def read_mapping(disp, domain_img, codomain_img, prealign=None):
"""
Read a syn registration mapping from a nifti file
Parameters
----------
disp : str, Nifti1Image, or ndarray
If string, file must of an image or ndarray.
If image, contains the mapping displacement field in each voxel
Shape (x, y, z, 3, 2)
If ndarray, contains affine transformation used for mapping
domain_img : str or Nifti1Image
codomain_img : str or Nifti1Image
Returns
-------
A :class:`DiffeomorphicMap` object
"""
if isinstance(disp, str):
if "nii.gz" in disp:
disp = nib.load(disp)
else:
disp = np.load(disp)
if isinstance(domain_img, str):
domain_img = nib.load(domain_img)
if isinstance(codomain_img, str):
codomain_img = nib.load(codomain_img)
if isinstance(disp, nib.Nifti1Image):
mapping = DiffeomorphicMap(3, disp.shape[:3],
disp_grid2world=np.linalg.inv(disp.affine),
domain_shape=domain_img.shape[:3],
domain_grid2world=domain_img.affine,
codomain_shape=codomain_img.shape,
codomain_grid2world=codomain_img.affine,
prealign=prealign)
disp_data = disp.get_fdata().astype(np.float32)
mapping.forward = disp_data[..., 0]
mapping.backward = disp_data[..., 1]
mapping.is_inverse = True
else:
from AFQ.definitions.mapping import ConformedAffineMapping
mapping = ConformedAffineMapping(
disp,
domain_grid_shape=reduce_shape(
domain_img.shape),
domain_grid2world=domain_img.affine,
codomain_grid_shape=reduce_shape(
codomain_img.shape),
codomain_grid2world=codomain_img.affine)
return mapping
def read_mapping(disp, domain_img, codomain_img, prealign=None):
"""
Read a syn registration mapping from a nifti file
Parameters
----------
disp : str or Nifti1Image
A file of image containing the mapping displacement field in each voxel
Shape (x, y, z, 3, 2)
domain_img : str or Nifti1Image
codomain_img : str or Nifti1Image
Returns
-------
A :class:`DiffeomorphicMap` object.
Notes
-----
See :func:`write_mapping` for the data format expected.
"""
if isinstance(disp, str):
disp_data, disp_affine = load_nifti(disp)
if isinstance(domain_img, str):
domain_img = nib.load(domain_img)
if isinstance(codomain_img, str):
codomain_img = nib.load(codomain_img)
mapping = DiffeomorphicMap(3,
disp_data.shape[:3],
disp_grid2world=np.linalg.inv(disp_affine),
domain_shape=domain_img.shape[:3],
domain_grid2world=domain_img.affine,
codomain_shape=codomain_img.shape,
codomain_grid2world=codomain_img.affine,
prealign=prealign)
mapping.forward = disp_data[..., 0]
mapping.backward = disp_data[..., 1]
mapping.is_inverse = True
return mapping
def read_mapping(disp, domain_img, codomain_img, prealign=None):
"""
Read a syn registration mapping from a nifti file
Parameters
----------
disp : str or Nifti1Image
A file of image containing the mapping displacement field in each voxel
Shape (x, y, z, 3, 2)
domain_img : str or Nifti1Image
codomain_img : str or Nifti1Image
Returns
-------
A :class:`DiffeomorphicMap` object
"""
if isinstance(disp, str):
disp = nib.load(disp)
if isinstance(domain_img, str):
domain_img = nib.load(domain_img)
if isinstance(codomain_img, str):
codomain_img = nib.load(codomain_img)
mapping = DiffeomorphicMap(3,
disp.shape[:3],
disp_grid2world=np.linalg.inv(disp.affine),
domain_shape=domain_img.shape[:3],
domain_grid2world=domain_img.affine,
codomain_shape=codomain_img.shape,
codomain_grid2world=codomain_img.affine,
prealign=prealign)
disp_data = disp.get_fdata().astype(np.float32)
mapping.forward = disp_data[..., 0]
mapping.backward = disp_data[..., 1]
mapping.is_inverse = True
return mapping
def read_mapping(disp, domain_img, codomain_img):
"""
Read a syn registration mapping from a nifti file
Parameters
----------
disp : str or Nifti1Image
A file of image containing the mapping displacement field in each voxel
Shape (x, y, z, 3, 2)
domain_img : str or Nifti1Image
codomain_img : str or Nifti1Image
Returns
-------
A :class:`DiffeomorphicMap` object
"""
if isinstance(disp, str):
disp = nib.load(disp)
if isinstance(domain_img, str):
domain_img = nib.load(domain_img)
if isinstance(codomain_img, str):
codomain_img = nib.load(codomain_img)
mapping = DiffeomorphicMap(3, disp.shape[:3],
disp_grid2world=np.linalg.inv(disp.affine),
domain_shape=domain_img.shape[:3],
domain_grid2world=domain_img.affine,
codomain_shape=codomain_img.shape,
codomain_grid2world=codomain_img.affine)
disp_data = disp.get_data()
mapping.forward = disp_data[..., 0]
mapping.backward = disp_data[..., 1]
mapping.is_inverse = True
return mapping
def register_demons_sym_diffeom(img_sense,
img_ref,
smooth_sigma=1.,
params=DIPY_DEAMONS_PARAMS,
inverse=False,
verbose=False):
""" Register the image and reconstruction from atlas
on the end we smooth the final deformation by a gaussian filter
:param ndarray img_sense:
:param ndarray img_ref:
:param float smooth_sigma:
:param dict params:
:param bool verbose: whether show debug time measurements
:return tuple(ndarray,ndarray):
>>> np.random.seed(0)
>>> img_ref = np.zeros((10, 10), dtype=int)
>>> img_ref[2:6, 1:7] = 1
>>> img_ref[5:9, 4:10] = 1
>>> img_ref
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> from skimage.morphology import erosion, dilation
>>> img_ref_fuz = np.zeros((10, 10), dtype=float)
>>> img_ref_fuz[dilation(img_ref, np.ones((3, 3))) == 1] = 0.1
>>> img_ref_fuz[img_ref == 1] = 0.5
>>> img_ref_fuz[erosion(img_ref, np.ones((3, 3))) == 1] = 1.0
>>> img_ref_fuz
array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0. , 0. ],
[ 0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.1, 0. , 0. ],
[ 0.1, 0.5, 1. , 1. , 1. , 1. , 0.5, 0.1, 0. , 0. ],
[ 0.1, 0.5, 1. , 1. , 1. , 1. , 0.5, 0.1, 0.1, 0.1],
[ 0.1, 0.5, 0.5, 0.5, 0.5, 1. , 0.5, 0.5, 0.5, 0.5],
[ 0.1, 0.1, 0.1, 0.1, 0.5, 1. , 1. , 1. , 1. , 1. ],
[ 0. , 0. , 0. , 0.1, 0.5, 1. , 1. , 1. , 1. , 1. ],
[ 0. , 0. , 0. , 0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
[ 0. , 0. , 0. , 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]])
>>> d_deform = register_demons_sym_diffeom(img_ref_fuz, img_ref,
... smooth_sigma=1.5, inverse=True, verbose=True)
>>> img_warp = warp2d_transform_image(img_ref, d_deform, method='nearest',
... inverse=True)
>>> np.round(img_warp.astype(float), 1)
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> img_sense = np.zeros(img_ref.shape, dtype=int)
>>> img_sense[4:9, 3:10] = 1
>>> img_sense
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> d_deform = register_demons_sym_diffeom(img_sense, img_ref, smooth_sigma=0.)
>>> img_warp = warp2d_transform_image(img_sense, d_deform)
>>> np.round(img_warp.astype(float), 1) # doctest: +SKIP
array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0.3, 0.5, 0.3, 0.1, 0. , 0. , 0. , 0. , 0. ],
[ 0. , 1. , 1. , 1. , 1. , 0.8, 0.4, 0.1, 0. , 0. ],
[ 0. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 0.5, 0. ],
[ 0. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ],
[ 0. , 0.2, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ],
[ 0. , 0. , 0.6, 0.9, 1. , 1. , 1. , 1. , 1. , 1. ],
[ 0. , 0. , 0.2, 0.4, 0.5, 0.8, 1. , 1. , 1. , 1. ],
[ 0. , 0. , 0. , 0.2, 0.2, 0.3, 0.4, 0.6, 0.7, 1. ],
[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
>>> np.round(img_warp - img_sense, 1) # doctest: +SKIP
"""
if img_ref.max() == 0 or img_sense.max() == 0:
logging.debug(
'skip image registration (demons): max values for '
'RECONST=%d and SENSE=%d', img_ref.max(), img_sense.max())
return {'mapping': None, 'mapping-inv': None, 'package': 'dipy'}
sdr_params = {k: params[k] for k in params if k in LIST_SDR_PARAMS}
sdr = SmoothSymmetricDiffeomorphicRegistration(metric=SSDMetric(
img_ref.ndim),
smooth_sigma=smooth_sigma,
**sdr_params)
sdr.verbosity = VerbosityLevels.NONE
t = time.time()
mapping = sdr.optimize(img_ref.astype(float), img_sense.astype(float))
if verbose:
logging.debug('demons took: %d s', time.time() - t)
mapping.forward = smooth_deform_field(mapping.forward, sigma=smooth_sigma)
mapping.backward = smooth_deform_field(mapping.backward,
sigma=smooth_sigma)
# img_warped = mapping.transform(img_moving, 'linear')
# mapping_inv = sdr.moving_to_ref
if inverse:
mapping_inv = DiffeomorphicMap(img_ref.ndim, img_ref.shape, None,
img_ref.shape, None, img_ref.shape,
None, None)
mapping_inv.forward = smooth_deform_field(sdr.moving_to_ref.forward,
sigma=smooth_sigma)
mapping_inv.backward = smooth_deform_field(sdr.moving_to_ref.backward,
sigma=smooth_sigma)
else:
mapping_inv = None
if verbose:
logging.debug('smoothing and warping took: %d s', time.time() - t)
dict_deform = {
'mapping': mapping,
'mapping-inv': mapping_inv,
'package': 'dipy'
}
return dict_deform
