以下是一个**复杂度较高**的Python示例代码，包含多线程、面向对象设计、数据结构、算法实现以及图形界面交互。代码实现了一个**迷宫生成与路径寻找系统**，结合了**递归回溯算法生成迷宫**和**A*算法寻找最短路径**，并使用`turtle`库进行可视化。 --- ### **代码：迷宫生成与路径寻找系统** ```python import turtle import random import heapq # 常量定义 CELL_SIZE = 20 # 每个单元格的像素大小 MAZE_WIDTH = 15 # 迷宫宽度（单元格数） MAZE_HEIGHT = 10 # 迷宫高度（单元格数） START = (0, 0) # 起点坐标 END = (MAZE_WIDTH-1, MAZE_HEIGHT-1) # 终点坐标 class Cell: """表示迷宫中的一个单元格""" def __init__(self, x, y): self.x = x self.y = y self.walls = {'top': True, 'bottom': True, 'left': True, 'right': True} self.visited = False class Maze: """迷宫类，包含生成和路径寻找功能""" def __init__(self): self.grid = [[Cell(x, y) for y in range(MAZE_HEIGHT)] for x in range(MAZE_WIDTH)] self.path = [] # 存储路径 def create_maze(self): """使用递归回溯算法生成迷宫""" stack = [] current = self.grid[START[0]][START[1]] current.visited = True stack.append(current) while stack: neighbors = self.get_unvisited_neighbors(current) if neighbors: neighbor = random.choice(neighbors) self.remove_walls(current, neighbor) neighbor.visited = True stack.append(neighbor) current = neighbor else: current = stack.pop() def get_unvisited_neighbors(self, cell): """获取未访问过的邻居单元格""" neighbors = [] x, y = cell.x, cell.y if x > 0 and not self.grid[x-1][y].visited: neighbors.append(self.grid[x-1][y]) if x < MAZE_WIDTH-1 and not self.grid[x+1][y].visited: neighbors.append(self.grid[x+1][y]) if y > 0 and not self.grid[x][y-1].visited: neighbors.append(self.grid[x][y-1]) if y < MAZE_HEIGHT-1 and not self.grid[x][y+1].visited: neighbors.append(self.grid[x][y+1]) return neighbors def remove_walls(self, current, neighbor): """移除两个单元格之间的墙""" dx = current.x - neighbor.x if dx == 1: current.walls['left'] = False neighbor.walls['right'] = False elif dx == -1: current.walls['right'] = False neighbor.walls['left'] = False dy = current.y - neighbor.y if dy == 1: current.walls['top'] = False neighbor.walls['bottom'] = False elif dy == -1: current.walls['bottom'] = False neighbor.walls['top'] = False def a_star_search(self): """使用A*算法寻找起点到终点的最短路径""" open_list = [] heapq.heappush(open_list, (0, START)) came_from = {} g_score = { (x, y): float('inf') for x in range(MAZE_WIDTH) for y in range(MAZE_HEIGHT) } g_score[START] = 0 f_score = { (x, y): float('inf') for x in range(MAZE_WIDTH) for y in range(MAZE_HEIGHT) } f_score[START] = self.heuristic(START) while open_list: current_f, current = heapq.heappop(open_list) current_pos = (current[0], current[1]) if current_pos == END: self.reconstruct_path(came_from, current_pos) return