Source code for ants.core.ants_transform_io
__all__ = [
"create_ants_transform",
"new_ants_transform",
"read_transform",
"write_transform",
"transform_from_displacement_field",
"transform_to_displacement_field"
]
import os
import numpy as np
from . import ants_image as iio
from . import ants_transform as tio
from .. import utils
[docs]def new_ants_transform(
precision="float", dimension=3, transform_type="AffineTransform", parameters=None,
fixed_parameters=None
):
"""
Create a new ANTsTransform
ANTsR function: None
This is a simplified method for creating an ANTsTransform, mostly used internally.
See create_ants_transform for more options.
Example
-------
>>> import ants
>>> tx = ants.new_ants_transform()
"""
libfn = utils.get_lib_fn(
"newAntsTransform%s%i" % (utils.short_ptype(precision), dimension)
)
itk_tx = libfn(precision, dimension, transform_type)
ants_tx = tio.ANTsTransform(
precision=precision,
dimension=dimension,
transform_type=transform_type,
pointer=itk_tx,
)
if parameters is not None:
ants_tx.set_parameters(parameters)
if fixed_parameters is not None:
ants_tx.set_fixed_parameters(fixed_parameters)
return ants_tx
[docs]def create_ants_transform(
transform_type="AffineTransform",
precision="float",
dimension=3,
matrix=None,
offset=None,
center=None,
translation=None,
parameters=None,
fixed_parameters=None,
displacement_field=None,
supported_types=False,
):
"""
Create and initialize an ANTsTransform
ANTsR function: `createAntsrTransform`
Arguments
---------
transform_type : string
type of transform(s)
precision : string
numerical precision
dimension : integer
spatial dimension of transform
matrix : ndarray
matrix for linear transforms
offset : tuple/list
offset for linear transforms
center : tuple/list
center for linear transforms
translation : tuple/list
translation for linear transforms
parameters : ndarray/list
array of parameters
fixed_parameters : ndarray/list
array of fixed parameters
displacement_field : ANTsImage
multichannel ANTsImage for non-linear transform
supported_types : boolean
flag that returns array of possible transforms types
Returns
-------
ANTsTransform or list of ANTsTransform types
Example
-------
>>> import ants
>>> translation = (3,4,5)
>>> tx = ants.create_ants_transform( type='Euler3DTransform', translation=translation )
"""
def _check_arg(arg, dim=1):
if arg is None:
if dim == 1:
return []
elif dim == 2:
return [[]]
elif isinstance(arg, np.ndarray):
return arg.tolist()
elif isinstance(arg, (tuple, list)):
return list(arg)
else:
raise ValueError("Incompatible input argument")
matrix = _check_arg(matrix, dim=2)
offset = _check_arg(offset)
center = _check_arg(center)
translation = _check_arg(translation)
parameters = _check_arg(parameters)
fixed_parameters = _check_arg(fixed_parameters)
matrix_offset_types = {
"AffineTransform",
"CenteredAffineTransform",
"Euler2DTransform",
"Euler3DTransform",
"Rigid3DTransform",
"Rigid2DTransform",
"QuaternionRigidTransform",
"Similarity2DTransform",
"CenteredSimilarity2DTransform",
"Similarity3DTransform",
"CenteredRigid2DTransform",
"CenteredEuler3DTransform",
}
# user_matrix_types = {'Affine','CenteredAffine',
# 'Euler', 'CenteredEuler',
# 'Rigid', 'CenteredRigid', 'QuaternionRigid',
# 'Similarity', 'CenteredSimilarity'}
if supported_types:
return set(list(matrix_offset_types) + ["DisplacementFieldTransform"])
# Check for valid dimension
if (dimension < 2) or (dimension > 4):
raise ValueError("Unsupported dimension: %i" % dimension)
# Check for valid precision
precision_types = ("float", "double")
if precision not in precision_types:
raise ValueError("Unsupported Precision %s" % str(precision))
# Check for supported transform type
if (transform_type not in matrix_offset_types) and (
transform_type != "DisplacementFieldTransform"
):
raise ValueError("Unsupported type %s" % str(transform_type))
# Check parameters with type
if transform_type == "Euler3DTransform":
dimension = 3
elif transform_type == "Euler2DTransform":
dimension = 2
elif transform_type == "Rigid3DTransform":
dimension = 3
elif transform_type == "QuaternionRigidTransform":
dimension = 3
elif transform_type == "Rigid2DTransform":
dimension = 2
elif transform_type == "CenteredRigid2DTransform":
dimension = 2
elif transform_type == "CenteredEuler3DTransform":
dimension = 3
elif transform_type == "Similarity3DTransform":
dimension = 3
elif transform_type == "Similarity2DTransform":
dimension = 2
elif transform_type == "CenteredSimilarity2DTransform":
dimension = 2
# If displacement field
if displacement_field is not None:
# raise ValueError('Displacement field transform not currently supported')
itk_tx = transform_from_displacement_field(displacement_field)
return tio.ants_transform(itk_tx)
# Transforms that derive from itk::MatrixOffsetTransformBase
libfn = utils.get_lib_fn(
"matrixOffset%s%i" % (utils.short_ptype(precision), dimension)
)
itk_tx = libfn(
transform_type,
precision,
dimension,
matrix,
offset,
center,
translation,
parameters,
fixed_parameters,
)
return tio.ANTsTransform(
precision=precision,
dimension=dimension,
transform_type=transform_type,
pointer=itk_tx,
)
[docs]def transform_from_displacement_field(field):
"""
Convert deformation field (multiChannel image) to ANTsTransform
ANTsR function: `antsrTransformFromDisplacementField`
Arguments
---------
field : ANTsImage
deformation field as multi-channel ANTsImage
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> fi = ants.image_read(ants.get_ants_data('r16') )
>>> mi = ants.image_read(ants.get_ants_data('r64') )
>>> fi = ants.resample_image(fi,(60,60),1,0)
>>> mi = ants.resample_image(mi,(60,60),1,0) # speed up
>>> mytx = ants.registration(fixed=fi, moving=mi, type_of_transform = ('SyN') )
>>> vec = ants.image_read( mytx['fwdtransforms'][0] )
>>> atx = ants.transform_from_displacement_field( vec )
"""
if not isinstance(field, iio.ANTsImage):
raise ValueError("field must be ANTsImage type")
libfn = utils.get_lib_fn("antsTransformFromDisplacementFieldF%i" % field.dimension)
field = field.clone("float")
txptr = libfn(field.pointer)
return tio.ANTsTransform(
precision="float",
dimension=field.dimension,
transform_type="DisplacementFieldTransform",
pointer=txptr,
)
[docs]def transform_to_displacement_field(xfrm, ref):
"""
Convert displacement field ANTsTransform to displacement field
ANTsR function: `antsrTransformToDisplacementField`
Arguments
---------
xfrm : displacement field ANTsTransform
displacement field ANTsTransform
ref : ANTs Image
Returns
-------
ANTsVectorImage
Example
-------
>>> import ants
>>> fi = ants.image_read(ants.get_ants_data('r16') )
>>> mi = ants.image_read(ants.get_ants_data('r64') )
>>> fi = ants.resample_image(fi,(60,60),1,0)
>>> mi = ants.resample_image(mi,(60,60),1,0) # speed up
>>> mytx = ants.registration(fixed=fi, moving=mi, type_of_transform = ('SyN') )
>>> vec = ants.image_read( mytx['fwdtransforms'][0] )
>>> atx = ants.transform_from_displacement_field( vec )
>>> field = ants.transform_to_displacement_field( atx, fi )
"""
if not xfrm.type == 'DisplacementFieldTransform':
raise ValueError("Transform must be of DisplacementFieldTransform type")
libfn = utils.get_lib_fn("antsTransformToDisplacementFieldF%i" % xfrm.dimension)
field_ptr = libfn(xfrm.pointer, ref.pointer)
return iio.ANTsImage( pixeltype=xfrm.precision,
dimension=xfrm.dimension,
components=xfrm.dimension,
pointer=field_ptr)
[docs]def read_transform(filename, precision="float"):
"""
Read a transform from file
ANTsR function: `readAntsrTransform`
Arguments
---------
filename : string
filename of transform
precision : string
numerical precision of transform
Returns
-------
ANTsTransform
Example
-------
>>> import ants
>>> tx = ants.new_ants_transform(dimension=2)
>>> tx.set_parameters((0.9,0,0,1.1,10,11))
>>> ants.write_transform(tx, '~/desktop/tx.mat')
>>> tx2 = ants.read_transform('~/desktop/tx.mat')
"""
filename = os.path.expanduser(filename)
if not os.path.exists(filename):
raise ValueError("filename does not exist!")
# intentionally ignore dimension
libfn1 = utils.get_lib_fn("getTransformDimensionFromFile")
dimensionUse = libfn1(filename)
libfn2 = utils.get_lib_fn("getTransformNameFromFile")
transform_type = libfn2(filename)
libfn3 = utils.get_lib_fn(
"readTransform%s%i" % (utils.short_ptype(precision), dimensionUse)
)
itk_tx = libfn3(filename, dimensionUse, precision)
return tio.ANTsTransform(
precision=precision,
dimension=dimensionUse,
transform_type=transform_type,
pointer=itk_tx,
)
[docs]def write_transform(transform, filename):
"""
Write ANTsTransform to file
ANTsR function: `writeAntsrTransform`
Arguments
---------
transform : ANTsTransform
transform to save
filename : string
filename of transform (file extension is ".mat" for affine transforms)
Returns
-------
N/A
Example
-------
>>> import ants
>>> tx = ants.new_ants_transform(dimension=2)
>>> tx.set_parameters((0.9,0,0,1.1,10,11))
>>> ants.write_transform(tx, '~/desktop/tx.mat')
>>> tx2 = ants.read_transform('~/desktop/tx.mat')
"""
filename = os.path.expanduser(filename)
libfn = utils.get_lib_fn("writeTransform%s" % (transform._libsuffix))
libfn(transform.pointer, filename)
