MOOSE/pyproject.toml at main · ENHANCE-PET/MOOSE · GitHub

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[build-system]
requires = ["setuptools>=64", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "moosez"
version = "3.1.6"
description = "An AI-inference engine for 3D clinical and preclinical whole-body segmentation tasks"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "Apache-2.0" }

authors = [
  { name = "Lalith Kumar Shiyam Sundar", email = "Lalith.shiyam@med.uni-muenchen.de" },
  { name = "Sebastian Gutschmayer", email = "sebastian.gutschmayer@meduniwien.ac.at"  },
  { name = "Manuel Pires", email = "manuel.pires@meduniwien.ac.at"  },
]

keywords = [
  "moosez", "model-zoo", "nnUNet", "medical-imaging",
  "tumor-segmentation", "organ-segmentation", "bone-segmentation",
  "lung-segmentation", "muscle-segmentation", "fat-segmentation",
  "vessel-segmentation", "vertebral-segmentation", "rib-segmentation",
  "preclinical-segmentation", "clinical-segmentation",
]

classifiers = [
  "Development Status :: 4 - Beta",
  "Intended Audience :: Developers",
  "Intended Audience :: Healthcare Industry",
  "License :: OSI Approved :: Apache Software License",
  "Programming Language :: Python :: 3 :: Only",
  "Programming Language :: Python :: 3.10",
  "Topic :: Scientific/Engineering :: Medical Science Apps.",
]

dependencies = [
  "torch",
  "SimpleITK",
  "nnunetv2>=2.6.0",
  "halo",
  "pydicom",
  "numpy",
  "pyfiglet",
  "natsort",
  "colorama",
  "dask",
  "rich",
  "pandas",
  "dicom2nifti",
  "emoji",
  "matplotlib",
  "psutil",
  "nibabel"
]

[project.scripts]
moosez = "moosez.moosez:main"

[tool.setuptools.packages.find]
where = ["."]
include = ["moosez*"]