Create README.md

DataAnnotation/labelme/README.md

+<h1 align="center">
+  <img src="labelme/icons/icon.png"><br/>labelme
+</h1>
+
+<h4 align="center">
+  Image Polygonal Annotation with Python
+</h4>
+
+<div align="center">
+  <a href="https://pypi.python.org/pypi/labelme"><img src="https://img.shields.io/pypi/v/labelme.svg"></a>
+  <a href="https://pypi.org/project/labelme"><img src="https://img.shields.io/pypi/pyversions/labelme.svg"></a>
+  <a href="https://travis-ci.org/wkentaro/labelme"><img src="https://travis-ci.org/wkentaro/labelme.svg?branch=master"></a>
+  <a href="https://hub.docker.com/r/wkentaro/labelme"><img src="https://img.shields.io/docker/build/wkentaro/labelme.svg"></a>
+</div>
+
+<br/>
+
+<div align="center">
+  <img src="examples/instance_segmentation/.readme/annotation.jpg" width="70%">
+</div>
+
+## 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.
+
+<img src="examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClassPNG/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObjectPNG/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg" width="19%" />  
+<i>VOC dataset example of instance segmentation.</i>
+
+<img src="examples/semantic_segmentation/.readme/annotation.jpg" width="32%" /> <img src="examples/bbox_detection/.readme/annotation.jpg" width="30%" /> <img src="examples/classification/.readme/annotation_cat.jpg" width="35%" />  
+<i>Other examples (semantic segmentation, bbox detection, and classification).</i>
+
+<img src="https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png" width="32%" />  
+<i>Various primitives (polygon, rectangle, circle, line, and point).</i>
+
+
+## Features
+
+- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](examples/tutorial))
+- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))
+- [x] Video annotation. ([video annotation](examples/video_annotation))
+- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144))
+- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation))
+- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](examples/instance_segmentation))
+
+
+
+## Requirements
+
+- Ubuntu / macOS / Windows
+- Python2 / Python3
+- [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) / [PySide2](https://wiki.qt.io/PySide2_GettingStarted)
+
+
+## Installation
+
+There are options:
+
+- Platform agonistic installation: [Anaconda](#anaconda), [Docker](#docker)
+- Platform specific installation: [Ubuntu](#ubuntu), [macOS](#macos), [Windows](#windows)
+
+### 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 -c conda-forge pyside2
+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 -c conda-forge pyside2
+# 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/labelme/cli/on_docker.py -O 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
+sudo apt-get install python-pyqt5  # PyQt5
+sudo pip install labelme
+# Python3
+sudo apt-get install python3-pyqt5  # PyQt5
+sudo pip3 install labelme
+```
+
+### Ubuntu 19.10+ / Debian (sid)
+
+```bash
+sudo apt-get install labelme
+```
+
+### macOS
+
+```bash
+# macOS Sierra
+brew install pyqt  # maybe pyqt5
+pip install labelme  # both python2/3 should work
+
+# or install standalone executable / app
+# NOTE: this only installs the `labelme` command
+brew install wkentaro/labelme/labelme
+brew cask install wkentaro/labelme/labelme
+```
+
+### Windows
+
+Firstly, follow instruction in [Anaconda](#anaconda).
+
+```bash
+# Pillow 5 causes dll load error on Windows.
+# https://github.com/wkentaro/labelme/pull/174
+conda install pillow=4.0.0
+```
+
+
+## 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)
+* [Video Annotation Example](examples/video_annotation)
+
+### Command Line Arguemnts
+- `--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](examples/classification)
+- Labels are assigned to a single polygon. [Example](examples/bbox_detection)
+
+## FAQ
+
+- **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial#convert-to-dataset).
+- **How to load label PNG file?** See [examples/tutorial](examples/tutorial#how-to-load-label-png-file).
+- **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).
+
+
+## Testing
+
+```bash
+pip install hacking pytest pytest-qt
+flake8 .
+pytest -v tests
+```
+
+
+## Developing
+
+```bash
+git clone https://github.com/wkentaro/labelme.git
+cd labelme
+
+# 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](https://github.com/wkentaro/labelme/releases).
+
+```bash
+# Setup conda
+conda create --name labelme python==3.6.0
+conda activate labelme
+
+# Build the standalone executable
+pip install .
+pip install pyinstaller
+pyinstaller labelme.spec
+dist/labelme --version
+```
+
+
+## Acknowledgement
+
+This repo is the fork of [mpitid/pylabelme](https://github.com/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.
+
+```bash
+@misc{labelme2016,
+  author =       {Ketaro Wada},
+  title =        {{labelme: Image Polygonal Annotation with Python}},
+  howpublished = {\url{https://github.com/wkentaro/labelme}},
+  year =         {2016}
+}
+```