void Box::rotate(double angle, glm::vec3 const& point) {
glm::mat4x4 translate_origin{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
-point.x, -point.y, -point.z, 1
};
double rad_angle = angle * M_PI / 180;
glm::mat4x4 rotate{
std::cos(rad_angle), std::sin(rad_angle), -std::sin(rad_angle), 0,
-std::sin(rad_angle), std::cos(rad_angle), std::sin(rad_angle), 0,
std::sin(rad_angle), -std::sin(rad_angle), std::cos(rad_angle), 0,
0, 0, 0, 1
};
glm::mat4x4 translate_back{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
point.x, point.y, point.z, 1
};
glm::mat4x4 trans_matrix = translate_back * rotate * translate_origin;
transformation_matrix(transformation_matrix() * trans_matrix);
// Transform min/max back to cartesian coordinates
glm::vec4 h_min = trans_matrix * glm::vec4(min(), 1);
glm::vec4 h_max = trans_matrix * glm::vec4(max(), 1);
min({ h_min.x / h_min.w, h_min.y / h_min.w, h_min.z / h_min.w });
max({ h_max.x / h_max.w, h_max.y / h_max.w, h_max.z / h_max.w });
}
/*!
Sets the brush to have a matrix and translation in its
transformation
\param p translation value for brush transformation
\param m matrix value for brush transformation
*/
PainterBrush&
transformation(const vec2 &p, const float2x2 &m)
{
transformation_translate(p);
transformation_matrix(m);
return *this;
}
void Sphere::rotate(double angle, glm::vec3 const& point) {
glm::mat4x4 translate_origin{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
-point.x, -point.y, -point.z, 1
};
double rad_angle = angle * M_PI / 180;
glm::mat4x4 rotate{
std::cos(rad_angle), std::sin(rad_angle), -std::sin(rad_angle), 0,
-std::sin(rad_angle), std::cos(rad_angle), std::sin(rad_angle), 0,
std::sin(rad_angle), -std::sin(rad_angle), std::cos(rad_angle), 0,
0, 0, 0, 1
};
glm::mat4x4 translate_back{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
point.x, point.y, point.z, 1
};
glm::mat4x4 trans_mat = translate_back * rotate * translate_origin;
transformation_matrix(transformation_matrix() * trans_mat);
// Transform center back to cartesian coordinates
glm::vec4 h_center = trans_mat * glm::vec4(center(), 1);
center(
glm::vec3{
h_center.x / h_center.w,
h_center.y / h_center.w,
h_center.z / h_center.w
}
);
// std::cout << trans_mat[0][0] << " " << trans_mat[1][0] << " "
// << trans_mat[2][0] << " " << trans_mat[3][0] << std::endl;
// std::cout << trans_mat[0][1] << " " << trans_mat[1][1] << " "
// << trans_mat[2][1] << " " << trans_mat[3][1] << std::endl;
// std::cout << trans_mat[0][2] << " " << trans_mat[1][2] << " "
// << trans_mat[2][2] << " " << trans_mat[3][2] << std::endl;
// std::cout << trans_mat[0][3] << " " << trans_mat[1][3] << " "
// << trans_mat[2][3] << " " << trans_mat[3][3] << std::endl;
}
void Box::translate(glm::vec3 const& distance) {
glm::mat4x4 trans_mat{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
distance.x, distance.y, distance.z, 1
};
// Multiply transformation matrix with min/max (in homogeneous coordinates)
glm::vec4 h_min = trans_mat * glm::vec4(min(), 1);
glm::vec4 h_max = trans_mat * glm::vec4(max(), 1);
// Set new transformation matrix
transformation_matrix(transformation_matrix() * trans_mat);
// Set new min/max (in cartesian coordinates)
min({ h_min.x / h_min.w, h_min.y / h_min.w, h_min.z / h_min.w });
max({ h_max.x / h_max.w, h_max.y / h_max.w, h_max.z / h_max.w });
}
void Box::scale(double scale_factor) {
// The center of the box is offset by the origin by the minimum plus
// half of the distance between minimum and maximum
glm::vec3 center_offset{
min().x + (max().x - min().x)/2,
min().y + (max().y - min().y)/2,
min().z + (max().z - min().z)/2
};
// Translate box to origin so that its center is in the origin
glm::mat4x4 translate_origin{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
-center_offset.x, -center_offset.y, -center_offset.z, 1
};
// Scaling the box in the center
glm::mat4x4 scale{
scale_factor, 0, 0, 0,
0, scale_factor, 0, 0,
0, 0, scale_factor, 0,
0, 0, 0, 1
};
// Translating back again
glm::mat4x4 translate_back{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
center_offset.x, center_offset.y, center_offset.z, 1
};
glm::mat4x4 trans_matrix = translate_back * scale * translate_origin;
transformation_matrix(transformation_matrix() * trans_matrix);
glm::vec4 h_min = trans_matrix * glm::vec4(min(), 1);
glm::vec4 h_max = trans_matrix * glm::vec4(max(), 1);
min(glm::vec3{ h_min.x / h_min.w, h_min.y / h_min.w, h_min.z / h_min.w });
max(glm::vec3{ h_max.x / h_max.w, h_max.y / h_max.w, h_max.z / h_max.w });
}
sensor_msgs::PointCloud2 TrajectoryScans2PointcloudAlgNode::laser_scan_to_point_cloud(const sensor_msgs::LaserScan& LScan, const geometry_msgs::Pose& pose)
{
sensor_msgs::PointCloud2 pcloud;
sensor_msgs::PointCloud2 transformed_pcloud;
laser_projector_.projectLaser(LScan, pcloud, LScan.range_max - 1e-6);
Matrix4f T_pose = transformation_matrix(pose.position.x, pose.position.y, pose.position.z, tf::getYaw(pose.orientation));
pcl_ros::transformPointCloud(T_pose * T_laser_frame_, pcloud, transformed_pcloud);
return transformed_pcloud;
}
void Sphere::translate(glm::vec3 const& distance) {
glm::mat4x4 trans_mat{
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
distance.x, distance.y, distance.z, 1
};
// Multiply transformation matrix with center (in homogeneous coordinates)
glm::vec4 h_center = trans_mat * glm::vec4(center(), 1);
// Set new transformation matrix
transformation_matrix(transformation_matrix() * trans_mat);
// Set new center (in cartesian coordinates)
center(
glm::vec3{
h_center.x / h_center.w,
h_center.y / h_center.w,
h_center.z / h_center.w
}
);
}
void shape::paint(graphics_context& aGraphicsContext) const
{
if (frame_count() == 0)
return;
auto tm = transformation_matrix();
auto m = map();
for (auto& vertex : m)
vertex = tm * vertex;
if (current_frame().texture() != boost::none)
{
if (current_frame().texture_rect() == boost::none)
aGraphicsContext.draw_texture(texture_map{ {m[0][0], m[0][1]}, {m[1][0], m[1][1]}, {m[2][0], m[2][1]}, {m[3][0], m[3][1]} }, *current_frame().texture());
else
aGraphicsContext.draw_texture(texture_map{ {m[0][0], m[0][1]}, {m[1][0], m[1][1]}, {m[2][0], m[2][1]}, {m[3][0], m[3][1]} }, *current_frame().texture(), *current_frame().texture_rect());
}
else if (current_frame().colour() != boost::none)
{
aGraphicsContext.fill_shape(position() - origin(), m, *current_frame().colour());
}
}
TrajectoryScans2PointcloudAlgNode::TrajectoryScans2PointcloudAlgNode(void) :
algorithm_base::IriBaseAlgorithm<TrajectoryScans2PointcloudAlgorithm>()
{
//init class attributes if necessary
emptyPointCloud_ = true;
new_trajectory_ = false;
public_node_handle_.param<bool>("publish_redundant", publish_redundant_, true);
double d[4];
public_node_handle_.param<double>("dx_base_2_laser", d[0], 0.57);
public_node_handle_.param<double>("dy_base_2_laser", d[1], 0.0);
public_node_handle_.param<double>("dz_base_2_laser", d[2], 0.145);
public_node_handle_.param<double>("dth_base_2_laser", d[3], 0.0);
T_laser_frame_ = transformation_matrix(d[0], d[1], d[2], d[3]);
ROS_DEBUG("TR 2 PC: Config updated");
PointCloud_msg_.header.frame_id = "/map";
//this->loop_rate_ = 2;//in [Hz]
// [init publishers]
this->laser_pointcloud_publisher_ = this->public_node_handle_.advertise<sensor_msgs::PointCloud2>("laser_pointcloud", 1);
// [init subscribers]
this->scan_subscriber_ = this->public_node_handle_.subscribe("scan", 10, &TrajectoryScans2PointcloudAlgNode::scan_callback, this);
this->trajectory_subscriber_ = this->public_node_handle_.subscribe("trajectory", 1, &TrajectoryScans2PointcloudAlgNode::trajectory_callback, this);
pthread_mutex_init(&this->last_trajectory_mutex_,NULL);
// [init services]
// [init clients]
// [init action servers]
// [init action clients]
}
template<template<class > class Distance, typename PointT, typename FeatureT> bool
GlobalNNPipelineROS<Distance,PointT,FeatureT>::classifyROS (classifier_srv_definitions::classify::Request & req, classifier_srv_definitions::classify::Response & response)
{
typename pcl::PointCloud<PointT>::Ptr cloud (new pcl::PointCloud<PointT>());
pcl::fromROSMsg(req.cloud, *cloud);
this->input_ = cloud;
pcl::PointCloud<pcl::PointXYZRGB>::Ptr pClusteredPCl (new pcl::PointCloud<pcl::PointXYZRGB>());
pcl::copyPointCloud(*cloud, *pClusteredPCl);
//clear all data from previous visualization steps and publish empty markers/point cloud
for (size_t i=0; i < markerArray_.markers.size(); i++)
{
markerArray_.markers[i].action = visualization_msgs::Marker::DELETE;
}
vis_pub_.publish( markerArray_ );
sensor_msgs::PointCloud2 scenePc2;
vis_pc_pub_.publish(scenePc2);
markerArray_.markers.clear();
for(size_t cluster_id=0; cluster_id < req.clusters_indices.size(); cluster_id++)
{
std::vector<int> cluster_indices_int;
Eigen::Vector4f centroid;
Eigen::Matrix3f covariance_matrix;
const float r = std::rand() % 255;
const float g = std::rand() % 255;
const float b = std::rand() % 255;
this->indices_.resize(req.clusters_indices[cluster_id].data.size());
for(size_t kk=0; kk < req.clusters_indices[cluster_id].data.size(); kk++)
{
this->indices_[kk] = static_cast<int>(req.clusters_indices[cluster_id].data[kk]);
pClusteredPCl->at(req.clusters_indices[cluster_id].data[kk]).r = 0.8*r;
pClusteredPCl->at(req.clusters_indices[cluster_id].data[kk]).g = 0.8*g;
pClusteredPCl->at(req.clusters_indices[cluster_id].data[kk]).b = 0.8*b;
}
this->classify ();
std::cout << "for cluster " << cluster_id << " with size " << cluster_indices_int.size() << ", I have following hypotheses: " << std::endl;
object_perception_msgs::classification class_tmp;
for(size_t kk=0; kk < this->categories_.size(); kk++)
{
std::cout << this->categories_[kk] << " with confidence " << this->confidences_[kk] << std::endl;
std_msgs::String str_tmp;
str_tmp.data = this->categories_[kk];
class_tmp.class_type.push_back(str_tmp);
class_tmp.confidence.push_back( this->confidences_[kk] );
}
response.class_results.push_back(class_tmp);
typename pcl::PointCloud<PointT>::Ptr pClusterPCl_transformed (new pcl::PointCloud<PointT>());
pcl::computeMeanAndCovarianceMatrix(*this->input_, cluster_indices_int, covariance_matrix, centroid);
Eigen::Matrix3f eigvects;
Eigen::Vector3f eigvals;
pcl::eigen33(covariance_matrix, eigvects, eigvals);
Eigen::Vector3f centroid_transformed = eigvects.transpose() * centroid.topRows(3);
Eigen::Matrix4f transformation_matrix = Eigen::Matrix4f::Zero(4,4);
transformation_matrix.block<3,3>(0,0) = eigvects.transpose();
transformation_matrix.block<3,1>(0,3) = -centroid_transformed;
transformation_matrix(3,3) = 1;
pcl::transformPointCloud(*this->input_, cluster_indices_int, *pClusterPCl_transformed, transformation_matrix);
//pcl::transformPointCloud(*frame_, cluster_indices_int, *frame_eigencoordinates_, eigvects);
PointT min_pt, max_pt;
pcl::getMinMax3D(*pClusterPCl_transformed, min_pt, max_pt);
std::cout << "Elongations along eigenvectors: " << max_pt.x - min_pt.x << ", " << max_pt.y - min_pt.y
<< ", " << max_pt.z - min_pt.z << std::endl;
geometry_msgs::Point32 centroid_ros;
centroid_ros.x = centroid[0];
centroid_ros.y = centroid[1];
centroid_ros.z = centroid[2];
response.centroid.push_back(centroid_ros);
// calculating the bounding box of the cluster
Eigen::Vector4f min;
Eigen::Vector4f max;
pcl::getMinMax3D (*this->input_, cluster_indices_int, min, max);
object_perception_msgs::BBox bbox;
geometry_msgs::Point32 pt;
pt.x = min[0]; pt.y = min[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = min[0]; pt.y = min[1]; pt.z = max[2]; bbox.point.push_back(pt);
pt.x = min[0]; pt.y = max[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = min[0]; pt.y = max[1]; pt.z = max[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = min[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = min[1]; pt.z = max[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = max[1]; pt.z = min[2]; bbox.point.push_back(pt);
pt.x = max[0]; pt.y = max[1]; pt.z = max[2]; bbox.point.push_back(pt);
response.bbox.push_back(bbox);
// getting the point cloud of the cluster
typename pcl::PointCloud<PointT>::Ptr cluster (new pcl::PointCloud<PointT>);
pcl::copyPointCloud(*this->input_, cluster_indices_int, *cluster);
sensor_msgs::PointCloud2 pc2;
pcl::toROSMsg (*cluster, pc2);
response.cloud.push_back(pc2);
// visualize the result as ROS topic
visualization_msgs::Marker marker;
marker.header.frame_id = camera_frame_;
marker.header.stamp = ros::Time::now();
//marker.header.seq = ++marker_seq_;
marker.ns = "object_classification";
marker.id = cluster_id;
marker.type = visualization_msgs::Marker::TEXT_VIEW_FACING;
marker.action = visualization_msgs::Marker::ADD;
marker.pose.position.x = centroid_ros.x;
marker.pose.position.y = centroid_ros.y - 0.1;
marker.pose.position.z = centroid_ros.z - 0.1;
marker.pose.orientation.x = 0;
marker.pose.orientation.y = 0;
marker.pose.orientation.z = 0;
marker.pose.orientation.w = 1.0;
marker.scale.z = 0.1;
marker.color.a = 1.0;
marker.color.r = r/255.f;
marker.color.g = g/255.f;
marker.color.b = b/255.f;
std::stringstream marker_text;
marker_text.precision(2);
marker_text << this->categories_[0] << this->confidences_[0];
marker.text = marker_text.str();
markerArray_.markers.push_back(marker);
}
pcl::toROSMsg (*pClusteredPCl, scenePc2);
vis_pc_pub_.publish(scenePc2);
vis_pub_.publish( markerArray_ );
response.clusters_indices = req.clusters_indices;
return true;
}
int update_face_numerics(int n_variables_old, int n_variables, int *variable_order_old, int *variable_order, int n_faces, struct FACE *face, int n_boundaries_old, struct BOUNDARY *boundary_old)
{
int a, b, f, g, h, i, j, v;
// lists of old face boundaries
int **face_n_boundaries_old = allocate_integer_matrix(NULL,n_faces,n_variables);
exit_if_false(face_n_boundaries_old != NULL,"allocating face_n_boundaries_old");
for(i = 0; i < n_faces; i ++) for(j = 0; j < n_variables; j ++) face_n_boundaries_old[i][j] = 0;
int ***face_boundary_old = allocate_integer_tensor(NULL,n_faces,n_variables,MAX_FACE_N_BOUNDARIES);
exit_if_false(face_boundary_old != NULL,"allocating face_boundary_old");
for(b = 0; b < n_boundaries_old; b ++)
{
for(i = 0; i < boundary_old[b].n_faces; i ++)
{
v = boundary_old[b].variable;
f = boundary_old[b].face[i] - &face[0];
face_boundary_old[f][v][face_n_boundaries_old[f][v]++] = b;
}
}
// order and numbers of integration points and bases
int max_variable_order = 0, max_variable_order_old = 0;
for(v = 0; v < n_variables; v ++) max_variable_order = MAX(max_variable_order,variable_order[v]);
for(v = 0; v < n_variables_old; v ++) max_variable_order_old = MAX(max_variable_order_old,variable_order_old[v]);
int n_gauss = ORDER_TO_N_GAUSS(max_variable_order), n_hammer = ORDER_TO_N_HAMMER(max_variable_order);
int n_points[MAX_FACE_N_BORDERS], sum_n_points[MAX_FACE_N_BORDERS+1];
int *n_basis = (int *)malloc(n_variables * sizeof(int)), max_n_basis = ORDER_TO_N_BASIS(max_variable_order);
exit_if_false(n_basis != NULL,"allocating n_basis");
for(v = 0; v < n_variables; v ++) n_basis[v] = ORDER_TO_N_BASIS(variable_order[v]);
// truth values for updating the interpolation on a face
int *update = (int *)malloc(n_variables * sizeof(int)), max_update = max_variable_order != max_variable_order_old, any_update;
exit_if_false(update != NULL,"allocating update");
// transformation
double **R = allocate_double_matrix(NULL,2,2), det_R, **inv_R = allocate_double_matrix(NULL,2,2);
exit_if_false(R != NULL,"allocating R");
exit_if_false(inv_R != NULL,"allocating inv_R");
double **T = allocate_double_matrix(NULL,max_n_basis,max_n_basis);
exit_if_false(T != NULL,"allocating T");
// centre
double **centre = allocate_double_matrix(NULL,2,1);
exit_if_false(centre != NULL,"allocating centre");
// differentials
int condition[2], none[2] = {0,0};
// integration locations in cartesian (x) and tranformed (y) coordinates
double **x, **y;
double **x_adj[MAX_FACE_N_BORDERS], ***y_adj;
exit_if_false(y = allocate_double_matrix(NULL,2,n_gauss),"allocating y");
exit_if_false(y_adj = allocate_double_tensor(NULL,MAX_FACE_N_BORDERS,2,n_hammer*(MAX_ELEMENT_N_FACES-2)),"allocating y_adj");
// adjacent elements and boundaries to the face
int n_adj, n_bnd;
struct ELEMENT **adj;
struct BOUNDARY **bnd;
// interpolation problem sizes
int n_adj_bases = MAX_FACE_N_BORDERS*max_n_basis; // number of adjacent bases
int n_int_terms = MAX_FACE_N_BORDERS*max_n_basis + MAX_FACE_N_BOUNDARIES; // number of terms from which to interpolate
int n_int_bases = MAX_FACE_N_BORDERS*max_n_basis + MAX_FACE_N_BOUNDARIES*max_variable_order; // number of interpolant bases
// face basis taylor indices
int *face_taylor = (int *)malloc(n_int_bases * sizeof(int));
exit_if_false(face_taylor != NULL,"allocating face_taylor");
// temporary matrices
int ldp, lds, ldq;
ldp = lds = ldq = MAX_FACE_N_BORDERS*(MAX_ELEMENT_N_FACES-2)*n_hammer;
int sizep = n_adj_bases*ldp;
double **P = allocate_double_matrix(NULL,n_adj_bases,ldp);
double **S = allocate_double_matrix(NULL,n_adj_bases,lds);
double **Q = allocate_double_matrix(NULL,n_int_bases,ldp);
int lda, ldb;
lda = ldb = n_int_bases;
double **A = allocate_double_matrix(NULL,n_int_bases,n_int_bases);
double **B = allocate_double_matrix(NULL,n_int_bases,n_int_bases);
int ldf, ldd;
ldf = ldd = n_gauss;
double **F = allocate_double_matrix(NULL,n_int_bases,n_gauss);
double ***D = allocate_double_tensor(NULL,max_n_basis,n_int_terms,n_gauss);
int incd = n_int_terms*n_gauss, incq;
exit_if_false(P != NULL && S != NULL && Q != NULL && A != NULL && B != NULL && F != NULL && D != NULL,"allocating working matrices");
// blas
char trans[2] = "NT";
int i_one = 1;
double d_one = 1.0, d_zero = 0.0;
// lapack
int info, *pivot = (int *)malloc(n_int_bases * sizeof(int));
exit_if_false(pivot != NULL,"allocating pivot");
int updated = 0;
for(f = 0; f < n_faces; f ++)
{
// see if face interpolation needs updating
any_update = 0;
for(v = 0; v < n_variables; v ++)
{
update[v] = 0;
if(v >= n_variables_old) update[v] = 1;
else if(variable_order[v] != variable_order_old[v]) update[v] = 1;
else if(face[f].n_boundaries[v] != face_n_boundaries_old[f][v]) update[v] = 1;
else for(i = 0; i < face[f].n_boundaries[v]; i ++)
for(j = 0; j < 2; j ++)
if(face[f].boundary[v][i]->condition[j] != boundary_old[face_boundary_old[f][v][i]].condition[j])
update[v] = 1;
any_update += update[v];
}
if(!any_update) continue;
// allocate matrices
exit_if_false(face[f].Q = allocate_face_q(&face[f],n_variables,n_basis,n_gauss),"allocating face Q");
// rotation to face coordinates
R[0][0] = + face[f].normal[0]; R[0][1] = + face[f].normal[1];
R[1][0] = - face[f].normal[1]; R[1][1] = + face[f].normal[0];
det_R = R[0][0]*R[1][1] - R[0][1]*R[1][0];
inv_R[0][0] = + R[1][1]/det_R; inv_R[0][1] = - R[0][1]/det_R;
inv_R[1][0] = - R[1][0]/det_R; inv_R[1][1] = + R[0][0]/det_R;
transformation_matrix(max_variable_order,T,inv_R);
// face integration locations
x = face[f].X;
for(g = 0; g < n_gauss; g ++)
{
for(i = 0; i < 2; i ++)
{
y[i][g] = face[f].centre[i];
for(j = 0; j < 2; j ++) y[i][g] += R[i][j]*(x[j][g] - face[f].centre[j]);
}
}
// numbers of element integration locations
for(a = 0; a < face[f].n_borders; a ++) n_points[a] = n_hammer*(face[f].border[a]->n_faces-2);
sum_n_points[0] = 0;
for(a = 0; a < face[f].n_borders; a ++) sum_n_points[a+1] = sum_n_points[a] + n_points[a];
// adjacent element integration locations
for(a = 0; a < face[f].n_borders; a ++)
{
x_adj[a] = face[f].border[a]->X;
for(h = 0; h < n_points[a]; h ++)
{
for(i = 0; i < 2; i ++)
{
y_adj[a][i][h] = face[f].centre[i];
for(j = 0; j < 2; j ++) y_adj[a][i][h] += R[i][j]*(x_adj[a][j][h] - face[f].centre[j]);
}
}
}
// for all variables
for(v = 0; v < n_variables; v ++)
{
if(!max_update && !update[v]) continue;
n_adj = face[f].n_borders;
adj = face[f].border;
n_bnd = face[f].n_boundaries[v];
bnd = face[f].boundary[v];
n_adj_bases = n_adj*n_basis[v];
n_int_terms = n_adj_bases + n_bnd;
// face basis indices
n_int_bases = 0;
for(i = 0; i < n_adj*variable_order[v] + n_bnd; i ++)
for(j = 0; j < n_adj*variable_order[v] + n_bnd; j ++)
if(i + n_adj*j < n_adj*variable_order[v] + n_bnd && j < variable_order[v])
face_taylor[n_int_bases ++] = powers_taylor[i][j];
exit_if_false(n_int_bases == n_adj_bases + n_bnd*variable_order[v],"mismatched number of interpolation unknowns");
// element bases at the integration locations
for(i = 0; i < n_adj*n_basis[v]; i ++) for(j = 0; j < sum_n_points[n_adj]; j ++) P[i][j] = 0.0;
for(a = 0; a < n_adj; a ++)
for(i = 0; i < n_basis[v]; i ++)
basis(n_points[a],&P[i+a*n_basis[v]][sum_n_points[a]],x_adj[a],adj[a]->centre,adj[a]->size,i,none);
// face bases at the integration locations
for(a = 0; a < n_adj; a ++)
for(i = 0; i < n_int_bases; i ++)
basis(n_points[a],&Q[i][sum_n_points[a]],y_adj[a],face[f].centre,face[f].size,face_taylor[i],none);
// centre of face in form which can be passed to basis
for(i = 0; i < 2; i ++) centre[i][0] = face[f].centre[i];
// integration matrix
dcopy_(&sizep,P[0],&i_one,S[0],&i_one);
for(a = 0; a < n_adj; a ++)
for(i = 0; i < n_points[a]; i ++)
dscal_(&n_basis[v],&adj[a]->W[i],&S[a*n_basis[v]][i+sum_n_points[a]],&lds);
// weak interpolation system
dgemm_(&trans[1],&trans[0],&n_adj_bases,&n_int_bases,&sum_n_points[n_adj],&d_one,S[0],&lds,Q[0],&ldq,&d_zero,A[0],&lda);
// weak interpolation rhs
dgemm_(&trans[1],&trans[0],&n_adj_bases,&n_adj_bases,&sum_n_points[n_adj],&d_one,S[0],&lds,P[0],&ldp,&d_zero,B[0],&ldb);
// boundary conditions
for(b = 0; b < n_bnd; b ++)
{
condition[0] = bnd[b]->condition[0];
for(i = 0; i < variable_order[v]; i ++)
{
condition[1] = bnd[b]->condition[1] + i;
for(j = 0; j < n_int_bases; j ++)
basis(1,&A[j][i+n_adj_bases],centre,face[f].centre,face[f].size,face_taylor[j],condition);
}
for(i = 0; i < variable_order[v]; i ++) for(j = 0; j < n_int_terms; j ++) B[j][i+n_adj_bases] = 0.0;
for(i = 0; i < n_adj_bases; i ++) B[n_adj_bases+b][i] = 0.0;
B[b+n_adj_bases][b*variable_order[v]+n_adj_bases] = 1.0;
}
// solve interpolation problem
dgesv_(&n_int_bases,&n_int_terms,A[0],&lda,pivot,B[0],&ldb,&info);
// interpolate values to the face integration locations
for(i = 0; i < n_basis[v]; i ++)
{
for(j = 0; j < n_int_bases; j ++) basis(n_gauss,F[j],y,face[f].centre,face[f].size,face_taylor[j],taylor_powers[i]);
dgemm_(&trans[0],&trans[0],&n_gauss,&n_int_terms,&n_int_bases,&d_one,F[0],&ldf,B[0],&ldb,&d_zero,D[i][0],&ldd);
}
// transform from face to cartesian coordinates
incq = n_int_terms*n_gauss;
for(i = 0; i < n_int_terms; i ++)
for(j = 0; j < n_gauss; j ++)
dgemv_(&trans[1],&n_basis[v],&n_basis[v],&d_one,T[0],&max_n_basis,&D[0][i][j],&incd,&d_zero,&face[f].Q[v][0][i][j],&incq);
updated ++;
}
}
// clean up
destroy_matrix((void *)face_n_boundaries_old);
destroy_tensor((void *)face_boundary_old);
free(n_basis);
free(update);
destroy_matrix((void *)R);
destroy_matrix((void *)inv_R);
destroy_matrix((void *)T);
destroy_matrix((void *)centre);
destroy_matrix((void *)y);
destroy_tensor((void *)y_adj);
free(face_taylor);
destroy_matrix((void *)P);
destroy_matrix((void *)Q);
destroy_matrix((void *)S);
destroy_matrix((void *)A);
destroy_matrix((void *)B);
destroy_matrix((void *)F);
destroy_tensor((void *)D);
free(pivot);
return updated;
}
int
main (int argc,
char* argv[])
{
// The point clouds we will be using
PointCloudT::Ptr cloud_in (new PointCloudT); // Original point cloud
PointCloudT::Ptr cloud_tr (new PointCloudT); // Transformed point cloud
PointCloudT::Ptr cloud_icp (new PointCloudT); // ICP output point cloud
// Checking program arguments
if (argc < 2)
{
printf ("Usage :\n");
printf ("\t\t%s file.ply number_of_ICP_iterations\n", argv[0]);
PCL_ERROR ("Provide one ply file.\n");
return (-1);
}
int iterations = 1; // Default number of ICP iterations
if (argc > 2)
{
// If the user passed the number of iteration as an argument
iterations = atoi (argv[2]);
if (iterations < 1)
{
PCL_ERROR ("Number of initial iterations must be >= 1\n");
return (-1);
}
}
pcl::console::TicToc time;
time.tic ();
if (pcl::io::loadPLYFile (argv[1], *cloud_in) < 0)
{
PCL_ERROR ("Error loading cloud %s.\n", argv[1]);
return (-1);
}
std::cout << "\nLoaded file " << argv[1] << " (" << cloud_in->size () << " points) in " << time.toc () << " ms\n" << std::endl;
// Defining a rotation matrix and translation vector
Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity ();
// A rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
double theta = M_PI / 8; // The angle of rotation in radians
transformation_matrix (0, 0) = cos (theta);
transformation_matrix (0, 1) = -sin (theta);
transformation_matrix (1, 0) = sin (theta);
transformation_matrix (1, 1) = cos (theta);
// A translation on Z axis (0.4 meters)
transformation_matrix (2, 3) = 0.4;
// Display in terminal the transformation matrix
std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl;
print4x4Matrix (transformation_matrix);
// Executing the transformation
pcl::transformPointCloud (*cloud_in, *cloud_icp, transformation_matrix);
*cloud_tr = *cloud_icp; // We backup cloud_icp into cloud_tr for later use
// The Iterative Closest Point algorithm
time.tic ();
pcl::IterativeClosestPoint<PointT, PointT> icp;
icp.setMaximumIterations (iterations);
icp.setInputSource (cloud_icp);
icp.setInputTarget (cloud_in);
icp.align (*cloud_icp);
icp.setMaximumIterations (1); // We set this variable to 1 for the next time we will call .align () function
std::cout << "Applied " << iterations << " ICP iteration(s) in " << time.toc () << " ms" << std::endl;
if (icp.hasConverged ())
{
std::cout << "\nICP has converged, score is " << icp.getFitnessScore () << std::endl;
std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
transformation_matrix = icp.getFinalTransformation ().cast<double>();
print4x4Matrix (transformation_matrix);
}
else
{
PCL_ERROR ("\nICP has not converged.\n");
return (-1);
}
// Visualization
pcl::visualization::PCLVisualizer viewer ("ICP demo");
// Create two vertically separated viewports
int v1 (0);
int v2 (1);
viewer.createViewPort (0.0, 0.0, 0.5, 1.0, v1);
viewer.createViewPort (0.5, 0.0, 1.0, 1.0, v2);
// The color we will be using
float bckgr_gray_level = 0.0; // Black
float txt_gray_lvl = 1.0 - bckgr_gray_level;
// Original point cloud is white
pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_in_color_h (cloud_in, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl,
(int) 255 * txt_gray_lvl);
viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v1", v1);
viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v2", v2);
// Transformed point cloud is green
pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_tr_color_h (cloud_tr, 20, 180, 20);
viewer.addPointCloud (cloud_tr, cloud_tr_color_h, "cloud_tr_v1", v1);
// ICP aligned point cloud is red
pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_icp_color_h (cloud_icp, 180, 20, 20);
viewer.addPointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2", v2);
// Adding text descriptions in each viewport
viewer.addText ("White: Original point cloud\nGreen: Matrix transformed point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
viewer.addText ("White: Original point cloud\nRed: ICP aligned point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_2", v2);
std::stringstream ss;
ss << iterations;
std::string iterations_cnt = "ICP iterations = " + ss.str ();
viewer.addText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt", v2);
// Set background color
viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);
// Set camera position and orientation
viewer.setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
viewer.setSize (1280, 1024); // Visualiser window size
// Register keyboard callback :
viewer.registerKeyboardCallback (&keyboardEventOccurred, (void*) NULL);
// Display the visualiser
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
// The user pressed "space" :
if (next_iteration)
{
// The Iterative Closest Point algorithm
time.tic ();
icp.align (*cloud_icp);
std::cout << "Applied 1 ICP iteration in " << time.toc () << " ms" << std::endl;
if (icp.hasConverged ())
{
printf ("\033[11A"); // Go up 11 lines in terminal output.
printf ("\nICP has converged, score is %+.0e\n", icp.getFitnessScore ());
std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;
transformation_matrix *= icp.getFinalTransformation ().cast<double>(); // WARNING /!\ This is not accurate! For "educational" purpose only!
print4x4Matrix (transformation_matrix); // Print the transformation between original pose and current pose
ss.str ("");
ss << iterations;
std::string iterations_cnt = "ICP iterations = " + ss.str ();
viewer.updateText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt");
viewer.updatePointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2");
}
else
{
PCL_ERROR ("\nICP has not converged.\n");
return (-1);
}
}
next_iteration = false;
}
return (0);
}
