Chess Position & Type Recognition System (SRTP)

• Developed a system that recognizes chess positions & types based on real-time captured images
• Used 1) Hough Circle algorithm for recognizing chess locations, and 2) a deep CNN model with Red-Black enhancement for recognizing chess types
• Achieved an identification accuracy of over 99.5%
• Demos for the entire SRTP, which included 3 studies, with the chess_simulator, and alphacc_zero.
  • YouTube Viedo Link
  • Project Website

Usage & Environment

• Usage:
  • init_mkdir.py to create repo needed
  • camera.py for capturing images which are saved to ./test_image_process/systemCamTest
  • circle_crop.py for croping chesses which are saved to ./test_image_process/image_circle_test
  • Next step requires HUMAN to move cropped chesses to corresponding folder under ./data_pre_360
  • generate_data.py to generate training data in ./data_360
  • trainModel.py to train deep CNN models which are saved to ./model_save
  • circle_multi.py to test the model using images in ./webcam
  • server_one.py to run the chess vision TCP server for communication with Qt digital twin platform
• Environment:
  • python 3.6
  • tensorflow 2.4
  • keras 2.4
  • opencv
  • pillow
• Whole Structure

• DataGenerator

• RedBlackReinforcement

• RealChessboardImage

• HoughCircleProcess

• ChessLocationResult & ChessboardMatrixResult