Metadata-Version: 1.1
Name: labelme
Version: 2.8.0
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: <img src="https://github.com/wkentaro/labelme/blob/master/labelme/icons/icon.png?raw=true" align="right" />
        
        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)
        [![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.
        
        
        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 apc2016_obj3.jpg \
          --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball  # Specify label list
        ```
        
        <img src=".readme/apc2016_obj3_screenshot.jpg" width="50%" /> <img src=".readme/apc2016_obj3_annotate_label.jpg" width="44%" />
        
        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
        ```
        
        <img src=".readme/apc2016_obj3_draw_json.jpg" width="70%" />
        
        ### 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
        ----------
        
        <img src=".readme/screencast.gif" width="70%"/>
        
        
        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
