# labelme: Image Polygonal Annotation with Python [](https://pypi.python.org/pypi/labelme) [](https://pypi.org/project/labelme) [](https://travis-ci.org/wkentaro/labelme) [](https://hub.docker.com/r/wkentaro/labelme) Labelme is a graphical image annotation tool inspired by . It is written in Python and uses Qt for its graphical interface. Fig 1. Annotation example of instance segmentation. Fig 2. VOC dataset example of instance segmentation. Fig 3. Other examples (semantic segmentation, bbox detection, and classification). Fig 4. Various primitives (polygon, rectangle, circle, line, and point). ## Features - [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](examples/tutorial)) - [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166)) - [x] Video annotation. ([video annotation](examples/video_annotation)) - [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](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation)) - [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](examples/instance_segmentation)) ## Requirements - Ubuntu / macOS / Windows - Python2 / Python3 - [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) / [PySide2](https://wiki.qt.io/PySide2_GettingStarted) ## Installation There are options: - Platform agonistic installation: [Anaconda](#anaconda), [Docker](#docker) - Platform specific installation: [Ubuntu](#ubuntu), [macOS](#macos), [Windows](#windows) ### Anaconda You need install [Anaconda](https://www.continuum.io/downloads), then run below: ```bash # python2 conda create --name=labelme python=2.7 source activate labelme # conda install -c conda-forge pyside2 conda install pyqt pip install labelme # if you'd like to use the latest version. run below: # pip install git+https://github.com/wkentaro/labelme.git # python3 conda create --name=labelme python=3.6 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 ``` ### Docker You need install [docker](https://www.docker.com), then run below: ```bash wget https://raw.githubusercontent.com/wkentaro/labelme/master/labelme/cli/on_docker.py -O labelme_on_docker chmod u+x labelme_on_docker # Maybe you need http://sourabhbajaj.com/blog/2017/02/07/gui-applications-docker-mac/ on macOS ./labelme_on_docker examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json ./labelme_on_docker examples/semantic_segmentation/data_annotated ``` ### Ubuntu ```bash # Ubuntu 14.04 / Ubuntu 16.04 # Python2 # sudo apt-get install python-qt4 # PyQt4 sudo apt-get install python-pyqt5 # PyQt5 sudo pip install labelme # Python3 sudo apt-get install python3-pyqt5 # PyQt5 sudo pip3 install labelme ``` ### macOS ```bash # macOS Sierra brew install pyqt # maybe pyqt5 pip install labelme # both python2/3 should work # or install standalone executable / app brew install wkentaro/labelme/labelme brew cask install wkentaro/labelme/labelme ``` ### Windows Firstly, follow instruction in [Anaconda](#anaconda). ```bash # Pillow 5 causes dll load error on Windows. # https://github.com/wkentaro/labelme/pull/174 conda install pillow=4.0.0 ``` ## 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)](examples/tutorial) * [Semantic Segmentation Example](examples/semantic_segmentation) * [Instance Segmentation Example](examples/instance_segmentation) * [Video Annotation Example](examples/video_annotation) ## FAQ - **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial#convert-to-dataset). - **How to load label PNG file?** See [examples/tutorial](examples/tutorial#how-to-load-label-png-file). - **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](examples/semantic_segmentation). - **How to get annotations for instance segmentation?** See [examples/instance_segmentation](examples/instance_segmentation). ## Screencast ## Testing ```bash pip install hacking pytest pytest-qt flake8 . pytest -v tests ``` ## Developing ```bash git clone https://github.com/wkentaro/labelme.git cd labelme # Install anaconda3 and labelme curl -L https://github.com/wkentaro/dotfiles/raw/master/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. Also, there are pre-built executables in [the release section](https://github.com/wkentaro/labelme/releases). ```bash # 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 [mpitid/pylabelme](https://github.com/mpitid/pylabelme), whose development has already stopped.