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Label images and video for Computer Vision applications
| Date | Stars |
|---|---|
| 2026-07-24 | 962 |
| 2026-07-25 | 962 |
| 2026-07-28 | 962 |
| 2026-07-30 | 962 |
| 2026-08-06 | 962 |
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# OpenLabeling: open-source image and video labeler
[](https://github.com/Cartucho/OpenLabeling)
Image labeling in multiple annotation formats:
- PASCAL VOC (= [darkflow](https://github.com/thtrieu/darkflow))
- [YOLO darknet](https://github.com/pjreddie/darknet)
- ask for more (create a new issue)...
<img src="https://media.giphy.com/media/l49JDgDSygJN369vW/giphy.gif" width="40%"><img src="https://media.giphy.com/media/3ohc1csRs9PoDgCeuk/giphy.gif" width="40%">
<img src="https://media.giphy.com/media/3o752fXKwTJJkhXP32/giphy.gif" width="40%"><img src="https://media.giphy.com/media/3ohc11t9auzSo6fwLS/giphy.gif" width="40%">
## Citation
This project was developed for the following paper, please consider citing it:
```bibtex
@INPROCEEDINGS{8594067,
author={J. {Cartucho} and R. {Ventura} and M. {Veloso}},
booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots},
year={2018},
pages={2336-2341},
}
```
## Latest Features
- Jun 2019: Deep Learning Object Detection Model
- May 2019: [ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking
- Jan 2019: easy and quick bounding-boxe's resizing!
- Jan 2019: video object tracking with OpenCV trackers!
- TODO: Label photos via Google drive to allow "team online labeling".
[New Features Discussion](https://github.com/Cartucho/OpenLabeling/issues/3)
## Table of contents
- [Quick start](#quick-start)
- [Prerequisites](#prerequisites)
- [Run project](#run-project)
- [GUI usage](#gui-usage)
- [Authors](#authors)
## Quick start
To start using the YOLO Bounding Box Tool you need to [download the latest release](https://github.com/Cartucho/OpenLabeling/archive/v1.3.zip) or clone the repo:
```
git clone --recurse-submodules [email protected]:Cartucho/OpenLabeling.git
```
### Prerequisites
You need to install:
- [Python](https://www.python.org/downloads/)
- [OpenCV](https://opencv.org/) version >= 3.0
1. `python -mpip install -U pip`
1. `python -mpip install -U opencv-python`
1. `python -mpip install -U opencv-contrib-python`
- numpy, tqdm and lxml:
1. `python -mpip install -U numpy`
1. `python -mpip install -U tqdm`
1. `python -mpip install -U lxml`
Alternatively, you can install everything at once by simply running:
```
python -mpip install -U pip
python -mpip install -U -r requirements.txt
```
- [PyTorch](https://pytorch.org/get-started/locally/)
Visit the link for a configurator for your setup.
### Run project
Step by step:
1. Open the `main/` directory
2. Insert the input images and videos in the folder **input/**
3. Insert the classes in the file **class_list.txt** (one class name per line)
4. Run the code:
5. You can find the annotations in the folder **output/**
python main.py [-h] [-i] [-o] [-t] [--tracker TRACKER_TYPE] [-n N_FRAMES]
optional arguments:
-h, --help Show this help message and exit
-i, --input Path to images and videos input folder | Default: input/
-o, --output Path to output folder (if using the PASCAL VOC format it's important to set this path correctly) | Default: output/
-t, --thickness Bounding box and cross line thickness (int) | Default: -t 1
--tracker tracker_type tracker_type being used: ['CSRT', 'KCF','MOSSE', 'MIL', 'BOOSTING', 'MEDIANFLOW', 'TLD', 'GOTURN', 'DASIAMRPN']
-n N_FRAMES number of frames to track object for
To use DASIAMRPN Tracker:
1. Install the [DaSiamRPN](https://github.com/foolwood/DaSiamRPN) submodule and download the model (VOT) from [google drive](https://drive.google.com/drive/folders/1BtIkp5pB6aqePQGlMb2_Z7bfPy6XEj6H)
2. copy it into 'DaSiamRPN/code/'
3. set default tracker in main.py or Excerpt of 5,782 characters
Read on GitHub
Cartucho · Imperial College London
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ba327119c9500aff, topic:object-detection, topic:yolo, topic:opencv