import cv2 import numpy as np from collections import deque def preprocess_maze_image(image_path): img = cv2.imread(image_path) if img is None: raise ValueError(f"无法读取图像: {image_path}") gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) binary = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2 ) kernel = np.ones((3, 3), np.uint8) cleaned = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_CLOSE, kernel) return img, cleaned def detect_maze_structure(binary_img): contours, _ = cv2.findContours( binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE ) max_contour = max(contours, key=cv2.contourArea) x, y, w, h = cv2.boundingRect(max_contour) maze_roi = binary_img[y:y+h, x:x+w] horizontal_projection = np.sum(maze_roi, axis=1) // 255 vertical_projection = np.sum(maze_roi, axis=0) // 255 rows = np.count_nonzero(horizontal_projection > 0.5 * np.max(horizontal_projection)) cols = np.count_nonzero(vertical_projection > 0.5 * np.max(vertical_projection)) cell_h = h // rows cell_w = w // cols start_x = x + cell_w // 2 start_y = y + h - cell_h // 2 end_x = x + w - cell_w // 2 end_y = y + cell_h // 2 start_cell = (rows - 1, 0) end_cell = (0, cols - 1) return { 'roi': (x, y, w, h), 'grid_size': (rows, cols), 'cell_size': (cell_h, cell_w), 'start': (start_x, start_y), 'end': (end_x, end_y), 'start_cell': start_cell, 'end_cell': end_cell } def create_maze_grid(binary_img, maze_info): x, y, w, h = maze_info['roi'] rows, cols = maze_info['grid_size'] cell_h, cell_w = maze_info['cell_size'] grid = np.zeros((rows, cols), dtype=np.uint8) for r in range(rows): for c in range(cols): cell_y = y + r * cell_h + cell_h // 4 cell_x = x + c * cell_w + cell_w // 4 roi_h = cell_h // 2 roi_w = cell_w // 2 cell_region = binary_img[ cell_y:cell_y+roi_h, cell_x:cell_x+roi_w ] if np.mean(cell_region) > 25: grid[r, c] = 1 return grid def bfs_pathfinding(grid, start, end): rows, cols = grid.shape directions = [(0, 1), (1, 0), (0, -1), (-1, 0)] queue = deque([(start, [start])]) visited = np.zeros_like(grid, dtype=bool) visited[start] = True while queue: (r, c), path = queue.popleft() if (r, c) == end: return path for dr, dc in directions: nr, nc = r + dr, c + dc if (0 <= nr < rows and 0 <= nc < cols and not visited[nr, nc] and grid[nr, nc] == 0): visited[nr, nc] = True queue.append(((nr, nc), path + [(nr, nc)])) return [] def visualize_results(original_img, maze_info, grid, path): x, y, w, h = maze_info['roi'] rows, cols = maze_info['grid_size'] cell_h, cell_w = maze_info['cell_size'] result_img = original_img.copy() cv2.rectangle(result_img, (x, y), (x+w, y+h), (0, 100, 255), 2) cv2.circle(result_img, maze_info['start'], 8, (0, 0, 255), -1) # 红色起点 cv2.circle(result_img, maze_info['end'], 8, (255, 0, 0), -1) # 蓝色终点 if path: for i in range(1, len(path)): r1, c1 = path[i-1] r2, c2 = path[i] y1 = y + r1 * cell_h + cell_h // 2 x1 = x + c1 * cell_w + cell_w // 2 y2 = y + r2 * cell_h + cell_h // 2 x2 = x + c2 * cell_w + cell_w // 2 cv2.line(result_img, (x1, y1), (x2, y2), (0, 255, 0), 2) return result_img def process_maze_image(image_path, output_path="output.png"): try: # 1. 图像预处理 original, binary = preprocess_maze_image(image_path) # 2. 检测迷宫结构 maze_info = detect_maze_structure(binary) # 3. 创建迷宫网格 grid = create_maze_grid(binary, maze_info) path = bfs_pathfinding( grid, maze_info['start_cell'], maze_info['end_cell'] ) if not path: print("警告：未找到从起点到终点的路径！") result_img = visualize_results(original, maze_info, grid, path) cv2.imwrite(output_path, result_img) print(f"处理完成！结果已保存至: {output_path}") return result_img except Exception as e: print(f"处理过程中出错: {str(e)}") return None if __name__ == "__main__": input_image = "maze.png" output_image = "solved_maze.png" result = process_maze_image(input_image, output_image) if result is not None: cv2.namedWindow("Solved Maze",cv2.WINDOW_NORMAL) cv2.imshow("Solved Maze", result) cv2.waitKey(0) cv2.destroyAllWindows()不改动代码，解释代码的步骤及语句用法