| 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'] | |
| ) | |