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labelme

Image Polygonal Annotation with Python


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.


VOC dataset example of instance segmentation.


Other examples (semantic segmentation, bbox detection, and classification).


Various primitives (polygon, rectangle, circle, line, and point).

Features

  • Image annotation for polygon, rectangle, circle, line and point. (tutorial)
  • Image flag annotation for classification and cleaning.
  • Video annotation. (video annotation)
  • GUI customization (predefined labels / flags, auto-saving, label validation, etc).
  • Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation)
  • Exporting COCO-format dataset for instance segmentation. (instance segmentation)

Requirements

Usage

Run labelme --help for detail.
The annotations are saved as a JSON file.

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:

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
  • Labels are assigned to a single polygon. Example

FAQ

Testing

pip install hacking pytest pytest-qt
flake8 .
pytest -v tests

How to build standalone executable

Below shows how to build the standalone executable on macOS, Linux and Windows.

# Setup conda
conda create --name labelme python==3.6.0
conda activate labelme

# Build the standalone executable
pip install .
pip install pyinstaller
pyinstaller labelme.spec
dist/labelme --version

Acknowledgement

This repo is the fork of wkentaro/labelme.

Cite This Project

If 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.

@misc{labelme2016,
  author =       {Kentaro Wada},
  title =        {{labelme: Image Polygonal Annotation with Python}},
  howpublished = {\url{https://github.com/wkentaro/labelme}},
  year =         {2016}
}