int main(int argc, char** argv)
{
double taubin_radius = 0.03; // radius of curvature-estimation neighborhood
double hand_radius = 0.08; // radius of hand configuration neighborhood
// std::string file = "/home/andreas/data/mlaffordance/round21l_reg.pcd";
std::string file = "/home/andreas/data/mlaffordance/training/rect31l_reg.pcd";
PointCloud::Ptr cloud(new PointCloud);
if (pcl::io::loadPCDFile<pcl::PointXYZ>(file, *cloud) == -1) //* load the file
{
PCL_ERROR("Couldn't read input PCD file\n");
return (-1);
}
pcl::search::OrganizedNeighbor<pcl::PointXYZ>::Ptr organized_neighbor(
new pcl::search::OrganizedNeighbor<pcl::PointXYZ>());
std::vector<int> nn_indices;
std::vector<float> nn_dists;
pcl::KdTreeFLANN<pcl::PointXYZ>::Ptr tree(new pcl::KdTreeFLANN<pcl::PointXYZ>());
// int sample_index = 0;
int sample_index = 731;
if (cloud->isOrganized())
{
organized_neighbor->setInputCloud(cloud);
organized_neighbor->radiusSearch(cloud->points[sample_index], taubin_radius, nn_indices, nn_dists);
}
else
{
tree->setInputCloud(cloud);
tree->radiusSearch(cloud->points[sample_index], taubin_radius, nn_indices, nn_dists);
}
std::cout << "Found point neighborhood for sample " << sample_index << "\n";
Eigen::Matrix4d T_base, T_sqrt;
T_base << 0, 0.445417, 0.895323, 0.22, 1, 0, 0, -0.02, 0, 0.895323, -0.445417, 0.24, 0, 0, 0, 1;
T_sqrt << 0.9366, -0.0162, 0.3500, -0.2863, 0.0151, 0.9999, 0.0058, 0.0058, -0.3501, -0.0002, 0.9367, 0.0554, 0, 0, 0, 1;
std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > T_cams;
T_cams.push_back(T_base * T_sqrt.inverse());
std::cout << T_cams[0] << std::endl;
// fit quadric
Eigen::Vector3d sample = cloud->points[sample_index].getVector3fMap().cast<double>();
Quadric quadric(T_cams, cloud, sample, true);
quadric.fitQuadric(nn_indices);
Eigen::MatrixXi cam_source = Eigen::MatrixXi::Zero(1, cloud->points.size());
quadric.findTaubinNormalAxis(nn_indices, cam_source);
quadric.print();
// fit hand
tree->radiusSearch(cloud->points[sample_index], hand_radius, nn_indices, nn_dists);
Eigen::VectorXi pts_cam_source = Eigen::VectorXi::Ones(1, cloud->size());
Eigen::Matrix3Xd nn_normals(3, nn_indices.size());
Eigen::VectorXi nn_cam_source(nn_indices.size());
Eigen::Matrix3Xd centered_neighborhood(3, nn_indices.size());
nn_normals.setZero();
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().cast<double>() - sample;
}
double finger_width_ = 0.01;
double hand_outer_diameter_ = 0.09;
double hand_depth_ = 0.06;
ParallelHand finger_hand(finger_width_, hand_outer_diameter_, hand_depth_);
double hand_height_ = 0.02;
double init_bite_ = 0.01;
RotatingHand rotating_hand(T_cams[0].block(0, 3, 3, 1) - sample,
T_cams[0].block(0, 3, 3, 1) - sample, finger_hand, true, pts_cam_source(sample_index));
rotating_hand.transformPoints(centered_neighborhood, quadric.getNormal(), quadric.getCurvatureAxis(), nn_normals, nn_cam_source,
hand_height_);
std::vector<GraspHypothesis> h = rotating_hand.evaluateHand(init_bite_, sample, 1);
for (int i = 0; i < h.size(); i++)
{
std::cout << "-- orientation " << i << " --\n";
h[i].print();
}
// std::cout << "\n";
// rotating_hand.evaluateHand(init_bite_, sample, 1);
// rotating_hand.print();
return 0;
}
int main(int argc, char** argv)
{
double taubin_radius = 0.03; // radius of curvature-estimation neighborhood
std::string file = "/home/andreas/data/mlaffordance/round21l_reg.pcd";
PointCloud::Ptr cloud(new PointCloud);
if (pcl::io::loadPCDFile<pcl::PointXYZRGBA>(file, *cloud) == -1) //* load the file
{
PCL_ERROR("Couldn't read input PCD file\n");
return (-1);
}
pcl::search::OrganizedNeighbor<pcl::PointXYZRGBA>::Ptr organized_neighbor(
new pcl::search::OrganizedNeighbor<pcl::PointXYZRGBA>());
std::vector<int> nn_outer_indices;
std::vector<float> nn_outer_dists;
pcl::KdTreeFLANN<pcl::PointXYZRGBA>::Ptr tree(new pcl::KdTreeFLANN<pcl::PointXYZRGBA>());
int sample_index = 0;
if (cloud->isOrganized())
{
organized_neighbor->setInputCloud(cloud);
organized_neighbor->radiusSearch(cloud->points[sample_index], taubin_radius, nn_outer_indices, nn_outer_dists);
}
else
{
tree->setInputCloud(cloud);
tree->radiusSearch(cloud->points[sample_index], taubin_radius, nn_outer_indices, nn_outer_dists);
}
Eigen::Matrix4d T_base, T_sqrt;
T_base << 0, 0.445417, 0.895323, 0.22, 1, 0, 0, -0.02, 0, 0.895323, -0.445417, 0.24, 0, 0, 0, 1;
T_sqrt << 0.9366, -0.0162, 0.3500, -0.2863, 0.0151, 0.9999, 0.0058, 0.0058, -0.3501, -0.0002, 0.9367, 0.0554, 0, 0, 0, 1;
std::vector<Eigen::Matrix4d, Eigen::aligned_allocator<Eigen::Matrix4d> > T_cams;
T_cams.push_back(T_base * T_sqrt.inverse());
Eigen::Vector3d sample = cloud->points[sample_index].getVector3fMap().cast<double>();
Quadric quadric(T_cams, cloud, sample, true);
quadric.fitQuadric(nn_outer_indices);
Eigen::MatrixXi cam_source = Eigen::MatrixXi::Zero(1, cloud->points.size());
quadric.findTaubinNormalAxis(nn_outer_indices, cam_source);
quadric.print();
std::set<Eigen::Vector3i, VectorComparator> s;
Eigen::Matrix3i M;
M << 1,1,1,2,3,4,1,1,1;
for (int i=0; i < M.rows(); i++)
s.insert(M.row(i));
std::cout << "M:" << std::endl;
std::cout << M << std::endl;
Eigen::Matrix<int, Eigen::Dynamic, 3> N(s.size(), 3);
int i = 0;
for (std::set<Eigen::Vector3i, VectorComparator>::iterator it=s.begin(); it!=s.end(); ++it)
{
N.row(i) = *it;
i++;
}
std::cout << "N:" << std::endl;
std::cout << N << std::endl;
return 0;
}
