♟ chess2fen-ai ♟

Forsyth–Edwards Notation (FEN) is a representation of an instance in a chess game. It allows you to represent the board state (red), the color-to-make-a-move (orange), the castling availability (green), the en passant option (light blue), the half-move clock (blue) and the full-move clock (purple).

In this project, we aim to calculate the FEN-string of an NxN image of a digital chess board.

Example

With the FEN-string r3k2r/p4p1p/b1p1pq2/2p5/4pP2/2P3P1/PP5P/RN1QK2R w KQkq - 1 16, we can render the following image (see /examples/input.png):

With this image, we can compute the FEN-string using python3 main.py -i ../examples/input.png.

The result of this is r3k2r/p4p1p/b1p1pq2/2p5/4pP2/2P3P1/PP5P/RN1QK2R w KQkq - 1 16, which can be converted into the following image (see /examples/output.png):

Models and Computations

All models are developed with TensorFlow and Keras, see card.md for more information.

Evaluation

The model can be evaluated with python3 evaluate.py, which will read FEN-strings from the preloaded lichess_game_data_test.txt file, convert them to images, and compare the predicted FEN-strings to the originals. Use -n to set the size of the test.

Example run:

Evaluated 1024 examples
'Board' accuracy: 1.000
'Active color' accuracy: 0.655
'Castling availability' accuracy: 0.994
'En passant' accuracy: 1.000
'Halfmove clock' accuracy: 0.279
Mean 'halfmove clock' difference: 1.524
'Fullmove clock' accuracy: 0.307
Mean 'fullmove clock' difference: 2.233

Drawbacks

• Since the program assumes that the board has already been cropped to an NxN image of only the board, "excess stuff" will cause problems.
• The model can struggle a little bit when being provided with images using piece styles other than the provided in /styles.
• Castling availability is naïvely computed (i.e. it is considered valid as long as the pieces are in their original squares).
• En passant is always assumed to not be possible.

Getting images of chess boards

The approach I use is to go to https://lichess.org/editor and insert the desired FEN-string. From there, select the desired piece style and theme. Then in Firefox, right-click on the page, and select Take Screenshot. Hover over the board element and select it. This will give a perfect NxN image of the chess board.

Downloading Lichess game data

Visit https://database.lichess.org/ and download the .pgn.zst of choice. Then, simply replace the empty file lichess_game_data.pgn.zst in /lichess with the new file using the same name.

I used January 2015, but any other should work equally well.

To train the models using your downloaded data, start by preloading the data using preload_lichess_data.py. This will generate lichess_game_data_train.txt and lichess_game_data_test.txt, containing one million randomly selected FEN-strings each.

Using the container

With Docker started, run docker compose run --rm app to start a container (you can also use run.sh). The project files are mounted to your local files, so any changes made will be reflected in the container (e.g. updated code) and vice versa (e.g. saved models).