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/*
* Copyright (c) 2008-2018 the MRtrix3 contributors.
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, you can obtain one at http://mozilla.org/MPL/2.0/
*
* MRtrix3 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty
* of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
*
* For more details, see http://www.mrtrix.org/
*/
#include "command.h"
#include "debug.h"
#include "image.h"
#include "math/average_space.h"
#include "registration/transform/initialiser_helpers.h"
#include "interp/nearest.h"
#include "algo/loop.h"
using namespace MR;
using namespace App;
using namespace Registration;
default_type PADDING_DEFAULT = 0.0;
enum RESOLUTION {MAX, MEAN};
const char* resolution_choices[] = { "max", "mean", nullptr };
void usage ()
{
AUTHOR = "Maximilian Pietsch (maximilian.pietsch@kcl.ac.uk)";
SYNOPSIS = "Calculate the average (unbiased) coordinate space of all input images";
ARGUMENTS
+ Argument ("input", "the input image(s).").type_image_in ().allow_multiple()
+ Argument ("output", "the output image").type_image_out ();
OPTIONS
+ Option ("padding", " boundary box padding in voxels. Default: " + str(PADDING_DEFAULT))
+ Argument ("value").type_float (0.0, std::numeric_limits<default_type>::infinity())
+ Option ("resolution", " subsampling of template compared to smallest voxel size in any input image. "
"Valid options are 'mean': unbiased but loss of resolution for individual images possible, "
"and 'max': smallest voxel size of any input image defines the resolution. Default: mean")
+ Argument ("type").type_choice (resolution_choices)
+ Option ("fill", " set the intensity in the first volume of the average space to 1")
+ DataType::options();
}
void run ()
{
const size_t num_inputs = argument.size()-1;
auto opt = get_options ("padding");
const default_type p = opt.size() ? default_type(opt[0][0]) : PADDING_DEFAULT;
auto padding = Eigen::Matrix<default_type, 4, 1>(p, p, p, 1.0);
INFO("padding in template voxels: " + str(padding.transpose().head<3>()));
opt = get_options ("resolution");
const int resolution = opt.size() ? int(opt[0][0]) : 1;
INFO("template voxel subsampling: " + str(resolution));
bool fill = get_options ("fill").size();
vector<Header> headers_in;
size_t dim (Header::open (argument[0]).ndim());
if (dim < 3 or dim > 4)
throw Exception ("Please provide 3D or 4D images");
ssize_t volumes (dim == 3 ? 1 : Header::open (argument[0]).size(3));
for (size_t i = 0; i != num_inputs; ++i) {
headers_in.push_back (Header::open (argument[i]));
if (fill) {
if (headers_in.back().ndim() != dim)
throw Exception ("Images do not have the same dimensionality");
if (dim == 4 and volumes != headers_in.back().size(3))
throw Exception ("Images do not have the same number of volumes");
}
}
vector<Eigen::Transform<default_type, 3, Eigen::Projective>> transform_header_with;
auto H = compute_minimum_average_header (headers_in, resolution, padding, transform_header_with);
H.datatype() = DataType::Bit;
if (fill) {
H.ndim() = dim;
if (dim == 4)
H.size(3) = headers_in.back().size(3);
}
auto out = Image<bool>::create(argument[argument.size()-1], H);
if (fill) {
for (auto l = Loop (0,3) (out); l; ++l)
out.value() = 1;
Eigen::Matrix<default_type, 3, 1> centre, vox;
Registration::Transform::Init::get_geometric_centre (out, centre);
vox = MR::Transform(out).scanner2voxel * centre;
INFO("centre scanner: " + str(centre.transpose()));
for (size_t i = 0; i < 3; ++i)
vox(i) = std::round (vox(i));
INFO("centre voxel: " + str(vox.transpose()));
}
INFO("average transformation:");
INFO(str(out.transform().matrix()));
INFO("average voxel to scanner transformation:");
INFO(str(MR::Transform(out).voxel2scanner.matrix()));
}
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