# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
#
# Copyright 2021 The NiPreps Developers <nipreps@gmail.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# We support and encourage derived works from this project, please read
# about our expectations at
#
# https://www.nipreps.org/community/licensing/
#
"""Nibabel-based interfaces."""
from pathlib import Path
from warnings import warn
import numpy as np
import nibabel as nb
from nipype import logging
from nipype.utils.filemanip import fname_presuffix
from nipype.interfaces.base import (
traits,
TraitedSpec,
BaseInterfaceInputSpec,
File,
SimpleInterface,
OutputMultiObject,
InputMultiObject,
)
IFLOGGER = logging.getLogger("nipype.interface")
class _ApplyMaskInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="an image")
in_mask = File(exists=True, mandatory=True, desc="a mask")
threshold = traits.Float(
0.5, usedefault=True, desc="a threshold to the mask, if it is nonbinary"
)
class _ApplyMaskOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="masked file")
[docs]
class ApplyMask(SimpleInterface):
"""Mask the input given a mask."""
input_spec = _ApplyMaskInputSpec
output_spec = _ApplyMaskOutputSpec
def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file)
msknii = nb.load(self.inputs.in_mask)
msk = msknii.get_fdata() > self.inputs.threshold
self._results["out_file"] = fname_presuffix(
self.inputs.in_file, suffix="_masked", newpath=runtime.cwd
)
if img.dataobj.shape[:3] != msk.shape:
raise ValueError("Image and mask sizes do not match.")
if not np.allclose(img.affine, msknii.affine):
raise ValueError("Image and mask affines are not similar enough.")
if img.dataobj.ndim == msk.ndim + 1:
msk = msk[..., np.newaxis]
masked = img.__class__(img.dataobj * msk, None, img.header)
masked.to_filename(self._results["out_file"])
return runtime
class _BinarizeInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="input image")
thresh_low = traits.Float(mandatory=True, desc="non-inclusive lower threshold")
class _BinarizeOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="masked file")
out_mask = File(exists=True, desc="output mask")
[docs]
class Binarize(SimpleInterface):
"""Binarizes the input image applying the given thresholds."""
input_spec = _BinarizeInputSpec
output_spec = _BinarizeOutputSpec
def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file)
self._results["out_file"] = fname_presuffix(
self.inputs.in_file, suffix="_masked", newpath=runtime.cwd
)
self._results["out_mask"] = fname_presuffix(
self.inputs.in_file, suffix="_mask", newpath=runtime.cwd
)
data = img.get_fdata()
mask = data > self.inputs.thresh_low
data[~mask] = 0.0
masked = img.__class__(data, img.affine, img.header)
masked.to_filename(self._results["out_file"])
img.header.set_data_dtype("uint8")
maskimg = img.__class__(mask.astype("uint8"), img.affine, img.header)
maskimg.to_filename(self._results["out_mask"])
return runtime
class _BinaryDilationInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="binary file to dilate")
radius = traits.Float(3, usedefault=True, desc="structure element (ball) radius")
iterations = traits.Range(low=0, value=1, usedefault=True, desc="repeat dilation")
class _BinaryDilationOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="the input file, after binary dilation")
[docs]
class BinaryDilation(SimpleInterface):
"""Morphological binary dilation using Scipy."""
# DEPRECATED in 1.7.0
# To remove in 1.9.0
input_spec = _BinaryDilationInputSpec
output_spec = _BinaryDilationOutputSpec
def __init__(self, from_file=None, resource_monitor=None, **inputs):
warn("""\
niworkflows.interfaces.nibabel.BinaryDilation is deprecated in favor of
niworkflows.interfaces.morphology.BinaryDilation. Please validate that
interface for your use case and switch.
""", DeprecationWarning, stacklevel=2)
super().__init__(from_file=from_file, resource_monitor=resource_monitor, **inputs)
def _run_interface(self, runtime):
self._results["out_file"] = _dilate(
self.inputs.in_file,
radius=self.inputs.radius,
iterations=self.inputs.iterations,
newpath=runtime.cwd,
)
return runtime
class _SplitSeriesInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="input 4d image")
class _SplitSeriesOutputSpec(TraitedSpec):
out_files = OutputMultiObject(File(exists=True), desc="output list of 3d images")
[docs]
class SplitSeries(SimpleInterface):
"""Split a 4D dataset along the last dimension into a series of 3D volumes."""
input_spec = _SplitSeriesInputSpec
output_spec = _SplitSeriesOutputSpec
def _run_interface(self, runtime):
in_file = self.inputs.in_file
img = nb.load(in_file)
extra_dims = tuple(dim for dim in img.shape[3:] if dim > 1) or (1,)
if len(extra_dims) != 1:
raise ValueError(f"Invalid shape {'x'.join(str(s) for s in img.shape)}")
img = img.__class__(
img.dataobj.reshape(img.shape[:3] + extra_dims), img.affine, img.header
)
self._results["out_files"] = []
for i, img_3d in enumerate(nb.four_to_three(img)):
out_file = fname_presuffix(
in_file, suffix=f"_idx-{i:03}", newpath=runtime.cwd
)
img_3d.to_filename(out_file)
self._results["out_files"].append(out_file)
return runtime
class _MergeSeriesInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiObject(
File(exists=True, mandatory=True, desc="input list of 3d images")
)
allow_4D = traits.Bool(
True, usedefault=True, desc="whether 4D images are allowed to be concatenated"
)
affine_tolerance = traits.Float(desc="Absolute tolerance allowed between image affines")
class _MergeSeriesOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="output 4d image")
[docs]
class MergeSeries(SimpleInterface):
"""Merge a series of 3D volumes along the last dimension into a single 4D image."""
input_spec = _MergeSeriesInputSpec
output_spec = _MergeSeriesOutputSpec
def _run_interface(self, runtime):
nii_list = []
aff0 = None
for f in self.inputs.in_files:
filenii = nb.squeeze_image(nb.load(f))
if self.inputs.affine_tolerance:
if aff0 is None:
aff0 = filenii.affine
elif not np.allclose(aff0, filenii.affine, atol=self.inputs.affine_tolerance):
raise ValueError(
"Difference in affines greater than allowed tolerance "
f"{self.inputs.affine_tolerance}"
)
ndim = filenii.dataobj.ndim
if ndim == 3:
nii_list.append(filenii)
continue
elif self.inputs.allow_4D and ndim == 4:
nii_list += nb.four_to_three(filenii)
continue
else:
raise ValueError(
f"Input image has an incorrect number of dimensions ({ndim})."
)
img_4d = nb.concat_images(
nii_list,
check_affines=not bool(self.inputs.affine_tolerance)
)
out_file = fname_presuffix(
self.inputs.in_files[0], suffix="_merged", newpath=runtime.cwd
)
img_4d.to_filename(out_file)
self._results["out_file"] = out_file
return runtime
class _MergeROIsInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiObject(File(exists=True), desc="ROI files to be merged")
class _MergeROIsOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="NIfTI containing all ROIs")
[docs]
class MergeROIs(SimpleInterface):
"""Combine multiple region of interest files (3D or 4D) into a single file"""
input_spec = _MergeROIsInputSpec
output_spec = _MergeROIsOutputSpec
def _run_interface(self, runtime):
self._results["out_file"] = _merge_rois(self.inputs.in_files, newpath=runtime.cwd)
return runtime
class _RegridToZoomsInputSpec(BaseInterfaceInputSpec):
in_file = File(
exists=True, mandatory=True, desc="a file whose resolution is to change"
)
zooms = traits.Tuple(
traits.Float,
traits.Float,
traits.Float,
mandatory=True,
desc="the new resolution",
)
order = traits.Int(3, usedefault=True, desc="order of interpolator")
clip = traits.Bool(
True,
usedefault=True,
desc="clip the data array within the original image's range",
)
smooth = traits.Either(
traits.Bool(),
traits.Float(),
default=False,
usedefault=True,
desc="apply gaussian smoothing before resampling",
)
class _RegridToZoomsOutputSpec(TraitedSpec):
out_file = File(exists=True, dec="the regridded file")
[docs]
class RegridToZooms(SimpleInterface):
"""Change the resolution of an image (regrid)."""
input_spec = _RegridToZoomsInputSpec
output_spec = _RegridToZoomsOutputSpec
def _run_interface(self, runtime):
from ..utils.images import resample_by_spacing
self._results["out_file"] = fname_presuffix(
self.inputs.in_file, suffix="_regrid", newpath=runtime.cwd
)
resample_by_spacing(
self.inputs.in_file,
self.inputs.zooms,
order=self.inputs.order,
clip=self.inputs.clip,
smooth=self.inputs.smooth,
).to_filename(self._results["out_file"])
return runtime
class _DemeanImageInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="image to be demeaned")
in_mask = File(
exists=True, mandatory=True, desc="mask where median will be calculated"
)
only_mask = traits.Bool(False, usedefault=True, desc="demean only within mask")
class _DemeanImageOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="demeaned image")
[docs]
class DemeanImage(SimpleInterface):
input_spec = _DemeanImageInputSpec
output_spec = _DemeanImageOutputSpec
def _run_interface(self, runtime):
from ..utils.images import demean
self._results["out_file"] = demean(
self.inputs.in_file,
self.inputs.in_mask,
only_mask=self.inputs.only_mask,
newpath=runtime.cwd,
)
return runtime
class _FilledImageLikeInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="image to be demeaned")
fill_value = traits.Float(1.0, usedefault=True, desc="value to fill")
dtype = traits.Enum(
"float32", "uint8", usedefault=True, desc="force output data type"
)
class _FilledImageLikeOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="demeaned image")
[docs]
class FilledImageLike(SimpleInterface):
input_spec = _FilledImageLikeInputSpec
output_spec = _FilledImageLikeOutputSpec
def _run_interface(self, runtime):
from ..utils.images import nii_ones_like
self._results["out_file"] = nii_ones_like(
self.inputs.in_file,
self.inputs.fill_value,
self.inputs.dtype,
newpath=runtime.cwd,
)
return runtime
class _GenerateSamplingReferenceInputSpec(BaseInterfaceInputSpec):
fixed_image = File(
exists=True, mandatory=True, desc="the reference file, defines the FoV"
)
moving_image = File(exists=True, mandatory=True, desc="the pixel size reference")
xform_code = traits.Enum(None, 2, 4, usedefault=True, desc="force xform code")
fov_mask = traits.Either(
None,
File(exists=True),
usedefault=True,
desc="mask to clip field of view (in fixed_image space)",
)
keep_native = traits.Bool(
True,
usedefault=True,
desc="calculate a grid with native resolution covering "
"the volume extent given by fixed_image, fast forward "
"fixed_image otherwise.",
)
class _GenerateSamplingReferenceOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="one file with all inputs flattened")
[docs]
class GenerateSamplingReference(SimpleInterface):
"""
Generates a reference grid for resampling one image keeping original resolution,
but moving data to a different space (e.g. MNI).
If the `fov_mask` optional input is provided, then the abbr:`FoV (field-of-view)`
is cropped to a bounding box containing the brain mask plus an offset of two
voxels along all dimensions. The `fov_mask` should be to the brain mask calculated
from the T1w, and should not contain the brain stem. The mask is resampled into
target space, and then the bounding box is calculated. Finally, the FoV is adjusted
to that bounding box.
"""
input_spec = _GenerateSamplingReferenceInputSpec
output_spec = _GenerateSamplingReferenceOutputSpec
def _run_interface(self, runtime):
if not self.inputs.keep_native:
self._results["out_file"] = self.inputs.fixed_image
return runtime
from .. import __version__
self._results["out_file"] = _gen_reference(
self.inputs.fixed_image,
self.inputs.moving_image,
fov_mask=self.inputs.fov_mask,
force_xform_code=self.inputs.xform_code,
message="%s (niworkflows v%s)" % (self.__class__.__name__, __version__),
newpath=runtime.cwd,
)
return runtime
class _IntensityClipInputSpec(BaseInterfaceInputSpec):
in_file = File(
exists=True, mandatory=True, desc="3D file which intensity will be clipped"
)
p_min = traits.Float(35.0, usedefault=True, desc="percentile for the lower bound")
p_max = traits.Float(99.98, usedefault=True, desc="percentile for the upper bound")
nonnegative = traits.Bool(
True, usedefault=True, desc="whether input intensities must be positive"
)
dtype = traits.Enum(
"int16", "float32", "uint8", usedefault=True, desc="output datatype"
)
invert = traits.Bool(False, usedefault=True, desc="finalize by inverting contrast")
class _IntensityClipOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="file after clipping")
[docs]
class IntensityClip(SimpleInterface):
"""Clip the intensity range as prescribed by the percentiles."""
input_spec = _IntensityClipInputSpec
output_spec = _IntensityClipOutputSpec
def _run_interface(self, runtime):
self._results["out_file"] = _advanced_clip(
self.inputs.in_file,
p_min=self.inputs.p_min,
p_max=self.inputs.p_max,
nonnegative=self.inputs.nonnegative,
dtype=self.inputs.dtype,
invert=self.inputs.invert,
newpath=runtime.cwd,
)
return runtime
class _MapLabelsInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, desc="Segmented NIfTI")
mappings = traits.Dict(
xor=["mappings_file"],
desc="Dictionary of label / replacement label pairs",
)
mappings_file = File(
exists=True, xor=["mappings"], help="JSON composed of label / replacement label pairs."
)
class _MapLabelsOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="Labeled file")
[docs]
class MapLabels(SimpleInterface):
"""Remap discrete labels"""
input_spec = _MapLabelsInputSpec
output_spec = _MapLabelsOutputSpec
def _run_interface(self, runtime):
mapping = self.inputs.mappings or _load_int_json(self.inputs.mappings_file)
self._results["out_file"] = _remap_labels(
self.inputs.in_file,
mapping,
newpath=runtime.cwd,
)
return runtime
class ReorientImageInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="Moving file")
target_file = File(
exists=True, xor=["target_orientation"], desc="Reference file to reorient to"
)
target_orientation = traits.Str(
xor=["target_file"], desc="Axis codes of coordinate system to reorient to"
)
class ReorientImageOutputSpec(TraitedSpec):
out_file = File(desc="Reoriented file")
[docs]
class ReorientImage(SimpleInterface):
input_spec = ReorientImageInputSpec
output_spec = ReorientImageOutputSpec
def _run_interface(self, runtime):
self._results["out_file"] = reorient_file(
self.inputs.in_file,
target_file=self.inputs.target_file,
target_ornt=self.inputs.target_orientation,
)
return runtime
[docs]
def reorient_file(
in_file: str, *, target_file: str = None, target_ornt: str = None, newpath: str = None,
) -> str:
"""
Reorient an image.
New orientation targets can be either another image, or a string representation of the
orientation axis.
Parameters
----------
in_file : Image to be reoriented
target_file : Reference image of desired orientation
target_ornt : Orientation denoted by the first letter of each axis (i.e., "RAS", "LPI")
"""
import nibabel as nb
img = nb.load(in_file)
if not target_file and not target_ornt:
raise TypeError("No target orientation or file is specified.")
if target_file:
target_img = nb.load(target_file)
target_ornt = nb.aff2axcodes(target_img.affine)
reoriented = reorient_image(img, target_ornt)
if newpath is None:
newpath = Path()
out_file = str((Path(newpath) / "reoriented.nii.gz").absolute())
reoriented.to_filename(out_file)
return out_file
[docs]
def reorient_image(img: nb.spatialimages.SpatialImage, target_ornt: str):
"""Reorient an image in memory."""
import nibabel as nb
img_axcodes = nb.aff2axcodes(img.affine)
in_ornt = nb.orientations.axcodes2ornt(img_axcodes)
out_ornt = nb.orientations.axcodes2ornt(target_ornt)
ornt_xfm = nb.orientations.ornt_transform(in_ornt, out_ornt)
r_img = img.as_reoriented(ornt_xfm)
return r_img
def _gen_reference(
fixed_image,
moving_image,
fov_mask=None,
out_file=None,
message=None,
force_xform_code=None,
newpath=None,
):
"""Generate a sampling reference, and makes sure xform matrices/codes are correct."""
import nilearn.image as nli
if out_file is None:
out_file = fname_presuffix(
fixed_image, suffix="_reference", newpath=newpath
)
# Moving images may not be RAS/LPS (more generally, transverse-longitudinal-axial)
reoriented_moving_img = nb.as_closest_canonical(nb.load(moving_image))
new_zooms = reoriented_moving_img.header.get_zooms()[:3]
# Avoid small differences in reported resolution to cause changes to
# FOV. See https://github.com/nipreps/fmriprep/issues/512
# A positive diagonal affine is RAS, hence the need to reorient above.
new_affine = np.diag(np.round(new_zooms, 3))
resampled = nli.resample_img(
fixed_image, target_affine=new_affine, interpolation="nearest"
)
if fov_mask is not None:
# If we have a mask, resample again dropping (empty) samples
# out of the FoV.
fixednii = nb.load(fixed_image)
masknii = nb.load(fov_mask)
if np.all(masknii.shape[:3] != fixednii.shape[:3]):
raise RuntimeError("Fixed image and mask do not have the same dimensions.")
if not np.allclose(masknii.affine, fixednii.affine, atol=1e-5):
raise RuntimeError("Fixed image and mask have different affines")
# Get mask into reference space
masknii = nli.resample_img(
masknii, target_affine=new_affine, interpolation="nearest"
)
res_shape = np.array(masknii.shape[:3])
# Calculate a bounding box for the input mask
# with an offset of 2 voxels per face
bbox = np.argwhere(np.asanyarray(masknii.dataobj) > 0)
new_origin = np.clip(bbox.min(0) - 2, a_min=0, a_max=None)
new_end = np.clip(bbox.max(0) + 2, a_min=0, a_max=res_shape - 1)
# Find new origin, and set into new affine
new_affine_4 = resampled.affine.copy()
new_affine_4[:3, 3] = new_affine_4[:3, :3].dot(new_origin) + new_affine_4[:3, 3]
# Calculate new shapes
new_shape = new_end - new_origin + 1
resampled = nli.resample_img(
fixed_image,
target_affine=new_affine_4,
target_shape=new_shape.tolist(),
interpolation="nearest",
)
xform = resampled.affine # nibabel will pick the best affine
_, qform_code = resampled.header.get_qform(coded=True)
_, sform_code = resampled.header.get_sform(coded=True)
xform_code = sform_code if sform_code > 0 else qform_code
if xform_code == 1:
xform_code = 2
if force_xform_code is not None:
xform_code = force_xform_code
# Keep 0, 2, 3, 4 unchanged
resampled.header.set_qform(xform, int(xform_code))
resampled.header.set_sform(xform, int(xform_code))
resampled.header["descrip"] = "reference image generated by %s." % (
message or "(unknown software)"
)
resampled.to_filename(out_file)
return out_file
def _advanced_clip(
in_file, p_min=35, p_max=99.98, nonnegative=True, dtype="int16", invert=False, newpath=None,
):
"""
Remove outliers at both ends of the intensity distribution and fit into a given dtype.
This interface tries to emulate ANTs workflows' massaging that truncate images into
the 0-255 range, and applies percentiles for clipping images.
For image registration, normalizing the intensity into a compact range (e.g., uint8)
is generally advised.
To more robustly determine the clipping thresholds, data are removed of spikes
with a median filter.
Once the thresholds are calculated, the denoised data are thrown away and the thresholds
are applied on the original image.
"""
from pathlib import Path
import nibabel as nb
import numpy as np
from scipy import ndimage
from skimage.morphology import ball
out_file = (Path(newpath or "") / "clipped.nii.gz").absolute()
# Load data
img = nb.squeeze_image(nb.load(in_file))
if len(img.shape) != 3:
raise RuntimeError(f"<{in_file}> is not a 3D file.")
data = img.get_fdata(dtype="float32")
# Calculate stats on denoised version, to preempt outliers from biasing
denoised = ndimage.median_filter(data, footprint=ball(3))
a_min = np.percentile(
denoised[denoised > 0] if nonnegative else denoised,
p_min
)
a_max = np.percentile(
denoised[denoised > 0] if nonnegative else denoised,
p_max
)
# Clip and cast
data = np.clip(data, a_min=a_min, a_max=a_max)
data -= data.min()
data /= data.max()
if invert:
data = 1.0 - data
if dtype in ("uint8", "int16"):
data = np.round(255 * data).astype(dtype)
hdr = img.header.copy()
hdr.set_data_dtype(dtype)
img.__class__(data, img.affine, hdr).to_filename(out_file)
return str(out_file)
def _dilate(in_file, radius=3, iterations=1, newpath=None):
"""Dilate (binary) input mask."""
from pathlib import Path
import numpy as np
import nibabel as nb
from scipy import ndimage
from skimage.morphology import ball
from nipype.utils.filemanip import fname_presuffix
mask = nb.load(in_file)
newdata = ndimage.binary_dilation(
np.asanyarray(mask.dataobj) > 0,
structure=ball(radius),
iterations=iterations,
)
hdr = mask.header.copy()
hdr.set_data_dtype("uint8")
out_file = fname_presuffix(in_file, suffix="_dil", newpath=newpath or Path.cwd())
mask.__class__(newdata.astype("uint8"), mask.affine, hdr).to_filename(out_file)
return out_file
def _merge_rois(in_files, newpath=None):
"""
Aggregate individual 4D ROI files together into a single subcortical NIfTI.
All ROI images are sanity checked with regards to:
1) Shape
2) Affine
3) Overlap
If any of these checks fail, an ``AssertionError`` will be raised.
"""
from pathlib import Path
import nibabel as nb
import numpy as np
img = nb.load(in_files[0])
data = np.array(img.dataobj)
affine = img.affine
header = img.header
nonzero = np.any(data, axis=3)
for roi in in_files[1:]:
img = nb.load(roi)
assert img.shape == data.shape, "Mismatch in image shape"
assert np.allclose(img.affine, affine), "Mismatch in affine"
roi_data = np.asanyarray(img.dataobj)
roi_nonzero = np.any(roi_data, axis=3)
assert not np.any(roi_nonzero & nonzero), "Overlapping ROIs"
nonzero |= roi_nonzero
data += roi_data
del roi_data
if newpath is None:
newpath = Path()
out_file = str((Path(newpath) / "combined.nii.gz").absolute())
img.__class__(data, affine, header).to_filename(out_file)
return out_file
def _remap_labels(in_file, mapping, newpath=None):
from pathlib import Path
import nibabel as nb
import numpy as np
dtype = np.int16
img = nb.load(in_file)
data = np.asarray(img.dataobj, dtype=dtype)
vec = data.ravel()
def _relabel(label):
return mapping.get(label, label)
labels = np.unique(vec) # include all labels present
# copy values and substitute mappings
subs = np.asarray(list(map(_relabel, labels)), dtype=dtype)
subbed = np.zeros(labels.max() + 1, dtype=dtype)
subbed[labels] = subs
out = subbed[vec].reshape(data.shape)
if newpath is None:
newpath = Path()
out_file = str((Path(newpath) / "relabeled.nii.gz").absolute())
img.__class__(out, img.affine, header=img.header).to_filename(out_file)
return out_file
def _load_int_json(json_file):
import json
def _keys_as_ints(d):
return {int(k): v for k, v in d.items()}
with open(json_file) as fp:
data = json.load(fp, object_hook=_keys_as_ints)
return data