Forked from Foxhole Inventory Report, which mostly just wraps their excellent work with a different UI.
This tool analyses screenshots from Foxhole containing tooltips of the base you are looking to supply using the Logi Pyramid as a basis.
- Open the map and hover the the base you want to supply
- Take a screenshot
- Paste it into https://pyramid.82dk.net
- Profit
Currently a bit of a hack around fir as a proof of concept but thanks to their solid image recognition, its fully functional and should even work with icon mods.
Feel free to open an issue if you notice anything not working or have a suggestion on how it can be improved.
To deploy a non-containerized server run:
cd fir
python3 -m http.server
To build the docker container run:
docker build -f Dockerfile.server --tag 'fir_server' .
If you'd like to override the override the port the server listens on run:
docker build -f Dockerfile.server --build-arg PORT=<override port> --tag 'fir_server' .
To run the FIR server in the built docker continer:
docker run -p <host port>:<fir port> fir_server
The -p argument maps the host port to the fir server port inside the container. FIR defaults to listening on port
8000. To override the port please see this section.
Standalone website:
cd fir
python3 -m http.server
To build the google spreadsheet sidebar, run ./sundial/gs-build.sh and find the files to be added to Google Apps Script in ./sundial/gs-build.
The standalone method will require you to manually install all the necessary dependencies, such as Node, NPM, TensorFlow, etc.
To begin training, simply run build.sh <FModel-Data-Directory>.
Training can also be performed using a Docker container instead. [https://docs.docker.com/desktop/features/wsl/] If you plan to use your GPU(s) for training, you will need to install NVIDIA drivers and the NVIDIA Container Toolkit. [https://docs.nvidia.com/ai-enterprise/deployment/vmware/latest/docker.html]
Build the docker container by running docker docker build -f Dockerfile.trainer --tag 'fir_trainer' .
If you only want to utilize your CPU for training, run docker run -f Dockerfile.trainer -it --rm -v $PWD:/tmp -w /tmp -e WAR_LOCATION=<FModel-Data-Directory> fir_trainer
If you want to utilize both your CPU and GPU(s) for training, run docker run -f Dockerfile.trainer --gpus all -it --rm -v $PWD:/tmp -w /tmp -e WAR_LOCATION=<FModel-Data-Directory> fir_trainer
All original source code and contributions available under MIT License.
Catalog details and icons processed from the game Foxhole (created by Siege Camp) are made available only under Fair Use.