{ "info": { "author": "Kentaro Wada", "author_email": "www.kentaro.wada@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Natural Language :: English", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy" ], "description": "

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labelme\n

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\n Image Polygonal Annotation with Python\n

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\n\n## Description\n\nLabelme is a graphical image annotation tool inspired by . \nIt is written in Python and uses Qt for its graphical interface.\n\n \nVOC dataset example of instance segmentation.\n\n \nOther examples (semantic segmentation, bbox detection, and classification).\n\n \nVarious primitives (polygon, rectangle, circle, line, and point).\n\n\n## Features\n\n- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial))\n- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))\n- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/master/examples/video_annotation?raw=true))\n- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144))\n- [x] Exporting VOC-format dataset for semantic/instance segmentation. ([semantic segmentation](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation?raw=true), [instance segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true))\n- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true))\n\n\n\n## Requirements\n\n- Ubuntu / macOS / Windows\n- Python2 / Python3\n- [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) / [PySide2](https://wiki.qt.io/PySide2_GettingStarted)\n\n\n## Installation\n\nThere are options:\n\n- Platform agonistic installation: [Anaconda](https://github.com/wkentaro/labelme/blob/master/#anaconda), [Docker](https://github.com/wkentaro/labelme/blob/master/#docker)\n- Platform specific installation: [Ubuntu](https://github.com/wkentaro/labelme/blob/master/#ubuntu), [macOS](https://github.com/wkentaro/labelme/blob/master/#macos), [Windows](https://github.com/wkentaro/labelme/blob/master/#windows)\n\n### Anaconda\n\nYou need install [Anaconda](https://www.continuum.io/downloads), then run below:\n\n```bash\n# python2\nconda create --name=labelme python=2.7\nsource activate labelme\n# conda install -c conda-forge pyside2\nconda install pyqt\npip install labelme\n# if you'd like to use the latest version. run below:\n# pip install git+https://github.com/wkentaro/labelme.git\n\n# python3\nconda create --name=labelme python=3.6\nsource activate labelme\n# conda install -c conda-forge pyside2\n# conda install pyqt\npip install pyqt5 # pyqt5 can be installed via pip on python3\npip install labelme\n```\n\n### Docker\n\nYou need install [docker](https://www.docker.com), then run below:\n\n```bash\nwget https://raw.githubusercontent.com/wkentaro/labelme/master/labelme/cli/on_docker.py -O labelme_on_docker\nchmod u+x labelme_on_docker\n\n# Maybe you need http://sourabhbajaj.com/blog/2017/02/07/gui-applications-docker-mac/ on macOS\n./labelme_on_docker examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json\n./labelme_on_docker examples/semantic_segmentation/data_annotated\n```\n\n### Ubuntu\n\n```bash\n# Ubuntu 14.04 / Ubuntu 16.04\n# Python2\n# sudo apt-get install python-qt4 # PyQt4\nsudo apt-get install python-pyqt5 # PyQt5\nsudo pip install labelme\n# Python3\nsudo apt-get install python3-pyqt5 # PyQt5\nsudo pip3 install labelme\n```\n\n### macOS\n\n```bash\n# macOS Sierra\nbrew install pyqt # maybe pyqt5\npip install labelme # both python2/3 should work\n\n# or install standalone executable / app\n# NOTE: this only installs the `labelme` command\nbrew install wkentaro/labelme/labelme\nbrew cask install wkentaro/labelme/labelme\n```\n\n### Windows\n\nFirstly, follow instruction in [Anaconda](https://github.com/wkentaro/labelme/blob/master/#anaconda).\n\n```bash\n# Pillow 5 causes dll load error on Windows.\n# https://github.com/wkentaro/labelme/pull/174\nconda install pillow=4.0.0\n```\n\n\n## Usage\n\nRun `labelme --help` for detail. \nThe annotations are saved as a [JSON](http://www.json.org/) file.\n\n```bash\nlabelme # just open gui\n\n# tutorial (single image example)\ncd examples/tutorial\nlabelme apc2016_obj3.jpg # specify image file\nlabelme apc2016_obj3.jpg -O apc2016_obj3.json # close window after the save\nlabelme apc2016_obj3.jpg --nodata # not include image data but relative image path in JSON file\nlabelme apc2016_obj3.jpg \\\n --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball # specify label list\n\n# semantic segmentation example\ncd examples/semantic_segmentation\nlabelme data_annotated/ # Open directory to annotate all images in it\nlabelme data_annotated/ --labels labels.txt # specify label list with a file\n```\n\nFor more advanced usage, please refer to the examples:\n\n* [Tutorial (Single Image Example)](https://github.com/wkentaro/labelme/blob/master/examples/tutorial)\n* [Semantic Segmentation Example](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation?raw=true)\n* [Instance Segmentation Example](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true)\n* [Video Annotation Example](https://github.com/wkentaro/labelme/blob/master/examples/video_annotation?raw=true)\n\n### Command Line Arguemnts\n- `--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.\n- 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.\n- 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.\n- Flags are assigned to an entire image. [Example](https://github.com/wkentaro/labelme/blob/master/examples/classification?raw=true)\n- Labels are assigned to a single polygon. [Example](https://github.com/wkentaro/labelme/blob/master/examples/bbox_detection?raw=true)\n\n## FAQ\n\n- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial#convert-to-dataset).\n- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial#how-to-load-label-png-file).\n- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation?raw=true).\n- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation?raw=true).\n\n\n## Testing\n\n```bash\npip install hacking pytest pytest-qt\nflake8 .\npytest -v tests\n```\n\n\n## Developing\n\n```bash\ngit clone https://github.com/wkentaro/labelme.git\ncd labelme\n\n# Install anaconda3 and labelme\ncurl -L https://github.com/wkentaro/dotfiles/raw/master/local/bin/install_anaconda3.sh | bash -s .\nsource .anaconda3/bin/activate\npip install -e .\n```\n\n\n## How to build standalone executable\n\nBelow shows how to build the standalone executable on macOS, Linux and Windows. \nAlso, there are pre-built executables in\n[the release section](https://github.com/wkentaro/labelme/releases).\n\n```bash\n# Setup conda\nconda create --name labelme python==3.6.0\nconda activate labelme\n\n# Build the standalone executable\npip install .\npip install pyinstaller\npyinstaller labelme.spec\ndist/labelme --version\n```\n\n\n## Acknowledgement\n\nThis repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme),\nwhose development has already stopped.\n\n\n## Cite This Project\n\nIf 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.\n\n```bash\n@misc{labelme2016,\n author = {Ketaro Wada},\n title = {{labelme: Image Polygonal Annotation with Python}},\n howpublished = {\\url{https://github.com/wkentaro/labelme}},\n year = {2016}\n}\n```", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/wkentaro/labelme", "keywords": "Image Annotation,Machine Learning", "license": "GPLv3", "maintainer": "", "maintainer_email": "", "name": "labelme-Test", "package_url": "https://pypi.org/project/labelme-Test/", "platform": "", "project_url": "https://pypi.org/project/labelme-Test/", "project_urls": { "Homepage": "https://github.com/wkentaro/labelme" }, "release_url": "https://pypi.org/project/labelme-Test/3.16.2/", "requires_dist": null, "requires_python": "", "summary": "Image Polygonal Annotation with Python", "version": "3.16.2" }, "last_serial": 5550502, "releases": { "3.16.2": [ { "comment_text": "", "digests": { "md5": "b70ba19edd3d74446d7d8423075af578", "sha256": "fd80c7adf0998c3360fa589cbfc25f1ce86caa809d2b2356741a01c11f604be0" }, "downloads": -1, "filename": "labelme-Test-3.16.2.tar.gz", "has_sig": false, "md5_digest": "b70ba19edd3d74446d7d8423075af578", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 1456517, "upload_time": "2019-07-18T11:04:44", "url": "https://files.pythonhosted.org/packages/c5/5d/fb87cc9b807875aa08d5e181fd805eac3bdcb1e9458c5e12c7ccd01c40f4/labelme-Test-3.16.2.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "b70ba19edd3d74446d7d8423075af578", "sha256": "fd80c7adf0998c3360fa589cbfc25f1ce86caa809d2b2356741a01c11f604be0" }, "downloads": -1, "filename": "labelme-Test-3.16.2.tar.gz", "has_sig": false, "md5_digest": "b70ba19edd3d74446d7d8423075af578", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 1456517, "upload_time": "2019-07-18T11:04:44", "url": "https://files.pythonhosted.org/packages/c5/5d/fb87cc9b807875aa08d5e181fd805eac3bdcb1e9458c5e12c7ccd01c40f4/labelme-Test-3.16.2.tar.gz" } ] }