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setup.py
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setup.py
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"""
Setup of SD Mask RCNN codebase
Author: Mike Danielczuk
"""
import os
from setuptools import setup
root_dir = os.path.dirname(os.path.realpath(__file__))
# load __version__
version_file = 'sd_maskrcnn/version.py'
exec(open(version_file).read())
# load README.md as long_description
long_description = ''
if os.path.exists('README.md'):
with open('README.md', 'r') as f:
long_description = f.read()
setup_requirements = [
'Cython',
'numpy'
]
requirements = [
'pycocotools>=2.0', # For benchmarking
'scikit-image>=0.14.2', # For image loading
'keras>=2.2<2.3', # For training
'tqdm', # For pretty progress bars
'matplotlib', # For visualization of results
'h5py<3.0.0', # Loading pretrained models
'autolab_core>=1.1.0', # For core utilities
'nvidia-tensorflow', # For training - need TF 1.15 so use nvidia
f'mask-rcnn @ file://localhost{root_dir}/maskrcnn'
# Underlying Mask RCNN model
]
generation_requirements = [
'gym>=0.11', # For sampling heaps
'pyglet==1.4.0b1', # For pyrender
'pyrender>=0.1.23', # For rendering images
'pybullet', # For dynamic sim
'trimesh[easy]', # For mesh loading/exporting
'scipy' # For random vars
]
setup(
name='sd_maskrcnn',
version=__version__,
description='SD Mask RCNN project code',
long_description=long_description,
long_description_content_type='text/markdown',
author='Michael Danielczuk',
author_email='mdanielczuk@berkeley.edu',
license='MIT',
url='http://github.com/BerkeleyAutomation/sd-maskrcnn',
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
'Natural Language :: English',
'Topic :: Scientific/Engineering'
],
packages=['sd_maskrcnn', 'sd_maskrcnn.envs'],
package_data={'sd_maskrcnn': ['data/plane/*', 'data/bin/*']},
setup_requires=setup_requirements,
install_requires=requirements,
extras_require={
'generation': generation_requirements
}
)