Metadata-Version: 1.1
Name: labelme
Version: 2.5.2
Summary: Annotation Tool for Object Segmentation.
Home-page: https://github.com/wkentaro/labelme
Author: Kentaro Wada
Author-email: www.kentaro.wada@gmail.com
License: GPLv3
Description: labelme: Image Annotation Tool 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)
        [![Appveyor Build status](https://ci.appveyor.com/api/projects/status/epxf9b6c47cw373y/branch/master?svg=true)](https://ci.appveyor.com/project/wkentaro/labelme/branch/master)
        [![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 <http://labelme.csail.mit.edu>.
        
        It is written in Python and uses Qt for its graphical interface.
        
        
        Dependencies
        ------------
        
        - [PyQt4 or 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
        conda create --name=labelme python=2.7
        source activate labelme
        conda install pyqt
        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
        sudo apt-get install python-qt4 pyqt4-dev-tools
        sudo pip install labelme
        ```
        
        **macOS**
        
        ```bash
        brew install qt qt4 || brew install pyqt  # qt4 is deprecated
        pip install labelme
        ```
        
        
        Usage
        -----
        
        **Annotation**
        
        Run `labelme --help` for detail.
        
        ```bash
        labelme  # Open GUI
        labelme static/apc2016_obj3.jpg  # Specify file
        labelme static/apc2016_obj3.jpg -O static/apc2016_obj3.json  # Close window after the save
        ```
        
        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 static/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 static/apc2016_obj3.json
        ```
        
        
        Sample
        ------
        
        - [Original Image](https://github.com/wkentaro/labelme/blob/master/static/apc2016_obj3.jpg)
        - [Screenshot](https://github.com/wkentaro/labelme/blob/master/static/apc2016_obj3_screenshot.jpg)
        - [Generated Json File](https://github.com/wkentaro/labelme/blob/master/static/apc2016_obj3.json)
        - [Visualized Json File](https://github.com/wkentaro/labelme/blob/master/static/apc2016_obj3_draw_json.jpg)
        
        
        Screencast
        ----------
        
        <img src="https://github.com/wkentaro/labelme/raw/master/static/screencast.gif" width="70%"/>
        
Keywords: Image Annotation,Machine Learning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Topic :: Internet :: WWW/HTTP
