def load(filename):
"""Load an image from the given filename.
Parameters
----------
filename : string
Should resolve to a complete filename path.
Returns
-------
image : An `Image` object
If successful, a new `Image` object is returned.
See Also
--------
save_image : function for saving images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> from nipy.io.api import load_image
>>> from nipy.testing import anatfile
>>> img = load_image(anatfile)
>>> img.shape
(33, 41, 25)
"""
img = formats.load(filename)
aff = img.get_affine()
shape = img.get_shape()
hdr = img.get_header()
# Get info from NIFTI header, if present, to tell which axes are
# which. This is a NIFTI-specific kludge, that might be abstracted
# out into the image backend in a general way. Similarly for
# getting zooms
# axis_renames is a dictionary: dict([(int, str)])
# that has keys in range(3)
# the axes of the Image are renamed from 'ijk'
# using these names
try:
axis_renames = hdr.get_axis_renames()
except (TypeError, AttributeError):
axis_renames = {}
try:
zooms = hdr.get_zooms()
except AttributeError:
zooms = np.ones(len(shape))
# affine_transform is a 3-d transform
affine_transform3d, affine_transform = \
affine_transform_from_array(aff, 'ijk', pixdim=zooms[3:])
img = Image(img.get_data(), affine_transform.renamed_domain(axis_renames))
img.header = hdr
return img
def load(filename):
"""Load an image from the given filename.
Parameters
----------
filename : string
Should resolve to a complete filename path.
Returns
-------
image : An `Image` object
If successful, a new `Image` object is returned.
See Also
--------
save_image : function for saving images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> from nipy.io.api import load_image
>>> from nipy.testing import anatfile
>>> img = load_image(anatfile)
>>> img.shape
(33, 41, 25)
"""
img = nib.load(filename)
aff = img.get_affine()
shape = img.get_shape()
hdr = img.get_header()
# If the header implements it, get a list of names, one per axis,
# and put this into the coordinate map. In fact, no image format
# implements this at the moment, so in practice, the following code
# is not currently called.
axis_renames = {}
try:
axis_names = hdr.axis_names
except AttributeError:
pass
else:
# axis_renames is a dictionary: dict([(int, str)]) that has keys
# in range(3). The axes of the Image are renamed from 'ijk' using
# these names
for i in range(min([len(axis_names), 3])):
name = axis_names[i]
if not (name is None or name == ''):
axis_renames[i] = name
zooms = hdr.get_zooms()
# affine_transform is a 3-d transform
affine_transform3d, affine_transform = \
affine_transform_from_array(aff, 'ijk', pixdim=zooms[3:])
img = Image(img.get_data(), affine_transform.renamed_domain(axis_renames))
img.header = hdr
return img
def load(filename):
"""Load an image from the given filename.
Parameters
----------
filename : string
Should resolve to a complete filename path.
Returns
-------
image : An `Image` object
If successful, a new `Image` object is returned.
See Also
--------
save_image : function for saving images
fromarray : function for creating images from numpy arrays
Examples
--------
>>> from nipy.io.api import load_image
>>> from nipy.testing import anatfile
>>> img = load_image(anatfile)
>>> img.shape
(33, 41, 25)
"""
img = formats.load(filename)
aff = img.get_affine()
shape = img.get_shape()
hdr = img.get_header()
# Get info from NIFTI header, if present, to tell which axes are
# which. This is a NIFTI-specific kludge, that might be abstracted
# out into the image backend in a general way. Similarly for
# getting zooms
try:
fps = hdr.get_dim_info()
except (TypeError, AttributeError):
fps = (None, None, None)
ijk = ijk_from_fps(fps)
try:
zooms = hdr.get_zooms()
except AttributeError:
zooms = np.ones(len(shape))
aff = _match_affine(aff, len(shape), zooms)
coordmap = coordmap_from_affine(aff, ijk)
img = Image(img.get_data(), coordmap)
img.header = hdr
return img