---

import cv2
import numpy as np

def extract_dominant_piece_color(cell_bgr):
    """Безопасное извлечение цвета центра клетки."""
    h, w = cell_bgr.shape[:2]
    center = cell_bgr[int(h * 0.25):int(h * 0.75), int(w * 0.25):int(w * 0.75)]
    # Используем mean вместо median для стабильности формата
    mean_color = np.mean(center, axis=(0, 1))
    return mean_color.astype(float).tolist()


def scan_frame(cell_board, color_database, templates_edges, ALL_PIECES, empty_threshold=12):
    """
    Сканирует доску. Возвращает (board_matrix, fen_string).
    """
    board = cv2.resize(cell_board, (400, 400))
    board_matrix = []
    
    for row in range(8):
        row_pieces = []
        for col in range(8):
            cell = board[row * 50:(row + 1) * 50, col * 50:(col + 1) * 50]
            gray_cell = cv2.cvtColor(cell, cv2.COLOR_BGR2GRAY)
            
            # Canny контуры
            cell_edge = cv2.Canny(gray_cell[7:43, 7:43], 30, 120)

            # 1. Отсекаем пустые клетки
            if np.count_nonzero(cell_edge) < empty_threshold:
                row_pieces.append('.')
                continue

            # 2. Безопасно определяем цвет
            cell_color_vec = np.array(extract_dominant_piece_color(cell))
            brightness = np.mean(cell_color_vec)
            
            # Разделение: белая или черная фигура
            is_white_piece = brightness > 110
            candidate_pieces = [p for p in ALL_PIECES if p.isupper() == is_white_piece]

            best_match, max_score = '.', -999.0

            for piece_symbol in candidate_pieces:
                if piece_symbol not in templates_edges or piece_symbol not in color_database:
                    continue
                
                # Сравнение формы Canny
                res = cv2.matchTemplate(cell_edge, templates_edges[piece_symbol], cv2.TM_CCOEFF_NORMED)
                _, shape_score, _, _ = cv2.minMaxLoc(res)
                
                # Штраф за расхождение цвета
                tpl_color = np.array(color_database[piece_symbol])
                color_dist = np.linalg.norm(cell_color_vec - tpl_color)
                
                score = shape_score - (color_dist / 120.0)
                
                if score > max_score:
                    max_score = score
                    best_match = piece_symbol
                    
            row_pieces.append(best_match)
        board_matrix.append(row_pieces)

    # 3. Формирование FEN
    fen_rows = []
    for r in board_matrix:
        empty, row_str = 0, ""
        for p in r:
            if p == '.': 
                empty += 1
            else:
                if empty > 0: 
                    row_str += str(empty)
                    empty = 0
                row_str += p
        if empty > 0: 
            row_str += str(empty)
        fen_rows.append(row_str)
        
    fen_string = "/".join(fen_rows) + " w - - 0 1"
    
    return board_matrix, fen_string


# --- БЕЗОПАСНЫЙ ТЕСТОВЫЙ ЗАПУСК ---
if __name__ == "__main__":
    try:
        # Здесь вызов твоей основной функции/скрипта
        print("[ИНФО] Запуск сканирования...")
        
        # Замени your_board_image, color_database, templates_edges, ALL_PIECES на свои переменные:
        # matrix, fen = scan_frame(your_board_image, color_database, templates_edges, ALL_PIECES)
        # print("FEN:", fen)

    except Exception as e:
        print(f"\n[КРИТИЧЕСКАЯ ОШИБКА]: {e}")
        import traceback
        traceback.print_exc()
        
    input("\nНажмите Enter, чтобы закрыть окно...")