def run(self,
static_image_files,
moving_image_files,
transform_map_file,
transform_type='affine',
out_dir='',
out_file='transformed.nii.gz'):
"""
Parameters
----------
static_image_files : string
Path of the static image file.
moving_image_files : string
Path of the moving image(s). It can be a single image or a
folder containing multiple images.
transform_map_file : string
For the affine case, it should be a text(*.txt) file containing
the affine matrix. For the diffeomorphic case,
it should be a nifti file containing the mapping displacement
field in each voxel with this shape (x, y, z, 3, 2)
transform_type : string, optional
Select the transformation type to apply between 'affine' or
'diffeomorphic'. (default affine)
out_dir : string, optional
Directory to save the transformed files (default '').
out_file : string, optional
Name of the transformed file (default 'transformed.nii.gz').
It is recommended to use the flag --mix-names to
prevent the output files from being overwritten.
"""
if transform_type.lower() not in ['affine', 'diffeomorphic']:
raise ValueError("Invalid transformation type: Please"
" provide a valid transform like 'affine'"
" or 'diffeomorphic'")
io = self.get_io_iterator()
for static_image_file, moving_image_file, transform_file, \
out_file in io:
# Loading the image data from the input files into object.
static_image, static_grid2world = load_nifti(static_image_file)
moving_image, moving_grid2world = load_nifti(moving_image_file)
# Doing a sanity check for validating the dimensions of the input
# images.
check_dimensions(static_image, moving_image)
if transform_type.lower() == 'affine':
# Loading the affine matrix.
affine_matrix = np.loadtxt(transform_file)
# Setting up the affine transformation object.
mapping = AffineMap(affine=affine_matrix,
domain_grid_shape=static_image.shape,
domain_grid2world=static_grid2world,
codomain_grid_shape=moving_image.shape,
codomain_grid2world=moving_grid2world)
elif transform_type.lower() == 'diffeomorphic':
# Loading the diffeomorphic map.
disp_data, disp_affine = load_nifti(transform_file)
mapping = DiffeomorphicMap(
3,
disp_data.shape[:3],
disp_grid2world=np.linalg.inv(disp_affine),
domain_shape=static_image.shape,
domain_grid2world=static_grid2world,
codomain_shape=moving_image.shape,
codomain_grid2world=moving_grid2world)
mapping.forward = disp_data[..., 0]
mapping.backward = disp_data[..., 1]
mapping.is_inverse = True
# Transforming the image/
transformed = mapping.transform(moving_image)
save_nifti(out_file, transformed, affine=static_grid2world)
def run(self, static_image_files, moving_image_files, transform_map_file,
transform_type='affine', out_dir='',
out_file='transformed.nii.gz'):
"""
Parameters
----------
static_image_files : string
Path of the static image file.
moving_image_files : string
Path of the moving image(s). It can be a single image or a
folder containing multiple images.
transform_map_file : string
For the affine case, it should be a text(*.txt) file containing
the affine matrix. For the diffeomorphic case,
it should be a nifti file containing the mapping displacement
field in each voxel with this shape (x, y, z, 3, 2)
transform_type : string, optional
Select the transformation type to apply between 'affine' or
'diffeomorphic'. (default affine)
out_dir : string, optional
Directory to save the transformed files (default '').
out_file : string, optional
Name of the transformed file (default 'transformed.nii.gz').
It is recommended to use the flag --mix-names to
prevent the output files from being overwritten.
"""
if transform_type.lower() not in ['affine', 'diffeomorphic']:
raise ValueError("Invalid transformation type: Please"
" provide a valid transform like 'affine'"
" or 'diffeomorphic'")
io = self.get_io_iterator()
for static_image_file, moving_image_file, transform_file, \
out_file in io:
# Loading the image data from the input files into object.
static_image, static_grid2world = load_nifti(static_image_file)
moving_image, moving_grid2world = load_nifti(moving_image_file)
# Doing a sanity check for validating the dimensions of the input
# images.
check_dimensions(static_image, moving_image)
if transform_type.lower() == 'affine':
# Loading the affine matrix.
affine_matrix = np.loadtxt(transform_file)
# Setting up the affine transformation object.
mapping = AffineMap(
affine=affine_matrix,
domain_grid_shape=static_image.shape,
domain_grid2world=static_grid2world,
codomain_grid_shape=moving_image.shape,
codomain_grid2world=moving_grid2world)
elif transform_type.lower() == 'diffeomorphic':
# Loading the diffeomorphic map.
disp = nib.load(transform_file)
mapping = DiffeomorphicMap(
3, disp.shape[:3],
disp_grid2world=np.linalg.inv(disp.affine),
domain_shape=static_image.shape,
domain_grid2world=static_grid2world,
codomain_shape=moving_image.shape,
codomain_grid2world=moving_grid2world)
disp_data = disp.get_data()
mapping.forward = disp_data[..., 0]
mapping.backward = disp_data[..., 1]
mapping.is_inverse = True
# Transforming the image/
transformed = mapping.transform(moving_image)
save_nifti(out_file, transformed, affine=static_grid2world)
