---

import cv2
import numpy as np
import os

class ChessAnalyzer:
    def __init__(self, templates_dir='templates', win_size=(64, 64)):
        self.templates_dir = templates_dir
        self.win_size = win_size
        
        self.piece_symbols = {
            'white_pawn': 'wP', 'white_knight': 'wN', 'white_bishop': 'wB',
            'white_rook': 'wR', 'white_queen': 'wQ', 'white_king': 'wK',
            'black_pawn': 'bP', 'black_knight': 'bN', 'black_bishop': 'bB',
            'black_rook': 'bR', 'black_queen': 'bQ', 'black_king': 'bK'
        }
        
        self.templates = {}
        self.load_templates()

    def load_templates(self):
        """Загрузка PNG-шаблонов"""
        if not os.path.exists(self.templates_dir):
            print(f"[!] Ошибка: Папка '{self.templates_dir}' не найдена.")
            return

        for filename in os.listdir(self.templates_dir):
            if filename.lower().endswith('.png'):
                piece_name = os.path.splitext(filename)[0]
                path = os.path.join(self.templates_dir, filename)
                
                img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
                if img is None:
                    continue

                img_resized = cv2.resize(img, self.win_size)

                # Наложение альфа-канала на серый фон для стандартизации
                if len(img_resized.shape) == 3 and img_resized.shape[2] == 4:
                    alpha = img_resized[:, :, 3] / 255.0
                    gray_piece = cv2.cvtColor(img_resized[:, :, :3], cv2.COLOR_BGR2GRAY)
                    bg = np.ones_like(gray_piece, dtype=np.uint8) * 128
                    composite_gray = (gray_piece * alpha + bg * (1 - alpha)).astype(np.uint8)
                else:
                    composite_gray = cv2.cvtColor(img_resized, cv2.COLOR_BGR2GRAY) if len(img_resized.shape) == 3 else img_resized

                self.templates[piece_name] = composite_gray

    def is_empty_cell(self, cell_img, stddev_threshold=12.0):
        """Проверка клетки на пустоту по дисперсии в центре"""
        gray = cv2.cvtColor(cell_img, cv2.COLOR_BGR2GRAY) if len(cell_img.shape) == 3 else cell_img
        h, w = gray.shape
        inner_crop = gray[int(h*0.25):int(h*0.75), int(w*0.25):int(w*0.75)]
        
        _, stddev = cv2.meanStdDev(inner_crop)
        return stddev[0][0] < stddev_threshold

    def identify_piece(self, cell_img):
        """Определение фигуры через Normalized Cross-Correlation (matchTemplate)"""
        if cell_img is None or cell_img.size == 0 or not self.templates:
            return ""

        if self.is_empty_cell(cell_img):
            return ""

        cell_resized = cv2.resize(cell_img, self.win_size)
        gray_cell = cv2.cvtColor(cell_resized, cv2.COLOR_BGR2GRAY) if len(cell_resized.shape) == 3 else cell_resized
        
        best_piece = ""
        max_corr = -1.0

        # Сравниваем клетку со всеми шаблонами
        for piece_name, template_img in self.templates.items():
            # Нормализованная взаимная корреляция (возвращает значение от -1.0 до 1.0)
            res = cv2.matchTemplate(gray_cell, template_img, cv2.TM_CCOEFF_NORMED)
            max_val = res[0][0]

            if max_val > max_corr:
                max_corr = max_val
                best_piece = piece_name

        # Порог уверенности совпадения
        if max_corr > 0.35 and best_piece:
            return self.piece_symbols.get(best_piece, "")
        
        return ""

    def get_board_state(self, image_input):
        """Получение матрицы 8x8"""
        if image_input is None:
            return []

        if isinstance(image_input, str):
            if not os.path.exists(image_input):
                print(f"[!] Файл '{image_input}' не найден!")
                return []
            board_img = cv2.imread(image_input)
        else:
            board_img = image_input

        if board_img is None:
            return []

        h, w = board_img.shape[:2]
        cell_h = h // 8
        cell_w = w // 8

        board_matrix = []

        for row in range(8):
            row_pieces = []
            for col in range(8):
                y1, y2 = row * cell_h, (row + 1) * cell_h
                x1, x2 = col * cell_w, (col + 1) * cell_w
                cell_crop = board_img[y1:y2, x1:x2]

                symbol = self.identify_piece(cell_crop)
                row_pieces.append(symbol)
            
            board_matrix.append(row_pieces)

        return board_matrix

    def analyze_initial_board(self, image_input):
        return self.get_board_state(image_input)