anylabeling-far

A fork of AnyLabeling with support for annotating bounding boxes with fixed aspect ratios (FAR)

https://github.com/rafeloya/anylabeling-far

Science Score: 44.0%

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  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
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  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
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  • Scientific vocabulary similarity
    Low similarity (12.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

A fork of AnyLabeling with support for annotating bounding boxes with fixed aspect ratios (FAR)

Basic Info
  • Host: GitHub
  • Owner: RafeLoya
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 14.4 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 10 months ago · Last pushed 10 months ago
Metadata Files
Readme Funding License Citation

README.md

AnyLabeling-FAR

This is a fork of AnyLabeling, slightly modified with new shapes to allow for the creation of bounding boxes at fixed aspect ratios (which I refer to with the acronym FAR) in a user-friendly way. Currently, it supports human-made annotations, auto-labeling support may come sometime in the future. The original README.md is included below this small section.

Installation

The regular installation instructions, if followed, will NOT install this version of AnyLabeling. To install this version, I recommend a modified version of the third method, which has been copied below.

**NOTE:* for the git URL, use HTTPS URL from this repository, not the original AnyLabeling repository.*

```bash

after navigating to a directory of your choosing...

git clone https://github.com/RafeLoya/anylabeling-FAR.git cd anylabeling-FAR pip install . ```

To start the program, simply do the following while in the .../anylabeling-FAR directory:

bash anylabeling

Original README.md

AnyLabeling

🌟 AnyLabeling 🌟

Effortless data labeling with AI support from YOLO and Segment Anything!

AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling

PyPI license open issues Pypi Downloads Documentation Follow

AnyLearning-Banner

ai-flow 62b3c222

AnyLabeling

Auto Labeling with Segment Anything

AnyLabeling-SegmentAnything

Features:

  • [x] Image annotation for polygon, rectangle, circle, line and point.
  • [x] Auto-labeling YOLOv8, Segment Anything (SAM, SAM2).
  • [x] Text detection, recognition and KIE (Key Information Extraction) labeling.
  • [x] Multiple languages availables: English, Vietnamese, Chinese.

Install and Run

1. Download and run executable

  • Download and run newest version from Releases.
  • For MacOS:
    • After installing, go to Applications folder
    • Right click on the app and select Open
    • From the second time, you can open the app normally using Launchpad

Install from Pypi

  • Requirements: Python 3.10+. Recommended: Python 3.12.
  • Recommended: Miniconda/Anaconda.

  • Create environment:

bash conda create -n anylabeling python=3.12 conda activate anylabeling

  • (For macOS only) Install PyQt5 using Conda:

bash conda install -c conda-forge pyqt==5.15.9

  • Install anylabeling:

bash pip install anylabeling # or pip install anylabeling-gpu for GPU support

  • Start labeling:

bash anylabeling

Documentation

Website: https://anylabeling.nrl.ai/

Applications

| Object Detection | Recognition | Facial Landmark Detection | 2D Pose Estimation | | :---: | :---: | :---: | :---: | | | | | | | 2D Lane Detection | OCR | Medical Imaging | Instance Segmentation | | | | | | | Image Tagging | Rotation | And more! | | | | Your applications here! |

Development

  • Install packages:

```bash pip install -r requirements-dev.txt

or pip install -r requirements-macos-dev.txt for MacOS

```

  • Generate resources:

bash pyrcc5 -o anylabeling/resources/resources.py anylabeling/resources/resources.qrc

  • Run app:

bash python anylabeling/app.py

Build executable

  • Install PyInstaller:

bash pip install -r requirements-dev.txt

  • Build:

bash bash build_executable.sh

  • Check the outputs in: dist/.

Contribution

If you want to contribute to AnyLabeling, please read Contribution Guidelines.

Star history

Star History Chart

References

Owner

  • Name: Rafe Loya
  • Login: RafeLoya
  • Kind: user

A Computer Science candidate at Baylor University. Proficiency in C, C++, Java

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Nguyen"
  given-names: "Viet Anh"
  orcid: https://orcid.org/0009-0002-0457-7811
title: "AnyLabeling - Effortless data labeling with AI support"
url: "https://github.com/vietanhdev/anylabeling"
license: GPL-3

GitHub Events

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Dependencies

.github/workflows/python-publish.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • pypa/gh-action-pypi-publish v1.8.5 composite
.github/workflows/release.yml actions
  • actions/checkout v2 composite
  • conda-incubator/setup-miniconda v2 composite
  • mikepenz/release-changelog-builder-action v3 composite
  • softprops/action-gh-release v2 composite
pyproject.toml pypi
requirements-dev.txt pypi
  • black * development
  • build * development
  • twine * development
requirements-gpu-dev.txt pypi
  • build * development
  • twine * development
requirements-gpu.txt pypi
  • PyQt5 ==5.15.7
  • PyYAML ==6.0.1
  • darkdetect ==0.8.0
  • imgviz ==1.5.0
  • natsort ==8.1.0
  • onnx ==1.16.1
  • onnxruntime-gpu ==1.18.1
  • opencv-contrib-python-headless ==4.7.0.72
  • qimage2ndarray ==1.10.0
  • termcolor ==1.1.0
requirements-macos-dev.txt pypi
  • build * development
  • twine * development
requirements-macos.txt pypi
  • PyYAML ==6.0.1
  • darkdetect ==0.8.0
  • imgviz ==1.5.0
  • natsort ==8.1.0
  • onnx ==1.16.1
  • onnxruntime ==1.18.1
  • opencv-contrib-python-headless ==4.7.0.72
  • qimage2ndarray ==1.10.0
  • termcolor ==1.1.0
requirements.txt pypi
  • PyQt5 ==5.15.7
  • PyYAML ==6.0.1
  • darkdetect ==0.8.0
  • imgviz ==1.5.0
  • natsort ==8.1.0
  • onnx ==1.16.1
  • onnxruntime ==1.18.1
  • opencv-contrib-python-headless ==4.7.0.72
  • qimage2ndarray ==1.10.0
  • termcolor ==1.1.0
setup.py pypi
  • Pillow >=2.8
  • PyQt5 >=5.15.7
  • PyYAML ==6.0.1
  • darkdetect ==0.8.0
  • imgviz >=0.11
  • natsort >=7.1.0
  • numpy ==1.26.4
  • onnx ==1.16.1
  • opencv-python-headless ==4.7.0.72
  • qimage2ndarray ==1.10.0
  • termcolor ==1.1.0