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from setuptools import setup, find_namespace_packages
setup(name='nnunet',
packages=find_namespace_packages(include=["nnunet", "nnunet.*"]),
version='1.7.0',
description='nnU-Net. Framework for out-of-the box biomedical image segmentation.',
url='https://github.com/MIC-DKFZ/nnUNet',
author='Division of Medical Image Computing, German Cancer Research Center',
author_email='f.isensee@dkfz-heidelberg.de',
license='Apache License Version 2.0, January 2004',
install_requires=[
"torch>=1.6.0a",
"tqdm",
"dicom2nifti",
"scikit-image>=0.14",
"medpy",
"scipy",
"batchgenerators>=0.23",
"numpy",
"SimpleITK",
"opencv-python~=4.7.0.72",
"pandas",
"requests",
"nibabel",
'tifffile',
"plotly~=5.14.1",
"scikit-learn~=1.2.2",
"astropy~=5.2.2",
"einops~=0.6.0",
"matplotlib~=3.7.1",
"h5py~=3.8.0",
"fil-finder~=1.7.2",
"psutil~=5.9.4",
"image~=1.5.33"
],
entry_points={
'console_scripts': [
'nnUNet_convert_decathlon_task = nnunet.experiment_planning.nnUNet_convert_decathlon_task:main',
'nnUNet_plan_and_preprocess = nnunet.experiment_planning.nnUNet_plan_and_preprocess:main',
'nnUNet_train = nnunet.run.run_training:main',
'nnUNet_train_DP = nnunet.run.run_training_DP:main',
'nnUNet_train_DDP = nnunet.run.run_training_DDP:main',
'nnUNet_predict = nnunet.inference.predict_simple:main',
'nnUNet_ensemble = nnunet.inference.ensemble_predictions:main',
'nnUNet_find_best_configuration = nnunet.evaluation.model_selection.figure_out_what_to_submit:main',
'nnUNet_print_available_pretrained_models = nnunet.inference.pretrained_models.download_pretrained_model:print_available_pretrained_models',
'nnUNet_print_pretrained_model_info = nnunet.inference.pretrained_models.download_pretrained_model:print_pretrained_model_requirements',
'nnUNet_download_pretrained_model = nnunet.inference.pretrained_models.download_pretrained_model:download_by_name',
'nnUNet_download_pretrained_model_by_url = nnunet.inference.pretrained_models.download_pretrained_model:download_by_url',
'nnUNet_determine_postprocessing = nnunet.postprocessing.consolidate_postprocessing_simple:main',
'nnUNet_export_model_to_zip = nnunet.inference.pretrained_models.collect_pretrained_models:export_entry_point',
'nnUNet_install_pretrained_model_from_zip = nnunet.inference.pretrained_models.download_pretrained_model:install_from_zip_entry_point',
'nnUNet_change_trainer_class = nnunet.inference.change_trainer:main',
'nnUNet_evaluate_folder = nnunet.evaluation.evaluator:nnunet_evaluate_folder',
'nnUNet_plot_task_pngs = nnunet.utilities.overlay_plots:entry_point_generate_overlay',
],
},
keywords=['deep learning', 'image segmentation', 'medical image analysis',
'medical image segmentation', 'nnU-Net', 'nnunet']
)
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