96 lines
3.4 KiB
Plaintext
96 lines
3.4 KiB
Plaintext
from PIL import Image, ImageDraw, ImageFont
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import chess
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import numpy as np
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import torch
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class ChessBoardNode:
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# وضعیت صفحه رو بیرون از کلاس یا به صورت متغیر استاتیک نگه می‌داریم تا ریست نشه
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board = chess.Board()
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"move": ("STRING", {"multiline": False, "default": ""}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "render"
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CATEGORY = "👾 OmiXDev/Games"
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# اصلاح دیکشنری برای تفکیک بهتر
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PIECE_UNICODE = {
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(chess.PAWN, chess.WHITE): "♙",
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(chess.ROOK, chess.WHITE): "♖",
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(chess.KNIGHT, chess.WHITE): "♘",
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(chess.BISHOP, chess.WHITE): "♗",
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(chess.QUEEN, chess.WHITE): "♕",
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(chess.KING, chess.WHITE): "♔",
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(chess.PAWN, chess.BLACK): "♟",
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(chess.ROOK, chess.BLACK): "♜",
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(chess.KNIGHT, chess.BLACK): "♞",
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(chess.BISHOP, chess.BLACK): "♝",
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(chess.QUEEN, chess.BLACK): "♛",
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(chess.KING, chess.BLACK): "♚",
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}
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def render(self, move):
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move = str(move).strip()
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if move:
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try:
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# سعی کن اول با SAN (مثل e4) امتحان کنه، اگه نشد با UCI (مثل e2e4)
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try:
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self.board.push_san(move)
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except:
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self.board.push_uci(move)
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except Exception:
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print(f"Invalid move: {move}")
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square_size = 64
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board_size = 8 * square_size
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image = Image.new("RGB", (board_size, board_size), "#F0D9B5")
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draw = ImageDraw.Draw(image)
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# رسم خانه‌ها و مهره‌ها
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colors = ["#F0D9B5", "#B58863"]
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for r in range(8):
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for c in range(8):
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# رسم مربع
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color = colors[(r + c) % 2]
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draw.rectangle([c * square_size, r * square_size, (c + 1) * square_size, (r + 1) * square_size],
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fill=color)
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# پیدا کردن مهره در این خانه
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square = chess.square(c, 7 - r)
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piece = self.board.piece_at(square)
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if piece:
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piece_char = self.PIECE_UNICODE.get((piece.piece_type, piece.color), "?")
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# تنظیم رنگ فونت بر اساس رنگ مهره (اختیاری چون یونیکدها خودشون پر و خالی هستن)
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text_color = "white" if piece.color == chess.WHITE else "black"
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# رسم مهره (استفاده از فونت پیش‌فرض در صورت نبود فایل)
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draw.text((c * square_size + 10, r * square_size), piece_char, fill="black", font=None)
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# --- اصلاح بخش اصلی (Tensor Format) ---
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# ComfyUI expects: [Batch, Height, Width, Channels]
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# values: 0.0 to 1.0
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np_image = np.array(image).astype(np.float32) / 255.0 # تبدیل به float و نرمال‌سازی
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tensor_image = torch.from_numpy(np_image).unsqueeze(0) # اضافه کردن Batch: [1, H, W, C]
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return (tensor_image,)
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# ثبت Node
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NODE_CLASS_MAPPINGS = {
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"ChessBoardNode": ChessBoardNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ChessBoardNode": "👾 OmiX Chess Board Renderer"
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} |