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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='0.1.7',
author='BreezeWhite',
author_email='miyasihta2010@tuta.io',
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'
]
)