update documents

DataAnnotation/README.md

 ### 数据标注
-在DataAnnotation模块下，我们依赖LabeMe标注工具，同时提供了数据处理脚本，帮助用户快速准备训练目标检测和语义分割任务所需的数据。
+您可以使用LabeMe标注工具对您的数据进行标注，同时提供了数据处理脚本，帮助用户快速准备训练目标检测和语义分割任务所需的数据。
 
 ### LabelMe
 LabelMe是目前广泛使用的数据标注工具，并且在GitHub上开源给用户使用。  

DataAnnotation/labelme/.github/stale.yaml

-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 30
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 7
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - bug
-# Label to use when marking an issue as stale
-staleLabel: stale
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Thank you
-  for your contributions.
-# Comment to post when removing the stale label. Set to `false` to disable
-unmarkComment: false
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: >
-  This issue is closed as announced. Feel free to re-open it if needed.

DataAnnotation/labelme/.gitignore

-/.cache/
-/.pytest_cache/
-
-/build/
-/dist/
-/*.egg-info/
-
-*.py[cdo]
-
-.DS_Store

DataAnnotation/labelme/.gitmodules

-[submodule "github2pypi"]
-	path = github2pypi
-	url = https://github.com/wkentaro/github2pypi.git

DataAnnotation/labelme/.travis.yml

-language: generic
-
-cache:
-  - pip
-
-sudo: required
-
-dist: trusty
-
-branches:
-  only:
-    - master
-    - /v\d+\.\d+.\d+/
-
-notifications:
-  email: false
-
-env:
-  global:
-    # used by ci-helpers
-    - SETUP_XVFB=true
-    - PIP_DEPENDENCIES='hacking pytest pytest-qt'
-
-    - MPLBACKEND=TkAgg  # for osx
-matrix:
-  include:
-    - os: osx
-      env:
-        - PYTEST_QT_API=pyqt5
-        - PYQT_PACKAGE='pyqt=5'
-        - PYTHON_VERSION=3.6
-        - RUN_PYINSTALLER=true
-    - os: linux
-      dist: trusty
-      env:
-        - PYTEST_QT_API=pyqt4v2
-        - PYQT_PACKAGE='pyqt=4'
-        - PYTHON_VERSION=2.7
-    - os: linux
-      dist: trusty
-      env:
-        - PYTEST_QT_API=pyside2
-        - CONDA_CHANNELS='conda-forge'
-        - PYQT_PACKAGE='pyside2!=5.12.4'
-        - PYTHON_VERSION=2.7
-    - os: linux
-      dist: trusty
-      env:
-        - PYTEST_QT_API=pyside2
-        - CONDA_CHANNELS='conda-forge'
-        - PYQT_PACKAGE='pyside2'
-        - PYTHON_VERSION=3.6
-    - os: linux
-      dist: trusty
-      env:
-        - PYTEST_QT_API=pyqt5
-        - PYQT_PACKAGE='pyqt=5'
-        - PYTHON_VERSION=2.7
-    - os: linux
-      dist: trusty
-      env:
-        - PYTEST_QT_API=pyqt5
-        - PYQT_PACKAGE='pyqt=5'
-        - PYTHON_VERSION=3.6
-        - RUN_PYINSTALLER=true
-
-install:
-  # Setup X
-  - |
-    if [ $TRAVIS_OS_NAME = "linux" ]; then
-      sudo apt-get update
-      # Xvfb / window manager
-      sudo apt-get install -y xvfb herbstluftwm
-    elif [ $TRAVIS_OS_NAME = "osx" ]; then
-      brew cask install xquartz
-    fi
-
-  # Setup miniconda
-  - git clone --depth 1 git://github.com/astropy/ci-helpers.git
-  - CONDA_DEPENDENCIES=$PYQT_PACKAGE source ci-helpers/travis/setup_conda.sh
-  - source activate test && export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
-  - pip install .
-  - rm -rf ci-helpers miniconda.sh
-
-before_script:
-  - if [ $TRAVIS_OS_NAME = "linux" ]; then (herbstluftwm )& fi
-  - if [ $TRAVIS_OS_NAME = "osx" ]; then (sudo Xvfb :99 -ac -screen 0 1024x768x8 )& fi
-  - sleep 1
-
-script:
-  # Run flake8
-  - flake8 examples labelme setup.py tests
-
-  # Run help2man
-  - conda install -y help2man
-
-  # Run pytest
-  - pytest -v tests
-
-  - labelme --help
-  - labelme --version
-
-  # Run examples
-  - (cd examples/primitives && labelme_json_to_dataset primitives.json && rm -rf primitives_json)
-  - (cd examples/tutorial && rm -rf apc2016_obj3_json && labelme_json_to_dataset apc2016_obj3.json && python load_label_png.py && git checkout -- .)
-  - (cd examples/semantic_segmentation && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
-  - (cd examples/instance_segmentation && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
-  - (cd examples/video_annotation && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
-
-  - pip install lxml  # for bbox_detection/labelme2voc.py
-  - (cd examples/bbox_detection && rm -rf data_dataset_voc && ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt && git checkout -- .)
-
-  - pip install cython && pip install pycocotools  # for instance_segmentation/labelme2coco.py
-  - (cd examples/instance_segmentation && rm -rf data_dataset_coco && ./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt && git checkout -- .)
-
-  # Run pyinstaller
-  - |
-    if [ "$RUN_PYINSTALLER" = "true" ]; then
-      # Cleanup
-      pip uninstall -y $PIP_DEPENDENCIES
-
-      # https://github.com/wkentaro/labelme/issues/183
-      if [ $TRAVIS_OS_NAME = "osx" ]; then
-        pip uninstall -y Pillow
-        conda install -y Pillow
-      fi
-
-      # Build the standalone executable
-      pip install 'pyinstaller!=3.4'  # 3.4 raises error
-
-      # numpy 1.17 raises error
-      # See https://github.com/wkentaro/labelme/issues/465
-      pip install 'numpy<1.17'
-
-      pyinstaller labelme.spec
-      dist/labelme --version
-    fi
-
-after_script:
-  - true  # noop

DataAnnotation/labelme/LICENSE

-Copyright (C) 2016-2018 Kentaro Wada.
-Copyright (C) 2011 Michael Pitidis, Hussein Abdulwahid.
-
-Labelme is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-Labelme is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with Labelme.  If not, see <http://www.gnu.org/licenses/>.

DataAnnotation/labelme/MANIFEST.in

-include 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}
-}
-```

DataAnnotation/labelme/docker/Dockerfile

-FROM ubuntu:xenial
-
-# http://fabiorehm.com/blog/2014/09/11/running-gui-apps-with-docker/
-RUN export uid=1000 gid=1000 && \
-    mkdir -p /home/developer && \
-    echo "developer:x:${uid}:${gid}:Developer,,,:/home/developer:/bin/bash" >> /etc/passwd && \
-    echo "developer:x:${uid}:" >> /etc/group && \
-    mkdir -p /etc/sudoers.d && \
-    echo "developer ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/developer && \
-    chmod 0440 /etc/sudoers.d/developer && \
-    chown ${uid}:${gid} -R /home/developer
-
-RUN \
-  apt-get update -qq && \
-  apt-get upgrade -qq -y && \
-  apt-get install -qq -y \
-    # requirements
-    git \
-    python3 \
-    python3-pip \
-    python3-matplotlib \
-    python3-pyqt5 \
-    # utilities
-    sudo
-
-RUN pip3 install -v git+https://github.com/wkentaro/labelme.git
-
-USER developer
-ENV HOME /home/developer

DataAnnotation/labelme/docs/man/labelme.1

-.\" DO NOT MODIFY THIS FILE!  It was generated by help2man 1.47.8.
-.TH LABELME "1" "August 2019" "labelme 3.16.3" "User Commands"
-.SH NAME
-labelme \- manual page for labelme 3.16.3
-.SH DESCRIPTION
-usage: labelme [\-h] [\-\-version] [\-\-reset\-config]
-.IP
-[\-\-logger\-level {debug,info,warning,fatal,error}]
-[\-\-output OUTPUT] [\-\-config CONFIG_FILE] [\-\-nodata]
-[\-\-autosave] [\-\-nosortlabels] [\-\-flags FLAGS]
-[\-\-labelflags LABEL_FLAGS] [\-\-labels LABELS]
-[\-\-validatelabel {exact,instance}] [\-\-keep\-prev]
-[\-\-epsilon EPSILON]
-[filename]
-.SS "positional arguments:"
-.TP
-filename
-image or label filename
-.SS "optional arguments:"
-.TP
-\fB\-h\fR, \fB\-\-help\fR
-show this help message and exit
-.TP
-\fB\-\-version\fR, \fB\-V\fR
-show version
-.TP
-\fB\-\-reset\-config\fR
-reset qt config
-.TP
-\fB\-\-logger\-level\fR {debug,info,warning,fatal,error}
-logger level
-.TP
-\fB\-\-output\fR OUTPUT, \fB\-O\fR OUTPUT, \fB\-o\fR OUTPUT
-output file or directory (if it ends with .json it is
-recognized as file, else as directory)
-.TP
-\fB\-\-config\fR CONFIG_FILE
-config file (default: /home/wkentaro/.labelmerc)
-.TP
-\fB\-\-nodata\fR
-stop storing image data to JSON file
-.TP
-\fB\-\-autosave\fR
-auto save
-.TP
-\fB\-\-nosortlabels\fR
-stop sorting labels
-.TP
-\fB\-\-flags\fR FLAGS
-comma separated list of flags OR file containing flags
-.TP
-\fB\-\-labelflags\fR LABEL_FLAGS
-yaml string of label specific flags OR file containing
-json string of label specific flags (ex. {person\-\ed+:
-[male, tall], dog\-\ed+: [black, brown, white], .*:
-[occluded]})
-.TP
-\fB\-\-labels\fR LABELS
-comma separated list of labels OR file containing
-labels
-.TP
-\fB\-\-validatelabel\fR {exact,instance}
-label validation types
-.TP
-\fB\-\-keep\-prev\fR
-keep annotation of previous frame
-.TP
-\fB\-\-epsilon\fR EPSILON
-epsilon to find nearest vertex on canvas
-.SH "SEE ALSO"
-The full documentation for
-.B labelme
-is maintained as a Texinfo manual.  If the
-.B info
-and
-.B labelme
-programs are properly installed at your site, the command
-.IP
-.B info labelme
-.PP
-should give you access to the complete manual.

DataAnnotation/labelme/examples/bbox_detection/README.md

-# Bounding Box Detection Example
-
-
-## Usage
-
-```bash
-labelme data_annotated --labels labels.txt --nodata --autosave
-```
-
-![](.readme/annotation.jpg)
-
-
-## Convert to VOC-format Dataset
-
-```bash
-# It generates:
-#   - data_dataset_voc/JPEGImages
-#   - data_dataset_voc/Annotations
-#   - data_dataset_voc/AnnotationsVisualization
-./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
-```
-
-<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/AnnotationsVisualization/2011_000003.jpg" width="33%" />
-
-<i>Fig1. JPEG image (left), Bounding box annotation visualization (right).</i>

DataAnnotation/labelme/examples/bbox_detection/data_annotated/2011_000003.json

-{
-  "flags": {},
-  "shapes": [
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          191,
-          107
-        ],
-        [
-          313,
-          329
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          365,
-          83
-        ],
-        [
-          500,
-          333
-        ]
-      ],
-      "shape_type": "rectangle"
-    }
-  ],
-  "lineColor": [
-    0,
-    255,
-    0,
-    128
-  ],
-  "fillColor": [
-    255,
-    0,
-    0,
-    128
-  ],
-  "imagePath": "2011_000003.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/bbox_detection/data_annotated/2011_000006.json

-{
-  "flags": {},
-  "shapes": [
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          91,
-          107
-        ],
-        [
-          240,
-          330
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          178,
-          110
-        ],
-        [
-          298,
-          282
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          254,
-          115
-        ],
-        [
-          369,
-          292
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          395,
-          81
-        ],
-        [
-          447,
-          117
-        ]
-      ],
-      "shape_type": "rectangle"
-    }
-  ],
-  "lineColor": [
-    0,
-    255,
-    0,
-    128
-  ],
-  "fillColor": [
-    255,
-    0,
-    0,
-    128
-  ],
-  "imagePath": "2011_000006.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/bbox_detection/data_annotated/2011_000025.json

-{
-  "flags": {},
-  "shapes": [
-    {
-      "label": "bus",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          84,
-          20
-        ],
-        [
-          435,
-          373
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "bus",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          1,
-          99
-        ],
-        [
-          107,
-          282
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "car",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          409,
-          167
-        ],
-        [
-          500,
-          266
-        ]
-      ],
-      "shape_type": "rectangle"
-    }
-  ],
-  "lineColor": [
-    0,
-    255,
-    0,
-    128
-  ],
-  "fillColor": [
-    255,
-    0,
-    0,
-    128
-  ],
-  "imagePath": "2011_000025.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/bbox_detection/data_dataset_voc/Annotations/2011_000003.xml

-<annotation>
-  <folder/>
-  <filename>2011_000003.jpg</filename>
-  <database/>
-  <annotation/>
-  <image/>
-  <size>
-    <height>338</height>
-    <width>500</width>
-    <depth>3</depth>
-  </size>
-  <segmented/>
-  <object>
-    <name>person</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>191</xmin>
-      <ymin>107</ymin>
-      <xmax>313</xmax>
-      <ymax>329</ymax>
-    </bndbox>
-  </object>
-  <object>
-    <name>person</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>365</xmin>
-      <ymin>83</ymin>
-      <xmax>500</xmax>
-      <ymax>333</ymax>
-    </bndbox>
-  </object>
-</annotation>

DataAnnotation/labelme/examples/bbox_detection/data_dataset_voc/Annotations/2011_000006.xml

-<annotation>
-  <folder/>
-  <filename>2011_000006.jpg</filename>
-  <database/>
-  <annotation/>
-  <image/>
-  <size>
-    <height>375</height>
-    <width>500</width>
-    <depth>3</depth>
-  </size>
-  <segmented/>
-  <object>
-    <name>person</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>91</xmin>
-      <ymin>107</ymin>
-      <xmax>240</xmax>
-      <ymax>330</ymax>
-    </bndbox>
-  </object>
-  <object>
-    <name>person</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>178</xmin>
-      <ymin>110</ymin>
-      <xmax>298</xmax>
-      <ymax>282</ymax>
-    </bndbox>
-  </object>
-  <object>
-    <name>person</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>254</xmin>
-      <ymin>115</ymin>
-      <xmax>369</xmax>
-      <ymax>292</ymax>
-    </bndbox>
-  </object>
-  <object>
-    <name>person</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>395</xmin>
-      <ymin>81</ymin>
-      <xmax>447</xmax>
-      <ymax>117</ymax>
-    </bndbox>
-  </object>
-</annotation>

DataAnnotation/labelme/examples/bbox_detection/data_dataset_voc/Annotations/2011_000025.xml

-<annotation>
-  <folder/>
-  <filename>2011_000025.jpg</filename>
-  <database/>
-  <annotation/>
-  <image/>
-  <size>
-    <height>375</height>
-    <width>500</width>
-    <depth>3</depth>
-  </size>
-  <segmented/>
-  <object>
-    <name>bus</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>84</xmin>
-      <ymin>20</ymin>
-      <xmax>435</xmax>
-      <ymax>373</ymax>
-    </bndbox>
-  </object>
-  <object>
-    <name>bus</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>1</xmin>
-      <ymin>99</ymin>
-      <xmax>107</xmax>
-      <ymax>282</ymax>
-    </bndbox>
-  </object>
-  <object>
-    <name>car</name>
-    <pose/>
-    <truncated/>
-    <difficult/>
-    <bndbox>
-      <xmin>409</xmin>
-      <ymin>167</ymin>
-      <xmax>500</xmax>
-      <ymax>266</ymax>
-    </bndbox>
-  </object>
-</annotation>

DataAnnotation/labelme/examples/bbox_detection/data_dataset_voc/class_names.txt

-_background_
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-potted plant
-sheep
-sofa
-train
-tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/bbox_detection/labelme2voc.py

-#!/usr/bin/env python
-
-from __future__ import print_function
-
-import argparse
-import glob
-import json
-import os
-import os.path as osp
-import sys
-
-try:
-    import lxml.builder
-    import lxml.etree
-except ImportError:
-    print('Please install lxml:\n\n    pip install lxml\n')
-    sys.exit(1)
-import numpy as np
-import PIL.Image
-
-import labelme
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument('input_dir', help='input annotated directory')
-    parser.add_argument('output_dir', help='output dataset directory')
-    parser.add_argument('--labels', help='labels file', required=True)
-    args = parser.parse_args()
-
-    if osp.exists(args.output_dir):
-        print('Output directory already exists:', args.output_dir)
-        sys.exit(1)
-    os.makedirs(args.output_dir)
-    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
-    os.makedirs(osp.join(args.output_dir, 'Annotations'))
-    os.makedirs(osp.join(args.output_dir, 'AnnotationsVisualization'))
-    print('Creating dataset:', args.output_dir)
-
-    class_names = []
-    class_name_to_id = {}
-    for i, line in enumerate(open(args.labels).readlines()):
-        class_id = i - 1  # starts with -1
-        class_name = line.strip()
-        class_name_to_id[class_name] = class_id
-        if class_id == -1:
-            assert class_name == '__ignore__'
-            continue
-        elif class_id == 0:
-            assert class_name == '_background_'
-        class_names.append(class_name)
-    class_names = tuple(class_names)
-    print('class_names:', class_names)
-    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
-    with open(out_class_names_file, 'w') as f:
-        f.writelines('\n'.join(class_names))
-    print('Saved class_names:', out_class_names_file)
-
-    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
-        print('Generating dataset from:', label_file)
-        with open(label_file) as f:
-            data = json.load(f)
-        base = osp.splitext(osp.basename(label_file))[0]
-        out_img_file = osp.join(
-            args.output_dir, 'JPEGImages', base + '.jpg')
-        out_xml_file = osp.join(
-            args.output_dir, 'Annotations', base + '.xml')
-        out_viz_file = osp.join(
-            args.output_dir, 'AnnotationsVisualization', base + '.jpg')
-
-        img_file = osp.join(osp.dirname(label_file), data['imagePath'])
-        img = np.asarray(PIL.Image.open(img_file))
-        PIL.Image.fromarray(img).save(out_img_file)
-
-        maker = lxml.builder.ElementMaker()
-        xml = maker.annotation(
-            maker.folder(),
-            maker.filename(base + '.jpg'),
-            maker.database(),    # e.g., The VOC2007 Database
-            maker.annotation(),  # e.g., Pascal VOC2007
-            maker.image(),       # e.g., flickr
-            maker.size(
-                maker.height(str(img.shape[0])),
-                maker.width(str(img.shape[1])),
-                maker.depth(str(img.shape[2])),
-            ),
-            maker.segmented(),
-        )
-
-        bboxes = []
-        labels = []
-        for shape in data['shapes']:
-            if shape['shape_type'] != 'rectangle':
-                print('Skipping shape: label={label}, shape_type={shape_type}'
-                      .format(**shape))
-                continue
-
-            class_name = shape['label']
-            class_id = class_names.index(class_name)
-
-            (xmin, ymin), (xmax, ymax) = shape['points']
-            bboxes.append([xmin, ymin, xmax, ymax])
-            labels.append(class_id)
-
-            xml.append(
-                maker.object(
-                    maker.name(shape['label']),
-                    maker.pose(),
-                    maker.truncated(),
-                    maker.difficult(),
-                    maker.bndbox(
-                        maker.xmin(str(xmin)),
-                        maker.ymin(str(ymin)),
-                        maker.xmax(str(xmax)),
-                        maker.ymax(str(ymax)),
-                    ),
-                )
-            )
-
-        captions = [class_names[l] for l in labels]
-        viz = labelme.utils.draw_instances(
-            img, bboxes, labels, captions=captions
-        )
-        PIL.Image.fromarray(viz).save(out_viz_file)
-
-        with open(out_xml_file, 'wb') as f:
-            f.write(lxml.etree.tostring(xml, pretty_print=True))
-
-
-if __name__ == '__main__':
-    main()

DataAnnotation/labelme/examples/bbox_detection/labels.txt

-__ignore__
-_background_
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-potted plant
-sheep
-sofa
-train
-tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/classification/README.md

-# Classification Example
-
-
-## Usage
-
-```bash
-labelme data_annotated --flags flags.txt --nodata
-```
-
-<img src=".readme/annotation_cat.jpg" width="100%" />
-<img src=".readme/annotation_dog.jpg" width="100%" />

DataAnnotation/labelme/examples/classification/data_annotated/0001.json

-{
-  "flags": {
-    "__ignore__": false,
-    "cat": true,
-    "dog": false
-  },
-  "shapes": [],
-  "lineColor": [
-    0,
-    255,
-    0,
-    128
-  ],
-  "fillColor": [
-    255,
-    0,
-    0,
-    128
-  ],
-  "imagePath": "0001.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/classification/data_annotated/0002.json

-{
-  "flags": {
-    "__ignore__": false,
-    "cat": false,
-    "dog": true
-  },
-  "shapes": [],
-  "lineColor": [
-    0,
-    255,
-    0,
-    128
-  ],
-  "fillColor": [
-    255,
-    0,
-    0,
-    128
-  ],
-  "imagePath": "0002.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/classification/flags.txt

-__ignore__
-cat
-dog

DataAnnotation/labelme/examples/instance_segmentation/README.md

-# Instance Segmentation Example
-
-## Annotation
-
-```bash
-labelme data_annotated --labels labels.txt --nodata
-labelme data_annotated --labels labels.txt --nodata --labelflags '{.*: [occluded, truncated], person-\d+: [male]}'
-```
-
-![](.readme/annotation.jpg)
-
-## Convert to VOC-format Dataset
-
-```bash
-# It generates:
-#   - data_dataset_voc/JPEGImages
-#   - data_dataset_voc/SegmentationClass
-#   - data_dataset_voc/SegmentationClassVisualization
-#   - data_dataset_voc/SegmentationObject
-#   - data_dataset_voc/SegmentationObjectVisualization
-./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
-```
-
-<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationObjectVisualization/2011_000003.jpg" width="33%" />  
-Fig 1. JPEG image (left), JPEG class label visualization (center), JPEG instance label visualization (right)
-
-
-Note that the label file contains only very low label values (ex. `0, 4, 14`), and
-`255` indicates the `__ignore__` label value (`-1` in the npy file).  
-You can see the label PNG file by following.
-
-```bash
-labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png   # left
-labelme_draw_label_png data_dataset_voc/SegmentationObjectPNG/2011_000003.png  # right
-```
-
-<img src=".readme/draw_label_png_class.jpg" width="33%" /> <img src=".readme/draw_label_png_object.jpg" width="33%" />
-
-
-## Convert to COCO-format Dataset
-
-```bash
-# It generates:
-#   - data_dataset_coco/JPEGImages
-#   - data_dataset_coco/annotations.json
-./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt
-```

DataAnnotation/labelme/examples/instance_segmentation/data_annotated/2011_000003.json

-{
-  "version": "3.14.2",
-  "flags": {},
-  "shapes": [
-    {
-      "label": "person-1",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          250.984126984127,
-          106.92063492063491
-        ],
-        [
-          229.984126984127,
-          118.92063492063491
-        ],
-        [
-          221.984126984127,
-          134.9206349206349
-        ],
-        [
-          223.984126984127,
-          147.9206349206349
-        ],
-        [
-          217.984126984127,
-          160.9206349206349
-        ],
-        [
-          202.984126984127,
-          167.9206349206349
-        ],
-        [
-          192.984126984127,
-          199.9206349206349
-        ],
-        [
-          194.984126984127,
-          221.9206349206349
-        ],
-        [
-          199.984126984127,
-          226.9206349206349
-        ],
-        [
-          191.984126984127,
-          233.9206349206349
-        ],
-        [
-          197.984126984127,
-          263.92063492063494
-        ],
-        [
-          213.984126984127,
-          294.92063492063494
-        ],
-        [
-          214.984126984127,
-          319.92063492063494
-        ],
-        [
-          221.984126984127,
-          326.92063492063494
-        ],
-        [
-          235.984126984127,
-          325.92063492063494
-        ],
-        [
-          240.984126984127,
-          322.92063492063494
-        ],
-        [
-          235.984126984127,
-          297.92063492063494
-        ],
-        [
-          238.984126984127,
-          286.92063492063494
-        ],
-        [
-          234.984126984127,
-          267.92063492063494
-        ],
-        [
-          257.984126984127,
-          257.92063492063494
-        ],
-        [
-          264.984126984127,
-          263.92063492063494
-        ],
-        [
-          256.984126984127,
-          272.92063492063494
-        ],
-        [
-          259.984126984127,
-          281.92063492063494
-        ],
-        [
-          284.984126984127,
-          287.92063492063494
-        ],
-        [
-          297.984126984127,
-          277.92063492063494
-        ],
-        [
-          288.984126984127,
-          269.92063492063494
-        ],
-        [
-          281.984126984127,
-          269.92063492063494
-        ],
-        [
-          283.984126984127,
-          263.92063492063494
-        ],
-        [
-          292.984126984127,
-          260.92063492063494
-        ],
-        [
-          308.984126984127,
-          235.9206349206349
-        ],
-        [
-          313.984126984127,
-          216.9206349206349
-        ],
-        [
-          309.984126984127,
-          207.9206349206349
-        ],
-        [
-          312.984126984127,
-          201.9206349206349
-        ],
-        [
-          308.984126984127,
-          184.9206349206349
-        ],
-        [
-          291.984126984127,
-          172.9206349206349
-        ],
-        [
-          269.984126984127,
-          158.9206349206349
-        ],
-        [
-          261.984126984127,
-          153.9206349206349
-        ],
-        [
-          264.984126984127,
-          141.9206349206349
-        ],
-        [
-          273.984126984127,
-          136.9206349206349
-        ],
-        [
-          278.984126984127,
-          129.9206349206349
-        ],
-        [
-          270.984126984127,
-          120.92063492063491
-        ]
-      ],
-      "shape_type": "polygon",
-      "flags": {
-        "occluded": true,
-        "truncated": false,
-        "male": true
-      }
-    },
-    {
-      "label": "person-2",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          482,
-          85
-        ],
-        [
-          468,
-          90
-        ],
-        [
-          460,
-          110
-        ],
-        [
-          460,
-          127
-        ],
-        [
-          444,
-          137
-        ],
-        [
-          419,
-          153
-        ],
-        [
-          410,
-          163
-        ],
-        [
-          403,
-          168
-        ],
-        [
-          394,
-          170
-        ],
-        [
-          386,
-          168
-        ],
-        [
-          386,
-          184
-        ],
-        [
-          392,
-          182
-        ],
-        [
-          410,
-          187
-        ],
-        [
-          414,
-          192
-        ],
-        [
-          437,
-          189
-        ],
-        [
-          434,
-          204
-        ],
-        [
-          390,
-          195
-        ],
-        [
-          386,
-          195
-        ],
-        [
-          387,
-          208
-        ],
-        [
-          381,
-          212
-        ],
-        [
-          372,
-          212
-        ],
-        [
-          372,
-          216
-        ],
-        [
-          400,
-          270
-        ],
-        [
-          389,
-          272
-        ],
-        [
-          389,
-          274
-        ],
-        [
-          403,
-          282
-        ],
-        [
-          444,
-          283
-        ],
-        [
-          443,
-          259
-        ],
-        [
-          426,
-          244
-        ],
-        [
-          462,
-          256
-        ],
-        [
-          474,
-          270
-        ],
-        [
-          477,
-          280
-        ],
-        [
-          473,
-          289
-        ],
-        [
-          471,
-          296
-        ],
-        [
-          472,
-          317
-        ],
-        [
-          480,
-          332
-        ],
-        [
-          494,
-          335
-        ],
-        [
-          498,
-          329
-        ],
-        [
-          494,
-          308
-        ],
-        [
-          499,
-          297
-        ],
-        [
-          499,
-          90
-        ]
-      ],
-      "shape_type": "polygon",
-      "flags": {
-        "occluded": true,
-        "truncated": true,
-        "male": true
-      }
-    },
-    {
-      "label": "bottle",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          374,
-          159
-        ],
-        [
-          369,
-          170
-        ],
-        [
-          369,
-          210
-        ],
-        [
-          375,
-          212
-        ],
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499.81422924901193, 90.33596837944665], [370.81422924901193, 170.33596837944665, 366.81422924901193, 173.33596837944665, 365.81422924901193, 182.33596837944665, 368.81422924901193, 185.33596837944665]], "area": 17254.0, "iscrowd": null, "image_id": 0, "category_id": 15}, {"id": 2, "segmentation": [[374.81422924901193, 159.33596837944665, 369.81422924901193, 170.33596837944665, 369.81422924901193, 210.33596837944665, 375.81422924901193, 212.33596837944665, 387.81422924901193, 209.33596837944665, 385.81422924901193, 185.33596837944665, 385.81422924901193, 168.33596837944665, 385.81422924901193, 165.33596837944665, 382.81422924901193, 159.33596837944665]], "area": 873.0, "iscrowd": null, "image_id": 0, "category_id": 5}, {"id": 3, "segmentation": [[260.936170212766, 22.563829787234056, 193.936170212766, 19.563829787234056, 124.93617021276599, 39.563829787234056, 89.93617021276599, 101.56382978723406, 81.93617021276599, 150.56382978723406, 108.93617021276599, 145.56382978723406, 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0.9361702127659877, 265.56382978723406, 27.936170212765987, 265.56382978723406, 29.936170212765987, 283.56382978723406, 63.93617021276599, 281.56382978723406, 89.93617021276599, 252.56382978723406, 100.93617021276599, 183.56382978723406, 108.93617021276599, 145.56382978723406, 81.93617021276599, 151.56382978723406]], "area": 15781.0, "iscrowd": null, "image_id": 1, "category_id": 6}, {"id": 5, "segmentation": [[413.936170212766, 168.56382978723406, 497.936170212766, 168.56382978723406, 497.936170212766, 256.56382978723406, 431.936170212766, 258.56382978723406, 430.936170212766, 236.56382978723406, 408.936170212766, 218.56382978723406]], "area": 7256.0, "iscrowd": null, "image_id": 1, "category_id": 7}, {"id": 6, "segmentation": [[204.936170212766, 108.56382978723406, 183.936170212766, 141.56382978723406, 166.936170212766, 150.56382978723406, 108.93617021276599, 203.56382978723406, 92.93617021276599, 228.56382978723406, 95.93617021276599, 244.56382978723406, 105.93617021276599, 244.56382978723406, 116.93617021276599, 223.56382978723406, 163.936170212766, 187.56382978723406, 147.936170212766, 212.56382978723406, 117.93617021276599, 222.56382978723406, 108.93617021276599, 243.56382978723406, 100.93617021276599, 325.56382978723406, 135.936170212766, 329.56382978723406, 148.936170212766, 319.56382978723406, 150.936170212766, 295.56382978723406, 169.936170212766, 272.56382978723406, 171.936170212766, 249.56382978723406, 178.936170212766, 246.56382978723406, 186.936170212766, 225.56382978723406, 214.936170212766, 219.56382978723406, 242.936170212766, 157.56382978723406, 228.936170212766, 146.56382978723406, 228.936170212766, 125.56382978723406, 216.936170212766, 112.56382978723406]], "area": 15203.0, "iscrowd": null, "image_id": 2, "category_id": 15}, {"id": 7, "segmentation": [[271.936170212766, 109.56382978723406, 249.936170212766, 110.56382978723406, 244.936170212766, 150.56382978723406, 215.936170212766, 219.56382978723406, 208.936170212766, 245.56382978723406, 214.936170212766, 220.56382978723406, 188.936170212766, 227.56382978723406, 170.936170212766, 246.56382978723406, 170.936170212766, 275.56382978723406, 221.936170212766, 278.56382978723406, 233.936170212766, 259.56382978723406, 246.936170212766, 253.56382978723406, 245.936170212766, 256.56382978723406, 242.936170212766, 251.56382978723406, 262.936170212766, 256.56382978723406, 304.936170212766, 226.56382978723406, 297.936170212766, 199.56382978723406, 308.936170212766, 164.56382978723406, 296.936170212766, 148.56382978723406]], "area": 11735.0, "iscrowd": null, "image_id": 2, "category_id": 15}, {"id": 8, "segmentation": [[308.936170212766, 115.56382978723406, 298.936170212766, 145.56382978723406, 309.936170212766, 166.56382978723406, 297.936170212766, 200.56382978723406, 305.936170212766, 228.56382978723406, 262.936170212766, 258.56382978723406, 252.936170212766, 284.56382978723406, 272.936170212766, 291.56382978723406, 281.936170212766, 250.56382978723406, 326.936170212766, 235.56382978723406, 351.936170212766, 239.56382978723406, 365.936170212766, 223.56382978723406, 371.936170212766, 187.56382978723406, 353.936170212766, 168.56382978723406, 344.936170212766, 143.56382978723406, 336.936170212766, 115.56382978723406]], "area": 7597.0, "iscrowd": null, "image_id": 2, "category_id": 15}, {"id": 9, "segmentation": [[308.936170212766, 242.56382978723406, 281.936170212766, 251.56382978723406, 270.936170212766, 287.56382978723406, 174.936170212766, 275.56382978723406, 148.936170212766, 296.56382978723406, 150.936170212766, 319.56382978723406, 159.936170212766, 328.56382978723406, 164.77327127659578, 375.0, 485.936170212766, 373.56382978723406, 497.936170212766, 336.56382978723406, 497.936170212766, 202.56382978723406, 453.936170212766, 193.56382978723406, 434.936170212766, 212.56382978723406, 367.936170212766, 224.56382978723406, 350.936170212766, 241.56382978723406]], "area": 44532.0, "iscrowd": null, "image_id": 2, "category_id": 9}, {"id": 10, "segmentation": [[425.936170212766, 82.56382978723406, 404.936170212766, 109.56382978723406, 400.936170212766, 114.56382978723406, 437.936170212766, 114.56382978723406, 448.936170212766, 102.56382978723406, 446.936170212766, 91.56382978723406]], "area": 996.0, "iscrowd": null, "image_id": 2, "category_id": 15}, {"id": 11, "segmentation": [[183.936170212766, 140.56382978723406, 125.93617021276599, 140.56382978723406, 110.93617021276599, 187.56382978723406, 22.936170212765987, 199.56382978723406, 18.936170212765987, 218.56382978723406, 22.936170212765987, 234.56382978723406, 93.93617021276599, 239.56382978723406, 91.93617021276599, 229.56382978723406, 110.93617021276599, 203.56382978723406], [103.93617021276599, 290.56382978723406, 58.93617021276599, 303.56382978723406, 97.93617021276599, 311.56382978723406], [348.936170212766, 146.56382978723406, 472.936170212766, 149.56382978723406, 477.936170212766, 162.56382978723406, 471.936170212766, 196.56382978723406, 453.936170212766, 192.56382978723406, 434.936170212766, 213.56382978723406, 368.936170212766, 226.56382978723406, 375.936170212766, 187.56382978723406, 353.936170212766, 164.56382978723406], [246.936170212766, 252.56382978723406, 219.936170212766, 277.56382978723406, 254.936170212766, 287.56382978723406, 261.936170212766, 256.56382978723406]], "area": 14001.0, "iscrowd": null, "image_id": 2, "category_id": 18}], "categories": [{"supercategory": null, "id": 0, "name": "_background_"}, {"supercategory": null, "id": 1, "name": "aeroplane"}, {"supercategory": null, "id": 2, "name": "bicycle"}, {"supercategory": null, "id": 3, "name": "bird"}, {"supercategory": null, "id": 4, "name": "boat"}, {"supercategory": null, "id": 5, "name": "bottle"}, {"supercategory": null, "id": 6, "name": "bus"}, {"supercategory": null, "id": 7, "name": "car"}, {"supercategory": null, "id": 8, "name": "cat"}, {"supercategory": null, "id": 9, "name": "chair"}, {"supercategory": null, "id": 10, "name": "cow"}, {"supercategory": null, "id": 11, "name": "diningtable"}, {"supercategory": null, "id": 12, "name": "dog"}, {"supercategory": null, "id": 13, "name": "horse"}, {"supercategory": null, "id": 14, "name": "motorbike"}, {"supercategory": null, "id": 15, "name": "person"}, {"supercategory": null, "id": 16, "name": "potted plant"}, {"supercategory": null, "id": 17, "name": "sheep"}, {"supercategory": null, "id": 18, "name": "sofa"}, {"supercategory": null, "id": 19, "name": "train"}, {"supercategory": null, "id": 20, "name": "tv/monitor"}]}
\ No newline at end of file

DataAnnotation/labelme/examples/instance_segmentation/data_dataset_voc/class_names.txt

-_background_
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-potted plant
-sheep
-sofa
-train
-tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/instance_segmentation/labelme2coco.py

-#!/usr/bin/env python
-
-import argparse
-import collections
-import datetime
-import glob
-import json
-import os
-import os.path as osp
-import sys
-
-import numpy as np
-import PIL.Image
-
-import labelme
-
-try:
-    import pycocotools.mask
-except ImportError:
-    print('Please install pycocotools:\n\n    pip install pycocotools\n')
-    sys.exit(1)
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument('input_dir', help='input annotated directory')
-    parser.add_argument('output_dir', help='output dataset directory')
-    parser.add_argument('--labels', help='labels file', required=True)
-    args = parser.parse_args()
-
-    if osp.exists(args.output_dir):
-        print('Output directory already exists:', args.output_dir)
-        sys.exit(1)
-    os.makedirs(args.output_dir)
-    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
-    print('Creating dataset:', args.output_dir)
-
-    now = datetime.datetime.now()
-
-    data = dict(
-        info=dict(
-            description=None,
-            url=None,
-            version=None,
-            year=now.year,
-            contributor=None,
-            date_created=now.strftime('%Y-%m-%d %H:%M:%S.%f'),
-        ),
-        licenses=[dict(
-            url=None,
-            id=0,
-            name=None,
-        )],
-        images=[
-            # license, url, file_name, height, width, date_captured, id
-        ],
-        type='instances',
-        annotations=[
-            # segmentation, area, iscrowd, image_id, bbox, category_id, id
-        ],
-        categories=[
-            # supercategory, id, name
-        ],
-    )
-
-    class_name_to_id = {}
-    for i, line in enumerate(open(args.labels).readlines()):
-        class_id = i - 1  # starts with -1
-        class_name = line.strip()
-        if class_id == -1:
-            assert class_name == '__ignore__'
-            continue
-        class_name_to_id[class_name] = class_id
-        data['categories'].append(dict(
-            supercategory=None,
-            id=class_id,
-            name=class_name,
-        ))
-
-    out_ann_file = osp.join(args.output_dir, 'annotations.json')
-    label_files = glob.glob(osp.join(args.input_dir, '*.json'))
-    for image_id, label_file in enumerate(label_files):
-        print('Generating dataset from:', label_file)
-        with open(label_file) as f:
-            label_data = json.load(f)
-
-        base = osp.splitext(osp.basename(label_file))[0]
-        out_img_file = osp.join(
-            args.output_dir, 'JPEGImages', base + '.jpg'
-        )
-
-        img_file = osp.join(
-            osp.dirname(label_file), label_data['imagePath']
-        )
-        img = np.asarray(PIL.Image.open(img_file))
-        PIL.Image.fromarray(img).save(out_img_file)
-        data['images'].append(dict(
-            license=0,
-            url=None,
-            file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)),
-            height=img.shape[0],
-            width=img.shape[1],
-            date_captured=None,
-            id=image_id,
-        ))
-
-        masks = {}                                     # for area
-        segmentations = collections.defaultdict(list)  # for segmentation
-        for shape in label_data['shapes']:
-            points = shape['points']
-            label = shape['label']
-            shape_type = shape.get('shape_type', None)
-            mask = labelme.utils.shape_to_mask(
-                img.shape[:2], points, shape_type
-            )
-
-            if label in masks:
-                masks[label] = masks[label] | mask
-            else:
-                masks[label] = mask
-
-            points = np.asarray(points).flatten().tolist()
-            segmentations[label].append(points)
-
-        for label, mask in masks.items():
-            cls_name = label.split('-')[0]
-            if cls_name not in class_name_to_id:
-                continue
-            cls_id = class_name_to_id[cls_name]
-
-            mask = np.asfortranarray(mask.astype(np.uint8))
-            mask = pycocotools.mask.encode(mask)
-            area = float(pycocotools.mask.area(mask))
-            bbox = pycocotools.mask.toBbox(mask).flatten().tolist()
-
-            data['annotations'].append(dict(
-                id=len(data['annotations']),
-                image_id=image_id,
-                category_id=cls_id,
-                segmentation=segmentations[label],
-                area=area,
-                bbox=bbox,
-                iscrowd=0,
-            ))
-
-    with open(out_ann_file, 'w') as f:
-        json.dump(data, f)
-
-
-if __name__ == '__main__':
-    main()

DataAnnotation/labelme/examples/instance_segmentation/labelme2voc.py

-#!/usr/bin/env python
-
-from __future__ import print_function
-
-import argparse
-import glob
-import json
-import os
-import os.path as osp
-import sys
-
-import numpy as np
-import PIL.Image
-
-import labelme
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    parser.add_argument('input_dir', help='input annotated directory')
-    parser.add_argument('output_dir', help='output dataset directory')
-    parser.add_argument('--labels', help='labels file', required=True)
-    args = parser.parse_args()
-
-    if osp.exists(args.output_dir):
-        print('Output directory already exists:', args.output_dir)
-        sys.exit(1)
-    os.makedirs(args.output_dir)
-    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
-    os.makedirs(osp.join(args.output_dir, 'SegmentationClass'))
-    os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG'))
-    os.makedirs(osp.join(args.output_dir, 'SegmentationClassVisualization'))
-    os.makedirs(osp.join(args.output_dir, 'SegmentationObject'))
-    os.makedirs(osp.join(args.output_dir, 'SegmentationObjectPNG'))
-    os.makedirs(osp.join(args.output_dir, 'SegmentationObjectVisualization'))
-    print('Creating dataset:', args.output_dir)
-
-    class_names = []
-    class_name_to_id = {}
-    for i, line in enumerate(open(args.labels).readlines()):
-        class_id = i - 1  # starts with -1
-        class_name = line.strip()
-        class_name_to_id[class_name] = class_id
-        if class_id == -1:
-            assert class_name == '__ignore__'
-            continue
-        elif class_id == 0:
-            assert class_name == '_background_'
-        class_names.append(class_name)
-    class_names = tuple(class_names)
-    print('class_names:', class_names)
-    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
-    with open(out_class_names_file, 'w') as f:
-        f.writelines('\n'.join(class_names))
-    print('Saved class_names:', out_class_names_file)
-
-    colormap = labelme.utils.label_colormap(255)
-
-    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
-        print('Generating dataset from:', label_file)
-        with open(label_file) as f:
-            base = osp.splitext(osp.basename(label_file))[0]
-            out_img_file = osp.join(
-                args.output_dir, 'JPEGImages', base + '.jpg')
-            out_cls_file = osp.join(
-                args.output_dir, 'SegmentationClass', base + '.npy')
-            out_clsp_file = osp.join(
-                args.output_dir, 'SegmentationClassPNG', base + '.png')
-            out_clsv_file = osp.join(
-                args.output_dir,
-                'SegmentationClassVisualization',
-                base + '.jpg',
-            )
-            out_ins_file = osp.join(
-                args.output_dir, 'SegmentationObject', base + '.npy')
-            out_insp_file = osp.join(
-                args.output_dir, 'SegmentationObjectPNG', base + '.png')
-            out_insv_file = osp.join(
-                args.output_dir,
-                'SegmentationObjectVisualization',
-                base + '.jpg',
-            )
-
-            data = json.load(f)
-
-            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
-            img = np.asarray(PIL.Image.open(img_file))
-            PIL.Image.fromarray(img).save(out_img_file)
-
-            cls, ins = labelme.utils.shapes_to_label(
-                img_shape=img.shape,
-                shapes=data['shapes'],
-                label_name_to_value=class_name_to_id,
-                type='instance',
-            )
-            ins[cls == -1] = 0  # ignore it.
-
-            # class label
-            labelme.utils.lblsave(out_clsp_file, cls)
-            np.save(out_cls_file, cls)
-            clsv = labelme.utils.draw_label(
-                cls, img, class_names, colormap=colormap)
-            PIL.Image.fromarray(clsv).save(out_clsv_file)
-
-            # instance label
-            labelme.utils.lblsave(out_insp_file, ins)
-            np.save(out_ins_file, ins)
-            instance_ids = np.unique(ins)
-            instance_names = [str(i) for i in range(max(instance_ids) + 1)]
-            insv = labelme.utils.draw_label(ins, img, instance_names)
-            PIL.Image.fromarray(insv).save(out_insv_file)
-
-
-if __name__ == '__main__':
-    main()

DataAnnotation/labelme/examples/instance_segmentation/labels.txt

-__ignore__
-_background_
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-potted plant
-sheep
-sofa
-train
-tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/primitives/primitives.json

-{
-  "version": "3.5.0",
-  "flags": {},
-  "shapes": [
-    {
-      "label": "rectangle",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          32,
-          35
-        ],
-        [
-          132,
-          135
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "circle",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          195,
-          84
-        ],
-        [
-          225,
-          125
-        ]
-      ],
-      "shape_type": "circle"
-    },
-    {
-      "label": "rectangle",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          391,
-          33
-        ],
-        [
-          542,
-          135
-        ]
-      ],
-      "shape_type": "rectangle"
-    },
-    {
-      "label": "polygon",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          69,
-          318
-        ],
-        [
-          45,
-          403
-        ],
-        [
-          173,
-          406
-        ],
-        [
-          198,
-          321
-        ]
-      ],
-      "shape_type": "polygon"
-    },
-    {
-      "label": "line",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          188,
-          178
-        ],
-        [
-          160,
-          224
-        ]
-      ],
-      "shape_type": "line"
-    },
-    {
-      "label": "point",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          345,
-          174
-        ]
-      ],
-      "shape_type": "point"
-    },
-    {
-      "label": "line_strip",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          441,
-          181
-        ],
-        [
-          403,
-          274
-        ],
-        [
-          545,
-          275
-        ]
-      ],
-      "shape_type": "linestrip"
-    }
-  ],
-  "lineColor": [
-    0,
-    255,
-    0,
-    128
-  ],
-  "fillColor": [
-    255,
-    0,
-    0,
-    128
-  ],
-  "imagePath": "primitives.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/semantic_segmentation/README.md

-# Semantic Segmentation Example
-
-## Annotation
-
-```bash
-labelme data_annotated --labels labels.txt --nodata
-```
-
-![](.readme/annotation.jpg)
-
-
-## Convert to VOC-format Dataset
-
-```bash
-# It generates:
-#   - data_dataset_voc/JPEGImages
-#   - data_dataset_voc/SegmentationClass
-#   - data_dataset_voc/SegmentationClassVisualization
-./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
-```
-
-<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassPNG/2011_000003.png" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" />
-
-Fig 1. JPEG image (left), PNG label (center), JPEG label visualization (right)  
-
-
-Note that the label file contains only very low label values (ex. `0, 4, 14`), and
-`255` indicates the `__ignore__` label value (`-1` in the npy file).  
-You can see the label PNG file by following.
-
-```bash
-labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png
-```
-
-<img src=".readme/draw_label_png.jpg" width="33%" />

DataAnnotation/labelme/examples/semantic_segmentation/data_annotated/2011_000003.json

-{
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-      "line_color": null,
-      "fill_color": null,
-      "points": [
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-        ],
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-        [
-          194.8142292490119,
-          222.33596837944665
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-          199.8142292490119,
-          227.33596837944665
-        ],
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-          234.33596837944665
-        ],
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-          309.81422924901193,
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-          130.33596837944665
-        ],
-        [
-          270.81422924901193,
-          121.33596837944665
-        ]
-      ]
-    },
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
-        [
-          482.81422924901193,
-          85.33596837944665
-        ],
-        [
-          468.81422924901193,
-          90.33596837944665
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-        [
-          460.81422924901193,
-          110.33596837944665
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-          460.81422924901193,
-          127.33596837944665
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-          444.81422924901193,
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-          184.33596837944665
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-          182.33596837944665
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-          187.33596837944665
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-          414.81422924901193,
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-      "fill_color": null,
-      "points": [
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-  "lineColor": [
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-    255,
-    0,
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-  "fillColor": [
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-    0,
-    128
-  ],
-  "imagePath": "2011_000003.jpg",
-  "imageData": null
-}
\ No newline at end of file

DataAnnotation/labelme/examples/semantic_segmentation/data_annotated/2011_000006.json

-{
-  "shapes": [
-    {
-      "label": "person",
-      "line_color": null,
-      "fill_color": null,
-      "points": [
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