From c39966f82fd80d2960a1cc1bcde9a9a8aa168549 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 11 Apr 2023 10:50:58 +0000 Subject: automatic import of python-labelme --- .gitignore | 1 + python-labelme.spec | 726 ++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 728 insertions(+) create mode 100644 python-labelme.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..778f9a7 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/labelme-5.2.0.tar.gz diff --git a/python-labelme.spec b/python-labelme.spec new file mode 100644 index 0000000..6cea43d --- /dev/null +++ b/python-labelme.spec @@ -0,0 +1,726 @@ +%global _empty_manifest_terminate_build 0 +Name: python-labelme +Version: 5.2.0 +Release: 1 +Summary: Image Polygonal Annotation with Python +License: GPLv3 +URL: https://github.com/wkentaro/labelme +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/cf/2b/4a06be33ed86cc5227945b8dabb7d8f9d9c6e854f0de966a601738ceda69/labelme-5.2.0.tar.gz +BuildArch: noarch + + +%description +

+
labelme +

+ +

+ Image Polygonal Annotation with Python +

+ +
+ + + +
+ +
+ Installation | + Usage | + Tutorial | + Examples | + Discussions | + Youtube FAQ +
+ +
+ +
+ +
+ +## Description + +Labelme is a graphical image annotation tool inspired by . +It is written in Python and uses Qt for its graphical interface. + + +VOC dataset example of instance segmentation. + + +Other examples (semantic segmentation, bbox detection, and classification). + + +Various primitives (polygon, rectangle, circle, line, and point). + + +## Features + +- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial)) +- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166)) +- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true)) +- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144)) +- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true), [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)) +- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)) + + + +## Requirements + +- Ubuntu / macOS / Windows +- Python3 +- [PyQt5 / PySide2](http://www.riverbankcomputing.co.uk/software/pyqt/intro) + + +## Installation + +There are options: + +- Platform agnostic installation: [Anaconda](https://github.com/wkentaro/labelme/blob/main/#anaconda) +- Platform specific installation: [Ubuntu](https://github.com/wkentaro/labelme/blob/main/#ubuntu), [macOS](https://github.com/wkentaro/labelme/blob/main/#macos), [Windows](https://github.com/wkentaro/labelme/blob/main/#windows) +- Pre-build binaries from [the release section](https://github.com/wkentaro/labelme/releases) + +### Anaconda + +You need install [Anaconda](https://www.continuum.io/downloads), then run below: + +```bash +# python3 +conda create --name=labelme python=3 +source activate labelme +# conda install -c conda-forge pyside2 +# conda install pyqt +# pip install pyqt5 # pyqt5 can be installed via pip on python3 +pip install labelme +# or you can install everything by conda command +# conda install labelme -c conda-forge +``` + +### Ubuntu + +```bash +sudo apt-get install labelme + +# or +sudo pip3 install labelme + +# or install standalone executable from: +# https://github.com/wkentaro/labelme/releases +``` + +### macOS + +```bash +brew install pyqt # maybe pyqt5 +pip install labelme + +# or +brew install wkentaro/labelme/labelme # command line interface +# brew install --cask wkentaro/labelme/labelme # app + +# or install standalone executable/app from: +# https://github.com/wkentaro/labelme/releases +``` + +### Windows + +Install [Anaconda](https://www.continuum.io/downloads), then in an Anaconda Prompt run: + +```bash +conda create --name=labelme python=3 +conda activate labelme +pip install labelme + +# or install standalone executable/app from: +# https://github.com/wkentaro/labelme/releases +``` + + +## Usage + +Run `labelme --help` for detail. +The annotations are saved as a [JSON](http://www.json.org/) file. + +```bash +labelme # just open gui + +# tutorial (single image example) +cd examples/tutorial +labelme apc2016_obj3.jpg # specify image file +labelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save +labelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file +labelme apc2016_obj3.jpg \ + --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list + +# semantic segmentation example +cd examples/semantic_segmentation +labelme data_annotated/ # Open directory to annotate all images in it +labelme data_annotated/ --labels labels.txt # specify label list with a file +``` + +For more advanced usage, please refer to the examples: + +* [Tutorial (Single Image Example)](https://github.com/wkentaro/labelme/blob/main/examples/tutorial) +* [Semantic Segmentation Example](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true) +* [Instance Segmentation Example](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true) +* [Video Annotation Example](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true) + +### Command Line Arguments +- `--output` specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on. +- The first time you run labelme, it will create a config file in `~/.labelmerc`. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the `--config` flag. +- Without the `--nosortlabels` flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided. +- Flags are assigned to an entire image. [Example](https://github.com/wkentaro/labelme/blob/main/examples/classification?raw=true) +- Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection?raw=true) + +## FAQ + +- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#convert-to-dataset). +- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#how-to-load-label-png-file). +- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true). +- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true). + + +## Developing + +```bash +git clone https://github.com/wkentaro/labelme.git +cd labelme + +# Install anaconda3 and labelme +curl -L https://github.com/wkentaro/dotfiles/raw/main/local/bin/install_anaconda3.sh | bash -s . +source .anaconda3/bin/activate +pip install -e . +``` + + +## How to build standalone executable + +Below shows how to build the standalone executable on macOS, Linux and Windows. + +```bash +# Setup conda +conda create --name labelme python=3.9 +conda activate labelme + +# Build the standalone executable +pip install . +pip install 'matplotlib<3.3' +pip install pyinstaller +pyinstaller labelme.spec +dist/labelme --version +``` + + +## How to contribute + +Make sure below test passes on your environment. +See `.github/workflows/ci.yml` for more detail. + +```bash +pip install -r requirements-dev.txt + +flake8 . +black --line-length 79 --check labelme/ +MPLBACKEND='agg' pytest -vsx tests/ +``` + + +## Acknowledgement + +This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme). + + +%package -n python3-labelme +Summary: Image Polygonal Annotation with Python +Provides: python-labelme +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-labelme +

+
labelme +

+ +

+ Image Polygonal Annotation with Python +

+ +
+ + + +
+ +
+ Installation | + Usage | + Tutorial | + Examples | + Discussions | + Youtube FAQ +
+ +
+ +
+ +
+ +## Description + +Labelme is a graphical image annotation tool inspired by . +It is written in Python and uses Qt for its graphical interface. + + +VOC dataset example of instance segmentation. + + +Other examples (semantic segmentation, bbox detection, and classification). + + +Various primitives (polygon, rectangle, circle, line, and point). + + +## Features + +- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial)) +- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166)) +- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true)) +- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144)) +- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true), [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)) +- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)) + + + +## Requirements + +- Ubuntu / macOS / Windows +- Python3 +- [PyQt5 / PySide2](http://www.riverbankcomputing.co.uk/software/pyqt/intro) + + +## Installation + +There are options: + +- Platform agnostic installation: [Anaconda](https://github.com/wkentaro/labelme/blob/main/#anaconda) +- Platform specific installation: [Ubuntu](https://github.com/wkentaro/labelme/blob/main/#ubuntu), [macOS](https://github.com/wkentaro/labelme/blob/main/#macos), [Windows](https://github.com/wkentaro/labelme/blob/main/#windows) +- Pre-build binaries from [the release section](https://github.com/wkentaro/labelme/releases) + +### Anaconda + +You need install [Anaconda](https://www.continuum.io/downloads), then run below: + +```bash +# python3 +conda create --name=labelme python=3 +source activate labelme +# conda install -c conda-forge pyside2 +# conda install pyqt +# pip install pyqt5 # pyqt5 can be installed via pip on python3 +pip install labelme +# or you can install everything by conda command +# conda install labelme -c conda-forge +``` + +### Ubuntu + +```bash +sudo apt-get install labelme + +# or +sudo pip3 install labelme + +# or install standalone executable from: +# https://github.com/wkentaro/labelme/releases +``` + +### macOS + +```bash +brew install pyqt # maybe pyqt5 +pip install labelme + +# or +brew install wkentaro/labelme/labelme # command line interface +# brew install --cask wkentaro/labelme/labelme # app + +# or install standalone executable/app from: +# https://github.com/wkentaro/labelme/releases +``` + +### Windows + +Install [Anaconda](https://www.continuum.io/downloads), then in an Anaconda Prompt run: + +```bash +conda create --name=labelme python=3 +conda activate labelme +pip install labelme + +# or install standalone executable/app from: +# https://github.com/wkentaro/labelme/releases +``` + + +## Usage + +Run `labelme --help` for detail. +The annotations are saved as a [JSON](http://www.json.org/) file. + +```bash +labelme # just open gui + +# tutorial (single image example) +cd examples/tutorial +labelme apc2016_obj3.jpg # specify image file +labelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save +labelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file +labelme apc2016_obj3.jpg \ + --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list + +# semantic segmentation example +cd examples/semantic_segmentation +labelme data_annotated/ # Open directory to annotate all images in it +labelme data_annotated/ --labels labels.txt # specify label list with a file +``` + +For more advanced usage, please refer to the examples: + +* [Tutorial (Single Image Example)](https://github.com/wkentaro/labelme/blob/main/examples/tutorial) +* [Semantic Segmentation Example](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true) +* [Instance Segmentation Example](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true) +* [Video Annotation Example](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true) + +### Command Line Arguments +- `--output` specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on. +- The first time you run labelme, it will create a config file in `~/.labelmerc`. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the `--config` flag. +- Without the `--nosortlabels` flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided. +- Flags are assigned to an entire image. [Example](https://github.com/wkentaro/labelme/blob/main/examples/classification?raw=true) +- Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection?raw=true) + +## FAQ + +- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#convert-to-dataset). +- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#how-to-load-label-png-file). +- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true). +- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true). + + +## Developing + +```bash +git clone https://github.com/wkentaro/labelme.git +cd labelme + +# Install anaconda3 and labelme +curl -L https://github.com/wkentaro/dotfiles/raw/main/local/bin/install_anaconda3.sh | bash -s . +source .anaconda3/bin/activate +pip install -e . +``` + + +## How to build standalone executable + +Below shows how to build the standalone executable on macOS, Linux and Windows. + +```bash +# Setup conda +conda create --name labelme python=3.9 +conda activate labelme + +# Build the standalone executable +pip install . +pip install 'matplotlib<3.3' +pip install pyinstaller +pyinstaller labelme.spec +dist/labelme --version +``` + + +## How to contribute + +Make sure below test passes on your environment. +See `.github/workflows/ci.yml` for more detail. + +```bash +pip install -r requirements-dev.txt + +flake8 . +black --line-length 79 --check labelme/ +MPLBACKEND='agg' pytest -vsx tests/ +``` + + +## Acknowledgement + +This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme). + + +%package help +Summary: Development documents and examples for labelme +Provides: python3-labelme-doc +%description help +

+
labelme +

+ +

+ Image Polygonal Annotation with Python +

+ +
+ + + +
+ +
+ Installation | + Usage | + Tutorial | + Examples | + Discussions | + Youtube FAQ +
+ +
+ +
+ +
+ +## Description + +Labelme is a graphical image annotation tool inspired by . +It is written in Python and uses Qt for its graphical interface. + + +VOC dataset example of instance segmentation. + + +Other examples (semantic segmentation, bbox detection, and classification). + + +Various primitives (polygon, rectangle, circle, line, and point). + + +## Features + +- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial)) +- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166)) +- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true)) +- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144)) +- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true), [instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)) +- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)) + + + +## Requirements + +- Ubuntu / macOS / Windows +- Python3 +- [PyQt5 / PySide2](http://www.riverbankcomputing.co.uk/software/pyqt/intro) + + +## Installation + +There are options: + +- Platform agnostic installation: [Anaconda](https://github.com/wkentaro/labelme/blob/main/#anaconda) +- Platform specific installation: [Ubuntu](https://github.com/wkentaro/labelme/blob/main/#ubuntu), [macOS](https://github.com/wkentaro/labelme/blob/main/#macos), [Windows](https://github.com/wkentaro/labelme/blob/main/#windows) +- Pre-build binaries from [the release section](https://github.com/wkentaro/labelme/releases) + +### Anaconda + +You need install [Anaconda](https://www.continuum.io/downloads), then run below: + +```bash +# python3 +conda create --name=labelme python=3 +source activate labelme +# conda install -c conda-forge pyside2 +# conda install pyqt +# pip install pyqt5 # pyqt5 can be installed via pip on python3 +pip install labelme +# or you can install everything by conda command +# conda install labelme -c conda-forge +``` + +### Ubuntu + +```bash +sudo apt-get install labelme + +# or +sudo pip3 install labelme + +# or install standalone executable from: +# https://github.com/wkentaro/labelme/releases +``` + +### macOS + +```bash +brew install pyqt # maybe pyqt5 +pip install labelme + +# or +brew install wkentaro/labelme/labelme # command line interface +# brew install --cask wkentaro/labelme/labelme # app + +# or install standalone executable/app from: +# https://github.com/wkentaro/labelme/releases +``` + +### Windows + +Install [Anaconda](https://www.continuum.io/downloads), then in an Anaconda Prompt run: + +```bash +conda create --name=labelme python=3 +conda activate labelme +pip install labelme + +# or install standalone executable/app from: +# https://github.com/wkentaro/labelme/releases +``` + + +## Usage + +Run `labelme --help` for detail. +The annotations are saved as a [JSON](http://www.json.org/) file. + +```bash +labelme # just open gui + +# tutorial (single image example) +cd examples/tutorial +labelme apc2016_obj3.jpg # specify image file +labelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save +labelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file +labelme apc2016_obj3.jpg \ + --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list + +# semantic segmentation example +cd examples/semantic_segmentation +labelme data_annotated/ # Open directory to annotate all images in it +labelme data_annotated/ --labels labels.txt # specify label list with a file +``` + +For more advanced usage, please refer to the examples: + +* [Tutorial (Single Image Example)](https://github.com/wkentaro/labelme/blob/main/examples/tutorial) +* [Semantic Segmentation Example](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true) +* [Instance Segmentation Example](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true) +* [Video Annotation Example](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true) + +### Command Line Arguments +- `--output` specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on. +- The first time you run labelme, it will create a config file in `~/.labelmerc`. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the `--config` flag. +- Without the `--nosortlabels` flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided. +- Flags are assigned to an entire image. [Example](https://github.com/wkentaro/labelme/blob/main/examples/classification?raw=true) +- Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection?raw=true) + +## FAQ + +- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#convert-to-dataset). +- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#how-to-load-label-png-file). +- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true). +- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true). + + +## Developing + +```bash +git clone https://github.com/wkentaro/labelme.git +cd labelme + +# Install anaconda3 and labelme +curl -L https://github.com/wkentaro/dotfiles/raw/main/local/bin/install_anaconda3.sh | bash -s . +source .anaconda3/bin/activate +pip install -e . +``` + + +## How to build standalone executable + +Below shows how to build the standalone executable on macOS, Linux and Windows. + +```bash +# Setup conda +conda create --name labelme python=3.9 +conda activate labelme + +# Build the standalone executable +pip install . +pip install 'matplotlib<3.3' +pip install pyinstaller +pyinstaller labelme.spec +dist/labelme --version +``` + + +## How to contribute + +Make sure below test passes on your environment. +See `.github/workflows/ci.yml` for more detail. + +```bash +pip install -r requirements-dev.txt + +flake8 . +black --line-length 79 --check labelme/ +MPLBACKEND='agg' pytest -vsx tests/ +``` + + +## Acknowledgement + +This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme). + + +%prep +%autosetup -n labelme-5.2.0 + +%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-labelme -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot - 5.2.0-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..557bccf --- /dev/null +++ b/sources @@ -0,0 +1 @@ +1303b0939e109f09b54c89f837d0d242 labelme-5.2.0.tar.gz -- cgit v1.2.3