93 lines
1.8 KiB
Python
93 lines
1.8 KiB
Python
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from __future__ import annotations
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import cv2
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from enum import Enum
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from functools import lru_cache
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from pathlib import Path
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from typing import NewType, NamedTuple, Dict, Tuple
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import numpy as np
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from sklearn.externals import joblib
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class COLOR(Enum):
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WHITE = "white"
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BLACK = "black"
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def __str__(self) -> str:
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return self.value
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class PIECE(Enum):
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PAWN = "pawn"
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ROOK = "rook"
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KNIGHT = "knight"
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BISHOP = "bishop"
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QUEEN = "queen"
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KING = "king"
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EMPTY = "empty"
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def __str__(self) -> str:
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return self.value
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PieceAndColor = Tuple[PIECE, COLOR]
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class FILE(int, Enum):
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A = 1
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B = 2
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C = 3
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D = 4
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E = 5
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F = 6
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G = 7
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H = 8
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class RANK(int, Enum):
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EIGHT = 8
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SEVEN = 7
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SIX = 6
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FIVE = 5
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FOUR = 4
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THREE = 3
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TWO = 2
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ONE = 1
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class _Position(NamedTuple):
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file: FILE
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rank: RANK
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def __str__(self) -> str:
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return f"{self.file.name}{self.rank}"
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@property
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def color(self):
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if (self.file + self.rank) % 2:
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return COLOR.WHITE
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return COLOR.BLACK
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# POSITION.{A8, A7, ..., H1}
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POSITION = Enum("POSITION", {str(_Position(f, r)): _Position(f, r) for f in FILE for r in RANK}, type=_Position)
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# Squares is a dict mapping positions to square images, i.e. a board container during image processing
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Squares = NewType("Squares", Dict[POSITION, np.ndarray])
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class Board(Dict[POSITION, PIECE]):
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"""Board is a dict mapping positions to a piece, i.e. a board configuration after all image processing"""
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def imwrite(*args, **kwargs):
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Path(args[0]).parent.mkdir(parents=True, exist_ok=True)
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return cv2.imwrite(*args, **kwargs)
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@lru_cache()
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def load_classifier(filename):
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print(f"Loading classifier {filename}")
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return joblib.load(filename)
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