zugzwang

A real-time machine learning-based Chess game recording system using Python3, OpenCV, and TensorFlow 2 deployed in a Raspberry Pi.
(For definition of "zugzwang" in terms of Chess, kindly visit https://en.wikipedia.org/wiki/Zugzwang)

Table of Contents

• Demonstration (Scholar's Mate)
• System Architecture Overview
• Hardware Setup
• Software Installations
• Official TensorFlow Docker Image
• TensorFlow Jupyter Notebook
• Machine Learning Model

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Demonstration (Scholar's Mate)

The following screenshots show the final position in a Scholar's Mate in a games of Chess.
For more information on the Scholar's Mate, kindly visit: https://en.wikipedia.org/wiki/Scholar%27s_mate

• Scholar's Mate (Checkmate Position)

• Scholar's Mate Game Record (using Standard Algebraic Notation)

System Architecture Overview

TODO

Hardware Setup

Devices	Information
Raspberry Pi 2 Model B	https://www.raspberrypi.org/products/raspberry-pi-2-model-b/
Logicool C922n Web Camera	https://www.logicool.co.jp/ja-jp/product/c922n-pro-stream-webcam
Webcam Clamp Mount	https://www.amazon.co.jp/gp/product/B07H2TJWW4/ref=ppx_yo_dt_b_asin_title_o03_s00?ie=UTF8&psc=1
Chess Board Game	https://www.amazon.co.jp/-/en/dp/B07S8ZLXQR/ref=lp_4108555051_1_5?srs=4108555051&ie=UTF8&qid=1596539909&sr=8-5

Software Installations

• Raspberry Pi 2

Library	Version	Install Command	Reference
OpenCV	3.4.6	pip3 install opencv-contrib-python==3.4.6.27	EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi#67 (comment)
TensorFlow Lite	2.1.0.post1	pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl	https://www.tensorflow.org/lite/guide/python
python-chess	0.31.3	pip3 install python-chess	https://python-chess.readthedocs.io/en/latest/#installing

• Microsoft Windows 10 Home

Application	Install Instructions
Docker Toolbox	https://docs.docker.com/toolbox/toolbox_install_windows/
VNC Viewer	https://www.realvnc.com/en/connect/download/viewer/

Official TensorFlow Docker Image

• https://hub.docker.com/r/tensorflow/tensorflow/
• docker pull tensorflow/tensorflow:latest-jupyter
• docker run -it --rm -v /c/zugzwang:/tf/zugzwang -p 8888:8888 tensorflow/tensorflow:latest-jupyter
  • Example:

TensorFlow Jupyter Notebook

• http://192.168.99.100:8888/tree
• To access the notebook for the first time, open link in a web browser with the following format: http://192.168.99.100:8888/?token=replace_this_with_actual_token_value
  • For the example above, open this link in a web browser:
    http://192.168.99.100:8888/?token=6757e503380afc7649c95c83b6dd6d1d0e7f25cada72e86b
  • Note:
    The token will change on every start of the TensorFlow Docker container.

Machine Learning Model

• Convolutional Neural Network Architecture

• Training
  • Uses a Convolutional Neural Network with TensorFlow 2 and Keras APIs.
  • Input = 50x50x3 RBG image
  • Output = 3-class softmax layer
• Inference
  • Uses TensorFlow Lite on Raspberry Pi 2 + Logicool C922n to classify each Chess board square into three categories:
    • A square with white piece
    • A square with no piece
    • A square with black piece

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Copyright © 2020 Rennel Ongcoy (Eli). All rights reserved.