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# Copyright 2021 HIP Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research Center
# (DKFZ), Heidelberg, Germany
#
# 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 Tuple, Union, List
import numpy as np
from nnunetv2.imageio.base_reader_writer import BaseReaderWriter
import SimpleITK as sitk
class SimpleITKIO(BaseReaderWriter):
supported_file_endings = [
'.nii.gz',
'.nrrd',
'.mha'
]
def read_images(self, image_fnames: Union[List[str], Tuple[str, ...]]) -> Tuple[np.ndarray, dict]:
images = []
spacings = []
origins = []
directions = []
spacings_for_nnunet = []
for f in image_fnames:
itk_image = sitk.ReadImage(f)
spacings.append(itk_image.GetSpacing())
origins.append(itk_image.GetOrigin())
directions.append(itk_image.GetDirection())
npy_image = sitk.GetArrayFromImage(itk_image)
if npy_image.ndim == 2:
# 2d
npy_image = npy_image[None, None]
max_spacing = max(spacings[-1])
spacings_for_nnunet.append((max_spacing * 999, *list(spacings[-1])[::-1]))
elif npy_image.ndim == 3:
# 3d, as in original nnunet
npy_image = npy_image[None]
spacings_for_nnunet.append(list(spacings[-1])[::-1])
elif npy_image.ndim == 4:
# 4d, multiple modalities in one file
spacings_for_nnunet.append(list(spacings[-1])[::-1][1:])
pass
else:
raise RuntimeError(f"Unexpected number of dimensions: {npy_image.ndim} in file {f}")
images.append(npy_image)
spacings_for_nnunet[-1] = list(np.abs(spacings_for_nnunet[-1]))
if not self._check_all_same([i.shape for i in images]):
print('ERROR! Not all input images have the same shape!')
print('Shapes:')
print([i.shape for i in images])
print('Image files:')
print(image_fnames)
raise RuntimeError()
if not self._check_all_same(spacings):
print('ERROR! Not all input images have the same spacing!')
print('Spacings:')
print(spacings)
print('Image files:')
print(image_fnames)
raise RuntimeError()
if not self._check_all_same(origins):
print('WARNING! Not all input images have the same origin!')
print('Origins:')
print(origins)
print('Image files:')
print(image_fnames)
print('It is up to you to decide whether that\'s a problem. You should run nnUNet_plot_dataset_pngs to verify '
'that segmentations and data overlap.')
if not self._check_all_same(directions):
print('WARNING! Not all input images have the same direction!')
print('Directions:')
print(directions)
print('Image files:')
print(image_fnames)
print('It is up to you to decide whether that\'s a problem. You should run nnUNet_plot_dataset_pngs to verify '
'that segmentations and data overlap.')
if not self._check_all_same(spacings_for_nnunet):
print('ERROR! Not all input images have the same spacing_for_nnunet! (This should not happen and must be a '
'bug. Please report!')
print('spacings_for_nnunet:')
print(spacings_for_nnunet)
print('Image files:')
print(image_fnames)
raise RuntimeError()
stacked_images = np.vstack(images)
dict = {
'sitk_stuff': {
# this saves the sitk geometry information. This part is NOT used by nnU-Net!
'spacing': spacings[0],
'origin': origins[0],
'direction': directions[0]
},
# the spacing is inverted with [::-1] because sitk returns the spacing in the wrong order lol. Image arrays
# are returned x,y,z but spacing is returned z,y,x. Duh.
'spacing': spacings_for_nnunet[0]
}
return stacked_images.astype(np.float32), dict
def read_seg(self, seg_fname: str) -> Tuple[np.ndarray, dict]:
return self.read_images((seg_fname, ))
def write_seg(self, seg: np.ndarray, output_fname: str, properties: dict) -> None:
assert seg.ndim == 3, 'segmentation must be 3d. If you are exporting a 2d segmentation, please provide it as shape 1,x,y'
output_dimension = len(properties['sitk_stuff']['spacing'])
assert 1 < output_dimension < 4
if output_dimension == 2:
seg = seg[0]
if seg.dtype=="float16":
itk_image = sitk.GetImageFromArray(seg.astype(np.float32)) #TODO: to improve
else:
itk_image = sitk.GetImageFromArray(seg.astype(np.uint8))
itk_image.SetSpacing(properties['sitk_stuff']['spacing'])
itk_image.SetOrigin(properties['sitk_stuff']['origin'])
itk_image.SetDirection(properties['sitk_stuff']['direction'])
sitk.WriteImage(itk_image, output_fname, True)
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