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CHESSCOGNITO ♔♚

This project tackles automatic chess game analysis through image recognition. It captures the chessboard, identifies pieces, and generates a FEN (board position) for analysis using the Lichess API. This analysis provides valuable feedback to players, including:

  • Move suggestions 🕵️
  • Strategic insights 🧠
  • Overall game assessment 👑

ChessCognito automates the process, saving time and offering real-time feedback for improved chess gameplay.

Software Installation

Installation instructions for a Windows computer are presented below.

  1. First clone the repository and cd into it:

    git clone https://github.com/farvath/ChessCognito.git
cd ChessCognito

  2. Create a python virtual environment, activate it and upgrade pip:

    python -m venv venv
source venv/bin/activate
pip install --upgrade pip

  3. Install the required python packages:

    pip install -r requirements_pc.txt

  4. Install Python 3.10 from Microsoft Store. It is important NOT to install Python 3.11 instead as it would create dependency (numpy) issues when we later install onnxruntime and tensorflow==2.12.0.

  5. If you see any warning about some directory not on PATH, follow this and restart the computer to resolve it.

  6. Install tensorflow and corresponding CUDA version.

  7. Now you can install all the relevant packages by running the following commands in Windows PowerShell:

    pip install numpy
    pip install opencv-python
    pip install chess
    pip install tensorflow==2.12.0
    pip install onnxruntime
    pip install matplotlib
    pip install pyclipper
    pip install scikit-learn
    pip install tqdm
    pip install pandas
    pip install onnx
    pip install tf2onnx
    pip install pytest

Note: the above commands would install all the latest-possible versions of the required packages (it was found that there might not be any restrictions on the versions of nontensorflow packages). Alternatively, you could use the "requirements_pc.txt" file (pip install -r requirements_pc.txt) to install the specific versions that have been tested to be 100% working.

  1. Finally, in order to successfully import tensorflow, you also need to install a Microsoft Visual C++ Redistributable package from here. If you are using Windows 11 (Windows 11 only has the 64-bit version), you can simply download and install this.

Also, depending on the inference engine you want to use, install the following dependencies:

  • Keras with tensorflow backend.
  • ONNX Runtime.
  • (Optional) TensorRT and PyCUDA.

USAGE INSTRUCTIONS

  1. Edit predict_fen.py and set the ACTIVATE_*, MODEL_PATH_*, IMG_SIZE_*, and PRE_INPUT_* constants.

    • ACTIVATE_KERAS = True will select Keras with tensorflow backend as the inference engine. The Keras engine is the slowest of the three.

    • ACTIVATE_ONNX = True will select ONNX Runtime as the inference engine. It is significantly faster than Keras but almost just as accurate. It is the recommended choice for any standard computer.

    • ACTIVATE_TRT = True will select TensorRT as the inference engine. It is the fastest of the three but only available on computers with Nvidia GPUs.

  2. Run the test_lc2fen.py script.

TRAINING NEW MODELS

To train new models, check the this repo . That directory contains the python scripts used to train the chess piece models. It also contains the scripts used to manipulate both the dataset and the models.

USAGE OF END-TO-END FRAMEWORK

To use the main program, app.py :

  1. Make sure your phone and Windows computer are in the connected to same camera . You may use third-party application to do so (one such is DroidCam ).

  2. Open the app on your phone (that turns your phone into an IP camera), mount the phone on some kind of physical structure (Tripod), and edit the IMAGE_SOURCE variable in app.py (Incase you want to try on a image you can directly use the predict_fen.py).

  3. Edit the FULL_FEN_OF_STARTING_POSITION, A1_POS, and BOARD_CORNERS variables in app.py (feel free to edit other variables as well, but these three are generally the most relevant to the user).

  4. Run the application .

  5. After the game, feel free to use SAVE option for postgame analysis.

NOTE: If BOARD_CORNERS is set to None, automatic (neural-network-based) chessboard detection is used, and each moves takes at most 8 seconds to register with Intel Core i7.

TECHNICAL DETAILS

The figure below shows a high-level flow diagram for the signal-processing workflow. Models details are mentioned in this paper:

"FLOW DIAGRAM"

SETUP

alt text

DEMONSTRATION

Demonstration

ACKNOWLEDGEMENTS

I give special thanks to David Mallasén Quintana. This project was made possible by Quintana's work: LiveChess2FEN. LiveChess2FEN provided me with the foundation for chess-piece identification. The "models.zip" file (in "ChessPieceModelTraining/ModelTrainer") came directly from the LiveChess2FEN repository, and the "SqueezeNet1p1_model_training.ipynb" notebook (in "ChessPieceModelTraining/ModelTrainer") was written largely based on the work in "cpmodels" folder in the repository as well.