<h1 align="center">
<img src="https://github.com/wkentaro/labelme/blob/main/labelme/icons/icon.png?raw=true"><br/>labelme-ytu
</h1>
<h4 align="center">
Image Polygonal Annotation with Python
</h4>
<div align="center">
<a href="https://pypi.python.org/pypi/labelme"><img src="https://img.shields.io/pypi/v/labelme.svg"></a>
<a href="https://pypi.org/project/labelme"><img src="https://img.shields.io/pypi/pyversions/labelme.svg"></a>
<a href="https://github.com/wkentaro/labelme/actions"><img src="https://github.com/wkentaro/labelme/workflows/ci/badge.svg?branch=main&event=push"></a>
</div>
<div align="center">
<a href="https://github.com/wkentaro/labelme/blob/main/#starter-guide"><b>Starter Guide</b></a>
| <a href="https://github.com/wkentaro/labelme/blob/main/#installation?raw=true"><b>Installation</b></a>
| <a href="https://github.com/wkentaro/labelme/blob/main/#usage"><b>Usage</b></a>
| <a href="https://github.com/wkentaro/labelme/blob/main/#examples"><b>Examples</b></a>
| <a href="https://x.com/labelmeai"><b>X/Twitter</b></a>
<!-- | <a href="https://github.com/wkentaro/labelme/discussions"><b>Community</b></a> -->
<!-- | <a href="https://www.youtube.com/playlist?list=PLI6LvFw0iflh3o33YYnVIfOpaO0hc5Dzw"><b>Youtube FAQ</b></a> -->
</div>
<br/>
<div align="center">
<img src="https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/.readme/annotation.jpg?raw=true" width="70%">
</div>
## Description
Labelme is a graphical image annotation tool inspired by <http://labelme.csail.mit.edu>.
It is written in Python and uses Qt for its graphical interface.
<img src="https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg?raw=true" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg" width="19%" />
<i>VOC dataset example of instance segmentation.</i>
<img src="https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation/.readme/annotation.jpg?raw=true" width="30%" /> <img src="examples/bbox_detection/.readme/annotation.jpg" width="30%" /> <img src="examples/classification/.readme/annotation_cat.jpg" width="35%" />
<i>Other examples (semantic segmentation, bbox detection, and classification).</i>
<img src="https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png" width="32%" />
<i>Various primitives (polygon, rectangle, circle, line, and point).</i>
## 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))
## Starter Guide
If you're new to Labelme, you can get started with [Labelme Starter Guide](https://labelme.gumroad.com/l/starter-guide) (FREE), which contains:
- **Installation guides** for all platforms: Windows, macOS, and Linux 💻
- **Step-by-step tutorials**: first annotation to editing, exporting, and integrating with other programs 📕
- **A compilation of valuable resources** for further exploration 🔗.
## 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
```
### 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).
## Examples
* [Image Classification](https://github.com/wkentaro/labelme/blob/main/examples/classification?raw=true)
* [Bounding Box Detection](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection?raw=true)
* [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)
* [Video Annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true)
## How to develop
```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
ruff format --check # `ruff format` to auto-fix
ruff check # `ruff check --fix` to auto-fix
MPLBACKEND='agg' pytest -vsx tests/
```
## Acknowledgement
This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme).
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"description": "<h1 align=\"center\">\r\n <img src=\"https://github.com/wkentaro/labelme/blob/main/labelme/icons/icon.png?raw=true\"><br/>labelme-ytu\r\n</h1>\r\n\r\n<h4 align=\"center\">\r\n Image Polygonal Annotation with Python\r\n</h4>\r\n\r\n<div align=\"center\">\r\n <a href=\"https://pypi.python.org/pypi/labelme\"><img src=\"https://img.shields.io/pypi/v/labelme.svg\"></a>\r\n <a href=\"https://pypi.org/project/labelme\"><img src=\"https://img.shields.io/pypi/pyversions/labelme.svg\"></a>\r\n <a href=\"https://github.com/wkentaro/labelme/actions\"><img src=\"https://github.com/wkentaro/labelme/workflows/ci/badge.svg?branch=main&event=push\"></a>\r\n</div>\r\n\r\n<div align=\"center\">\r\n <a href=\"https://github.com/wkentaro/labelme/blob/main/#starter-guide\"><b>Starter Guide</b></a>\r\n | <a href=\"https://github.com/wkentaro/labelme/blob/main/#installation?raw=true\"><b>Installation</b></a>\r\n | <a href=\"https://github.com/wkentaro/labelme/blob/main/#usage\"><b>Usage</b></a>\r\n | <a href=\"https://github.com/wkentaro/labelme/blob/main/#examples\"><b>Examples</b></a>\r\n | <a href=\"https://x.com/labelmeai\"><b>X/Twitter</b></a>\r\n <!-- | <a href=\"https://github.com/wkentaro/labelme/discussions\"><b>Community</b></a> -->\r\n <!-- | <a href=\"https://www.youtube.com/playlist?list=PLI6LvFw0iflh3o33YYnVIfOpaO0hc5Dzw\"><b>Youtube FAQ</b></a> -->\r\n</div>\r\n\r\n<br/>\r\n\r\n<div align=\"center\">\r\n <img src=\"https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/.readme/annotation.jpg?raw=true\" width=\"70%\">\r\n</div>\r\n\r\n## Description\r\n\r\nLabelme is a graphical image annotation tool inspired by <http://labelme.csail.mit.edu>. \r\nIt is written in Python and uses Qt for its graphical interface.\r\n\r\n<img src=\"https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg?raw=true\" width=\"19%\" /> <img src=\"examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png\" width=\"19%\" /> <img src=\"examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg\" width=\"19%\" /> <img src=\"examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png\" width=\"19%\" /> <img src=\"examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg\" width=\"19%\" /> \r\n<i>VOC dataset example of instance segmentation.</i>\r\n\r\n<img src=\"https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation/.readme/annotation.jpg?raw=true\" width=\"30%\" /> <img src=\"examples/bbox_detection/.readme/annotation.jpg\" width=\"30%\" /> <img src=\"examples/classification/.readme/annotation_cat.jpg\" width=\"35%\" /> \r\n<i>Other examples (semantic segmentation, bbox detection, and classification).</i>\r\n\r\n<img src=\"https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif\" width=\"30%\" /> <img src=\"https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif\" width=\"30%\" /> <img src=\"https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png\" width=\"32%\" /> \r\n<i>Various primitives (polygon, rectangle, circle, line, and point).</i>\r\n\r\n\r\n## Features\r\n\r\n- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial))\r\n- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))\r\n- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true))\r\n- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144))\r\n- [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))\r\n- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true))\r\n\r\n\r\n## Starter Guide\r\n\r\nIf you're new to Labelme, you can get started with [Labelme Starter Guide](https://labelme.gumroad.com/l/starter-guide) (FREE), which contains:\r\n\r\n- **Installation guides** for all platforms: Windows, macOS, and Linux \u011f\u0178\u2019\u00bb\r\n- **Step-by-step tutorials**: first annotation to editing, exporting, and integrating with other programs \u011f\u0178\u201c\u2022\r\n- **A compilation of valuable resources** for further exploration \u011f\u0178\u201d\u2014.\r\n\r\n\r\n## Installation\r\n\r\nThere are options:\r\n\r\n- Platform agnostic installation: [Anaconda](https://github.com/wkentaro/labelme/blob/main/#anaconda)\r\n- 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)\r\n- Pre-build binaries from [the release section](https://github.com/wkentaro/labelme/releases)\r\n\r\n### Anaconda\r\n\r\nYou need install [Anaconda](https://www.continuum.io/downloads), then run below:\r\n\r\n```bash\r\n# python3\r\nconda create --name=labelme python=3\r\nsource activate labelme\r\n# conda install -c conda-forge pyside2\r\n# conda install pyqt\r\n# pip install pyqt5 # pyqt5 can be installed via pip on python3\r\npip install labelme\r\n# or you can install everything by conda command\r\n# conda install labelme -c conda-forge\r\n```\r\n\r\n### Ubuntu\r\n\r\n```bash\r\nsudo apt-get install labelme\r\n\r\n# or\r\nsudo pip3 install labelme\r\n\r\n# or install standalone executable from:\r\n# https://github.com/wkentaro/labelme/releases\r\n```\r\n\r\n### macOS\r\n\r\n```bash\r\nbrew install pyqt # maybe pyqt5\r\npip install labelme\r\n\r\n# or\r\nbrew install wkentaro/labelme/labelme # command line interface\r\n# brew install --cask wkentaro/labelme/labelme # app\r\n\r\n# or install standalone executable/app from:\r\n# https://github.com/wkentaro/labelme/releases\r\n```\r\n\r\n### Windows\r\n\r\nInstall [Anaconda](https://www.continuum.io/downloads), then in an Anaconda Prompt run:\r\n\r\n```bash\r\nconda create --name=labelme python=3\r\nconda activate labelme\r\npip install labelme\r\n\r\n# or install standalone executable/app from:\r\n# https://github.com/wkentaro/labelme/releases\r\n```\r\n\r\n\r\n## Usage\r\n\r\nRun `labelme --help` for detail. \r\nThe annotations are saved as a [JSON](http://www.json.org/) file.\r\n\r\n```bash\r\nlabelme # just open gui\r\n\r\n# tutorial (single image example)\r\ncd examples/tutorial\r\nlabelme apc2016_obj3.jpg # specify image file\r\nlabelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save\r\nlabelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file\r\nlabelme apc2016_obj3.jpg \\\r\n --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list\r\n\r\n# semantic segmentation example\r\ncd examples/semantic_segmentation\r\nlabelme data_annotated/ # Open directory to annotate all images in it\r\nlabelme data_annotated/ --labels labels.txt # specify label list with a file\r\n```\r\n\r\n### Command Line Arguments\r\n- `--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.\r\n- 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.\r\n- 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.\r\n- Flags are assigned to an entire image. [Example](https://github.com/wkentaro/labelme/blob/main/examples/classification?raw=true)\r\n- Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection?raw=true)\r\n\r\n### FAQ\r\n\r\n- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#convert-to-dataset).\r\n- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/main/examples/tutorial#how-to-load-label-png-file).\r\n- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true).\r\n- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true).\r\n\r\n\r\n## Examples\r\n\r\n* [Image Classification](https://github.com/wkentaro/labelme/blob/main/examples/classification?raw=true)\r\n* [Bounding Box Detection](https://github.com/wkentaro/labelme/blob/main/examples/bbox_detection?raw=true)\r\n* [Semantic Segmentation](https://github.com/wkentaro/labelme/blob/main/examples/semantic_segmentation?raw=true)\r\n* [Instance Segmentation](https://github.com/wkentaro/labelme/blob/main/examples/instance_segmentation?raw=true)\r\n* [Video Annotation](https://github.com/wkentaro/labelme/blob/main/examples/video_annotation?raw=true)\r\n\r\n## How to develop\r\n\r\n```bash\r\ngit clone https://github.com/wkentaro/labelme.git\r\ncd labelme\r\n\r\n# Install anaconda3 and labelme\r\ncurl -L https://github.com/wkentaro/dotfiles/raw/main/local/bin/install_anaconda3.sh | bash -s .\r\nsource .anaconda3/bin/activate\r\npip install -e .\r\n```\r\n\r\n\r\n### How to build standalone executable\r\n\r\nBelow shows how to build the standalone executable on macOS, Linux and Windows. \r\n\r\n```bash\r\n# Setup conda\r\nconda create --name labelme python=3.9\r\nconda activate labelme\r\n\r\n# Build the standalone executable\r\npip install .\r\npip install 'matplotlib<3.3'\r\npip install pyinstaller\r\npyinstaller labelme.spec\r\ndist/labelme --version\r\n```\r\n\r\n\r\n### How to contribute\r\n\r\nMake sure below test passes on your environment. \r\nSee `.github/workflows/ci.yml` for more detail.\r\n\r\n```bash\r\npip install -r requirements-dev.txt\r\n\r\nruff format --check # `ruff format` to auto-fix\r\nruff check # `ruff check --fix` to auto-fix\r\nMPLBACKEND='agg' pytest -vsx tests/\r\n```\r\n\r\n\r\n## Acknowledgement\r\n\r\nThis repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme).\r\n",
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"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "labelme-ytu"
}
JSON
