Foxhole Inventory Report (fir)

This tool prepares Foxhole stockpile screenshots into a visual report and machine-readable numbers.

1. Screenshot multiple inventories from the map view in-game. Do this in the order you'd like them to appear in the report.
2. Select your screenshots in the tool.
3. Wait for processing.
4. Edit the titles for each inventory in the report by clicking on them.
5. Download the result as a PNG, text report, TSV, or append to a google spreadsheet.

Status

Under development. However, it is already being used "in production" within regiment(s) for evaluation.

Deployment

Local

To deploy a non-containerized server run:

cd fir
python3 -m http.server

Docker

Building the Docker Container

To build the docker container run:

docker build -f Dockerfile.server --tag 'fir_server' .

Overriding the listen port

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' .

Running the Docker Container

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.

Development

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.

Training

Standalone

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>.

Docker

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

License

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.