https://github.com/daniel-lima-lopez/chess-pieces-classification-with-area-correlation

https://github.com/daniel-lima-lopez/chess-pieces-classification-with-area-correlation

Last synced: 20 days ago
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• Host: GitHub
• URL: https://github.com/daniel-lima-lopez/chess-pieces-classification-with-area-correlation
• Owner: daniel-lima-lopez
• Created: 2023-08-04T20:10:22.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-05T23:04:59.000Z (over 1 year ago)
• Last Synced: 2023-08-06T00:24:07.926Z (over 1 year ago)
• Language: Python
• Size: 747 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Chess pieces classification with area correlation

[Versión en español](README_ES.md)

In this repository, is presented a simple method to classify chess pieces on a board. Unlike traditional deep learning-based approaches, the problem is approached using area correlation and k-NN classifier, which allows us to implement a straightforward and intuitive method that performs well in controlled environments.

## Method description

The classification is carried out using the information of each piece's area. To gather such information, we work with boards from the page. [Chess](https://www.chess.com/es):

![alt](imgs/board.jpeg)

To build the dataset to work with _k-NN_ classifier, the board image is binarized, and the information of each square is divided:

![alt](imgs/Tablero.png)

Subsequently, for each piece, the image is divided into windows, and the number of black pixels in each window is counted:

![alt](imgs/KingArea.png)

Consequently, we obtain nine-entry vectors that represent the information of each piece.

Each board is processed using the method described above to calculate the area of each square, finally, *k-NN* is used to predict the most similar piece for each square and assign a label:

![alt](imgs/Prediccion.png)

## Instalation

Clone or download this repository:

```bash

git clone [email protected]:daniel-lima-lopez/Chess-pieces-classification-with-area-correlation.git

```

## Example

To instantiate the classifier, it is necessary to import the class:

```python

from ChessClassifier import Classifier

classifier = Classifier()

```

Subsequently, to make a prediction, you must include the location of the board to be classified.:

```python

classifier.predict('path')

```

Example [boards](test/) are included to test the classifier.