labelmewithAI

README.md

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+<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=main&event=push"></a>
+</div>
+
+<div align="center">
+  <a href="#starter-guide"><b>Starter Guide</b></a>
+  | <a href="#installation"><b>Installation</b></a>
+  | <a href="#usage"><b>Usage</b></a>
+  | <a href="#examples"><b>Examples</b></a>
+  <!-- | <a href="https://github.com/wkentaro/labelme/discussions"><b>Community</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/SegmentationClass/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/SegmentationObject/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))
+
+
+## Starter Guide
+
+If you're new to Labelme, you can get started with [Labelme Starter Guide](https://gumroad.labelme.io/l/starter-guide) (FREE), which contains:
+
+- **Installation guides** for all platforms: Windows, macOS, and Linux 💻
+- **Step-by-step tutorials**: first annotation to editing, exporting, and integrating with other programs 📕
+- **A compilation of valuable resources** for further exploration 🔗.
+
+
+## Installation
+
+There are options:
+
+- Platform agnostic installation: [Anaconda](#anaconda)
+- 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
+# python3
+conda create --name=labelme python=3
+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
+```
+
+### Ubuntu
+
+```bash
+sudo apt-get install labelme
+
+# or
+sudo pip3 install labelme
+
+# or install standalone executable from:
+# https://github.com/wkentaro/labelme/releases
+```
+
+### macOS
+
+```bash
+brew install pyqt  # maybe pyqt5
+pip install labelme
+
+# or
+brew install wkentaro/labelme/labelme  # command line interface
+# brew install --cask wkentaro/labelme/labelme  # app
+
+# 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
+conda create --name=labelme python=3
+conda activate labelme
+pip install labelme
+
+# or install standalone executable/app from:
+# https://github.com/wkentaro/labelme/releases
+```
+
+
+## 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
+```
+
+### 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 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).
+
+
+## Examples
+
+* [Image Classification](examples/classification)
+* [Bounding Box Detection](examples/bbox_detection)
+* [Semantic Segmentation](examples/semantic_segmentation)
+* [Instance Segmentation](examples/instance_segmentation)
+* [Video Annotation](examples/video_annotation)
+
+## How to develop
+
+```bash
+git clone https://github.com/wkentaro/labelme.git
+cd labelme
+
+# Install anaconda3 and labelme
+curl -L https://github.com/wkentaro/dotfiles/raw/main/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.  
+
+```bash
+# Setup conda
+conda create --name labelme python=3.9
+conda activate labelme
+
+# Build the standalone executable
+pip install .
+pip install 'matplotlib<3.3'
+pip install pyinstaller
+pyinstaller labelme.spec
+dist/labelme --version
+```
+
+
+### How to contribute
+
+Make sure below test passes on your environment.  
+See `.github/workflows/ci.yml` for more detail.
+
+```bash
+pip install -r requirements-dev.txt
+
+ruff format --check  # `ruff format` to auto-fix
+ruff check  # `ruff check --fix` to auto-fix
+MPLBACKEND='agg' pytest -vsx tests/
+```
+
+
+## Acknowledgement
+
+This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme).