---

import os
import cv2
import numpy as np

# --- 1. ВСПОМОГАТЕЛЬНЫЕ ФУНКЦИИ ---

def extract_dominant_piece_color(cell_bgr):
    """Извлечение BGR-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)]
    mean_color = np.mean(center, axis=(0, 1))
    return mean_color.astype(float).tolist()


def print_board(board_matrix, fen):
    """Печать 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 safe_match(image, template):
    """Безопасный вызов matchTemplate (не даёт OpenCV упасть)."""
    if image.shape[0] < template.shape[0] or image.shape[1] < template.shape[1]:
        return 0.0
    res = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
    _, max_val, _, _ = cv2.minMaxLoc(res)
    return max_val if not np.isnan(max_val) else 0.0


def scan_frame(cell_board, color_database, templates_edges, ALL_PIECES, empty_threshold=12):
    """Сканирование доски с точным разделением верхушек фигур."""
    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 (окно 36x36)
            cell_edge = cv2.Canny(gray_cell[7:43, 7:43], 50, 150)

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

            # 2. Определяем цвет фигуры (белая / черная)
            cell_color_vec = np.array(extract_dominant_piece_color(cell))
            brightness = np.mean(cell_color_vec)
            
            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
                
                tpl_edge = templates_edges[piece_symbol]

                # Общая форма (Canny)
                shape_score = safe_match(cell_edge, tpl_edge)
                
                # Штраф за разницу цвета
                tpl_color = np.array(color_database[piece_symbol])
                color_dist = np.linalg.norm(cell_color_vec - tpl_color)
                score = shape_score - (color_dist / 150.0)
                
                # Дополнительная проверка верхушки (верхние 12px)
                top_score = safe_match(cell_edge[:12, :], tpl_edge[:12, :])
                
                # Итоговая оценка
                final_score = score + (top_score * 0.3)
                
                if final_score > max_score:
                    max_score = final_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)
        
    return board_matrix, "/".join(fen_rows) + " w - - 0 1"


# --- 2. ГЛАВНЫЙ ИСПОЛНЯЕМЫЙ БЛОК ---

def main():
    TEMPLATES_DIR = "extracted_pieces"
    BOARD_IMAGE_PATH = "board.png"
    
    ALL_PIECES = ['K', 'Q', 'R', 'B', 'N', 'P', 'k', 'q', 'r', 'b', 'n', 'p']
    
    PIECES_CONFIG = {
        "K": "white_K.png", "Q": "white_Q.png", "R": "white_R.png",
        "B": "white_B.png", "N": "white_N.png", "P": "white_P.png",
        "k": "black_k.png", "q": "black_q.png", "r": "black_r.png",
        "b": "black_b.png", "n": "black_n.png", "p": "black_p.png"
    }

    print("[1/3] Загрузка шаблонов...")
    if not os.path.exists(TEMPLATES_DIR):
        print(f"[ОШИБКА] Папка '{TEMPLATES_DIR}' не найдена!")
        return

    templates_edges = {}
    color_database = {}

    for sym, filename in PIECES_CONFIG.items():
        path = os.path.join(TEMPLATES_DIR, filename)
        if not os.path.exists(path):
            print(f"[ПРЕДУПРЕЖДЕНИЕ] Нет файла: {filename}")
            continue

        img = cv2.imread(path)
        if img is None:
            continue

        img_50 = cv2.resize(img, (50, 50))
        gray = cv2.cvtColor(img_50, cv2.COLOR_BGR2GRAY)
        
        templates_edges[sym] = cv2.Canny(gray[7:43, 7:43], 50, 150)
        color_database[sym] = extract_dominant_piece_color(img_50)

    print(f"[ОК] Загружено шаблонов: {len(templates_edges)}/12")

    print(f"[2/3] Чтение картинки доски '{BOARD_IMAGE_PATH}'...")
    if not os.path.exists(BOARD_IMAGE_PATH):
        print(f"[ОШИБКА] Файл '{BOARD_IMAGE_PATH}' не найден рядом со скриптом!")
        return

    board_img = cv2.imread(BOARD_IMAGE_PATH)
    if board_img is None:
        print("[ОШИБКА] Файл картинки повреждён или не читается!")
        return

    print("[3/3] Распознавание...")
    matrix, fen = scan_frame(board_img, color_database, templates_edges, ALL_PIECES)
    
    print_board(matrix, fen)


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