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177 lines (144 loc) · 4.92 KB
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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.
"""Generic image functionality."""
__all__ = [
"ConditionSpec",
"SingleConditionSpec",
"mask_image",
"multimask_images",
]
import itertools
from typing import Iterable, Sequence, Type, TypeVar
import numpy as np
from nibabel.spatialimages import SpatialImage
T = TypeVar("T", bound="MaskedMultiSubjectData")
class MaskedMultiSubjectData(np.ndarray):
"""Array in shape n_voxels, n_trs, n_subjects."""
@classmethod
def from_masked_images(cls: Type[T], masked_images: Iterable[np.ndarray],
n_sub: int) -> T:
"""Create a new instance from masked images.
Parameters
----------
masked_images
Images to concatenate.
n_sub
Number of subjects. Must match the number of images.
Returns
-------
T
A new instance.
Raises
------
ValueError
Images have different shapes.
The number of images differs from n_sub.
"""
images_iterator = iter(masked_images)
first_image = next(images_iterator)
result = np.empty((first_image.shape[0], first_image.shape[1], n_sub))
for n_images, image in enumerate(itertools.chain([first_image],
images_iterator)):
if image.shape != first_image.shape:
raise ValueError("Image {} has different shape from first "
"image: {} != {}".format(n_images,
image.shape,
first_image.shape))
result[:, :, n_images] = image
n_images += 1
if n_images != n_sub:
raise ValueError("n_sub != number of images: {} != {}"
.format(n_sub, n_images))
return result.view(cls)
class ConditionSpec(np.ndarray):
"""One-hot representation of conditions across epochs and TRs.
The shape is (n_conditions, n_epochs, n_trs).
"""
class SingleConditionSpec(ConditionSpec):
"""ConditionSpec with a single condition applicable to an epoch."""
def extract_labels(self) -> np.ndarray:
"""Extract condition labels.
Returns
-------
np.ndarray
The condition label of each epoch.
"""
condition_idxs, epoch_idxs, _ = np.where(self)
_, unique_epoch_idxs = np.unique(epoch_idxs, return_index=True)
return condition_idxs[unique_epoch_idxs]
def mask_image(image: SpatialImage, mask: np.ndarray, data_type: type = None
) -> np.ndarray:
"""Mask image after optionally casting its type.
Parameters
----------
image
Image to mask. Can include time as the last dimension.
mask
Mask to apply. Must have the same shape as the image data.
data_type
Type to cast image to.
Returns
-------
np.ndarray
Masked image.
Raises
------
ValueError
Image data and masks have different shapes.
"""
image_data = image.get_data()
if image_data.shape[:3] != mask.shape:
raise ValueError("Image data and mask have different shapes.")
if data_type is not None:
cast_data = image_data.astype(data_type)
else:
cast_data = image_data
return cast_data[mask]
def multimask_images(images: Iterable[SpatialImage],
masks: Sequence[np.ndarray], image_type: type = None
) -> Iterable[Sequence[np.ndarray]]:
"""Mask images with multiple masks.
Parameters
----------
images:
Images to mask.
masks:
Masks to apply.
image_type:
Type to cast images to.
Yields
------
Sequence[np.ndarray]
For each mask, a masked image.
"""
for image in images:
yield [mask_image(image, mask, image_type) for mask in masks]
def mask_images(images: Iterable[SpatialImage], mask: np.ndarray,
image_type: type = None) -> Iterable[np.ndarray]:
"""Mask images.
Parameters
----------
images:
Images to mask.
mask:
Mask to apply.
image_type:
Type to cast images to.
Yields
------
np.ndarray
Masked image.
"""
for images in multimask_images(images, (mask,), image_type):
yield images[0]