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Chess Position & Type Recognition System (SRTP)
Home Page: https://haodong-li.com/zju/projects/alphacc_zero/
chess_vision's Introduction
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.
- 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
![Screenshot 2022-05-27 180653](https://user-images.githubusercontent.com/67775090/170679176-e01f23dd-6be9-42be-af77-70f440031236.png)
![1](https://github.com/lebronlihd/chess_vision/raw/master/chess_cnn/1.png)
![1](https://github.com/lebronlihd/chess_vision/raw/master/chess_cnn/2.png)
![2](https://github.com/lebronlihd/chess_vision/raw/master/chess_cnn/3.jpg)
![2](https://github.com/lebronlihd/chess_vision/raw/master/chess_cnn/4.jpg)
- ChessLocationResult & ChessboardMatrixResult
![4](https://github.com/lebronlihd/chess_vision/raw/master/chess_cnn/5.png)
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