN search: " << time_nn << " sec.\n";
//std::cout << " avg time for rotating_hand.evaluate(): " << time_eval_hand << " sec.\n";
//std::cout << " avg time per iteration: " << time_iter << " sec.\n";
std::vector<GraspHypothesis> grasp_list;
for (std::size_t i = 0; i < grasp_lists.size(); i++)
{
// std::cout << i << " " << grasp_lists[i].size() << "\n";
if (grasp_lists[i].size() > 0)
grasp_list.insert(grasp_list.end(), grasp_lists[i].begin(), grasp_lists[i].end());
}
double t2 = omp_get_wtime();
//std::cout << " Found " << grasp_list.size() << " robot hand poses in " << t2 - t1 << " sec.\n";
return grasp_list;
}