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https://github.com/ahmedheakl/drone-vis
Full compatible drone library to automate computer vision algorithms on parrot drones.
https://github.com/ahmedheakl/drone-vis
Last synced: 5 days ago
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Full compatible drone library to automate computer vision algorithms on parrot drones.
- Host: GitHub
- URL: https://github.com/ahmedheakl/drone-vis
- Owner: ahmedheakl
- License: mit
- Created: 2022-11-30T08:00:10.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-21T13:01:17.000Z (6 months ago)
- Last Synced: 2024-12-28T08:07:57.462Z (12 days ago)
- Language: Python
- Size: 29.1 MB
- Stars: 97
- Watchers: 6
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://drone-vis.readthedocs.io/) 
[](https://github.com/psf/black)
[](https://arxiv.org/abs/2406.00447)
# DroneVision
Drone Vision (DroneVis) is a full compatible drone library to automate computer vision algorithms on parrot drones. You can read a detailed documentation of Drone Vision [docs](https://drone-vis.readthedocs.io/en/latest).
**DroneVis** is a cutting-edge drone software library that has been specifically designed for use with the AR. Drone. It has been extensively tested both indoors and outdoors, and offers a wide range of features including adaptability in connecting to the drone, advanced computer vision algorithms, and 3 user-friendly interfaces. This makes it easy for users to take full advantage of the drone's capabilities and control it with simple commands. All of the implemented real-time data, inference, and detection achieve a minimum ``fps >= 4.5`` on an Intel core 8 CPU.
## Features
- Unified state-of-the art computer vision algoritms
- Full control over the drone
- PEP8 compliant (unified code style)
- Documented functions and classes
- Tests, high code coverage and type hints
- Clean code
- Multiple implementations for the same models
- Logger with timestamps
- Three UI for easier usage (GUI, CLI, Gesture Recognition)
### Computer Vision Models
You provide a wide variety of 20+ computer vision and recommends users to the best models in terms of accuracy and speed, whilst giving users the chance to use any model of their choice.
### Drone Control
- Right ➡️, Left :arrow_left:
- Up ⬆️, Down :arrow_down:
- Forward ▶️, Backward ◀️
- Takeoff 🚀, Land 🛬
- Reset 🔄, Emergency 🚨
- Rotate Left ↩️ /Right :arrow_right_hook:
- Hover 🔍, Calibrate 🔧
- Camera Stream 📹/Record ⏺️
- Hand Gesture Control 🖌️
## How to Install
Install PyTorch
You should consider installing the version of Pytorch that corresponds to your cuda version.
You start controling your drone now with just two commands:
```bash
pip install dronevis # install the library
dronevis-gui # run library GUI
```
Press the ``start`` button to start a demo drone simulation, and run your favourite algorithms with the ``stream`` button.
You can control your drone with our ``CLI``:
```bash
dronevis
```
## Getting Started
Dronevis is built with multiple modes for customizibility. You can view all the options for either runnning our ``GUI`` or ``CLI`` as follows:
```bash
dronevis --help
```
The default mode for running either the CLI or the GUI is the ``demo`` mode. You can alter the mode by providing "real" to ``--drone`` argument.
```bash
dronevis --drone=real # cli real drone mode
```
or for GUI,
```bash
dronevis-gui --drone=real # gui real drone mode
```
## Documentation
Dronevis is developed with an extensive documentation for easier user contributions. You can check our full documentation in [here](drone-vis.readthedocs.io/en/latest) to go more in-depth of **how the library is structure** and **how to contribute your favourite model**.
## Citing the Project
To cite this repository:
```bibtex
@software{drone-vis,
author = {Ahmed Heakl, Fatma Youssef, Abdallah-Elbarkokry},
title = {Dronevis: Full compatible drone library to automate computer vision algorithms on parrot drones},
year = {2023},
url = {github.com/ahmedheakl/drone-vis},
version = {1.3.0}
}
```