Lunar is a neural network aim assist that uses real-time object detection accelerated with CUDA on Nvidia GPUs.
Lunar can be modified to work with a variety of FPS games; however, it is currently configured for Fortnite. Besides being general purpose, the main advantage of using Lunar is that it does not meddle with the memory of other processes.
The basis of Lunar's player detection is the YOLOv5 architecture written in PyTorch.
A demo video (outdated) can be found here.
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Install a version of Python 3.8 or later.
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Navigate to the root directory. Use the package manager pip to install the necessary dependencies.
pip install -r requirements.txt
python lunar.py
To update sensitivity settings:
python lunar.py setup
To collect image data for annotating and training:
python lunar.py collect_data
- The method of mouse movement (SendInput) is slow. For this reason, the crosshair often lags behind a moving detection. This problem can be lessened by increasing the pixel_increment (e.g. to 4) so fewer calls to that function are made.
- False positives can also happen under certain lighting conditions.
Pull requests are welcome. If you have any suggestions, questions, or find any issues, please open an issue and provide some detail. If you find this project interesting or helpful, please star the repository.
This project is distributed under GNU General Public License v3.0 license.