File size: 4,751 Bytes
5ccd75a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | # Copyright 2020 MONAI Consortium
# 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.
from typing import Any, Callable, Optional, Sequence
import numpy as np
from torch.utils.data import Dataset
from monai.transforms import LoadNifti, Randomizable, apply_transform
from monai.utils import get_seed
class NiftiDataset(Dataset, Randomizable):
"""
Loads image/segmentation pairs of Nifti files from the given filename lists. Transformations can be specified
for the image and segmentation arrays separately.
"""
def __init__(
self,
image_files: Sequence[str],
seg_files: Optional[Sequence[str]] = None,
labels: Optional[Sequence[float]] = None,
as_closest_canonical: bool = False,
transform: Optional[Callable] = None,
seg_transform: Optional[Callable] = None,
image_only: bool = True,
dtype: Optional[np.dtype] = np.float32,
) -> None:
"""
Initializes the dataset with the image and segmentation filename lists. The transform `transform` is applied
to the images and `seg_transform` to the segmentations.
Args:
image_files: list of image filenames
seg_files: if in segmentation task, list of segmentation filenames
labels: if in classification task, list of classification labels
as_closest_canonical: if True, load the image as closest to canonical orientation
transform: transform to apply to image arrays
seg_transform: transform to apply to segmentation arrays
image_only: if True return only the image volume, other return image volume and header dict
dtype: if not None convert the loaded image to this data type
Raises:
ValueError: When ``seg_files`` length differs from ``image_files``.
"""
if seg_files is not None and len(image_files) != len(seg_files):
raise ValueError(
"Must have same the number of segmentation as image files: "
f"images={len(image_files)}, segmentations={len(seg_files)}."
)
self.image_files = image_files
self.seg_files = seg_files
self.labels = labels
self.as_closest_canonical = as_closest_canonical
self.transform = transform
self.seg_transform = seg_transform
self.image_only = image_only
self.dtype = dtype
self.set_random_state(seed=get_seed())
self._seed = 0 # transform synchronization seed
def __len__(self) -> int:
return len(self.image_files)
def randomize(self, data: Optional[Any] = None) -> None:
self._seed = self.R.randint(np.iinfo(np.int32).max)
def __getitem__(self, index: int):
self.randomize()
meta_data = None
img_loader = LoadNifti(
as_closest_canonical=self.as_closest_canonical, image_only=self.image_only, dtype=self.dtype
)
if self.image_only:
img = img_loader(self.image_files[index])
else:
img, meta_data = img_loader(self.image_files[index])
seg = None
if self.seg_files is not None:
seg_loader = LoadNifti(image_only=True)
seg = seg_loader(self.seg_files[index])
label = None
if self.labels is not None:
label = self.labels[index]
if self.transform is not None:
if isinstance(self.transform, Randomizable):
self.transform.set_random_state(seed=self._seed)
img = apply_transform(self.transform, img)
data = [img]
if self.seg_transform is not None:
if isinstance(self.seg_transform, Randomizable):
self.seg_transform.set_random_state(seed=self._seed)
seg = apply_transform(self.seg_transform, seg)
if seg is not None:
data.append(seg)
if label is not None:
data.append(label)
if not self.image_only and meta_data is not None:
data.append(meta_data)
if len(data) == 1:
return data[0]
# use tuple instead of list as the default collate_fn callback of MONAI DataLoader flattens nested lists
return tuple(data)
|