/**
* Constructor using std::vector (for python exposition purposes)
*
* @param[in] p base::problem to be rotated
* @param[in] rotation std::vector<std::vector<double> > expressing the problem rotation
*
* @see problem::base constructors.
*/
rotated::rotated(const base &p,
const std::vector<std::vector<double> > &rotation):
base_meta(
p,
p.get_dimension(),
p.get_i_dimension(),
p.get_f_dimension(),
p.get_c_dimension(),
p.get_ic_dimension(),
p.get_c_tol()),
m_Rotate(),m_normalize_translation(), m_normalize_scale()
{
if(!(rotation.size()==get_dimension())){
pagmo_throw(value_error,"The input matrix dimensions seem incorrect");
}
if(p.get_i_dimension()>0){
pagmo_throw(value_error,"Input problem has an integer dimension. Cannot rotate it.");
}
m_Rotate.resize(rotation.size(),rotation.size());
for (base::size_type i = 0; i < rotation.size(); ++i) {
if(!(rotation.size()==rotation[i].size())){
pagmo_throw(value_error,"The input matrix seems not to be square");
}
for (base::size_type j = 0; j < rotation[i].size(); ++j) {
m_Rotate(i,j) = rotation[i][j];
}
}
m_InvRotate = m_Rotate.transpose();
Eigen::MatrixXd check = m_InvRotate * m_Rotate;
if(!check.isIdentity(1e-5)){
pagmo_throw(value_error,"The input matrix seems not to be orthonormal (to a tolerance of 1e-5)");
}
configure_new_bounds();
}
rotated::rotated(const base &p, const Eigen::MatrixXd &rotation ):
base_meta(
p,
p.get_dimension(),
p.get_i_dimension(),
p.get_f_dimension(),
p.get_c_dimension(),
p.get_ic_dimension(),
p.get_c_tol()),
m_Rotate(rotation), m_normalize_translation(), m_normalize_scale()
{
m_InvRotate = m_Rotate.transpose();
Eigen::MatrixXd check = m_InvRotate * m_Rotate;
if(!check.isIdentity(1e-5)){
pagmo_throw(value_error,"The input matrix seems not to be orthonormal (to a tolerance of 1e-5)");
}
if(p.get_i_dimension()>0){
pagmo_throw(value_error,"Input problem has an integer dimension. Cannot rotate it.");
}
configure_new_bounds();
}
void run ()
{
auto input_header = Header::open (argument[0]);
Header output_header (input_header);
output_header.datatype() = DataType::from_command_line (DataType::from<float> ());
// Linear
transform_type linear_transform;
bool linear = false;
auto opt = get_options ("linear");
if (opt.size()) {
linear = true;
linear_transform = load_transform (opt[0][0]);
} else {
linear_transform.setIdentity();
}
// Replace
const bool replace = get_options ("replace").size();
if (replace && !linear) {
INFO ("no linear is supplied so replace with the default (identity) transform");
linear = true;
}
// Template
opt = get_options ("template");
Header template_header;
if (opt.size()) {
if (replace)
throw Exception ("you cannot use the -replace option with the -template option");
template_header = Header::open (opt[0][0]);
for (size_t i = 0; i < 3; ++i) {
output_header.size(i) = template_header.size(i);
output_header.spacing(i) = template_header.spacing(i);
}
output_header.transform() = template_header.transform();
add_line (output_header.keyval()["comments"], std::string ("regridded to template image \"" + template_header.name() + "\""));
}
// Warp 5D warp
// TODO add reference to warp format documentation
opt = get_options ("warp_full");
Image<default_type> warp;
if (opt.size()) {
warp = Image<default_type>::open (opt[0][0]).with_direct_io();
if (warp.ndim() != 5)
throw Exception ("the input -warp_full image must be a 5D file.");
if (warp.size(3) != 3)
throw Exception ("the input -warp_full image must have 3 volumes (x,y,z) in the 4th dimension.");
if (warp.size(4) != 4)
throw Exception ("the input -warp_full image must have 4 volumes in the 5th dimension.");
if (linear)
throw Exception ("the -warp_full option cannot be applied in combination with -linear since the "
"linear transform is already included in the warp header");
}
// Warp from image1 or image2
int from = 1;
opt = get_options ("from");
if (opt.size()) {
from = opt[0][0];
if (!warp.valid())
WARN ("-from option ignored since no 5D warp was input");
}
// Warp deformation field
opt = get_options ("warp");
if (opt.size()) {
if (warp.valid())
throw Exception ("only one warp field can be input with either -warp or -warp_mid");
warp = Image<default_type>::open (opt[0][0]).with_direct_io (Stride::contiguous_along_axis(3));
if (warp.ndim() != 4)
throw Exception ("the input -warp file must be a 4D deformation field");
if (warp.size(3) != 3)
throw Exception ("the input -warp file must have 3 volumes in the 4th dimension (x,y,z positions)");
}
// Inverse
const bool inverse = get_options ("inverse").size();
if (inverse) {
if (!(linear || warp.valid()))
throw Exception ("no linear or warp transformation provided for option '-inverse'");
if (warp.valid())
if (warp.ndim() == 4)
throw Exception ("cannot apply -inverse with the input -warp_df deformation field.");
linear_transform = linear_transform.inverse();
}
// Half
const bool half = get_options ("half").size();
if (half) {
if (!(linear))
throw Exception ("no linear transformation provided for option '-half'");
{
Eigen::Matrix<default_type, 4, 4> temp;
temp.row(3) << 0, 0, 0, 1.0;
temp.topLeftCorner(3,4) = linear_transform.matrix().topLeftCorner(3,4);
linear_transform.matrix() = temp.sqrt().topLeftCorner(3,4);
}
}
// Flip
opt = get_options ("flip");
if (opt.size()) {
std::vector<int> axes = opt[0][0];
transform_type flip;
flip.setIdentity();
for (size_t i = 0; i < axes.size(); ++i) {
if (axes[i] < 0 || axes[i] > 2)
throw Exception ("axes supplied to -flip are out of bounds (" + std::string (opt[0][0]) + ")");
flip(axes[i],3) += flip(axes[i],axes[i]) * input_header.spacing(axes[i]) * (input_header.size(axes[i])-1);
flip(axes[i], axes[i]) *= -1.0;
}
if (!replace)
flip = input_header.transform() * flip * input_header.transform().inverse();
linear_transform = linear_transform * flip;
linear = true;
}
Stride::List stride = Stride::get (input_header);
// Detect FOD image for reorientation
opt = get_options ("noreorientation");
bool fod_reorientation = false;
Eigen::MatrixXd directions_cartesian;
if (!opt.size() && (linear || warp.valid() || template_header.valid()) && input_header.ndim() == 4 &&
input_header.size(3) >= 6 &&
input_header.size(3) == (int) Math::SH::NforL (Math::SH::LforN (input_header.size(3)))) {
CONSOLE ("SH series detected, performing apodised PSF reorientation");
fod_reorientation = true;
Eigen::MatrixXd directions_az_el;
opt = get_options ("directions");
if (opt.size())
directions_az_el = load_matrix (opt[0][0]);
else
directions_az_el = DWI::Directions::electrostatic_repulsion_300();
Math::SH::spherical2cartesian (directions_az_el, directions_cartesian);
// load with SH coeffients contiguous in RAM
stride = Stride::contiguous_along_axis (3, input_header);
}
// Modulate FODs
bool modulate = false;
if (get_options ("modulate").size()) {
modulate = true;
if (!fod_reorientation)
throw Exception ("modulation can only be performed with FOD reorientation");
}
// Rotate/Flip gradient directions if present
if (linear && input_header.ndim() == 4 && !warp && !fod_reorientation) {
try {
auto grad = DWI::get_DW_scheme (input_header);
if (input_header.size(3) == (ssize_t) grad.rows()) {
INFO ("DW gradients detected and will be reoriented");
Eigen::MatrixXd rotation = linear_transform.linear();
Eigen::MatrixXd test = rotation.transpose() * rotation;
test = test.array() / test.diagonal().mean();
if (!test.isIdentity (0.001))
WARN ("the input linear transform contains shear or anisotropic scaling and "
"therefore should not be used to reorient diffusion gradients");
if (replace)
rotation = linear_transform.linear() * input_header.transform().linear().inverse();
for (ssize_t n = 0; n < grad.rows(); ++n) {
Eigen::Vector3 grad_vector = grad.block<1,3>(n,0);
grad.block<1,3>(n,0) = rotation * grad_vector;
}
DWI::set_DW_scheme (output_header, grad);
}
}
catch (Exception& e) {
e.display (2);
}
}
// Interpolator
int interp = 2; // cubic
opt = get_options ("interp");
if (opt.size()) {
interp = opt[0][0];
if (!warp && !template_header)
WARN ("interpolator choice ignored since the input image will not be regridded");
}
// Out of bounds value
float out_of_bounds_value = 0.0;
opt = get_options ("nan");
if (opt.size()) {
out_of_bounds_value = NAN;
if (!warp && !template_header)
WARN ("Out of bounds value ignored since the input image will not be regridded");
}
auto input = input_header.get_image<float>().with_direct_io (stride);
// Reslice the image onto template
if (template_header.valid() && !warp) {
INFO ("image will be regridded");
if (get_options ("midway_space").size()) {
INFO("regridding to midway space");
std::vector<Header> headers;
headers.push_back(input_header);
headers.push_back(template_header);
std::vector<Eigen::Transform<default_type, 3, Eigen::Projective>> void_trafo;
auto padding = Eigen::Matrix<double, 4, 1>(1.0, 1.0, 1.0, 1.0);
int subsampling = 1;
auto midway_header = compute_minimum_average_header (headers, subsampling, padding, void_trafo);
for (size_t i = 0; i < 3; ++i) {
output_header.size(i) = midway_header.size(i);
output_header.spacing(i) = midway_header.spacing(i);
}
output_header.transform() = midway_header.transform();
}
if (interp == 0)
output_header.datatype() = DataType::from_command_line (input_header.datatype());
auto output = Image<float>::create (argument[1], output_header).with_direct_io();
switch (interp) {
case 0:
Filter::reslice<Interp::Nearest> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
case 1:
Filter::reslice<Interp::Linear> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
case 2:
Filter::reslice<Interp::Cubic> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
case 3:
Filter::reslice<Interp::Sinc> (input, output, linear_transform, Adapter::AutoOverSample, out_of_bounds_value);
break;
default:
assert (0);
break;
}
if (fod_reorientation)
Registration::Transform::reorient ("reorienting", output, output, linear_transform, directions_cartesian.transpose(), modulate);
} else if (warp.valid()) {
if (replace)
throw Exception ("you cannot use the -replace option with the -warp or -warp_df option");
if (!template_header) {
for (size_t i = 0; i < 3; ++i) {
output_header.size(i) = warp.size(i);
output_header.spacing(i) = warp.spacing(i);
}
output_header.transform() = warp.transform();
add_line (output_header.keyval()["comments"], std::string ("resliced using warp image \"" + warp.name() + "\""));
}
auto output = Image<float>::create(argument[1], output_header).with_direct_io();
if (warp.ndim() == 5) {
Image<default_type> warp_deform;
// Warp to the midway space defined by the warp grid
if (get_options ("midway_space").size()) {
warp_deform = Registration::Warp::compute_midway_deformation (warp, from);
// Use the full transform to warp from the image image to the template
} else {
warp_deform = Registration::Warp::compute_full_deformation (warp, template_header, from);
}
apply_warp (input, output, warp_deform, interp, out_of_bounds_value);
if (fod_reorientation)
Registration::Transform::reorient_warp ("reorienting", output, warp_deform, directions_cartesian.transpose(), modulate);
// Compose and apply input linear and 4D deformation field
} else if (warp.ndim() == 4 && linear) {
auto warp_composed = Image<default_type>::scratch (warp);
Registration::Warp::compose_linear_deformation (linear_transform, warp, warp_composed);
apply_warp (input, output, warp_composed, interp, out_of_bounds_value);
if (fod_reorientation)
Registration::Transform::reorient_warp ("reorienting", output, warp_composed, directions_cartesian.transpose(), modulate);
// Apply 4D deformation field only
} else {
apply_warp (input, output, warp, interp, out_of_bounds_value);
if (fod_reorientation)
Registration::Transform::reorient_warp ("reorienting", output, warp, directions_cartesian.transpose(), modulate);
}
// No reslicing required, so just modify the header and do a straight copy of the data
} else {
if (get_options ("midway").size())
throw Exception ("midway option given but no template image defined");
INFO ("image will not be regridded");
Eigen::MatrixXd rotation = linear_transform.linear();
Eigen::MatrixXd temp = rotation.transpose() * rotation;
if (!temp.isIdentity (0.001))
WARN("the input linear transform is not orthonormal and therefore applying this without the -template"
"option will mean the output header transform will also be not orthonormal");
add_line (output_header.keyval()["comments"], std::string ("transform modified"));
if (replace)
output_header.transform() = linear_transform;
else
output_header.transform() = linear_transform.inverse() * output_header.transform();
auto output = Image<float>::create (argument[1], output_header).with_direct_io();
copy_with_progress (input, output);
if (fod_reorientation) {
transform_type transform = linear_transform;
if (replace)
transform = linear_transform * output_header.transform().inverse();
Registration::Transform::reorient ("reorienting", output, output, transform, directions_cartesian.transpose());
}
}
}