/usr/lib/python2.7/dist-packages/nibabel/processing.py is in python-nibabel 2.1.0-1.
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#
# See COPYING file distributed along with the NiBabel package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
""" Image processing functions for:
* smoothing
* resampling
* converting sd to and from FWHM
Smoothing and resampling routines need scipy
"""
from __future__ import print_function, division, absolute_import
import numpy as np
import numpy.linalg as npl
from .optpkg import optional_package
spnd, _, _ = optional_package('scipy.ndimage')
from .affines import AffineError, to_matvec, from_matvec, append_diag
from .spaces import vox2out_vox
from .nifti1 import Nifti1Image
from .imageclasses import spatial_axes_first
SIGMA2FWHM = np.sqrt(8 * np.log(2))
def fwhm2sigma(fwhm):
""" Convert a FWHM value to sigma in a Gaussian kernel.
Parameters
----------
fwhm : array-like
FWHM value or values
Returns
-------
sigma : array or float
sigma values corresponding to `fwhm` values
Examples
--------
>>> sigma = fwhm2sigma(6)
>>> sigmae = fwhm2sigma([6, 7, 8])
>>> sigma == sigmae[0]
True
"""
return np.asarray(fwhm) / SIGMA2FWHM
def sigma2fwhm(sigma):
""" Convert a sigma in a Gaussian kernel to a FWHM value
Parameters
----------
sigma : array-like
sigma value or values
Returns
-------
fwhm : array or float
fwhm values corresponding to `sigma` values
Examples
--------
>>> fwhm = sigma2fwhm(3)
>>> fwhms = sigma2fwhm([3, 4, 5])
>>> fwhm == fwhms[0]
True
"""
return np.asarray(sigma) * SIGMA2FWHM
def adapt_affine(affine, n_dim):
""" Adapt input / output dimensions of spatial `affine` for `n_dims`
Adapts a spatial (4, 4) affine that is being applied to an image with fewer
than 3 spatial dimensions, or more than 3 dimensions. If there are more
than three dimensions, assume an identity transformation for these
dimensions.
Parameters
----------
affine : array-like
affine transform. Usually shape (4, 4). For what follows ``N, M =
affine.shape``
n_dims : int
Number of dimensions of underlying array, and therefore number of input
dimensions for affine.
Returns
-------
adapted : shape (M, n_dims+1) array
Affine array adapted to number of input dimensions. Columns of the
affine corresponding to missing input dimensions have been dropped,
columns corresponding to extra input dimensions have an extra identity
column added
"""
affine = np.asarray(affine)
rzs, trans = to_matvec(affine)
# For missing input dimensions, drop columns in rzs
rzs = rzs[:, :n_dim]
adapted = from_matvec(rzs, trans)
n_extra_columns = n_dim - adapted.shape[1] + 1
if n_extra_columns > 0:
adapted = append_diag(adapted, np.ones((n_extra_columns,)))
return adapted
def resample_from_to(from_img,
to_vox_map,
order=3,
mode='constant',
cval=0.,
out_class=Nifti1Image):
""" Resample image `from_img` to mapped voxel space `to_vox_map`
Resample using N-d spline interpolation.
Parameters
----------
from_img : object
Object having attributes ``dataobj``, ``affine``, ``header`` and
``shape``. If `out_class` is not None, ``img.__class__`` should be able
to construct an image from data, affine and header.
to_vox_map : image object or length 2 sequence
If object, has attributes ``shape`` giving input voxel shape, and
``affine`` giving mapping of input voxels to output space. If length 2
sequence, elements are (shape, affine) with same meaning as above. The
affine is a (4, 4) array-like.
order : int, optional
The order of the spline interpolation, default is 3. The order has to
be in the range 0-5 (see ``scipy.ndimage.affine_transform``)
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant' (see ``scipy.ndimage.affine_transform``)
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0 (see
``scipy.ndimage.affine_transform``)
out_class : None or SpatialImage class, optional
Class of output image. If None, use ``from_img.__class__``.
Returns
-------
out_img : object
Image of instance specified by `out_class`, containing data output from
resampling `from_img` into axes aligned to the output space of
``from_img.affine``
"""
# This check requires `shape` attribute of image
if not spatial_axes_first(from_img):
raise ValueError('Cannot predict position of spatial axes for Image '
'type ' + str(type(from_img)))
try:
to_shape, to_affine = to_vox_map.shape, to_vox_map.affine
except AttributeError:
to_shape, to_affine = to_vox_map
a_to_affine = adapt_affine(to_affine, len(to_shape))
if out_class is None:
out_class = from_img.__class__
from_n_dim = len(from_img.shape)
if from_n_dim < 3:
raise AffineError('from_img must be at least 3D')
a_from_affine = adapt_affine(from_img.affine, from_n_dim)
to_vox2from_vox = npl.inv(a_from_affine).dot(a_to_affine)
rzs, trans = to_matvec(to_vox2from_vox)
data = spnd.affine_transform(from_img.dataobj,
rzs,
trans,
to_shape,
order=order,
mode=mode,
cval=cval)
return out_class(data, to_affine, from_img.header)
def resample_to_output(in_img,
voxel_sizes=None,
order=3,
mode='constant',
cval=0.,
out_class=Nifti1Image):
""" Resample image `in_img` to output voxel axes (world space)
Parameters
----------
in_img : object
Object having attributes ``dataobj``, ``affine``, ``header``. If
`out_class` is not None, ``img.__class__`` should be able to construct
an image from data, affine and header.
voxel_sizes : None or sequence
Gives the diagonal entries of ``out_img.affine` (except the trailing 1
for the homogenous coordinates) (``out_img.affine ==
np.diag(voxel_sizes + [1])``). If None, return identity
`out_img.affine`. If scalar, interpret as vector ``[voxel_sizes] *
len(in_img.shape)``.
order : int, optional
The order of the spline interpolation, default is 3. The order has to
be in the range 0-5 (see ``scipy.ndimage.affine_transform``).
mode : str, optional
Points outside the boundaries of the input are filled according to the
given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default is
'constant' (see ``scipy.ndimage.affine_transform``).
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0 (see
``scipy.ndimage.affine_transform``).
out_class : None or SpatialImage class, optional
Class of output image. If None, use ``in_img.__class__``.
Returns
-------
out_img : object
Image of instance specified by `out_class`, containing data output from
resampling `in_img` into axes aligned to the output space of
``in_img.affine``
"""
if out_class is None:
out_class = in_img.__class__
in_shape = in_img.shape
n_dim = len(in_shape)
if voxel_sizes is not None:
voxel_sizes = np.asarray(voxel_sizes)
if voxel_sizes.ndim == 0: # Scalar
voxel_sizes = np.repeat(voxel_sizes, n_dim)
# Allow 2D images by promoting to 3D. We might want to see what a slice
# looks like when resampled into world coordinates
if n_dim < 3: # Expand image to 3D, make voxel sizes match
new_shape = in_shape + (1,) * (3 - n_dim)
data = in_img.get_data().reshape(new_shape) # 2D data should be small
in_img = out_class(data, in_img.affine, in_img.header)
if voxel_sizes is not None and len(voxel_sizes) == n_dim:
# Need to pad out voxel sizes to match new image dimensions
voxel_sizes = tuple(voxel_sizes) + (1,) * (3 - n_dim)
out_vox_map = vox2out_vox((in_img.shape, in_img.affine), voxel_sizes)
return resample_from_to(in_img, out_vox_map, order, mode, cval, out_class)
def smooth_image(img,
fwhm,
mode='nearest',
cval=0.,
out_class=Nifti1Image):
""" Smooth image `img` along voxel axes by FWHM `fwhm` millimeters
Parameters
----------
img : object
Object having attributes ``dataobj``, ``affine``, ``header`` and
``shape``. If `out_class` is not None, ``img.__class__`` should be able
to construct an image from data, affine and header.
fwhm : scalar or length 3 sequence
FWHM *in mm* over which to smooth. The smoothing applies to the voxel
axes, not to the output axes, but is in millimeters. The function
adjusts the FWHM to voxels using the voxel sizes calculated from the
affine. A scalar implies the same smoothing across the spatial
dimensions of the image, but 0 smoothing over any further dimensions
such as time. A vector should be the same length as the number of
image dimensions.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'nearest'. This is different from the default for
``scipy.ndimage.affine_transform``, which is 'constant'. 'nearest'
might be a better choice when smoothing to the edge of an image where
there is still strong brain signal, otherwise this signal will get
blurred towards zero.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0 (see
``scipy.ndimage.affine_transform``).
out_class : None or SpatialImage class, optional
Class of output image. If None, use ``img.__class__``.
Returns
-------
smoothed_img : object
Image of instance specified by `out_class`, containing data output from
smoothing `img` data by given FWHM kernel.
"""
# This check requires `shape` attribute of image
if not spatial_axes_first(img):
raise ValueError('Cannot predict position of spatial axes for Image '
'type ' + str(type(img)))
if out_class is None:
out_class = img.__class__
n_dim = len(img.shape)
# TODO: make sure time axis is last
# Pad out fwhm from scalar, adding 0 for fourth etc (time etc) dimensions
fwhm = np.asarray(fwhm)
if fwhm.size == 1:
fwhm_scalar = fwhm
fwhm = np.zeros((n_dim,))
fwhm[:3] = fwhm_scalar
# Voxel sizes
RZS = img.affine[:-1, :n_dim]
vox = np.sqrt(np.sum(RZS ** 2, 0))
# Smoothing in terms of voxels
vox_fwhm = fwhm / vox
vox_sd = fwhm2sigma(vox_fwhm)
# Do the smoothing
sm_data = spnd.gaussian_filter(img.dataobj,
vox_sd,
mode=mode,
cval=cval)
return out_class(sm_data, img.affine, img.header)
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