CV Chess

Link to weekly log

This is the page for the CV Chess project.

Project Overview

This project aims to use a camera, a raspberry pi, and computer vision software to recognize the movements of chess pieces in a game of chess. The final product will be able to recognize individual pieces, as well as determine the change in positions (squares) they occupy. This will ultimately yield a project that can verify valid moves, transcribe games, and perhaps implement an AI to act as an opponent of a lone player.

Team Members

Robert Goodloe
Nhut Dang
TA: Ethan Shry
Instructor: Prof. Jim Feher

Objectives

1. Use OpenCV software to recognize chess board.
   
2. Use OpenCV software to recognize the chess pieces.
   
3. Use OpenCV software to recognize the movement of the pieces.
   
4. Transcribe game of chess and present in user-friendly fashion.
   
5. Add an AI component that responds to a users movements. It would display a move which the user must execute on behalf of the AI.
   

Challenges

1. Limited knowledge of Raspberry Pi
2. Zero knowledge using OpenCV or any computer vision software
3. Have been told that nobody has gotten OpenCV compiled and running in ESE 205 despite several attempts
4. Using object recognition to differentiate between similar pieces i.e. bishop versus pawn
5. testing

Gantt Chart

Budget

Item needed	Unit	Price	Total(max)	Description	Link
Chessboard	1	9.76	9.76	wood	https://www.amazon.com/Yellow-Mountain-Imports-Magnetic-Travel/dp/B0019FATKG/ref=sr_1_10?s=toys-and-games&ie=UTF8&qid=1538161920&sr=1-10&keywords=chess+set
color dot(optional)	1	9.99	9.99	may needed if we are going to color code the chessboard's pieces.	https://www.amazon.com/gp/aw/d/B06X6LT9G9/ref=sspa_mw_detail_4?ie=UTF8&psc=1
Pi touchscreen (optional)	1	79.95+tax+ship	79.95+tax+ship	may needed if we are going to color code the chessboard's pieces.	https://www.amazon.com/gp/aw/d/B06X6LT9G9/ref=sspa_mw_detail_4?ie=UTF8&psc=1
Plexiglass(optional)	1	12	12	clear board	https://www.amazon.com/gp/aw/d/B06X6LT9G9/ref=sspa_mw_detail_4?ie=UTF8&psc=1
			181.95

References

https://www.pyimagesearch.com/2018/04/09/how-to-quickly-build-a-deep-learning-image-dataset/

https://web.stanford.edu/class/cs231a/prev_projects_2016/CS_231A_Final_Report.pdf

https://www.pyimagesearch.com/2017/09/04/raspbian-stretch-install-opencv-3-python-on-your-raspberry-pi

https://www.pyimagesearch.com/2018/04/16/keras-and-convolutional-neural-networks-cnns/

https://www.pyimagesearch.com/2017/10/02/deep-learning-on-the-raspberry-pi-with-opencv/

https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/