labelme
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
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
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.post4
%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
* Sun Apr 23 2023 Python_Bot