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#
# See COPYING file distributed along with the NiBabel package for the
# copyright and license terms.
#
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''' Processor functions for images '''
import numpy as np
from .orientations import (io_orientation, inv_ornt_aff, flip_axis,
apply_orientation, OrientationError)
from .loadsave import load
def squeeze_image(img):
''' Return image, remove axes length 1 at end of image shape
For example, an image may have shape (10,20,30,1,1). In this case
squeeze will result in an image with shape (10,20,30). See doctests
for further description of behavior.
Parameters
----------
img : ``SpatialImage``
Returns
-------
squeezed_img : ``SpatialImage``
Copy of img, such that data, and data shape have been squeezed,
for dimensions > 3rd, and at the end of the shape list
Examples
--------
>>> import nibabel as nf
>>> shape = (10,20,30,1,1)
>>> data = np.arange(np.prod(shape)).reshape(shape)
>>> affine = np.eye(4)
>>> img = nf.Nifti1Image(data, affine)
>>> img.shape == (10, 20, 30, 1, 1)
True
>>> img2 = squeeze_image(img)
>>> img2.shape == (10, 20, 30)
True
If the data are 3D then last dimensions of 1 are ignored
>>> shape = (10,1,1)
>>> data = np.arange(np.prod(shape)).reshape(shape)
>>> img = nf.ni1.Nifti1Image(data, affine)
>>> img.shape == (10, 1, 1)
True
>>> img2 = squeeze_image(img)
>>> img2.shape == (10, 1, 1)
True
Only *final* dimensions of 1 are squeezed
>>> shape = (1, 1, 5, 1, 2, 1, 1)
>>> data = data.reshape(shape)
>>> img = nf.ni1.Nifti1Image(data, affine)
>>> img.shape == (1, 1, 5, 1, 2, 1, 1)
True
>>> img2 = squeeze_image(img)
>>> img2.shape == (1, 1, 5, 1, 2)
True
'''
klass = img.__class__
shape = img.shape
slen = len(shape)
if slen < 4:
return klass.from_image(img)
for bdim in shape[3::][::-1]:
if bdim == 1:
slen -= 1
else:
break
if slen == len(shape):
return klass.from_image(img)
shape = shape[:slen]
data = img.get_data()
data = data.reshape(shape)
return klass(data,
img.get_affine(),
img.get_header(),
img.extra)
def concat_images(images, check_affines=True):
''' Concatenate images in list to single image, along last dimension
Parameters
----------
images : sequence
sequence of ``SpatialImage`` or of filenames\s
check_affines : {True, False}, optional
If True, then check that all the affines for `images` are nearly
the same, raising a ``ValueError`` otherwise. Default is True
Returns
-------
concat_img : ``SpatialImage``
New image resulting from concatenating `images` across last
dimension
'''
n_imgs = len(images)
img0 = images[0]
is_filename = False
if not hasattr(img0, 'get_data'):
img0 = load(img0)
is_filename = True
i0shape = img0.shape
affine = img0.get_affine()
header = img0.get_header()
out_shape = (n_imgs, ) + i0shape
out_data = np.empty(out_shape)
for i, img in enumerate(images):
if is_filename:
img = load(img)
if check_affines:
if not np.all(img.get_affine() == affine):
raise ValueError('Affines do not match')
out_data[i] = img.get_data()
out_data = np.rollaxis(out_data, 0, len(i0shape)+1)
klass = img0.__class__
return klass(out_data, affine, header)
def four_to_three(img):
''' Create 3D images from 4D image by slicing over last axis
Parameters
----------
img : image
4D image instance of some class with methods ``get_data``,
``get_header`` and ``get_affine``, and a class constructor
allowing Klass(data, affine, header)
Returns
-------
imgs : list
list of 3D images
'''
arr = img.get_data()
header = img.get_header()
affine = img.get_affine()
image_maker = img.__class__
if arr.ndim != 4:
raise ValueError('Expecting four dimensions')
imgs = []
for i in range(arr.shape[3]):
arr3d = arr[..., i]
img3d = image_maker(arr3d, affine, header)
imgs.append(img3d)
return imgs
def as_closest_canonical(img, enforce_diag=False):
''' Return `img` with data reordered to be closest to canonical
Canonical order is the ordering of the output axes.
Parameters
----------
img : ``spatialimage``
enforce_diag : {False, True}, optional
If True, before transforming image, check if the resulting image
affine will be close to diagonal, and if not, raise an error
Returns
-------
canonical_img : ``spatialimage``
Version of `img` where the underlying array may have been
reordered and / or flipped so that axes 0,1,2 are those axes in
the input data that are, respectively, closest to the output axis
orientation. We modify the affine accordingly. If `img` is
already has the correct data ordering, we just return `img`
unmodified.
'''
aff = img.get_affine()
ornt = io_orientation(aff)
if np.all(ornt == [[0, 1],
[1,1],
[2,1]]): # canonical already
# however, the affine may not be diagonal
if enforce_diag and not _aff_is_diag(aff):
raise OrientationError('Transformed affine is not diagonal')
return img
shape = img.shape
t_aff = inv_ornt_aff(ornt, shape)
out_aff = np.dot(aff, t_aff)
# check if we are going to end up with something diagonal
if enforce_diag and not _aff_is_diag(aff):
raise OrientationError('Transformed affine is not diagonal')
# we need to transform the data
arr = img.get_data()
t_arr = apply_orientation(arr, ornt)
return img.__class__(t_arr, out_aff, img.get_header())
def _aff_is_diag(aff):
''' Utility function returning True if affine is nearly diagonal '''
rzs_aff = aff[:3, :3]
return np.allclose(rzs_aff, np.diag(np.diag(rzs_aff)))
