labelme: Image Polygonal Annotation with Python =============================================== [![PyPI Version](https://img.shields.io/pypi/v/labelme.svg)](https://pypi.python.org/pypi/labelme) [![Travis Build Status](https://travis-ci.org/wkentaro/labelme.svg?branch=master)](https://travis-ci.org/wkentaro/labelme) [![Docker Build Status](https://img.shields.io/docker/build/wkentaro/labelme.svg)](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. Requirements ------------ - Ubuntu / macOS / Windows - Python2 / Python3 - [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) Installation ------------ There are options: - Platform agonistic installation: Anaconda, Docker - Platform specific installation: Ubuntu, macOS **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 pyqt pip install labelme # python3 conda create --name=labelme python=3.6 source activate labelme # 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/scripts/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 static/apc2016_obj3.jpg -O static/apc2016_obj3.json ``` **Ubuntu** ```bash # Ubuntu 14.04 sudo apt-get install python-qt4 pyqt4-dev-tools sudo pip install labelme # python2 works ``` **macOS** ```bash # macOS Sierra brew install pyqt # maybe pyqt5 pip install labelme # both python2/3 should work ``` Usage ----- ### Annotation Run `labelme --help` for detail. ```bash labelme # Open GUI labelme tutorial/apc2016_obj3.jpg # Specify file labelme tutorial/apc2016_obj3.jpg -O tutorial/apc2016_obj3.json # Close window after the save labelme tutorial/apc2016_obj3.jpg --nodata # Not include image data but relative image path in JSON file labelme tutorial/apc2016_obj3.jpg \ --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # Specify label list ``` The annotations are saved as a [JSON](http://www.json.org/) file. The file includes the image itself. ### Visualization To view the json file quickly, you can use utility script: ```bash labelme_draw_json tutorial/apc2016_obj3.json ``` ### Convert to Dataset To convert the json to set of image and label, you can run following: ```bash labelme_json_to_dataset tutorial/apc2016_obj3.json -o tutorial/apc2016_obj3_json ``` It generates standard files from the JSON file. - [img.png](tutorial/apc2016_obj3_json/img.png): Image file. - [label.png](tutorial/apc2016_obj3_json/label.png): Int32 label file. - [label_viz.png](tutorial/apc2016_obj3_json/label_viz.png): Visualization of `label.png`. - [label_names.txt](tutorial/apc2016_obj3_json/label_names.txt): Label names for values in `label.png`. Note that loading `label.png` is a bit difficult (`scipy.misc.imread`, `skimage.io.imread` may not work correctly), and please use `PIL.Image.open` to avoid unexpected behavior: ```python # see tutorial/load_label_png.py also. >>> import numpy as np >>> import PIL.Image >>> label_png = 'tutorial/apc2016_obj3_json/label.png' >>> lbl = np.asarray(PIL.Image.open(label_png)) >>> print(lbl.dtype) dtype('int32') >>> np.unique(lbl) array([0, 1, 2, 3], dtype=int32) >>> lbl.shape (907, 1210) ``` Screencast ---------- Acknowledgement --------------- This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme), whose development has already stopped.