%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 https://nheri-simcenter.github.io/BRAILS-Documentation. ## 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 https://nheri-simcenter.github.io/BRAILS-Documentation. ## 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 https://nheri-simcenter.github.io/BRAILS-Documentation. ## 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 - 3.0.1-1 - Package Spec generated