---

import cv2
import numpy as np

def extract_dominant_piece_color(cell_bgr):
    """Извлекает медианный BGR-цвет центральной части клетки."""
    h, w, _ = cell_bgr.shape
    center = cell_bgr[int(h * 0.25):int(h * 0.75), int(w * 0.25):int(w * 0.75)]
    return np.median(center, axis=(0, 1)).tolist()


def print_board(board_matrix, fen):
    """Печать красивой таблицы доски в консоль."""
    divider = "  +" + "---+" * 8
    print("\n    a   b   c   d   e   f   g   h")
    print(divider)
    for idx, row in enumerate(board_matrix):
        rank = 8 - idx
        row_str = " | ".join([f"{p}" if p != '.' else " " for p in row])
        print(f"{rank} | {row_str} | {rank}")
        print(divider)
    print("    a   b   c   d   e   f   g   h")
    print(f"\nFEN: {fen}\n")


def scan_frame(cell_board, color_database, templates_edges, ALL_PIECES, empty_threshold=12):
    """
    Сканирует доску с разделением на заглавные (белые) и строчные (черные) фигуры.
    """
    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)
            
            # Контуры Canny
            cell_edge = cv2.Canny(gray_cell[7:43, 7:43], 30, 120)

            # 1. Проверка на пустую клетку
            if np.count_nonzero(cell_edge) < empty_threshold:
                row_pieces.append('.')
                continue

            cell_color = np.array(extract_dominant_piece_color(cell))
            
            # Определяем, светлая фигура или темная по яркости (V в HSV или среднему BGR)
            is_white_piece = np.mean(cell_color) > 110

            best_match, max_score = '.', -999.0

            # 2. Фильтруем список кандидатов: белые (P, R, N...) или черные (p, r, n...)
            candidate_pieces = [p for p in ALL_PIECES if p.isupper() == is_white_piece]

            for piece_symbol in candidate_pieces:
                if piece_symbol not in templates_edges or piece_symbol not in color_database:
                    continue
                
                # Сравнение Canny-контуров
                res = cv2.matchTemplate(cell_edge, templates_edges[piece_symbol], cv2.TM_CCOEFF_NORMED)
                _, shape_score, _, _ = cv2.minMaxLoc(res)
                
                # Штраф за разницу цвета
                tpl_color = np.array(color_database[piece_symbol])
                color_dist = np.linalg.norm(cell_color - 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. Сборка 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