def save_nifti(
image: Any,
uri: str,
compress: bool = True,
partition: Optional[str] = None,
# Format-specific kwargs
nifti_version: int = 2):
if partition is None:
partition = "xyz"
for img, _uri in partition_gen(image, partition, uri):
if nifti_version == 1:
header = nib.Nifti1Header()
else:
header = nib.Nifti2Header()
header.set_data_shape(img.shape[::-1])
header.set_data_dtype(img.dtype)
header.set_xyzt_units(xyz=get_spacing_units(image, "x") or None,
t=get_spacing_units(image, "t") or None)
affine = np.eye(img.ndim + 1, img.ndim + 1)
for i, ax in enumerate(get_axes(img)):
affine[i, i] = get_spacing(img, ax) or 1.0
if nifti_version == 1:
ni = nib.Nifti1Image(img.data, affine=affine, header=header)
else:
ni = nib.Nifti2Image(img.data, affine=affine, header=header)
nib.save(ni, _uri)
def _Image_to_Nifti2Image(image, affine=None):
nifti2header = nib.Nifti2Header()
nifti2header["dim"][1 : 1 + len(image.nxyz)] = image.nxyz
nifti2header["pixdim"][1 : 1 + len(image.dxyz)] = image.dxyz
nifti2image = nib.Nifti2Image(image.data, affine=affine, header=nifti2header)
return nifti2image
def make_random_image(filename=None,
dims=(10, 10, 10),
xform=None,
imgtype=1,
pixdims=None,
dtype=np.float32):
"""Convenience function which makes an image containing random data.
Saves and returns the nibabel object.
imgtype == 0: ANALYZE
imgtype == 1: NIFTI1
imgtype == 2: NIFTI2
"""
if imgtype == 0: hdr = nib.AnalyzeHeader()
elif imgtype == 1: hdr = nib.Nifti1Header()
elif imgtype == 2: hdr = nib.Nifti2Header()
if pixdims is None:
pixdims = [1] * len(dims)
pixdims = pixdims[:len(dims)]
zooms = [abs(p) for p in pixdims]
hdr.set_data_dtype(dtype)
hdr.set_data_shape(dims)
hdr.set_zooms(zooms)
if xform is None:
xform = np.eye(4)
for i, p in enumerate(pixdims[:3]):
xform[i, i] = p
data = np.array(np.random.random(dims) * 100, dtype=dtype)
if imgtype == 0: img = nib.AnalyzeImage(data, xform, hdr)
elif imgtype == 1: img = nib.Nifti1Image( data, xform, hdr)
elif imgtype == 2: img = nib.Nifti2Image( data, xform, hdr)
if filename is not None:
if op.splitext(filename)[1] == '':
if imgtype == 0: filename = '{}.img'.format(filename)
else: filename = '{}.nii'.format(filename)
nib.save(img, filename)
return img
def _readSpace(group):
"""Reads a *space* group, defining a source or reference space.
:arg group: A ``h5py.Group`` object.
:returns: :class:`.Nifti` object. defining the mapping
"""
if group.attrs.get('Type') != 'image':
raise X5Error('Not an image space')
shape = group.attrs['Size']
pixdim = group.attrs['Scales']
xform = _readAffine(group['Mapping'])
hdr = nib.Nifti2Header()
hdr.set_data_shape(shape)
hdr.set_zooms( pixdim)
hdr.set_sform( xform, 'aligned')
return fslimage.Nifti(hdr)
def save_nifti(
image: Any,
uri: str,
compress: bool = True,
partition: Optional[str] = None,
# Format-specific kwargs
nifti_version: int = 2,
header: Optional[Any] = None):
if partition is None:
partition = "xyz"
for img, _uri in partition_gen(image, partition, uri):
if header is None:
if nifti_version == 1:
header = nib.Nifti1Header()
else:
header = nib.Nifti2Header()
header.set_data_shape(img.shape[::-1])
header.set_data_dtype(img.dtype)
header.set_xyzt_units(xyz=get_spacing_units(image, "x") or None,
t=get_spacing_units(image, "t") or None)
affine = np.diag((
*[get_spacing(img, ax) or 1.0
for ax in get_spatial_axes(img)], 1.0))
else:
affine = None
nifti_version = 1 if int(header["sizeof_hdr"]) == 348 else 2
if nifti_version == 1:
ni = nib.Nifti1Image(img.data, affine=affine, header=header)
else:
ni = nib.Nifti2Image(img.data, affine=affine, header=header)
nib.save(ni, _uri)
project_dir = '/nfs/s2/userhome/chenxiayu/workingdir/study/FFA_clustering'
acti_file = pjoin(
project_dir,
'data/HCP_face-avg/s2/S1200.1080.FACE-AVG_level2_zstat_hp200_s2_MSMAll.dscalar.nii'
)
patch_dir = pjoin(project_dir, 'data/HCP_face-avg/s2/patches_15/crg')
patch_file = pjoin(patch_dir, 'rFFA_patch_maps_lt15.nii.gz')
max_maps_file = pjoin(patch_dir, 'rFFA_max_maps_lt15.nii.gz')
prob_max_map_file = pjoin(patch_dir, 'rFFA_prob_max_map_lt15.nii.gz')
brain_structure = 'CIFTI_STRUCTURE_CORTEX_RIGHT'
acti_maps = CiftiReader(acti_file).get_data(brain_structure, True)
patch_maps = nib.load(patch_file).get_data()
max_maps = np.zeros_like(patch_maps)
for row in range(acti_maps.shape[0]):
labels = np.unique(patch_maps[row])
for label in labels:
if label == 0:
continue
acti_map_tmp = acti_maps[row].copy()
not_label_indices = np.logical_not(patch_maps[row] == label)
acti_map_tmp[not_label_indices] = -np.inf
max_idx = np.argmax(acti_map_tmp)
max_maps[row, max_idx] = label
prob_max_map = np.mean(max_maps > 0, 0)
header = nib.Nifti2Header()
header['descrip'] = 'FreeROI label'
save2nifti(max_maps_file, max_maps, header=header)
save2nifti(prob_max_map_file, prob_max_map)
