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nnUNet_calvingfront_detection / nnunet /dataset_conversion /Task061_CREMI.py
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# Copyright 2020 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 collections import OrderedDict
from batchgenerators.utilities.file_and_folder_operations import *
import numpy as np
from nnunet.paths import nnUNet_raw_data, preprocessing_output_dir
import shutil
import SimpleITK as sitk
try:
import h5py
except ImportError:
h5py = None
def load_sample(filename):
# we need raw data and seg
f = h5py.File(filename, 'r')
data = np.array(f['volumes']['raw'])
if 'labels' in f['volumes'].keys():
labels = np.array(f['volumes']['labels']['clefts'])
# clefts are low values, background is high
labels = (labels < 100000).astype(np.uint8)
else:
labels = None
return data, labels
def save_as_nifti(arr, filename, spacing):
itk_img = sitk.GetImageFromArray(arr)
itk_img.SetSpacing(spacing)
sitk.WriteImage(itk_img, filename)
def prepare_submission():
from cremi.io import CremiFile
from cremi.Volume import Volume
base = "/home/fabian/drives/datasets/results/nnUNet/test_sets/Task061_CREMI/"
# a+
pred = sitk.GetArrayFromImage(sitk.ReadImage(join(base, 'results_3d_fullres', "sample_a+.nii.gz"))).astype(np.uint64)
pred[pred == 0] = 0xffffffffffffffff
out_a = CremiFile(join(base, 'sample_A+_20160601.hdf'), 'w')
clefts = Volume(pred, (40., 4., 4.))
out_a.write_clefts(clefts)
out_a.close()
pred = sitk.GetArrayFromImage(sitk.ReadImage(join(base, 'results_3d_fullres', "sample_b+.nii.gz"))).astype(np.uint64)
pred[pred == 0] = 0xffffffffffffffff
out_b = CremiFile(join(base, 'sample_B+_20160601.hdf'), 'w')
clefts = Volume(pred, (40., 4., 4.))
out_b.write_clefts(clefts)
out_b.close()
pred = sitk.GetArrayFromImage(sitk.ReadImage(join(base, 'results_3d_fullres', "sample_c+.nii.gz"))).astype(np.uint64)
pred[pred == 0] = 0xffffffffffffffff
out_c = CremiFile(join(base, 'sample_C+_20160601.hdf'), 'w')
clefts = Volume(pred, (40., 4., 4.))
out_c.write_clefts(clefts)
out_c.close()
if __name__ == "__main__":
assert h5py is not None, "you need h5py for this. Install with 'pip install h5py'"
foldername = "Task061_CREMI"
out_base = join(nnUNet_raw_data, foldername)
imagestr = join(out_base, "imagesTr")
imagests = join(out_base, "imagesTs")
labelstr = join(out_base, "labelsTr")
maybe_mkdir_p(imagestr)
maybe_mkdir_p(imagests)
maybe_mkdir_p(labelstr)
base = "/media/fabian/My Book/datasets/CREMI"
# train
img, label = load_sample(join(base, "sample_A_20160501.hdf"))
save_as_nifti(img, join(imagestr, "sample_a_0000.nii.gz"), (4, 4, 40))
save_as_nifti(label, join(labelstr, "sample_a.nii.gz"), (4, 4, 40))
img, label = load_sample(join(base, "sample_B_20160501.hdf"))
save_as_nifti(img, join(imagestr, "sample_b_0000.nii.gz"), (4, 4, 40))
save_as_nifti(label, join(labelstr, "sample_b.nii.gz"), (4, 4, 40))
img, label = load_sample(join(base, "sample_C_20160501.hdf"))
save_as_nifti(img, join(imagestr, "sample_c_0000.nii.gz"), (4, 4, 40))
save_as_nifti(label, join(labelstr, "sample_c.nii.gz"), (4, 4, 40))
save_as_nifti(img, join(imagestr, "sample_d_0000.nii.gz"), (4, 4, 40))
save_as_nifti(label, join(labelstr, "sample_d.nii.gz"), (4, 4, 40))
save_as_nifti(img, join(imagestr, "sample_e_0000.nii.gz"), (4, 4, 40))
save_as_nifti(label, join(labelstr, "sample_e.nii.gz"), (4, 4, 40))
# test
img, label = load_sample(join(base, "sample_A+_20160601.hdf"))
save_as_nifti(img, join(imagests, "sample_a+_0000.nii.gz"), (4, 4, 40))
img, label = load_sample(join(base, "sample_B+_20160601.hdf"))
save_as_nifti(img, join(imagests, "sample_b+_0000.nii.gz"), (4, 4, 40))
img, label = load_sample(join(base, "sample_C+_20160601.hdf"))
save_as_nifti(img, join(imagests, "sample_c+_0000.nii.gz"), (4, 4, 40))
json_dict = OrderedDict()
json_dict['name'] = foldername
json_dict['description'] = foldername
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "see challenge website"
json_dict['licence'] = "see challenge website"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "EM",
}
json_dict['labels'] = {i: str(i) for i in range(2)}
json_dict['numTraining'] = 5
json_dict['numTest'] = 1
json_dict['training'] = [{'image': "./imagesTr/sample_%s.nii.gz" % i, "label": "./labelsTr/sample_%s.nii.gz" % i} for i in
['a', 'b', 'c', 'd', 'e']]
json_dict['test'] = ["./imagesTs/sample_a+.nii.gz", "./imagesTs/sample_b+.nii.gz", "./imagesTs/sample_c+.nii.gz"]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
out_preprocessed = join(preprocessing_output_dir, foldername)
maybe_mkdir_p(out_preprocessed)
# manual splits. we train 5 models on all three datasets
splits = [{'train': ["sample_a", "sample_b", "sample_c"], 'val': ["sample_a", "sample_b", "sample_c"]},
{'train': ["sample_a", "sample_b", "sample_c"], 'val': ["sample_a", "sample_b", "sample_c"]},
{'train': ["sample_a", "sample_b", "sample_c"], 'val': ["sample_a", "sample_b", "sample_c"]},
{'train': ["sample_a", "sample_b", "sample_c"], 'val': ["sample_a", "sample_b", "sample_c"]},
{'train': ["sample_a", "sample_b", "sample_c"], 'val': ["sample_a", "sample_b", "sample_c"]}]
save_pickle(splits, join(out_preprocessed, "splits_final.pkl"))