labelme-3.16.1.zip

<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 ``` ### macOS ```bash # macOS Sierra brew install pyqt # maybe pyqt5 pip install labelme # both python2/3 should work # or install standalone executable / app 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