---

import os
import cv2
import numpy as np

def is_empty_cell(cell_gray, std_threshold=14.0):
    """
    Проверяет, пустая ли клетка.
    У пустой клетки фон однородный (маленькое стандартное отклонение).
    """
    h, w = cell_gray.shape
    margin_h, margin_w = int(h * 0.25), int(w * 0.25)
    center = cell_gray[margin_h:h-margin_h, margin_w:w-margin_w]
    return np.std(center) < std_threshold


def detect_color_group(cell_gray):
    """Определяет, фигура белая ('white') или чёрная ('black')."""
    h, w = cell_gray.shape
    center = cell_gray[int(h * 0.2):int(h * 0.8), int(w * 0.2):int(w * 0.8)]
    median_val = np.median(center)
    return "white" if median_val > 110 else "black"


def load_templates(templates_folder="extracted_pieces"):
    symbol_map = {
        "white_K": "K", "white_Q": "Q", "white_R": "R", "white_B": "B", "white_N": "N", "white_P": "P",
        "black_k": "k", "black_q": "q", "black_r": "r", "black_b": "b", "black_n": "n", "black_p": "p",
    }

    templates = {"white": {}, "black": {}}
    if not os.path.exists(templates_folder):
        print(f"[!] Ошибка: Папка '{templates_folder}' не найдена!")
        return templates

    for filename in os.listdir(templates_folder):
        if filename.endswith(".png"):
            name_without_ext = os.path.splitext(filename)[0]
            symbol = symbol_map.get(name_without_ext)
            if not symbol:
                continue

            file_path = os.path.join(templates_folder, filename)
            tpl_img = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
            
            if tpl_img is not None:
                color_group = "white" if name_without_ext.startswith("white") else "black"
                templates[color_group][symbol] = tpl_img

    return templates


def generate_fen(board_matrix):
    fen_rows = []
    for row in board_matrix:
        empty_count = 0
        row_str = ""
        for cell in row:
            if cell == ".":
                empty_count += 1
            else:
                if empty_count > 0:
                    row_str += str(empty_count)
                    empty_count = 0
                row_str += cell
        if empty_count > 0:
            row_str += str(empty_count)
        fen_rows.append(row_str)

    return "/".join(fen_rows) + " w - - 0 1"


def scan_board(
    board_path="board.png",
    templates_folder="extracted_pieces",
    empty_std_threshold=14.0,  # Порог определения пустой клетки
    inner_margin_pct=0.02      # Минимальный отступ (2%), чтобы фигура не резалась
):
    templates = load_templates(templates_folder)
    if not templates["white"] and not templates["black"]:
        print("[!] Ошибка: Нет шаблонов для сравнения.")
        return

    board_img = cv2.imread(board_path, cv2.IMREAD_GRAYSCALE)
    if board_img is None:
        print(f"[!] Ошибка: Не удалось открыть файл '{board_path}'.")
        return

    img_h, img_w = board_img.shape
    cell_w = img_w / 8.0
    cell_h = img_h / 8.0

    board_matrix = []

    print("Сканирование методом попиксельного вычитания (absdiff)...\n")

    for row in range(8):
        row_symbols = []
        for col in range(8):
            # Вычисляем точные координаты клетки
            x1 = int(round(col * cell_w + cell_w * inner_margin_pct))
            y1 = int(round(row * cell_h + cell_h * inner_margin_pct))
            x2 = int(round((col + 1) * cell_w - cell_w * inner_margin_pct))
            y2 = int(round((row + 1) * cell_h - cell_h * inner_margin_pct))

            cell_crop = board_img[y1:y2, x1:x2]

            # 1. Проверяем, есть ли на клетке фигура
            if is_empty_cell(cell_crop, std_threshold=empty_std_threshold):
                row_symbols.append(".")
            else:
                # 2. Определяем цвет фигуры (white / black)
                color_group = detect_color_group(cell_crop)

                best_symbol = "."
                min_difference = float("inf")

                # 3. Сравниваем ТОЛЬКО с шаблонами соответствующего цвета
                target_templates = templates[color_group]

                for symbol, tpl in target_templates.items():
                    tpl_h, tpl_w = tpl.shape
                    cell_resized = cv2.resize(cell_crop, (tpl_w, tpl_h))

                    # Попиксельный модуль разности
                    diff = cv2.absdiff(cell_resized, tpl)
                    total_diff = np.sum(diff)  # Сумма всех несовпадений

                    if total_diff < min_difference:
                        min_difference = total_diff
                        best_symbol = symbol

                row_symbols.append(best_symbol)

        board_matrix.append(row_symbols)

    # Вывод доски в консоль
    divider = "  +" + "---+" * 8
    print(divider)
    for i, row in enumerate(board_matrix):
        rank_label = 8 - i
        row_content = " | ".join(row)
        print(f"{rank_label} | {row_content} |")
        print(divider)
    print("    a   b   c   d   e   f   g   h\n")

    # Генерация FEN
    fen_string = generate_fen(board_matrix)
    print(f"FEN: {fen_string}\n")


if __name__ == "__main__":
    try:
        scan_board()
    except Exception as e:
        print(f"\n[!] Ошибка: {e}")
    
    input("\nНажмите Enter, чтобы закрыть программу...")