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setup.py
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setup.py
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from setuptools import setup, find_namespace_packages
setup(name='nnunetv2',
packages=find_namespace_packages(include=["nnunetv2", "nnunetv2.*"]),
version='2.0',
description='nnU-Net. Framework for out-of-the box biomedical image segmentation.',
url='https://github.com/MIC-DKFZ/nnUNet',
author='Helmholtz Imaging Applied Computer Vision Lab, Division of Medical Image Computing, German Cancer Research Center',
author_email='[email protected]',
license='Apache License Version 2.0, January 2004',
install_requires=[
"torch>1.12",
"acvl-utils>=0.2",
"dynamic-network-architectures>=0.2",
"tqdm",
"dicom2nifti",
"scikit-image>=0.14",
"medpy",
"scipy",
"batchgenerators>=0.25",
"numpy",
"scikit-learn",
"scikit-image>=0.19.3",
"SimpleITK>=2.2.1",
"pandas",
"graphviz",
'tifffile',
'requests',
"nibabel",
"matplotlib",
"seaborn",
"imagecodecs",
"yacs"
],
entry_points={
'console_scripts': [
'nnUNetv2_plan_and_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:plan_and_preprocess_entry', # api available
'nnUNetv2_extract_fingerprint = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:extract_fingerprint_entry', # api available
'nnUNetv2_plan_experiment = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:preprocess_entry', # api available
'nnUNetv2_preprocess = nnunetv2.experiment_planning.plan_and_preprocess_entrypoints:preprocess_entry', # api available
'nnUNetv2_train = nnunetv2.run.run_training:run_training_entry', # api available
'nnUNetv2_predict_from_modelfolder = nnunetv2.inference.predict_from_raw_data:predict_entry_point_modelfolder', # api available
'nnUNetv2_predict = nnunetv2.inference.predict_from_raw_data:predict_entry_point', # api available
'nnUNetv2_convert_old_nnUNet_dataset = nnunetv2.dataset_conversion.convert_raw_dataset_from_old_nnunet_format:convert_entry_point', # api available
'nnUNetv2_find_best_configuration = nnunetv2.evaluation.find_best_configuration:find_best_configuration_entry_point', # api available
'nnUNetv2_determine_postprocessing = nnunetv2.postprocessing.remove_connected_components:entry_point_determine_postprocessing_folder', # api available
'nnUNetv2_apply_postprocessing = nnunetv2.postprocessing.remove_connected_components:entry_point_apply_postprocessing', # api available
'nnUNetv2_ensemble = nnunetv2.ensembling.ensemble:entry_point_ensemble_folders', # api available
'nnUNetv2_accumulate_crossval_results = nnunetv2.evaluation.find_best_configuration:accumulate_crossval_results_entry_point', # api available
'nnUNetv2_plot_overlay_pngs = nnunetv2.utilities.overlay_plots:entry_point_generate_overlay', # api available
'nnUNetv2_download_pretrained_model_by_url = nnunetv2.model_sharing.entry_points:download_by_url', # api available
'nnUNetv2_install_pretrained_model_from_zip = nnunetv2.model_sharing.entry_points:install_from_zip_entry_point', # api available
'nnUNetv2_export_model_to_zip = nnunetv2.model_sharing.entry_points:export_pretrained_model_entry', # api available
'nnUNetv2_move_plans_between_datasets = nnunetv2.experiment_planning.plans_for_pretraining.move_plans_between_datasets:entry_point_move_plans_between_datasets', # api available
'nnUNetv2_evaluate_folder = nnunetv2.evaluation.evaluate_predictions:evaluate_folder_entry_point', # api available
'nnUNetv2_evaluate_simple = nnunetv2.evaluation.evaluate_predictions:evaluate_simple_entry_point', # api available
'nnUNetv2_convert_MSD_dataset = nnunetv2.dataset_conversion.convert_MSD_dataset:entry_point' # api available
],
},
keywords=['deep learning', 'image segmentation', 'medical image analysis',
'medical image segmentation', 'nnU-Net', 'nnunet']
)