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https://github.com/alturosdestinations/alturos.imageannotation

A collaborative tool for labeling image data for yolo
https://github.com/alturosdestinations/alturos.imageannotation

annotation-tool bounding-boxes boundingbox dotnet image-annotation image-classification image-tagging labeling labeling-tool labelling tagging taggingtools yolo yolov2 yolov3

Last synced: 5 days ago
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A collaborative tool for labeling image data for yolo

Host: GitHub
URL: https://github.com/alturosdestinations/alturos.imageannotation
Owner: AlturosDestinations
License: mit
Created: 2019-05-24T15:37:53.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2021-01-29T20:30:23.000Z (almost 4 years ago)
Last Synced: 2024-11-09T18:41:44.524Z (6 days ago)
Topics: annotation-tool, bounding-boxes, boundingbox, dotnet, image-annotation, image-classification, image-tagging, labeling, labeling-tool, labelling, tagging, taggingtools, yolo, yolov2, yolov3
Language: C#
Homepage:
Size: 860 KB
Stars: 62
Watchers: 6
Forks: 19
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        ![Alturos.ImageAnnotation](doc/logo-banner.png)

# Alturos.ImageAnnotation

The purpose of this project is to manage training data for Neural Networks. The images are stored in an object storage for example Amazon S3.

In our case we have image data for different runs that we want to annotate together. You can upload a folder into a package.

For every package you can set your own tags... this information is stored in a database for example Amazon DynamoDB.

![object detection result](/doc/AlturosImageAnnotation.png)

## Features

 - Collaborative annotation of images

 - Verification of image annotation data

 - Export for yolo (train.txt, test.txt, obj.names) with filters

 - No requirement for a custom server

## Installation

You have the choice to use AWS or install your own environment.

- [Cloud Installation](doc/CLOUD_INSTALLATION.md)

- [Local Installation](doc/LOCAL_INSTALLATION.md)

- [Docker Installation](doc/DOCKER_INSTALLATION.md)

## Keyboard Shortcuts

Shortcut | Description | 

--- | --- |

↓ | Next image |

↑ | Previous image |

→ | Next Object Class |

← | Previous Object Class |

0-9 | Select Object Class |

WASD
+Shift
+Ctrl
+Alt | Move Bounding Box
Resize
Quick
Invert

## Data preperation

### How many images are required

You should preferably have 2000 different images for each class or more.

### Extract images from a video

If you have a video file and need the individual frames you can use [ffmpeg](https://ffmpeg.org) to extract the images. This command exports every 10th frame in the video.

`ffmpeg -i input.mp4 -vf "select=not(mod(n\,10))" -vsync vfr 1_every_10/img_%03d.jpg`

## Articles of interest

- [Training YOLOv3 : Deep Learning based Custom Object Detector](https://www.learnopencv.com/training-yolov3-deep-learning-based-custom-object-detector/)

## Credits

This program uses icons from the Silk icon set created by Mark James, which can be found [here](http://www.famfamfam.com/lab/icons/silk/).

The icon set is licensed under a [CC BY 3.0 license](https://creativecommons.org/licenses/by/3.0/). Some changes were made to the icons.

## Other Image Annotation Tools

[List of annotation tools for machine learning research](https://simonwenkel.com/2019/07/19/list-of-annotation-tools-for-machine-learning-research.html)