Metadata-Version: 1.1
Name: labelme
Version: 2.10.0
Summary: Image Polygonal Annotation with Python.
Home-page: https://github.com/wkentaro/labelme
Author: Kentaro Wada
Author-email: www.kentaro.wada@gmail.com
License: GPLv3
Description: <img src="https://github.com/wkentaro/labelme/blob/master/labelme/icons/icon.png?raw=true" align="right" />
        
        # 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 <http://labelme.csail.mit.edu>.  
        It is written in Python and uses Qt for its graphical interface,  
        and supports annotations for semantic and instance segmentation.
        
        <img src="examples/instance_segmentation/.readme/annotation.jpg" width="80%" />  
        Fig 1. Example of annotations for instance segmentation.
        
        
        ## 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
        # 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 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 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-dev-tools  # PyQt4
        sudo apt-get install python-pyqt5 pyqt5-dev-tools  # PyQt5
        sudo pip install labelme
        # Python3
        sudo apt-get install python3-pyqt5 pyqt5-dev-tools  # PyQt5
        sudo pip3 install labelme
        ```
        
        ### macOS
        
        ```bash
        # macOS Sierra
        brew install pyqt  # maybe pyqt5
        pip install labelme  # both python2/3 should work
        ```
        
        
        ## 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)
        
        
        ## FAQ
        
        - **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial).
        - **How to load label.png generated by labelme_json_to_dataset?** See [examples/tutorial](examples/tutorial).
        - **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
        
        <img src=".readme/screencast.gif" width="70%"/>
        
        
        ## Testing
        
        ```bash
        pip install hacking pytest pytest-qt
        flake8 .
        pytest -v tests
        ```
        
        
        ## How to build standalone app
        
        Below is an example on macOS.
        
        ```bash
        git clone https://github.com/wkentaro/labelme.git
        cd labelme
        
        virtualenv venv --python /usr/local/bin/python3
        . venv/bin/activate
        pip install -e .
        pip uninstall matplotlib
        pip install pyinstaller
        
        pyinstaller app.py \
          --onefile \
          --windowed \
          --name labelme \
          --icon labelme/icons/icon.icns \
          --specpath $(mktemp -d) \
          --noconfirm
        open dist/labelme.app
        ```
        
        
        ## Acknowledgement
        
        This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme),
        whose development has already stopped.
        
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
