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02456-deep-learning-project

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    docker
    examples
    labelme
    tests
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    LICENSE
    MANIFEST.in
    README.md
    setup.cfg
    setup.py
    README.md

    labelme: Image Polygonal Annotation with Python

    PyPI Version Travis Build Status Docker Build Status

    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

    Installation

    There are options:

    • Platform agonistic installation: Anaconda, Docker
    • Platform specific installation: Ubuntu, macOS

    Anaconda

    You need install Anaconda, then run below:

    # 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, then run below:

    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/single_image/apc2016_obj3.jpg -O examples/single_image/apc2016_obj3.json
labelme_on_docker examples/semantic_segmentation/data_annotated

    Ubuntu

    # Ubuntu 14.04
sudo apt-get install python-qt4 pyqt4-dev-tools
sudo pip install labelme  # python2 works

    macOS

    # macOS Sierra
brew install pyqt  # maybe pyqt5
pip install labelme  # both python2/3 should work

    Usage

    Annotation

    Run labelme --help for detail.

    labelme  # Open GUI
labelme examples/single_image/apc2016_obj3.jpg  # Specify file
labelme examples/single_image/apc2016_obj3.jpg -O examples/single_image/apc2016_obj3.json  # Close window after the save
labelme examples/single_image/apc2016_obj3.jpg --nodata  # Not include image data but relative image path in JSON file
labelme examples/single_image/apc2016_obj3.jpg \
  --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball  # Specify label list
labelme examples/single_image/  # Open directory to annotate all images in it

    The annotations are saved as a JSON file. The file includes the image itself.

    For more advanced usage, please refer to the examples.

    Visualization

    To view the json file quickly, you can use utility script:

    labelme_draw_json examples/single_image/apc2016_obj3.json

    Convert to Dataset

    To convert the json to set of image and label, you can run following:

    labelme_json_to_dataset examples/single_image/apc2016_obj3.json -o examples/single_image/apc2016_obj3_json

    It generates standard files from the JSON file.

    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:

    # see examples/single_image/load_label_png.py also.
>>> import numpy as np
>>> import PIL.Image

>>> label_png = 'examples/single_image/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, whose development has already stopped.