template <typename PointInT, typename PointOutT, typename NormalT, typename IntensityT> bool
pcl::SUSANKeypoint<PointInT, PointOutT, NormalT, IntensityT>::isWithinNucleusCentroid (const Eigen::Vector3f& nucleus,
const Eigen::Vector3f& centroid,
const Eigen::Vector3f& nc,
const PointInT& point) const
{
Eigen::Vector3f pc = centroid - point.getVector3fMap ();
Eigen::Vector3f pn = nucleus - point.getVector3fMap ();
Eigen::Vector3f pc_cross_nc = pc.cross (nc);
return ((pc_cross_nc.norm () <= tolerance_) && (pc.dot (nc) >= 0) && (pn.dot (nc) <= 0));
}
template <typename PointInT> double
CombinedCoherence<PointInT>::computeCoherence (PointInT &source, PointInT &target)
{
// convert color space from RGB to HSV
RGBValue source_rgb, target_rgb;
source_rgb.int_value = source.rgba;
target_rgb.int_value = target.rgba;
float source_h, source_s, source_v, target_h, target_s, target_v;
RGB2HSV (source_rgb.Red, source_rgb.Blue, source_rgb.Green,
source_h, source_s, source_v);
RGB2HSV (target_rgb.Red, target_rgb.Blue, target_rgb.Green,
target_h, target_s, target_v);
// hue value is in 0 ~ 2pi, but circulated.
const float _h_diff = fabsf (source_h - target_h);
// Also need to compute distance other way around circle - but need to check which is closer to 0
float _h_diff2;
if (source_h < target_h)
_h_diff2 = fabsf (1.0f + source_h - target_h); //Add 2pi to source, subtract target
else
_h_diff2 = fabsf (1.0f + target_h - source_h); //Add 2pi to target, subtract source
float h_diff;
//Now we need to choose the smaller distance
if (_h_diff < _h_diff2)
h_diff = static_cast<float> (h_weight_) * _h_diff * _h_diff;
else
h_diff = static_cast<float> (h_weight_) * _h_diff2 * _h_diff2;
const float s_diff = static_cast<float> (s_weight_) * (source_s - target_s) * (source_s - target_s);
const float v_diff = static_cast<float> (v_weight_) * (source_v - target_v) * (source_v - target_v);
//const float color_diff = h_diff + s_diff + v_diff;
//if (color_diff > 0.1)
// return 0;
Eigen::Vector4f p = source.getVector4fMap ();
Eigen::Vector4f p_dash = target.getVector4fMap ();
double d = (p - p_dash).norm () * dist_weight_;
const float diff2 = h_diff + s_diff + v_diff + d;
return (1.0 / (1.0 + weight_ * diff2));
}
template <typename PointInT, typename PointNT, typename PointOutT> bool
pcl::BoundaryEstimation<PointInT, PointNT, PointOutT>::isBoundaryPoint (
const pcl::PointCloud<PointInT> &cloud, const PointInT &q_point,
const std::vector<int> &indices,
const Eigen::Vector4f &u, const Eigen::Vector4f &v,
const float angle_threshold)
{
if (indices.size () < 3)
return (false);
if (!pcl_isfinite (q_point.x) || !pcl_isfinite (q_point.y) || !pcl_isfinite (q_point.z))
return (false);
// Compute the angles between each neighboring point and the query point itself
std::vector<float> angles (indices.size ());
float max_dif = FLT_MIN, dif;
int cp = 0;
for (size_t i = 0; i < indices.size (); ++i)
{
if (!pcl_isfinite (cloud.points[indices[i]].x) ||
!pcl_isfinite (cloud.points[indices[i]].y) ||
!pcl_isfinite (cloud.points[indices[i]].z))
continue;
Eigen::Vector4f delta = cloud.points[indices[i]].getVector4fMap () - q_point.getVector4fMap ();
if (delta == Eigen::Vector4f::Zero())
continue;
angles[cp++] = atan2f (v.dot (delta), u.dot (delta)); // the angles are fine between -PI and PI too
}
if (cp == 0)
return (false);
angles.resize (cp);
std::sort (angles.begin (), angles.end ());
// Compute the maximal angle difference between two consecutive angles
for (size_t i = 0; i < angles.size () - 1; ++i)
{
dif = angles[i + 1] - angles[i];
if (max_dif < dif)
max_dif = dif;
}
// Get the angle difference between the last and the first
dif = 2 * static_cast<float> (M_PI) - angles[angles.size () - 1] + angles[0];
if (max_dif < dif)
max_dif = dif;
// Check results
if (max_dif > angle_threshold)
return (true);
else
return (false);
}
template <typename PointInT> double
pcl::tracking::NormalCoherence<PointInT>::computeCoherence (PointInT &source, PointInT &target)
{
Eigen::Vector4f n = source.getNormalVector4fMap ();
Eigen::Vector4f n_dash = target.getNormalVector4fMap ();
if ( n.norm () <= 1e-5 || n_dash.norm () <= 1e-5 )
{
PCL_ERROR("norm might be ZERO!\n");
std::cout << "source: " << source << std::endl;
std::cout << "target: " << target << std::endl;
exit (1);
return 0.0;
}
else
{
n.normalize ();
n_dash.normalize ();
double theta = pcl::getAngle3D (n, n_dash);
if (!pcl_isnan (theta))
return 1.0 / (1.0 + weight_ * theta * theta);
else
return 0.0;
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
// Compute the number of coefficients
nr_coeff_ = (order_ + 1) * (order_ + 2) / 2;
// Allocate enough space to hold the results of nearest neighbor searches
// \note resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
// For all points
for (size_t cp = 0; cp < indices_->size (); ++cp)
{
// Get the initial estimates of point positions and their neighborhoods
if (!searchForNeighbors (int (cp), nn_indices, nn_sqr_dists))
continue;
// Check the number of nearest neighbors for normal estimation (and later
// for polynomial fit as well)
if (nn_indices.size () < 3)
continue;
PointCloudOut projected_points;
NormalCloud projected_points_normals;
// Get a plane approximating the local surface's tangent and project point onto it
computeMLSPointNormal (int (cp), *input_, nn_indices, nn_sqr_dists, projected_points, projected_points_normals);
// Append projected points to output
output.insert (output.end (), projected_points.begin (), projected_points.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals.begin (), projected_points_normals.end ());
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
BOOST_FOREACH (typename MLSVoxelGrid::HashMap::value_type voxel, voxel_grid.voxel_grid_)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (voxel.first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3f add_point = p.getVector3fMap (),
input_point = input_->points[input_index].getVector3fMap ();
Eigen::Vector3d aux = mls_results_[input_index].u;
Eigen::Vector3f u = aux.cast<float> ();
aux = mls_results_[input_index].v;
Eigen::Vector3f v = aux.cast<float> ();
float u_disp = (add_point - input_point).dot (u),
v_disp = (add_point - input_point).dot (v);
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u, mls_results_[input_index].v,
mls_results_[input_index].plane_normal,
mls_results_[input_index].curvature,
input_point,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
float d_before = (pos - input_point).norm (),
d_after = (result_point.getVector3fMap () - input_point). norm();
if (d_after > d_before)
continue;
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performUpsampling (PointCloudOut &output)
{
if (upsample_method_ == DISTINCT_CLOUD)
{
for (size_t dp_i = 0; dp_i < distinct_cloud_->size (); ++dp_i) // dp_i = distinct_point_i
{
// Distinct cloud may have nan points, skip them
if (!pcl_isfinite (distinct_cloud_->points[dp_i].x))
continue;
// Get 3D position of point
//Eigen::Vector3f pos = distinct_cloud_->points[dp_i].getVector3fMap ();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (distinct_cloud_->points[dp_i], 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3d add_point = distinct_cloud_->points[dp_i].getVector3fMap ().template cast<double> ();
float u_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].u_axis)),
v_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].v_axis));
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u_axis, mls_results_[input_index].v_axis,
mls_results_[input_index].plane_normal,
mls_results_[input_index].mean,
mls_results_[input_index].curvature,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
// Copy additional point information if available
copyMissingFields (input_->points[input_index], result_point);
// Store the id of the original point
corresponding_input_indices_->indices.push_back (input_index);
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
for (typename MLSVoxelGrid::HashMap::iterator m_it = voxel_grid.voxel_grid_.begin (); m_it != voxel_grid.voxel_grid_.end (); ++m_it)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (m_it->first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3d add_point = p.getVector3fMap ().template cast<double> ();
float u_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].u_axis)),
v_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].v_axis));
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u_axis, mls_results_[input_index].v_axis,
mls_results_[input_index].plane_normal,
mls_results_[input_index].mean,
mls_results_[input_index].curvature,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
// Copy additional point information if available
copyMissingFields (input_->points[input_index], result_point);
// Store the id of the original point
corresponding_input_indices_->indices.push_back (input_index);
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
// Compute the number of coefficients
nr_coeff_ = (order_ + 1) * (order_ + 2) / 2;
// Allocate enough space to hold the results of nearest neighbor searches
// \note resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
typedef typename pcl::traits::fieldList<typename PointCloudIn::PointType>::type FieldListInput;
typedef typename pcl::traits::fieldList<typename PointCloudOut::PointType>::type FieldListOutput;
// For all points
for (size_t cp = 0; cp < indices_->size (); ++cp)
{
// Get the initial estimates of point positions and their neighborhoods
if (!searchForNeighbors (int (cp), nn_indices, nn_sqr_dists))
continue;
// Check the number of nearest neighbors for normal estimation (and later
// for polynomial fit as well)
if (nn_indices.size () < 3)
continue;
PointCloudOut projected_points;
NormalCloud projected_points_normals;
// Get a plane approximating the local surface's tangent and project point onto it
computeMLSPointNormal (int (cp), *input_, nn_indices, nn_sqr_dists, projected_points, projected_points_normals);
/// Copy RGB information if available
float rgb_input;
bool rgb_exists_input;
pcl::for_each_type<FieldListInput> (pcl::CopyIfFieldExists<typename PointCloudIn::PointType, float> (
input_->points[(*indices_)[cp]], "rgb", rgb_exists_input, rgb_input));
if (rgb_exists_input)
{
for (size_t pp = 0; pp < projected_points.size (); ++pp)
pcl::for_each_type<FieldListOutput> (pcl::SetIfFieldExists<typename PointCloudOut::PointType, float> (
projected_points.points[pp], "rgb", rgb_input));
}
// Append projected points to output
output.insert (output.end (), projected_points.begin (), projected_points.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals.begin (), projected_points_normals.end ());
}
if (upsample_method_ == DISTINCT_CLOUD)
{
for (size_t dp_i = 0; dp_i < distinct_cloud_->size (); ++dp_i) // dp_i = distinct_point_i
{
// Distinct cloud may have nan points, skip them
if (!pcl_isfinite (distinct_cloud_->points[dp_i].x))
continue;
// Get 3D position of point
//Eigen::Vector3f pos = distinct_cloud_->points[dp_i].getVector3fMap ();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (distinct_cloud_->points[dp_i], 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3f add_point = distinct_cloud_->points[dp_i].getVector3fMap (),
input_point = input_->points[input_index].getVector3fMap ();
Eigen::Vector3d aux = mls_results_[input_index].u;
Eigen::Vector3f u = aux.cast<float> ();
aux = mls_results_[input_index].v;
Eigen::Vector3f v = aux.cast<float> ();
float u_disp = (add_point - input_point).dot (u),
v_disp = (add_point - input_point).dot (v);
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u, mls_results_[input_index].v,
mls_results_[input_index].plane_normal,
mls_results_[input_index].curvature,
input_point,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
/// Copy RGB information if available
float rgb_input;
bool rgb_exists_input;
pcl::for_each_type<FieldListInput> (pcl::CopyIfFieldExists<typename PointCloudIn::PointType, float> (
input_->points[input_index], "rgb", rgb_exists_input, rgb_input));
if (rgb_exists_input)
{
pcl::for_each_type<FieldListOutput> (pcl::SetIfFieldExists<typename PointCloudOut::PointType, float> (
result_point, "rgb", rgb_input));
}
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
for (typename MLSVoxelGrid::HashMap::iterator m_it = voxel_grid.voxel_grid_.begin (); m_it != voxel_grid.voxel_grid_.end (); ++m_it)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (m_it->first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3f add_point = p.getVector3fMap (),
input_point = input_->points[input_index].getVector3fMap ();
Eigen::Vector3d aux = mls_results_[input_index].u;
Eigen::Vector3f u = aux.cast<float> ();
aux = mls_results_[input_index].v;
Eigen::Vector3f v = aux.cast<float> ();
float u_disp = (add_point - input_point).dot (u),
v_disp = (add_point - input_point).dot (v);
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u, mls_results_[input_index].v,
mls_results_[input_index].plane_normal,
mls_results_[input_index].curvature,
input_point,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
float d_before = (pos - input_point).norm (),
d_after = (result_point.getVector3fMap () - input_point). norm();
if (d_after > d_before)
continue;
/// Copy RGB information if available
float rgb_input;
bool rgb_exists_input;
pcl::for_each_type<FieldListInput> (pcl::CopyIfFieldExists<typename PointCloudIn::PointType, float> (
input_->points[input_index], "rgb", rgb_exists_input, rgb_input));
if (rgb_exists_input)
{
pcl::for_each_type<FieldListOutput> (pcl::SetIfFieldExists<typename PointCloudOut::PointType, float> (
result_point, "rgb", rgb_input));
}
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performUpsampling (PointCloudOut &output)
{
if (upsample_method_ == DISTINCT_CLOUD)
{
corresponding_input_indices_.reset (new PointIndices);
for (size_t dp_i = 0; dp_i < distinct_cloud_->size (); ++dp_i) // dp_i = distinct_point_i
{
// Distinct cloud may have nan points, skip them
if (!pcl_isfinite (distinct_cloud_->points[dp_i].x))
continue;
// Get 3D position of point
//Eigen::Vector3f pos = distinct_cloud_->points[dp_i].getVector3fMap ();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (distinct_cloud_->points[dp_i], 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3d add_point = distinct_cloud_->points[dp_i].getVector3fMap ().template cast<double> ();
MLSResult::MLSProjectionResults proj = mls_results_[input_index].projectPoint (add_point, projection_method_, 5 * nr_coeff_);
addProjectedPointNormal (input_index, proj.point, proj.normal, mls_results_[input_index].curvature, output, *normals_, *corresponding_input_indices_);
}
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
corresponding_input_indices_.reset (new PointIndices);
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
for (typename MLSVoxelGrid::HashMap::iterator m_it = voxel_grid.voxel_grid_.begin (); m_it != voxel_grid.voxel_grid_.end (); ++m_it)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (m_it->first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3d add_point = p.getVector3fMap ().template cast<double> ();
MLSResult::MLSProjectionResults proj = mls_results_[input_index].projectPoint (add_point, projection_method_, 5 * nr_coeff_);
addProjectedPointNormal (input_index, proj.point, proj.normal, mls_results_[input_index].curvature, output, *normals_, *corresponding_input_indices_);
}
}
}
