def __init__(self, param=None, hdr=None, orientation=None, absolutepath=None, dim=None, verbose=1):
from nibabel import Nifti1Header
# initialization of all parameters
self.im_file = None
self.data = None
self._path = None
self.ext = ""
if hdr is None:
hdr = self.hdr = Nifti1Header() # an empty header
else:
self.hdr = hdr
if absolutepath is not None:
self._path = os.path.abspath(absolutepath)
self.verbose = verbose
# load an image from file
if isinstance(param, str) or (sys.hexversion < 0x03000000 and isinstance(param, unicode)):
self.loadFromPath(param, verbose)
# copy constructor
elif isinstance(param, type(self)):
self.copy(param)
# create an empty image (full of zero) of dimension [dim]. dim must be [x,y,z] or (x,y,z). No header.
elif isinstance(param, list):
self.data = np.zeros(param)
self.hdr = hdr
# create a copy of im_ref
elif isinstance(param, (np.ndarray, np.generic)):
self.data = param
self.hdr = hdr
else:
raise TypeError('Image constructor takes at least one argument.')
def test_output_dtypes():
shape = (4, 2, 3)
rng = np.random.RandomState(19441217) # IN-S BD
data = rng.normal(4, 20, size=shape)
aff = np.diag([2.2, 3.3, 4.1, 1])
cmap = vox2mni(aff)
img = Image(data, cmap)
fname_root = 'my_file'
with InTemporaryDirectory():
for ext in 'img', 'nii':
out_fname = fname_root + '.' + ext
# Default is for data to come from data dtype
save_image(img, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), np.float)
del img_back # lets window re-use the file
# All these types are OK for both output formats
for out_dt in 'i2', 'i4', np.int16, '<f4', '>f8':
# Specified output dtype
save_image(img, out_fname, out_dt)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back # windows file re-use
# Output comes from data by default
data_typed = data.astype(out_dt)
img_again = Image(data_typed, cmap)
save_image(img_again, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back
# Even if header specifies otherwise
in_hdr = Nifti1Header()
in_hdr.set_data_dtype(np.dtype('c8'))
img_more = Image(data_typed, cmap, metadata={'header': in_hdr})
save_image(img_more, out_fname)
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), out_dt)
del img_back
# But can come from header if specified
save_image(img_more, out_fname, dtype_from='header')
img_back = load_image(out_fname)
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), 'c8')
del img_back
# u2 only OK for nifti
save_image(img, 'my_file.nii', 'u2')
img_back = load_image('my_file.nii')
hdr = img_back.metadata['header']
assert_dt_no_end_equal(hdr.get_data_dtype(), 'u2')
# Check analyze can't save u2 datatype
assert_raises(HeaderDataError, save_image, img, 'my_file.img', 'u2')
del img_back
def from_h5obj(cls, fileobj, check=True):
"""Read the struct from a file object."""
xfm_list = []
h5group = fileobj["TransformGroup"]
typo_fallback = "Transform"
try:
h5group["1"][f"{typo_fallback}Parameters"]
except KeyError:
typo_fallback = "Tranform"
for xfm in list(h5group.values())[1:]:
if xfm["TransformType"][0].startswith(b"AffineTransform"):
_params = np.asanyarray(xfm[f"{typo_fallback}Parameters"])
xfm_list.append(
ITKLinearTransform(
parameters=from_matvec(_params[:-3].reshape(3, 3),
_params[-3:]),
offset=np.asanyarray(
xfm[f"{typo_fallback}FixedParameters"]),
))
continue
if xfm["TransformType"][0].startswith(
b"DisplacementFieldTransform"):
_fixed = np.asanyarray(xfm[f"{typo_fallback}FixedParameters"])
shape = _fixed[:3].astype("uint16").tolist()
offset = _fixed[3:6].astype("uint16")
zooms = _fixed[6:9].astype("float")
directions = _fixed[9:].astype("float").reshape((3, 3))
affine = from_matvec(directions * zooms, offset)
field = np.asanyarray(
xfm[f"{typo_fallback}Parameters"]).reshape(
tuple(shape + [1, -1]))
hdr = Nifti1Header()
hdr.set_intent("vector")
hdr.set_data_dtype("float")
xfm_list.append(
ITKDisplacementsField.from_image(
Nifti1Image(field.astype("float"), affine, hdr)))
continue
raise NotImplementedError(
f"Unsupported transform type {xfm['TransformType'][0]}")
return xfm_list
def save_ics(icas, mask, threshold, output_dir, header, mean=None):
""" Save the independant compnents to Nifti.
Parameters
-----------
icas: 2D ndarray
The independant components, as returned by CanICA.
mask: 3D ndarray
The 3D boolean array used to go from index to voxel space for
each IC.
threshold: float
The threshold value used to threshold the ICs
output_dir: string
The directory in which maps and nifti files will be saved.
header: dictionnary or Nifti1Header object
The header for the nifti file, as returned by pynifti's
header attribute, or
nipy.io.imageformats.load().get_header().
"""
# put in order n_ic, n_voxels
icas = icas.T
mask = mask.astype(np.bool)
try:
affine = header.get_best_affine()
except:
affine = header['sform']
# XXX: I don't know how to convert a dictionnary to a
# Nifti1Header.
h = Nifti1Header()
for k, v in header.iteritems():
try:
h[k] = v
except ValueError:
pass
header = h
header['cal_max'] = np.nanmax(icas)
header['cal_min'] = np.nanmin(icas)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
icas = icas.copy()
for ic in icas:
ic *= auto_sign(ic, threshold=threshold)
maps3d = np.zeros(list(mask.shape) + [len(icas)])
maps3d[mask] = icas.T
save(Nifti1Image(maps3d, affine, header=header),
pjoin(output_dir, 'icas_no_thr.nii'))
maps3d[np.abs(maps3d) < threshold] = 0
save(Nifti1Image(maps3d, affine, header=header),
pjoin(output_dir, 'icas.nii'))
# Save the mask
mask_header = header.copy()
mask_header['cal_min'] = 0
mask_header['cal_max'] = 1
save(Nifti1Image(mask.astype(np.int), affine, header=mask_header),
pjoin(output_dir, 'mask.nii'))
if mean is not None:
# save the mean
mean_img = np.zeros(maps3d.shape[:-1])
mean_img[mask] = mean
mean_header = header.copy()
mean_header['cal_min'] = mean.min()
mean_header['cal_max'] = mean.max()
save(Nifti1Image(mean_img, affine, header=mean_header),
pjoin(output_dir, 'mean.nii'))
return maps3d, affine, mean_img
return maps3d, affine