PHP Chess

A chess library for PHP offering move validation, common formats, multiple variants, UCI engine support, explanation of chess positions, and image recognition.

Installation

Requirements

  • PHP >= 8.1
  • Stockfish >= 15.1

Composer installation

composer require chesslablab/php-chess

Features

Formats Supported

  • Chess moves in LAN and PGN formats.
  • Movetext processing in LAN, SAN and RAV formats.
  • NAG support for SAN and RAV movetexts.
  • UCI protocol.
  • FEN conversion to chess board.
  • Chess board conversion to PNG and JPG image.
  • Chess board conversion to MP4 video.
Acronym Description
LAN Long Algebraic Notation
PGN Portable Game Notation
SAN Standard Algebraic Notation
RAV Recursive Annotation Variation
NAG Numeric Annotation Glyphs
UCI Universal Chess Interface
FEN Forsyth-Edwards Notation

Chess Variants

Multiple variants are supported with the default one being classical chess.

Variant Chessboard
Capablanca Chess\Variant\Capablanca\Board
Capablanca-Fischer Chess\Variant\CapablancaFischer\Board
Chess960 Chess\Variant\Chess960\Board
Classical Chess\Variant\Classical\Board
Dunsany Chess\Variant\Dunsany\Board
Losing Chess Chess\Variant\Losing\Board
RacingKings Chess\Variant\RacingKings\Board

UCI Engines

Listed below are the UCI engines available at the moment.

  • Stockfish

Object-Oriented

The chess board representation is an object of type SplObjectStorage as opposed to a bitboard.

Thoroughly Tested

PHP Chess has been developed with a test-driven development (TDD) approach.

The tests/unit folder contains plenty of real examples. Almost every class in the src folder represents a concept that is tested accordingly in the tests/unit folder, in other words, the structure of the tests/unit folder is mirroring the structure of the src folder.

The PHP Chess docs are more of a tutorial rather than an API description. The unit tests are the best documentation. For further details on how to use a particular class, please feel free to browse the codebase and check out the corresponding tests.

Lightweight

PHP dependencies required:

  • Rubix ML for machine learning.
  • Imagine for image processing.