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|
%global _empty_manifest_terminate_build 0
Name: python-BRAILS
Version: 3.0.1
Release: 1
Summary: Building Recognition Using AI at Large-Scale
License: BSD 3-Clause
URL: https://github.com/NHERI-SimCenter/BRAILS
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f8/0c/9f3c76990a0ab965337435f2b2d57d202d880a5731b6f54eb477a28c5b87/BRAILS-3.0.1.tar.gz
BuildArch: noarch
%description
## What is BRAILS?
BRAILS (Building Recognition using AI at Large-Scale) provides a set of Python modules that utilize deep learning (DL), and computer vision (CV) techniques to extract information from satellite and street level images. The BRAILS framework also provides turn-key applications allowing users to put individual modules together to determine multiple attributes in a single pass or train general-purpose image classification, object detection, or semantic segmentation models.
## Documentation
Online documentation is available at <a href="https://nheri-simcenter.github.io/BRAILS-Documentation/index.html">https://nheri-simcenter.github.io/BRAILS-Documentation</a>.
## Quickstart
### Installation
The easiest way to install the latest version of BRAILS is using ``pip``:
```
pip install git+https://github.com/NHERI-SimCenter/BRAILS
```
### Example: InventoryGenerator Workflow
This example demonstrates how to use the ``InventoryGenerator`` method embedded in BRAILS to generate regional-level inventories.
The primary input to ``InventoryGenerator`` is location. ``InventoryGenerator`` accepts four different location input: 1) region name, 2) list of region names, 3) bounding box of a region, 4) A GeoJSON file containing building footprints.
Please note that you will need a Google API Key to run ``InventoryGenerator``.
```python
#import InventoryGenerator:
from brails.InventoryGenerator import InventoryGenerator
# Initialize InventoryGenerator:
invGenerator = InventoryGenerator(location='Berkeley, CA',
nbldgs=100, randomSelection=True,
GoogleAPIKey="")
# Run InventoryGenerator to generate an inventory for the entered location:
# To run InventoryGenerator for all enabled attributes set attributes='all':
invGenerator.generate(attributes=['numstories','roofshape','buildingheight'])
# View generated inventory:
invGenerator.inventory
```
## Acknowledgements
This work is based on material supported by the National Science Foundation under grants CMMI 1612843 and CMMI 2131111.
## Contact
NHERI-SimCenter nheri-simcenter@berkeley.edu
## How to cite
```
@software{cetiner_2022_7132010,
author = {Barbaros Cetiner and
Charles Wang and
Frank McKenna and
Sascha Hornauer and
Yunhui Guo},
title = {BRAILS Release v3.0.0},
month = sep,
year = 2022,
note = {{This work is based on material supported by the
National Science Foundation under grants CMMI
1612843 and CMMI 2131111}},
publisher = {Zenodo},
version = {v3.0.0},
doi = {10.5281/zenodo.7132010},
url = {https://doi.org/10.5281/zenodo.7132010}
}
```
%package -n python3-BRAILS
Summary: Building Recognition Using AI at Large-Scale
Provides: python-BRAILS
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-BRAILS
## What is BRAILS?
BRAILS (Building Recognition using AI at Large-Scale) provides a set of Python modules that utilize deep learning (DL), and computer vision (CV) techniques to extract information from satellite and street level images. The BRAILS framework also provides turn-key applications allowing users to put individual modules together to determine multiple attributes in a single pass or train general-purpose image classification, object detection, or semantic segmentation models.
## Documentation
Online documentation is available at <a href="https://nheri-simcenter.github.io/BRAILS-Documentation/index.html">https://nheri-simcenter.github.io/BRAILS-Documentation</a>.
## Quickstart
### Installation
The easiest way to install the latest version of BRAILS is using ``pip``:
```
pip install git+https://github.com/NHERI-SimCenter/BRAILS
```
### Example: InventoryGenerator Workflow
This example demonstrates how to use the ``InventoryGenerator`` method embedded in BRAILS to generate regional-level inventories.
The primary input to ``InventoryGenerator`` is location. ``InventoryGenerator`` accepts four different location input: 1) region name, 2) list of region names, 3) bounding box of a region, 4) A GeoJSON file containing building footprints.
Please note that you will need a Google API Key to run ``InventoryGenerator``.
```python
#import InventoryGenerator:
from brails.InventoryGenerator import InventoryGenerator
# Initialize InventoryGenerator:
invGenerator = InventoryGenerator(location='Berkeley, CA',
nbldgs=100, randomSelection=True,
GoogleAPIKey="")
# Run InventoryGenerator to generate an inventory for the entered location:
# To run InventoryGenerator for all enabled attributes set attributes='all':
invGenerator.generate(attributes=['numstories','roofshape','buildingheight'])
# View generated inventory:
invGenerator.inventory
```
## Acknowledgements
This work is based on material supported by the National Science Foundation under grants CMMI 1612843 and CMMI 2131111.
## Contact
NHERI-SimCenter nheri-simcenter@berkeley.edu
## How to cite
```
@software{cetiner_2022_7132010,
author = {Barbaros Cetiner and
Charles Wang and
Frank McKenna and
Sascha Hornauer and
Yunhui Guo},
title = {BRAILS Release v3.0.0},
month = sep,
year = 2022,
note = {{This work is based on material supported by the
National Science Foundation under grants CMMI
1612843 and CMMI 2131111}},
publisher = {Zenodo},
version = {v3.0.0},
doi = {10.5281/zenodo.7132010},
url = {https://doi.org/10.5281/zenodo.7132010}
}
```
%package help
Summary: Development documents and examples for BRAILS
Provides: python3-BRAILS-doc
%description help
## What is BRAILS?
BRAILS (Building Recognition using AI at Large-Scale) provides a set of Python modules that utilize deep learning (DL), and computer vision (CV) techniques to extract information from satellite and street level images. The BRAILS framework also provides turn-key applications allowing users to put individual modules together to determine multiple attributes in a single pass or train general-purpose image classification, object detection, or semantic segmentation models.
## Documentation
Online documentation is available at <a href="https://nheri-simcenter.github.io/BRAILS-Documentation/index.html">https://nheri-simcenter.github.io/BRAILS-Documentation</a>.
## Quickstart
### Installation
The easiest way to install the latest version of BRAILS is using ``pip``:
```
pip install git+https://github.com/NHERI-SimCenter/BRAILS
```
### Example: InventoryGenerator Workflow
This example demonstrates how to use the ``InventoryGenerator`` method embedded in BRAILS to generate regional-level inventories.
The primary input to ``InventoryGenerator`` is location. ``InventoryGenerator`` accepts four different location input: 1) region name, 2) list of region names, 3) bounding box of a region, 4) A GeoJSON file containing building footprints.
Please note that you will need a Google API Key to run ``InventoryGenerator``.
```python
#import InventoryGenerator:
from brails.InventoryGenerator import InventoryGenerator
# Initialize InventoryGenerator:
invGenerator = InventoryGenerator(location='Berkeley, CA',
nbldgs=100, randomSelection=True,
GoogleAPIKey="")
# Run InventoryGenerator to generate an inventory for the entered location:
# To run InventoryGenerator for all enabled attributes set attributes='all':
invGenerator.generate(attributes=['numstories','roofshape','buildingheight'])
# View generated inventory:
invGenerator.inventory
```
## Acknowledgements
This work is based on material supported by the National Science Foundation under grants CMMI 1612843 and CMMI 2131111.
## Contact
NHERI-SimCenter nheri-simcenter@berkeley.edu
## How to cite
```
@software{cetiner_2022_7132010,
author = {Barbaros Cetiner and
Charles Wang and
Frank McKenna and
Sascha Hornauer and
Yunhui Guo},
title = {BRAILS Release v3.0.0},
month = sep,
year = 2022,
note = {{This work is based on material supported by the
National Science Foundation under grants CMMI
1612843 and CMMI 2131111}},
publisher = {Zenodo},
version = {v3.0.0},
doi = {10.5281/zenodo.7132010},
url = {https://doi.org/10.5281/zenodo.7132010}
}
```
%prep
%autosetup -n BRAILS-3.0.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-BRAILS -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.1-1
- Package Spec generated
|
