javacollection_java freemarker

labelme

Image Polygonal Annotation with Python

Description

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.

VOC dataset example of instance segmentation.

Other examples (semantic segmentation, bbox detection, and classification).

Various primitives (polygon, rectangle, circle, line, and point).

Features

Image annotation for polygon, rectangle, circle, line and point. (tutorial)

Image flag annotation for classification and cleaning. (#166)

Video annotation. (video annotation)

GUI customization (predefined labels / flags, auto-saving, label validation, etc). (#144)

Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation)

Exporting COCO-format dataset for instance segmentation. (instance segmentation)

Requirements

Ubuntu / macOS / Windows

Python2 / Python3

Installation

There are options:

Platform agonistic installation: Anaconda, Docker

Platform specific installation: Ubuntu, macOS, Windows

Anaconda

You need install Anaconda, then run below:

# python2

conda create –name=labelme python=2.7

sourceactivate labelme

# conda install -c conda-forge pyside2

conda installpyqt

pip installlabelme

# 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

sourceactivate labelme

# conda install -c conda-forge pyside2

# conda install pyqt

# pip install pyqt5 # pyqt5 can be installed via pip on python3

pip installlabelme

# or you can install everything by conda command

# conda install labelme -c conda-forge

Docker

You need install docker, then run below:

# on macOS

socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:\”$DISPLAY\” &

docker run -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=docker.for.mac.host.internal:0 -v $(pwd):/root/workdir wkentaro/labelme

# on Linux

xhost +

docker run -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=:0 -v $(pwd):/root/workdir wkentaro/labelme

Ubuntu

# Ubuntu 14.04 / Ubuntu 16.04

# Python2

# sudo apt-get install python-qt4 # PyQt4

sudoapt-get installpython-pyqt5 # PyQt5

sudopip installlabelme

# Python3

sudoapt-get installpython3-pyqt5 # PyQt5

sudopip3 installlabelme

Ubuntu 19.10+ / Debian (sid)

sudoapt-get installlabelme

macOS

# macOS Sierra

brew installpyqt # maybe pyqt5

pip installlabelme # both python2/3 should work

# or install standalone executable / app

# NOTE: this only installs the `labelme` command

brew installwkentaro/labelme/labelme

brew cask installwkentaro/labelme/labelme

Windows

Install Anaconda, then in an Anaconda Prompt run:

# python3

conda create –name=labelme python=3.6

conda activate labelme

pip installlabelme

Usage

Run labelme –help for detail.

The annotations are saved as a JSON file.

labelme # just open gui

# tutorial (single image example)

cdexamples/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

cdexamples/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:

Command Line Arguments

–output specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on.

The first time you run labelme, it will create a config file in ~/.labelmerc. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the –config flag.

Without the –nosortlabels flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided.

Flags are assigned to an entire image. Example

Labels are assigned to a single polygon. Example

FAQ

How to convert JSON file to numpy array? See examples/tutorial.

How to load label PNG file? See examples/tutorial.

How to get annotations for semantic segmentation? See examples/semantic_segmentation.

How to get annotations for instance segmentation? See examples/instance_segmentation.

Testing

pip installhacking pytest pytest-qt

flake8 .

pytest -v tests

Developing

git clone https://github.com/wkentaro/labelme.git

cdlabelme

# Install anaconda3 and labelme

curl -L https://github.com/wkentaro/dotfiles/raw/master/local/bin/install_anaconda3.sh | bash -s .

source .anaconda3/bin/activate

pip install -e .

How to build standalone executable

Below shows how to build the standalone executable on macOS, Linux and Windows.

Also, there are pre-built executables in

the release section.

# Setup conda

conda create –name labelme python==3.6.0

conda activate labelme

# Build the standalone executable

pip install .

pip installpyinstaller

pyinstaller labelme.spec

dist/labelme –version

Acknowledgement

This repo is the fork of mpitid/pylabelme,

whose development has already stopped.

Cite This Project

If you use this project in your research or wish to refer to the baseline results published in the README, please use the following BibTeX entry.

@misc{labelme2016,

author = {Kentaro Wada},

title = {
{labelme: Image Polygonal Annotation with Python}},

howpublished = {\url{https://github.com/wkentaro/labelme}},

year = {2016}

}