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setup.py
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from setuptools import setup, find_packages
import setuptools
from distutils.command.build_ext import build_ext as DistUtilsBuildExt
NAME = 'deepforest'
VERSION = '1.2.7'
DESCRIPTION = 'Tree crown prediction using deep learning retinanets'
URL = 'https://github.com/Weecology/DeepForest'
AUTHOR = 'Ben Weinstein'
LICENCE = 'MIT'
LONG_DESCRIPTION = """
# Deepforest
## Full documentation
[http://deepforest.readthedocs.io/en/latest/](http://deepforest.readthedocs.io/en/latest/)
## Installation
Compiled wheels have been made for linux, osx and windows
```
#Install DeepForest
pip install deepforest
```
## Get in touch
See the [GitHub Repo](https://github.com/Weecology/DeepForest) to see the
source code or submit issues and feature requests.
## Citation
If you use this software in your research please cite it as:
Geographic Generalization in Airborne RGB Deep Learning Tree Detection
Ben. G. Weinstein, Sergio Marconi, Stephanie A. Bohlman, Alina Zare, Ethan P. White
bioRxiv 790071; doi: https://doi.org/10.1101/790071
## Acknowledgments
Development of this software was funded by
[the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative](http://www.moore.org/programs/science/data-driven-discovery) through
[Grant GBMF4563](http://www.moore.org/grants/list/GBMF4563) to Ethan P. White.
"""
setup(name=NAME,
version=VERSION,
python_requires='>3.5',
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
long_description_content_type='text/markdown',
url=URL,
author=AUTHOR,
license=LICENCE,
packages=find_packages(),
include_package_data=True,
install_requires=[
"torch",
"torchvision>=0.13",
"matplotlib",
"Pillow>6.2.0",
"pandas",
"progressbar2",
"six",
"scipy>1.5",
"slidingwindow",
"geopandas",
'rasterio',
'rtree',
"pytorch_lightning",
"tqdm",
"xmltodict",
"numpy",
"imagecodecs",
"albumentations>=1.0.0",
"opencv-python>=4.5.4"
],
zip_safe=False)