labelme深度学习数据集制作工具

<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://github.com/wkentaro/labelme/actions"><img src="https://github.com/wkentaro/labelme/workflows/ci/badge.svg?branch=master&event=push"></a> <a href="https://hub.docker.com/r/wkentaro/labelme"><img src="https://img.shields.io/docker/build/wkentaro/labelme.svg"></a> </div> <div align="center"> <a href="#installation"><b>Installation</b></a> | <a href="#usage"><b>Usage</b></a> | <a href="https://github.com/wkentaro/labelme/tree/master/examples/tutorial#tutorial-single-image-example"><b>Tutorial</b></a> | <a href="https://github.com/wkentaro/labelme/tree/master/examples"><b>Examples</b></a> | <a href="https://www.youtube.com/playlist?list=PLI6LvFw0iflh3o33YYnVIfOpaO0hc5Dzw"><b>Youtube FAQ</b></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="30%" /> <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) - Pre-build binaries from [the release section](https://github.com/wkentaro/labelme/releases) ### 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 # or you can install everything by conda command # conda install labelme -c conda-forge ``` ### Docker You need install [docker](https://www.docker.com), then run below: ```bash # 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 ```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 # or install standalone executable from: # https://github.com/wkentaro/labelme/releases ``` ### 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 from: # https://github.com/wkentaro/labelme/releases ``` ### Windows Install [Anaconda](https://www.continuum.io/downloads), then in an Anaconda Prompt run: ```bash # python3 conda create --name=labelme python=3.6 conda activate labelme pip install labelme ``` ## 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 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 or