chess.ipynb

This Jupyter Notebook (chess.ipynb) is focused on recognizing chess pieces from images of a chessboard. It employs image processing techniques to identify the position and type of each piece on the board.

Features:

Image Capture:

The notebook is designed to take images of a chessboard as input, either through a camera feed or pre-captured images.

Piece Recognition:

Utilizes computer vision techniques to detect and classify the chess pieces on the board. The notebook can distinguish between different types (pawn, rook, knight, bishop, queen, king) and colors (black or white).

Board Mapping:

Maps the recognized pieces to their corresponding positions on the chessboard, translating the visual data into a structured format (e.g., FEN notation).

Preprocessing:

Includes image preprocessing steps such as resizing, cropping, and filtering to enhance recognition accuracy.

Usage:

Chess Analysis:

Useful for automatically setting up the board position in chess analysis software by capturing the current state of a physical board.

Training Data:

Can be used to generate training data for machine learning models focused on chess piece recognition.

Automation:

Helps in automating the setup process for physical chessboards in various applications, such as tournaments or chess training tools.

Requirements:

Python 3.x

Jupyter Notebook

YOLO V8 and other relevant image processing libraries

A camera or a collection of chessboard images for input

This the sample image used for testing

This is our result