labelme
v5.5.0
Starter Guide | Installation | Usage | Examples
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).
Image annotation for polygon, rectangle, circle, line and point. (tutorial)
Image flag annotation for classification and cleaning. (#166)
Video annotation. (video annotation)
GUI customization (predefined labels / flags, auto-saving, label validation, etc). (#144)
Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation)
Exporting COCO-format dataset for instance segmentation. (instance segmentation)
If you're new to Labelme, you can get started with Labelme Starter, 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 ?.
There are options:
Platform agnostic installation: Anaconda
Platform specific installation: Ubuntu, macOS, Windows
Pre-build binaries from the release section
You need install Anaconda, then run below:
# python3conda create --name=labelme python=3source activate labelme# conda install -c conda-forge pyside2# conda install pyqt# pip install pyqt5 # pyqt5 can be installed via pip on python3pip install labelme# or you can install everything by conda command# conda install labelme -c conda-forge
sudo apt-get install labelme# orsudo pip3 install labelme# or install standalone executable from:# https://github.com/labelmeai/labelme/releases# or install from sourcepip3 install git+https://github.com/labelmeai/labelme
brew install pyqt # maybe pyqt5pip install labelme# or install standalone executable/app from:# https://github.com/labelmeai/labelme/releases# or install from sourcepip3 install git+https://github.com/labelmeai/labelme
Install Anaconda, then in an Anaconda Prompt run:
conda create --name=labelme python=3 conda activate labelme pip install labelme# or install standalone executable/app from:# https://github.com/labelmeai/labelme/releases# or install from sourcepip3 install git+https://github.com/labelmeai/labelme
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 filelabelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the savelabelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON filelabelme apc2016_obj3.jpg --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list# semantic segmentation examplecd examples/semantic_segmentation labelme data_annotated/ # Open directory to annotate all images in itlabelme data_annotated/ --labels labels.txt # specify label list with a file
--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
How to convert JSON file to numpy array? See examples/tutorial.
How to load label PNG file? See examples/tutorial.
How to get annotations for semantic segmentation? See examples/semantic_segmentation.
How to get annotations for instance segmentation? See examples/instance_segmentation.
Image Classification
Bounding Box Detection
Semantic Segmentation
Instance Segmentation
Video Annotation
git clone https://github.com/labelmeai/labelme.gitcd labelme# Install anaconda3 and labelmecurl -L https://github.com/wkentaro/dotfiles/raw/main/local/bin/install_anaconda3.sh | bash -s .source .anaconda3/bin/activate pip install -e .
Below shows how to build the standalone executable on macOS, Linux and Windows.
# Setup condaconda create --name labelme python=3.9 conda activate labelme# Build the standalone executablepip install .pip install 'matplotlib<3.3'pip install pyinstaller pyinstaller labelme.spec dist/labelme --version
Make sure below test passes on your environment.
See .github/workflows/ci.yml for more detail.
pip install -r requirements-dev.txt ruff format --check # `ruff format` to auto-fixruff check # `ruff check --fix` to auto-fixMPLBACKEND='agg' pytest -vsx tests/
This repo is the fork of mpitid/pylabelme.
