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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.
There are options: Platform agonistic installation: Anaconda, Docker Platform specific installation: Ubuntu, macOS, Windows AnacondaYou need install Anaconda, then run below: # 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 DockerYou need install docker, then run below: 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 # 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 # macOS Sierra brew install pyqt # maybe pyqt5 pip install labelme # both python2/3 should work # or install standalone executable / app # NOTE: this only installs the `labelme` command brew install wkentaro/labelme/labelme brew cask install wkentaro/labelme/labelme WindowsFirstly, follow instruction in Anaconda. # Pillow 5 causes dll load error on Windows. # https://github.com/wkentaro/labelme/pull/174 conda install pillow=4.0.0 UsageRun 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 fileFor more advanced usage, please refer to the examples: Tutorial (Single Image Example) Semantic Segmentation Example Instance Segmentation Example Video Annotation Example Command Line Arguemnts --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 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. Testing pip install hacking pytest pytest-qt flake8 . pytest -v tests Developing 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 executableBelow shows how to build the standalone executable on macOS, Linux and Windows. Also, there are pre-built executables in the release section. # 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 AcknowledgementThis repo is the fork of mpitid/pylabelme, whose development has already stopped. Cite This ProjectIf 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 = {Ketaro Wada}, title = {{labelme: Image Polygonal Annotation with Python}}, howpublished = {\url{https://github.com/wkentaro/labelme}}, year = {2016} } |
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