![](\"https://github.com/wkentaro/labelme/blob/master/labelme/icons/icon.png?raw=true\"){kind=icon}
labelme\n
![](\"https://img.shields.io/pypi/v/labelme.svg\")
\n ![](\"https://img.shields.io/pypi/pyversions/labelme.svg\")
\n ![](\"https://github.com/wkentaro/labelme/workflows/CI/badge.svg\"){kind=icon}
\n ![](\"https://img.shields.io/docker/build/wkentaro/labelme.svg\")
\n![](\"https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/.readme/annotation.jpg?raw=true\")
\n![](\"https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg?raw=true\")
![](\"https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationClassPNG/2011_000006.png?raw=true\")
![](\"https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg?raw=true\")
![](\"https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationObjectPNG/2011_000006.png?raw=true\")
![](\"https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg?raw=true\")
\nVOC dataset example of instance segmentation.\n\n![](\"https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation/.readme/annotation.jpg?raw=true\")
![](\"https://github.com/wkentaro/labelme/blob/master/examples/bbox_detection/.readme/annotation.jpg?raw=true\")
![](\"https://github.com/wkentaro/labelme/blob/master/examples/classification/.readme/annotation_cat.jpg?raw=true\")
\nOther examples (semantic segmentation, bbox detection, and classification).\n\n![](\"https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif\")
![](\"https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif\")
![](\"https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png\")
\nVarious primitives (polygon, rectangle, circle, line, and point).\n\n\n## Features\n\n- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial))\n- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))\n- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/master/examples/video_annotation?raw=true))\n- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144))\n- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation?raw=true), [instance segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true))\n- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true))\n\n\n\n## Requirements\n\n- Ubuntu / macOS / Windows\n- Python2 / Python3\n- [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) / [PySide2](https://wiki.qt.io/PySide2_GettingStarted)\n\n\n## Installation\n\nThere are options:\n\n- Platform agonistic installation: [Anaconda](https://github.com/wkentaro/labelme/blob/master/#anaconda), [Docker](https://github.com/wkentaro/labelme/blob/master/#docker)\n- Platform specific installation: [Ubuntu](https://github.com/wkentaro/labelme/blob/master/#ubuntu), [macOS](https://github.com/wkentaro/labelme/blob/master/#macos), [Windows](https://github.com/wkentaro/labelme/blob/master/#windows)\n\n### Anaconda\n\nYou need install [Anaconda](https://www.continuum.io/downloads), then run below:\n\n```bash\n# python2\nconda create --name=labelme python=2.7\nsource activate labelme\n# conda install -c conda-forge pyside2\nconda install pyqt\npip install labelme\n# if you'd like to use the latest version. run below:\n# pip install git+https://github.com/wkentaro/labelme.git\n\n# python3\nconda create --name=labelme python=3.6\nsource activate labelme\n# conda install -c conda-forge pyside2\n# conda install pyqt\n# pip install pyqt5 # pyqt5 can be installed via pip on python3\npip install labelme\n# or you can install everything by conda command\n# conda install labelme -c conda-forge\n```\n\n### Docker\n\nYou need install [docker](https://www.docker.com), then run below:\n\n```bash\nwget https://raw.githubusercontent.com/wkentaro/labelme/master/labelme/cli/on_docker.py -O labelme_on_docker\nchmod u+x labelme_on_docker\n\n# Maybe you need http://sourabhbajaj.com/blog/2017/02/07/gui-applications-docker-mac/ on macOS\n./labelme_on_docker examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json\n./labelme_on_docker examples/semantic_segmentation/data_annotated\n```\n\n### Ubuntu\n\n```bash\n# Ubuntu 14.04 / Ubuntu 16.04\n# Python2\n# sudo apt-get install python-qt4 # PyQt4\nsudo apt-get install python-pyqt5 # PyQt5\nsudo pip install labelme\n# Python3\nsudo apt-get install python3-pyqt5 # PyQt5\nsudo pip3 install labelme\n```\n\n### Ubuntu 19.10+ / Debian (sid)\n\n```bash\nsudo apt-get install labelme\n```\n\n### macOS\n\n```bash\n# macOS Sierra\nbrew install pyqt # maybe pyqt5\npip install labelme # both python2/3 should work\n\n# or install standalone executable / app\n# NOTE: this only installs the `labelme` command\nbrew install wkentaro/labelme/labelme\nbrew cask install wkentaro/labelme/labelme\n```\n\n### Windows\n\nFirstly, follow instruction in [Anaconda](https://github.com/wkentaro/labelme/blob/master/#anaconda).\n\n```bash\n# Pillow 5 causes dll load error on Windows.\n# https://github.com/wkentaro/labelme/pull/174\nconda install pillow=4.0.0\n```\n\n\n## Usage\n\nRun `labelme --help` for detail. \nThe annotations are saved as a [JSON](http://www.json.org/) file.\n\n```bash\nlabelme # just open gui\n\n# tutorial (single image example)\ncd examples/tutorial\nlabelme apc2016_obj3.jpg # specify image file\nlabelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save\nlabelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file\nlabelme apc2016_obj3.jpg \\\n --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list\n\n# semantic segmentation example\ncd examples/semantic_segmentation\nlabelme data_annotated/ # Open directory to annotate all images in it\nlabelme data_annotated/ --labels labels.txt # specify label list with a file\n```\n\nFor more advanced usage, please refer to the examples:\n\n* [Tutorial (Single Image Example)](https://github.com/wkentaro/labelme/blob/master/examples/tutorial)\n* [Semantic Segmentation Example](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation?raw=true)\n* [Instance Segmentation Example](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true)\n* [Video Annotation Example](https://github.com/wkentaro/labelme/blob/master/examples/video_annotation?raw=true)\n\n### Command Line Arguemnts\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.\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.\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.\n- Flags are assigned to an entire image. [Example](https://github.com/wkentaro/labelme/blob/master/examples/classification?raw=true)\n- Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/master/examples/bbox_detection?raw=true)\n\n## FAQ\n\n- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial#convert-to-dataset).\n- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial#how-to-load-label-png-file).\n- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation?raw=true).\n- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true).\n\n\n## Testing\n\n```bash\npip install hacking pytest pytest-qt\nflake8 .\npytest -v tests\n```\n\n\n## Developing\n\n```bash\ngit clone https://github.com/wkentaro/labelme.git\ncd labelme\n\n# Install anaconda3 and labelme\ncurl -L https://github.com/wkentaro/dotfiles/raw/master/local/bin/install_anaconda3.sh | bash -s .\nsource .anaconda3/bin/activate\npip install -e .\n```\n\n\n## How to build standalone executable\n\nBelow shows how to build the standalone executable on macOS, Linux and Windows. \nAlso, there are pre-built executables in\n[the release section](https://github.com/wkentaro/labelme/releases).\n\n```bash\n# Setup conda\nconda create --name labelme python==3.6.0\nconda activate labelme\n\n# Build the standalone executable\npip install .\npip install pyinstaller\npyinstaller labelme.spec\ndist/labelme --version\n```\n\n\n## Acknowledgement\n\nThis repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme),\nwhose development has already stopped.\n\n\n## Cite This Project\n\nIf you use this project in your research or wish to refer to the baseline results published in the README, please use the following BibTeX entry.\n\n```bash\n@misc{labelme2016,\n author = {Ketaro Wada},\n title = {{labelme: Image Polygonal Annotation with Python}},\n howpublished = {\\url{https://github.com/wkentaro/labelme}},\n year = {2016}\n}\n```",
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