%global _empty_manifest_terminate_build 0
Name:		python-omnidata-tools
Version:	0.0.23
Release:	1
Summary:	Tooling for Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
License:	omnidata # Add licenses and see current list in `setup.py`
URL:		https://github.com/alexsax/omnidata_tools/tree/main/
Source0:	https://mirrors.aliyun.com/pypi/web/packages/f5/31/2748e589f1ff82f8fa42e340dd9033e04a80190562a2915f4d6b2bdc8315/omnidata_tools-0.0.23.tar.gz
BuildArch:	noarch

Requires:	python3-tqdm
Requires:	python3-fastcore
Requires:	python3-multiprocess
Requires:	python3-aria2p[tui]

%description
# Omnidata Docs
> <strong>Quick links to docs</strong>: [ <a href='/omnidata-tools/pretrained.html'>Pretrained Models</a> ]  [ <a href='/omnidata-tools/starter_dataset.html'>Starter Dataset ]  [ <a href='//omnidata-tools/annotator_usage.html'>Annotator Demo</a> ] 



**This site is intended to be a wiki/documentation site for everything that we open-sourced from the paper.** There are three main folders: the annotator, utilities (dataloaders, download tools, pretrained models, etc), and a code dump of stuff from the paper that is just for reference. 

(Check out the main site for an overview of 'steerable datastes' and the 3D → 2D rendering pipeline).



<br>

#### Download the code
If you want to see and edit the code, then you can clone the github and install with: 

```bash
git clone https://github.com/EPFL-VILAB/omnidata-tools
cd omnidata-tools
pip install -e .    # this will install the python requirements (and also install the CLI)
```
This is probably the best option for you if you want to use the pretrained models, dataloaders, etc in other work.

<br>


#### Install just CLI tools (`omnitools`)
If you are only interested in using the [CLI tools](/omnidata-tools/omnitools.html), you can install them with: `pip install omnidata-tools`. This might be preferable if you only want to quickly download the starter data, or if you just want a simple way to manipulate the vision datasets output by the annotator.

_Note:_ The annotator can also be used with a [docker-based](/omnidata-tools/annotator_usage.html) CLI, but you don't need to use the annotator to use the starter dataset, pretrained models, or training code.


<br>


> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)? 

<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->




%package -n python3-omnidata-tools
Summary:	Tooling for Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
Provides:	python-omnidata-tools
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-omnidata-tools
# Omnidata Docs
> <strong>Quick links to docs</strong>: [ <a href='/omnidata-tools/pretrained.html'>Pretrained Models</a> ]  [ <a href='/omnidata-tools/starter_dataset.html'>Starter Dataset ]  [ <a href='//omnidata-tools/annotator_usage.html'>Annotator Demo</a> ] 



**This site is intended to be a wiki/documentation site for everything that we open-sourced from the paper.** There are three main folders: the annotator, utilities (dataloaders, download tools, pretrained models, etc), and a code dump of stuff from the paper that is just for reference. 

(Check out the main site for an overview of 'steerable datastes' and the 3D → 2D rendering pipeline).



<br>

#### Download the code
If you want to see and edit the code, then you can clone the github and install with: 

```bash
git clone https://github.com/EPFL-VILAB/omnidata-tools
cd omnidata-tools
pip install -e .    # this will install the python requirements (and also install the CLI)
```
This is probably the best option for you if you want to use the pretrained models, dataloaders, etc in other work.

<br>


#### Install just CLI tools (`omnitools`)
If you are only interested in using the [CLI tools](/omnidata-tools/omnitools.html), you can install them with: `pip install omnidata-tools`. This might be preferable if you only want to quickly download the starter data, or if you just want a simple way to manipulate the vision datasets output by the annotator.

_Note:_ The annotator can also be used with a [docker-based](/omnidata-tools/annotator_usage.html) CLI, but you don't need to use the annotator to use the starter dataset, pretrained models, or training code.


<br>


> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)? 

<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->




%package help
Summary:	Development documents and examples for omnidata-tools
Provides:	python3-omnidata-tools-doc
%description help
# Omnidata Docs
> <strong>Quick links to docs</strong>: [ <a href='/omnidata-tools/pretrained.html'>Pretrained Models</a> ]  [ <a href='/omnidata-tools/starter_dataset.html'>Starter Dataset ]  [ <a href='//omnidata-tools/annotator_usage.html'>Annotator Demo</a> ] 



**This site is intended to be a wiki/documentation site for everything that we open-sourced from the paper.** There are three main folders: the annotator, utilities (dataloaders, download tools, pretrained models, etc), and a code dump of stuff from the paper that is just for reference. 

(Check out the main site for an overview of 'steerable datastes' and the 3D → 2D rendering pipeline).



<br>

#### Download the code
If you want to see and edit the code, then you can clone the github and install with: 

```bash
git clone https://github.com/EPFL-VILAB/omnidata-tools
cd omnidata-tools
pip install -e .    # this will install the python requirements (and also install the CLI)
```
This is probably the best option for you if you want to use the pretrained models, dataloaders, etc in other work.

<br>


#### Install just CLI tools (`omnitools`)
If you are only interested in using the [CLI tools](/omnidata-tools/omnitools.html), you can install them with: `pip install omnidata-tools`. This might be preferable if you only want to quickly download the starter data, or if you just want a simple way to manipulate the vision datasets output by the annotator.

_Note:_ The annotator can also be used with a [docker-based](/omnidata-tools/annotator_usage.html) CLI, but you don't need to use the annotator to use the starter dataset, pretrained models, or training code.


<br>


> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)? 

<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->




%prep
%autosetup -n omnidata_tools-0.0.23

%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-omnidata-tools -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.23-1
- Package Spec generated