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diff --git a/python-brails.spec b/python-brails.spec new file mode 100644 index 0000000..938dd2c --- /dev/null +++ b/python-brails.spec @@ -0,0 +1,306 @@ +%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 |