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# Copyright 2017 Intel Corporation
#
# 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.
"""I/O functionality."""
__all__ = [
"load_boolean_mask",
"load_images_from_dir",
"load_labels",
"save_as_nifti_file",
]
from pathlib import Path
from typing import Callable, Iterable, List, Union
import logging
import nibabel as nib
import numpy as np
from nibabel.nifti1 import Nifti1Pair
from nibabel.spatialimages import SpatialImage
from .image import SingleConditionSpec
logger = logging.getLogger(__name__)
def load_images_from_dir(in_dir: Union[str, Path], suffix: str = "nii.gz",
) -> Iterable[SpatialImage]:
"""Load images from directory.
For efficiency, returns an iterator, not a sequence, so the results cannot
be accessed by indexing.
For every new iteration through the images, load_images_from_dir must be
called again.
Parameters
----------
in_dir:
Path to directory.
suffix:
Only load images with names that end like this.
Yields
------
SpatialImage
Image.
"""
if isinstance(in_dir, str):
in_dir = Path(in_dir)
files = sorted(in_dir.glob("*" + suffix))
for f in files:
logger.debug(
'Starting to read file %s', f
)
yield nib.load(str(f))
8712
def load_images(image_paths: Iterable[Union[str, Path]]
) -> Iterable[SpatialImage]:
"""Load images from paths.
For efficiency, returns an iterator, not a sequence, so the results cannot
be accessed by indexing.
For every new iteration through the images, load_images must be called
again.
Parameters
----------
image_paths:
Paths to images.
Yields
------
SpatialImage
Image.
"""
for image_path in image_paths:
if isinstance(image_path, Path):
string_path = str(image_path)
else:
string_path = image_path
logger.debug(
'Starting to read file %s', string_path
)
yield nib.load(string_path)
def load_boolean_mask(path: Union[str, Path],
predicate: Callable[[np.ndarray], np.ndarray] = None
) -> np.ndarray:
"""Load boolean nibabel.SpatialImage mask.
Parameters
----------
path
Mask path.
predicate
Callable used to create boolean values, e.g. a threshold function
``lambda x: x > 50``.
Returns
-------
np.ndarray
Boolean array corresponding to mask.
"""
if not isinstance(path, str):
path = str(path)
data = nib.load(path).get_data()
if predicate is not None:
mask = predicate(data)
else:
mask = data.astype(np.bool)
return mask
def load_labels(path: Union[str, Path]) -> List[SingleConditionSpec]:
"""Load labels files.
Parameters
----------
path
Path of labels file.
Returns
-------
List[SingleConditionSpec]
List of SingleConditionSpec stored in labels file.
"""
condition_specs = np.load(str(path))
return [c.view(SingleConditionSpec) for c in condition_specs]
def save_as_nifti_file(data: np.ndarray, affine: np.ndarray,
path: Union[str, Path]) -> None:
"""Create a Nifti file and save it.
Parameters
----------
data
Brain data.
affine
Affine of the image, usually inherited from an existing image.
path
Output filename.
"""
if not isinstance(path, str):
path = str(path)
img = Nifti1Pair(data, affine)
nib.nifti1.save(img, path)