---

import os
import cv2
import numpy as np

def calculate_dhash(image, hash_size=8):
    """Вычисляет разностный хэш (dHash) для вырезки."""
    # Приводим к оттенкам серого и урезаем до (hash_size + 1, hash_size)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) == 3 else image
    resized = cv2.resize(gray, (hash_size + 1, hash_size))
    
    # Сравниваем соседние пиксели по горизонтали
    diff = resized[:, 1:] > resized[:, :-1]
    return diff.flatten()


def hamming_distance(hash1, hash2):
    """Считает число несовпадающих бит между двумя хэшами."""
    return np.count_nonzero(hash1 != hash2)


def get_top_crop(cell_img):
    """
    Вырезает верхушку фигуры, минимизируя фон клетки.
    Захватываем верхние 65% высоты и центральные 70% ширины.
    """
    h, w = cell_img.shape[:2]
    return cell_img[int(h * 0.05):int(h * 0.70), int(w * 0.15):int(w * 0.85)]


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 = {}
    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)
            
            if tpl_img is not None:
                top_part = get_top_crop(tpl_img)
                templates[symbol] = calculate_dhash(top_part)

    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",
    max_hash_diff=18  # Порог допустимой разницы dHash (12 - 22)
):
    templates = load_templates(templates_folder)
    if not templates:
        print("[!] Ошибка: Нет шаблонов для сравнения.")
        return

    board_img = cv2.imread(board_path)
    if board_img is None:
        print(f"[!] Ошибка: Файл '{board_path}' не найден.")
        return

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

    board_matrix = []

    print("Сканирование формы верхушек через dHash...\n")

    for row in range(8):
        row_symbols = []
        for col in range(8):
            x1 = int(round(col * cell_w))
            y1 = int(round(row * cell_h))
            x2 = int(round((col + 1) * cell_w))
            y2 = int(round((row + 1) * cell_h))

            cell_crop = board_img[y1:y2, x1:x2]
            cell_top = get_top_crop(cell_crop)
            cell_hash = calculate_dhash(cell_top)

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

            for symbol, tpl_hash in templates.items():
                dist = hamming_distance(cell_hash, tpl_hash)
                if dist < min_dist:
                    min_dist = dist
                    best_symbol = symbol

            # Если наименьшая разница не превышает порог — фиксируем фигуру
            if min_dist <= max_hash_diff:
                row_symbols.append(best_symbol)
            else:
                row_symbols.append(".")

        board_matrix.append(row_symbols)

    # Отрисовка в консоли
    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...")