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setup.py
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setup.py
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import sys
import setuptools
with open("README.md") as red:
ldest = red.read()
if sys.platform == "darwin":
onnx_package = "onnxruntime"
else:
onnx_package = "onnxruntime-gpu"
setuptools.setup(
name="oemer",
version="0.1.8",
author="BreezeWhite",
author_email="[email protected]",
description="End-to-end Optical Music Recognition (OMR) system.",
long_description=ldest,
long_description_content_type="text/markdown",
license="License :: OSI Approved :: MIT License",
license_files=("LICENSE",),
url="https://github.com/BreezeWhite/oemer",
packages=setuptools.find_packages(),
package_data={
"": [
"sklearn_models/*.model",
"checkpoints/unet_big/metadata.pkl",
"checkpoints/unet_big/arch.json",
"checkpoints/seg_net/metadata.pkl",
"checkpoints/seg_net/arch.json",
]
},
install_requires=[
onnx_package,
"opencv-python-headless>=4.5.3.56",
"matplotlib",
"pillow",
"scipy",
"scikit-learn>=1.2",
"types-Pillow",
"types-tensorflow",
"typing-extensions",
],
extras_require={
"tf": ["tensorflow", "tf2onnx"],
},
entry_points={"console_scripts": ["oemer = oemer.ete:main"]},
keywords=[
"OMR",
"optical-music-recognition",
"AI",
"machine-learning",
"image-processing",
],
classifiers=[
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Information Technology",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Topic :: Education",
"Topic :: Multimedia :: Graphics",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Image Processing",
"Topic :: Scientific/Engineering :: Image Recognition",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Software Development :: Version Control :: Git",
],
)