From a2648e76807ce5f46a82b8a9af8f4cca81e80d12 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Sun, 21 Dec 2025 12:30:32 +0800
Subject: [PATCH 01/11] =?UTF-8?q?=E6=97=A0=E4=BA=BA=E6=9C=BA=E5=AF=BC?=
 =?UTF-8?q?=E8=88=AA=E7=B3=BB=E7=BB=9F?=
MIME-Version: 1.0
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简化app
---
 src/UAV_navigation_system/app.py | 1963 +++++++++++++++++++++++++++++-
 1 file changed, 1945 insertions(+), 18 deletions(-)

diff --git a/src/UAV_navigation_system/app.py b/src/UAV_navigation_system/app.py
index e6a0cdbc11..a6066686fc 100644
--- a/src/UAV_navigation_system/app.py
+++ b/src/UAV_navigation_system/app.py
@@ -1,33 +1,1960 @@
-# app.py - Flask Web应用主程序
-from flask import Flask, render_template, Response, jsonify
+#!/usr/bin/env python3
+"""
+🚁 无人机导航系统 - 完整演示版
+无需真实数据，立即展示效果
+"""
+
+from flask import Flask, render_template, Response, jsonify, request, send_file
 import cv2
+import numpy as np
 import threading
+import time
+import json
+import os
+import io
+from datetime import datetime
+from PIL import Image, ImageDraw, ImageFont
+import random
 
 app = Flask(__name__)
 
-def generate_frames():
+
+# ==================== 配置参数 ====================
+class Config:
+    """演示配置"""
+    # 演示模式配置
+    DEMO_MODE = True  # 演示模式，使用虚拟数据
+    USE_VIRTUAL_CAMERA = True  # 使用虚拟摄像头
+
+    # 类别配置
+    CLASS_NAMES = ['森林 Forest', '火灾 Fire', '城市 City', '动物 Animal', '车辆 Vehicle', '水域 Water']
+    CLASS_COLORS = {
+        '森林 Forest': (0, 128, 0),
+        '火灾 Fire': (255, 0, 0),
+        '城市 City': (128, 128, 128),
+        '动物 Animal': (255, 165, 0),
+        '车辆 Vehicle': (255, 0, 255),
+        '水域 Water': (0, 191, 255)
+    }
+
+    # 无人机状态
+    DRONE_STATUS = {
+        'battery': 100,
+        'altitude': 0,
+        'speed': 0,
+        'location': {'x': 0, 'y': 0, 'z': 0},
+        'mode': 'LANDED',  # LANDED, TAKEOFF, FLYING, LANDING
+        'detected_class': '正在检测...',
+        'confidence': 0,
+        'timestamp': None,
+        'temperature': 25,
+        'wind_speed': 5,
+        'gps_signal': '强'
+    }
+
+
+config = Config()
+
+
+# ==================== 虚拟摄像头和检测系统 ====================
+class VirtualCamera:
+    """虚拟摄像头系统 - 生成模拟的无人机画面"""
+
+    def __init__(self):
+        self.frame_width = 640
+        self.frame_height = 480
+        self.frame_count = 0
+        self.current_scene = '城市 City'
+        self.scene_transition = 0
+        self.scene_history = []
+
+        # 场景切换概率
+        self.scene_change_prob = 0.05
+
+        # 创建虚拟场景图像
+        self.scene_images = self.create_scene_images()
+
+        print("🎥 虚拟摄像头初始化完成")
+        print("📊 可检测场景:", ", ".join(config.CLASS_NAMES))
+
+    def create_scene_images(self):
+        """创建虚拟场景图像"""
+        images = {}
+
+        for scene in config.CLASS_NAMES:
+            # 创建基础图像
+            img = np.zeros((self.frame_height, self.frame_width, 3), dtype=np.uint8)
+
+            # 根据场景类型设置不同颜色和模式
+            if '森林' in scene:
+                # 森林 - 绿色系
+                img[:, :, 1] = random.randint(100, 200)  # 绿色
+                # 添加树木纹理
+                for i in range(20):
+                    x = random.randint(50, self.frame_width - 50)
+                    y = random.randint(50, self.frame_height - 50)
+                    cv2.circle(img, (x, y), 15, (0, random.randint(150, 255), 0), -1)
+
+            elif '火灾' in scene:
+                # 火灾 - 红色系
+                img[:, :, 2] = random.randint(150, 255)  # 红色
+                img[:, :, 1] = random.randint(50, 150)  # 黄色
+                # 添加火焰效果
+                for i in range(30):
+                    x = random.randint(50, self.frame_width - 50)
+                    y = random.randint(50, self.frame_height - 50)
+                    size = random.randint(5, 20)
+                    cv2.circle(img, (x, y), size, (0, random.randint(100, 200), random.randint(200, 255)), -1)
+
+            elif '城市' in scene:
+                # 城市 - 灰色系
+                gray = random.randint(100, 200)
+                img[:, :, 0] = gray
+                img[:, :, 1] = gray
+                img[:, :, 2] = gray
+                # 添加建筑
+                for i in range(10):
+                    x = random.randint(50, self.frame_width - 50)
+                    width = random.randint(20, 60)
+                    height = random.randint(40, 150)
+                    cv2.rectangle(img, (x, self.frame_height - height),
+                                  (x + width, self.frame_height),
+                                  (gray + 20, gray + 20, gray + 20), -1)
+
+            elif '动物' in scene:
+                # 动物 - 棕色系
+                img[:, :, 0] = random.randint(30, 60)  # 蓝色通道（棕色偏黄）
+                img[:, :, 1] = random.randint(80, 120)  # 绿色通道
+                img[:, :, 2] = random.randint(140, 180)  # 红色通道
+                # 添加动物轮廓
+                for i in range(5):
+                    x = random.randint(50, self.frame_width - 50)
+                    y = random.randint(50, self.frame_height - 50)
+                    cv2.ellipse(img, (x, y), (30, 20), 0, 0, 360, (100, 70, 40), -1)
+
+            elif '车辆' in scene:
+                # 车辆 - 各种颜色
+                img[:, :, 0] = random.randint(50, 100)  # 蓝色
+                img[:, :, 1] = random.randint(50, 100)  # 绿色
+                img[:, :, 2] = random.randint(50, 100)  # 红色
+                # 添加车辆
+                for i in range(8):
+                    x = random.randint(50, self.frame_width - 50)
+                    y = random.randint(100, self.frame_height - 50)
+                    cv2.rectangle(img, (x - 25, y - 15), (x + 25, y + 15),
+                                  (random.randint(100, 255), random.randint(100, 255), random.randint(100, 255)), -1)
+
+            elif '水域' in scene:
+                # 水域 - 蓝色系
+                img[:, :, 0] = random.randint(150, 255)  # 蓝色
+                # 添加波纹效果
+                for i in range(15):
+                    x = random.randint(50, self.frame_width - 50)
+                    y = random.randint(50, self.frame_height - 50)
+                    cv2.circle(img, (x, y), random.randint(10, 40),
+                               (random.randint(100, 200), random.randint(100, 200), 255), 2)
+
+            images[scene] = img
+
+        return images
+
+    def get_frame(self):
+        """获取虚拟摄像头帧"""
+        self.frame_count += 1
+
+        # 随机切换场景（模拟摄像头移动）
+        if random.random() < self.scene_change_prob and self.scene_transition == 0:
+            self.scene_history.append(self.current_scene)
+            if len(self.scene_history) > 5:
+                self.scene_history.pop(0)
+
+            # 随机选择新场景（排除当前场景）
+            available_scenes = [s for s in config.CLASS_NAMES if s != self.current_scene]
+            self.current_scene = random.choice(available_scenes)
+            self.scene_transition = 30  # 30帧的过渡效果
+
+        # 获取当前场景图像
+        frame = self.scene_images[self.current_scene].copy()
+
+        # 添加过渡效果
+        if self.scene_transition > 0:
+            old_scene = self.scene_history[-1] if self.scene_history else self.current_scene
+            old_frame = self.scene_images[old_scene].copy()
+
+            alpha = self.scene_transition / 30.0
+            frame = cv2.addWeighted(frame, 1 - alpha, old_frame, alpha, 0)
+            self.scene_transition -= 1
+
+        # 模拟摄像头噪声
+        noise = np.random.normal(0, 3, frame.shape).astype(np.uint8)
+        frame = cv2.add(frame, noise)
+
+        # 添加时间戳
+        cv2.putText(frame, f"帧: {self.frame_count}", (10, 20),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+        return frame, self.current_scene
+
+    def simulate_detection(self, scene):
+        """模拟深度学习检测结果"""
+        # 基础置信度
+        if scene in config.CLASS_NAMES:
+            base_confidence = 0.85 + random.random() * 0.12  # 85-97%
+        else:
+            base_confidence = 0.3 + random.random() * 0.4  # 30-70%
+
+        # 添加一些随机波动
+        confidence = max(0.1, min(0.99, base_confidence + (random.random() - 0.5) * 0.1))
+
+        return scene, confidence
+
+
+# 创建虚拟摄像头实例
+virtual_camera = VirtualCamera()
+
+
+# ==================== 无人机智能系统 ====================
+class IntelligentDroneSystem:
+    """无人机智能控制系统"""
+
+    def __init__(self):
+        self.emergency_level = 0  # 0-10，10为最高紧急级别
+        self.flight_log = []
+        self.last_detection_time = time.time()
+        self.detection_interval = 2.0  # 每2秒检测一次
+        self.response_actions = {
+            '森林 Forest': self.normal_flight,
+            '火灾 Fire': self.emergency_response,
+            '城市 City': self.urban_flight,
+            '动物 Animal': self.avoid_obstacle,
+            '车辆 Vehicle': self.traffic_awareness,
+            '水域 Water': self.water_precaution
+        }
+
+        print("🤖 无人机智能系统初始化完成")
+
+    def normal_flight(self):
+        """正常飞行模式"""
+        return {
+            'action': '正常飞行',
+            'speed': 5,
+            'altitude': '维持',
+            'message': '森林环境，保持正常飞行模式'
+        }
+
+    def emergency_response(self):
+        """火灾应急响应"""
+        self.emergency_level = min(10, self.emergency_level + 2)
+        return {
+            'action': '紧急响应',
+            'speed': 8,
+            'altitude': '升高',
+            'message': '检测到火灾！正在升高避让并发送警报'
+        }
+
+    def urban_flight(self):
+        """城市飞行模式"""
+        return {
+            'action': '谨慎飞行',
+            'speed': 3,
+            'altitude': '维持',
+            'message': '城市环境，注意建筑物和人群'
+        }
+
+    def avoid_obstacle(self):
+        """避障模式"""
+        return {
+            'action': '避障飞行',
+            'speed': 4,
+            'altitude': '微调',
+            'message': '检测到动物，保持安全距离'
+        }
+
+    def traffic_awareness(self):
+        """交通感知模式"""
+        return {
+            'action': '交通感知',
+            'speed': 2,
+            'altitude': '维持',
+            'message': '检测到车辆，注意交通状况'
+        }
+
+    def water_precaution(self):
+        """水域预防模式"""
+        return {
+            'action': '水域预防',
+            'speed': 3,
+            'altitude': '升高',
+            'message': '检测到水域，升高飞行高度避免接触'
+        }
+
+    def analyze_scene(self, scene, confidence):
+        """分析场景并制定飞行策略"""
+        current_time = time.time()
+
+        # 检查是否需要更新检测
+        if current_time - self.last_detection_time < self.detection_interval:
+            return None
+
+        self.last_detection_time = current_time
+
+        # 根据置信度调整响应
+        if confidence < 0.6:
+            response = {
+                'action': '待确认',
+                'speed': 1,
+                'altitude': '悬停',
+                'message': '检测置信度较低，悬停待确认'
+            }
+        else:
+            # 获取对应场景的响应
+            if scene in self.response_actions:
+                response = self.response_actions[scene]()
+            else:
+                response = self.normal_flight()
+
+        # 记录飞行日志
+        log_entry = {
+            'timestamp': datetime.now().strftime("%H:%M:%S"),
+            'scene': scene,
+            'confidence': confidence,
+            'action': response['action'],
+            'message': response['message']
+        }
+        self.flight_log.append(log_entry)
+
+        # 保持日志大小
+        if len(self.flight_log) > 20:
+            self.flight_log = self.flight_log[-20:]
+
+        return response
+
+
+# 创建无人机智能系统
+drone_system = IntelligentDroneSystem()
+
+
+# ==================== 无人机模拟器 ====================
+class DroneSimulator(threading.Thread):
+    """无人机状态模拟器线程"""
+
+    def __init__(self):
+        super().__init__()
+        self.running = True
+        self.daemon = True
+        self.simulation_speed = 1.0
+        self.last_update = time.time()
+
+        print("🚁 无人机模拟器启动")
+
+    def run(self):
+        """运行模拟器"""
+        while self.running:
+            try:
+                current_time = time.time()
+                delta_time = min(1.0, current_time - self.last_update) * self.simulation_speed
+                self.last_update = current_time
+
+                # 更新无人机状态
+                self.update_drone_status(delta_time)
+
+                # 根据当前模式更新状态
+                self.update_by_mode(delta_time)
+
+                # 更新环境参数
+                self.update_environment()
+
+                time.sleep(0.1)
+
+            except Exception as e:
+                print(f"模拟器错误: {e}")
+                time.sleep(1)
+
+    def update_drone_status(self, delta_time):
+        """更新无人机状态"""
+        # 根据飞行模式更新电池
+        if config.DRONE_STATUS['mode'] == 'FLYING':
+            config.DRONE_STATUS['battery'] = max(0, config.DRONE_STATUS['battery'] - 0.1 * delta_time)
+
+        # 自动充电（如果着陆且电量低于20%）
+        elif config.DRONE_STATUS['mode'] == 'LANDED' and config.DRONE_STATUS['battery'] < 20:
+            config.DRONE_STATUS['battery'] = min(100, config.DRONE_STATUS['battery'] + 0.5 * delta_time)
+
+    def update_by_mode(self, delta_time):
+        """根据飞行模式更新"""
+        mode = config.DRONE_STATUS['mode']
+
+        if mode == 'TAKEOFF':
+            config.DRONE_STATUS['altitude'] = min(50, config.DRONE_STATUS['altitude'] + 10 * delta_time)
+            config.DRONE_STATUS['speed'] = 2
+
+            if config.DRONE_STATUS['altitude'] >= 50:
+                config.DRONE_STATUS['mode'] = 'FLYING'
+                print("🛫 起飞完成，进入飞行模式")
+
+        elif mode == 'FLYING':
+            # 随机飞行路径
+            config.DRONE_STATUS['altitude'] = 50 + random.uniform(-3, 3)
+            config.DRONE_STATUS['speed'] = 3 + random.uniform(-1, 1)
+
+            # 随机位置变化
+            config.DRONE_STATUS['location']['x'] += random.uniform(-2, 2) * delta_time
+            config.DRONE_STATUS['location']['y'] += random.uniform(-2, 2) * delta_time
+
+        elif mode == 'LANDING':
+            config.DRONE_STATUS['altitude'] = max(0, config.DRONE_STATUS['altitude'] - 8 * delta_time)
+            config.DRONE_STATUS['speed'] = 1
+
+            if config.DRONE_STATUS['altitude'] <= 0:
+                config.DRONE_STATUS['mode'] = 'LANDED'
+                config.DRONE_STATUS['speed'] = 0
+                print("🛬 降落完成")
+
+    def update_environment(self):
+        """更新环境参数"""
+        # 模拟温度变化
+        config.DRONE_STATUS['temperature'] = 20 + random.uniform(-2, 2)
+
+        # 模拟风速变化
+        config.DRONE_STATUS['wind_speed'] = max(0, 3 + random.uniform(-1, 1))
+
+        # 更新GPS信号（受环境影响）
+        if config.DRONE_STATUS['detected_class'] == '城市 City':
+            config.DRONE_STATUS['gps_signal'] = random.choice(['中', '强'])
+        else:
+            config.DRONE_STATUS['gps_signal'] = '强'
+
+    def stop(self):
+        """停止模拟器"""
+        self.running = False
+
+
+# 启动无人机模拟器
+drone_simulator = DroneSimulator()
+drone_simulator.start()
+
+
+# ==================== Flask路由和功能 ====================
+def generate_video_feed():
     """生成视频流"""
-    camera = cv2.VideoCapture(0)  # 使用默认摄像头
     while True:
-        success, frame = camera.read()
-        if not success:
-            break
-        else:
-            ret, buffer = cv2.imencode('.jpg', frame)
-            frame = buffer.tobytes()
-            yield (b'--frame\r\n'
-                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+        # 获取虚拟摄像头帧
+        frame, current_scene = virtual_camera.get_frame()
+
+        # 模拟AI检测
+        detected_scene, confidence = virtual_camera.simulate_detection(current_scene)
+
+        # 更新无人机状态
+        config.DRONE_STATUS['detected_class'] = detected_scene
+        config.DRONE_STATUS['confidence'] = confidence
+        config.DRONE_STATUS['timestamp'] = datetime.now().strftime("%H:%M:%S")
+
+        # 获取智能响应
+        response = drone_system.analyze_scene(detected_scene, confidence)
+        if response:
+            # 更新飞行模式（如果响应建议）
+            if response['action'] == '紧急响应' and config.DRONE_STATUS['mode'] == 'FLYING':
+                config.DRONE_STATUS['mode'] = 'LANDING'
+
+        # 添加检测信息到帧
+        self.add_detection_overlay(frame, detected_scene, confidence)
+
+        # 编码为JPEG
+        ret, buffer = cv2.imencode('.jpg', frame)
+        frame_bytes = buffer.tobytes()
+
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
+
+
+def add_detection_overlay(frame, scene, confidence):
+    """添加检测信息到视频帧"""
+    height, width = frame.shape[:2]
+
+    # 添加半透明状态栏
+    overlay = frame.copy()
+    cv2.rectangle(overlay, (0, 0), (width, 100), (0, 0, 0), -1)
+    cv2.addWeighted(overlay, 0.6, frame, 0.4, 0, frame)
+
+    # 添加标题
+    cv2.putText(frame, "🚁 无人机视觉导航演示系统", (10, 30),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
+
+    # 添加检测结果
+    color = config.CLASS_COLORS.get(scene, (255, 255, 255))
+    detection_text = f"检测: {scene}"
+    confidence_text = f"置信度: {confidence:.1%}"
+
+    cv2.putText(frame, detection_text, (10, 60),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
+    cv2.putText(frame, confidence_text, (10, 85),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
+
+    # 添加无人机状态
+    status_text = f"状态: {config.DRONE_STATUS['mode']} | 电量: {config.DRONE_STATUS['battery']:.1f}%"
+    cv2.putText(frame, status_text, (width - 300, 30),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)
+
+    # 添加场景指示器
+    for i, scene_name in enumerate(config.CLASS_NAMES):
+        x = 10 + (i * 100)
+        if x + 90 < width:
+            color = config.CLASS_COLORS.get(scene_name, (128, 128, 128))
+            thickness = 3 if scene_name == scene else 1
+            cv2.rectangle(frame, (x, height - 30), (x + 90, height - 10), color, thickness)
+
+            # 简化显示
+            scene_short = scene_name.split()[0]
+            cv2.putText(frame, scene_short, (x + 5, height - 15),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
+
 
 @app.route('/')
 def index():
-    """主页"""
-    return render_template('index.html')
+    """主页面"""
+    return render_template('index.html',
+                           class_names=config.CLASS_NAMES,
+                           drone_status=config.DRONE_STATUS,
+                           class_colors=config.CLASS_COLORS)
+
 
 @app.route('/video_feed')
 def video_feed():
-    """视频流路由"""
-    return Response(generate_frames(),
-                   mimetype='multipart/x-mixed-replace; boundary=frame')
+    """视频流端点"""
+    return Response(generate_video_feed(),
+                    mimetype='multipart/x-mixed-replace; boundary=frame')
+
+
+@app.route('/drone_status')
+def get_drone_status():
+    """获取无人机状态"""
+    return jsonify(config.DRONE_STATUS)
+
+
+@app.route('/flight_log')
+def get_flight_log():
+    """获取飞行日志"""
+    return jsonify(drone_system.flight_log)
+
+
+@app.route('/system_info')
+def get_system_info():
+    """获取系统信息"""
+    info = {
+        'demo_mode': config.DEMO_MODE,
+        'virtual_camera': config.USE_VIRTUAL_CAMERA,
+        'fps': 30,
+        'detection_accuracy': '演示模式',
+        'system_time': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        'uptime': time.time() - drone_simulator.last_update,
+        'emergency_level': drone_system.emergency_level
+    }
+    return jsonify(info)
+
+
+@app.route('/control', methods=['POST'])
+def control_drone():
+    """控制无人机"""
+    data = request.json
+    command = data.get('command', '')
+
+    response = {
+        'success': True,
+        'message': '',
+        'command': command,
+        'timestamp': datetime.now().strftime("%H:%M:%S")
+    }
+
+    try:
+        current_mode = config.DRONE_STATUS['mode']
+
+        if command == 'takeoff':
+            if current_mode == 'LANDED':
+                config.DRONE_STATUS['mode'] = 'TAKEOFF'
+                response['message'] = '无人机正在起飞...'
+            else:
+                response['success'] = False
+                response['message'] = f'无法起飞，当前状态: {current_mode}'
+
+        elif command == 'land':
+            if current_mode in ['FLYING', 'TAKEOFF']:
+                config.DRONE_STATUS['mode'] = 'LANDING'
+                response['message'] = '无人机正在降落...'
+            else:
+                response['success'] = False
+                response['message'] = f'无法降落，当前状态: {current_mode}'
+
+        elif command == 'emergency_land':
+            config.DRONE_STATUS['mode'] = 'LANDING'
+            response['message'] = '紧急降落已启动'
+
+        elif command == 'hover':
+            response['message'] = '悬停模式已激活'
+
+        elif command == 'charge':
+            config.DRONE_STATUS['battery'] = 100
+            response['message'] = '电池已充满'
+
+        elif command == 'auto_pilot':
+            response['message'] = '自动驾驶模式已激活'
+
+        elif command == 'return_home':
+            config.DRONE_STATUS['location'] = {'x': 0, 'y': 0, 'z': config.DRONE_STATUS['altitude']}
+            response['message'] = '正在返回起始点'
+
+        else:
+            response['success'] = False
+            response['message'] = f'未知命令: {command}'
 
+    except Exception as e:
+        response['success'] = False
+        response['message'] = f'控制错误: {str(e)}'
+
+    return jsonify(response)
+
+
+@app.route('/simulate_scene', methods=['POST'])
+def simulate_scene():
+    """手动模拟特定场景"""
+    data = request.json
+    scene = data.get('scene', '')
+
+    if scene in config.CLASS_NAMES:
+        virtual_camera.current_scene = scene
+        virtual_camera.scene_transition = 15
+
+        response = {
+            'success': True,
+            'message': f'已切换到 {scene} 场景',
+            'scene': scene
+        }
+    else:
+        response = {
+            'success': False,
+            'message': f'未知场景: {scene}',
+            'available_scenes': config.CLASS_NAMES
+        }
+
+    return jsonify(response)
+
+
+@app.route('/capture_image')
+def capture_image():
+    """捕获当前帧图像"""
+    frame, _ = virtual_camera.get_frame()
+
+    # 编码为PNG
+    ret, buffer = cv2.imencode('.png', frame)
+
+    # 创建内存文件
+    img_io = io.BytesIO(buffer.tobytes())
+    img_io.seek(0)
+
+    return send_file(img_io, mimetype='image/png',
+                     as_attachment=True,
+                     download_name=f'drone_capture_{datetime.now().strftime("%Y%m%d_%H%M%S")}.png')
+
+
+# ==================== 主函数 ====================
 if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=5000)
\ No newline at end of file
+    print("=" * 70)
+    print("🚁 无人机导航系统 - 完整演示版")
+    print("=" * 70)
+    print("🎯 无需真实数据，立即展示效果")
+    print("📊 检测场景:", ", ".join(config.CLASS_NAMES))
+    print("🌐 访问地址: http://localhost:5000")
+    print("=" * 70)
+    print("🎮 控制功能:")
+    print("  - 起飞/降落/紧急降落")
+    print("  - 自动驾驶/返航")
+    print("  - 手动切换场景")
+    print("  - 实时视频流")
+    print("=" * 70)
+
+    # 检查模板是否存在
+    if not os.path.exists("templates/index.html"):
+        print("⚠️  未找到模板文件，正在创建...")
+        create_default_template()
+
+    # 检查静态目录
+    os.makedirs("static/css", exist_ok=True)
+    os.makedirs("static/js", exist_ok=True)
+
+    # 创建静态文件
+    create_static_files()
+
+    # 运行Flask应用
+    app.run(debug=True, host='0.0.0.0', port=5000, threaded=True)
+
+
+# ==================== 辅助函数 ====================
+def create_default_template():
+    """创建默认HTML模板"""
+    template_dir = "templates"
+    os.makedirs(template_dir, exist_ok=True)
+
+    html_content = """<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>🚁 无人机视觉导航演示系统</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
+</head>
+<body>
+    <div class="container">
+        <!-- 头部 -->
+        <header class="header">
+            <div class="header-content">
+                <h1><i class="fas fa-drone"></i> 无人机视觉导航演示系统</h1>
+                <p class="subtitle">基于深度学习的实时环境识别与智能飞行控制</p>
+                <div class="demo-badge">
+                    <i class="fas fa-rocket"></i> 演示模式 | 实时效果展示
+                </div>
+            </div>
+        </header>
+
+        <!-- 主内容区 -->
+        <div class="main-content">
+            <!-- 左侧：视频和检测 -->
+            <div class="video-section">
+                <div class="section-header">
+                    <h2><i class="fas fa-video"></i> 实时视觉识别</h2>
+                    <div class="fps-indicator">30 FPS</div>
+                </div>
+
+                <div class="video-container">
+                    <img id="video-feed" src="{{ url_for('video_feed') }}" alt="实时视频流">
+                    <div class="video-overlay">
+                        <div class="detection-info">
+                            <div class="detection-title">实时检测结果</div>
+                            <div class="detection-result">
+                                <span id="live-class">{{ drone_status.detected_class }}</span>
+                                <span id="live-confidence">{{ "%.1f"|format(drone_status.confidence * 100) }}%</span>
+                            </div>
+                        </div>
+                        <button id="capture-btn" class="btn-capture">
+                            <i class="fas fa-camera"></i> 截图
+                        </button>
+                    </div>
+                </div>
+
+                <div class="detection-panel">
+                    <h3><i class="fas fa-search"></i> 场景识别面板</h3>
+                    <div class="confidence-meter">
+                        <div class="meter-label">检测置信度</div>
+                        <div class="meter-bar">
+                            <div class="meter-fill" id="confidence-fill" 
+                                 style="width: {{ drone_status.confidence * 100 }}%"></div>
+                        </div>
+                        <div class="meter-value" id="confidence-value">
+                            {{ "%.1f"|format(drone_status.confidence * 100) }}%
+                        </div>
+                    </div>
+
+                    <div class="scene-controls">
+                        <h4>手动场景切换</h4>
+                        <div class="scene-buttons">
+                            {% for scene in class_names %}
+                            <button class="scene-btn" data-scene="{{ scene }}"
+                                    style="border-color: rgb{{ class_colors[scene] }}">
+                                {{ scene }}
+                            </button>
+                            {% endfor %}
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- 右侧：控制和状态 -->
+            <div class="control-section">
+                <div class="section-header">
+                    <h2><i class="fas fa-gamepad"></i> 飞行控制</h2>
+                    <div class="mode-indicator" id="mode-indicator">{{ drone_status.mode }}</div>
+                </div>
+
+                <!-- 无人机状态 -->
+                <div class="status-grid">
+                    <div class="status-card">
+                        <div class="status-icon"><i class="fas fa-battery-full"></i></div>
+                        <div class="status-content">
+                            <div class="status-label">电池电量</div>
+                            <div class="status-value" id="battery-value">{{ drone_status.battery|round(1) }}%</div>
+                            <div class="status-bar">
+                                <div class="bar-fill" id="battery-fill" 
+                                     style="width: {{ drone_status.battery }}%"></div>
+                            </div>
+                        </div>
+                    </div>
+
+                    <div class="status-card">
+                        <div class="status-icon"><i class="fas fa-mountain"></i></div>
+                        <div class="status-content">
+                            <div class="status-label">飞行高度</div>
+                            <div class="status-value" id="altitude-value">{{ drone_status.altitude|round(1) }} m</div>
+                        </div>
+                    </div>
+
+                    <div class="status-card">
+                        <div class="status-icon"><i class="fas fa-tachometer-alt"></i></div>
+                        <div class="status-content">
+                            <div class="status-label">飞行速度</div>
+                            <div class="status-value" id="speed-value">{{ drone_status.speed|round(1) }} m/s</div>
+                        </div>
+                    </div>
+
+                    <div class="status-card">
+                        <div class="status-icon"><i class="fas fa-map-marker-alt"></i></div>
+                        <div class="status-content">
+                            <div class="status-label">位置坐标</div>
+                            <div class="status-value" id="position-value">
+                                ({{ drone_status.location.x|round(1) }}, {{ drone_status.location.y|round(1) }})
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- 飞行控制按钮 -->
+                <div class="control-panel">
+                    <div class="control-row">
+                        <button class="control-btn btn-takeoff" id="takeoff-btn">
+                            <i class="fas fa-rocket"></i>
+                            <span>起飞</span>
+                        </button>
+                        <button class="control-btn btn-land" id="land-btn">
+                            <i class="fas fa-plane-arrival"></i>
+                            <span>降落</span>
+                        </button>
+                        <button class="control-btn btn-emergency" id="emergency-btn">
+                            <i class="fas fa-exclamation-triangle"></i>
+                            <span>紧急降落</span>
+                        </button>
+                    </div>
+
+                    <div class="control-row">
+                        <button class="control-btn btn-direction" id="forward-btn">
+                            <i class="fas fa-arrow-up"></i>
+                            <span>前进</span>
+                        </button>
+                        <button class="control-btn btn-direction" id="backward-btn">
+                            <i class="fas fa-arrow-down"></i>
+                            <span>后退</span>
+                        </button>
+                        <button class="control-btn btn-direction" id="left-btn">
+                            <i class="fas fa-arrow-left"></i>
+                            <span>左转</span>
+                        </button>
+                        <button class="control-btn btn-direction" id="right-btn">
+                            <i class="fas fa-arrow-right"></i>
+                            <span>右转</span>
+                        </button>
+                    </div>
+
+                    <div class="control-row">
+                        <button class="control-btn btn-action" id="hover-btn">
+                            <i class="fas fa-pause-circle"></i>
+                            <span>悬停</span>
+                        </button>
+                        <button class="control-btn btn-action" id="charge-btn">
+                            <i class="fas fa-charging-station"></i>
+                            <span>充电</span>
+                        </button>
+                        <button class="control-btn btn-action" id="auto-btn">
+                            <i class="fas fa-robot"></i>
+                            <span>自动驾驶</span>
+                        </button>
+                        <button class="control-btn btn-action" id="home-btn">
+                            <i class="fas fa-home"></i>
+                            <span>返航</span>
+                        </button>
+                    </div>
+                </div>
+
+                <!-- 环境信息 -->
+                <div class="environment-panel">
+                    <h3><i class="fas fa-cloud-sun"></i> 环境监测</h3>
+                    <div class="env-grid">
+                        <div class="env-item">
+                            <i class="fas fa-thermometer-half"></i>
+                            <span>温度: <span id="temp-value">{{ drone_status.temperature|round(1) }}°C</span></span>
+                        </div>
+                        <div class="env-item">
+                            <i class="fas fa-wind"></i>
+                            <span>风速: <span id="wind-value">{{ drone_status.wind_speed|round(1) }} m/s</span></span>
+                        </div>
+                        <div class="env-item">
+                            <i class="fas fa-satellite"></i>
+                            <span>GPS: <span id="gps-value">{{ drone_status.gps_signal }}</span></span>
+                        </div>
+                        <div class="env-item">
+                            <i class="fas fa-shield-alt"></i>
+                            <span>紧急等级: <span id="emergency-value">0</span>/10</span>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- 底部：日志和系统信息 -->
+        <div class="footer-section">
+            <div class="logs-panel">
+                <h3><i class="fas fa-clipboard-list"></i> 飞行日志</h3>
+                <div class="logs-container" id="logs-container">
+                    <!-- 日志将通过JavaScript动态加载 -->
+                    <div class="log-entry">
+                        <span class="log-time">--:--:--</span>
+                        <span class="log-message">系统启动中...</span>
+                    </div>
+                </div>
+            </div>
+
+            <div class="system-info">
+                <h3><i class="fas fa-info-circle"></i> 系统信息</h3>
+                <div class="info-grid">
+                    <div class="info-item">
+                        <i class="fas fa-microchip"></i>
+                        <span>运行模式: <span id="system-mode">演示模式</span></span>
+                    </div>
+                    <div class="info-item">
+                        <i class="fas fa-clock"></i>
+                        <span>系统时间: <span id="system-time">--:--:--</span></span>
+                    </div>
+                    <div class="info-item">
+                        <i class="fas fa-eye"></i>
+                        <span>检测精度: <span id="detection-accuracy">演示模式</span></span>
+                    </div>
+                    <div class="info-item">
+                        <i class="fas fa-wifi"></i>
+                        <span>连接状态: <span id="connection-status">已连接</span></span>
+                    </div>
+                </div>
+
+                <div class="system-controls">
+                    <button class="sys-btn" id="refresh-btn">
+                        <i class="fas fa-sync-alt"></i> 刷新状态
+                    </button>
+                    <button class="sys-btn" id="help-btn">
+                        <i class="fas fa-question-circle"></i> 使用帮助
+                    </button>
+                    <button class="sys-btn" id="fullscreen-btn">
+                        <i class="fas fa-expand"></i> 全屏显示
+                    </button>
+                </div>
+            </div>
+        </div>
+
+        <!-- 页脚 -->
+        <footer class="page-footer">
+            <p>🚁 无人机视觉导航演示系统 | 基于深度学习的实时环境识别 | © 2025</p>
+            <p>演示版本 v2.0 | 最后更新: <span id="last-update">--:--:--</span></p>
+        </footer>
+    </div>
+
+    <!-- 通知容器 -->
+    <div id="notification-container"></div>
+
+    <!-- JavaScript文件 -->
+    <script src="{{ url_for('static', filename='js/main.js') }}"></script>
+</body>
+</html>
+"""
+
+    template_path = os.path.join(template_dir, "index.html")
+    with open(template_path, "w", encoding="utf-8") as f:
+        f.write(html_content)
+
+    print(f"✅ 已创建模板文件: {template_path}")
+
+
+def create_static_files():
+    """创建静态CSS和JS文件"""
+    # 创建CSS文件
+    css_content = """/* 无人机导航系统样式 */
+:root {
+    --primary-color: #00b4d8;
+    --secondary-color: #0077b6;
+    --success-color: #00b894;
+    --warning-color: #fdcb6e;
+    --danger-color: #e17055;
+    --dark-color: #2d3436;
+    --light-color: #f5f5f5;
+    --gray-color: #636e72;
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background: linear-gradient(135deg, #0a1929 0%, #1a365d 50%, #2d3748 100%);
+    color: white;
+    min-height: 100vh;
+    overflow-x: hidden;
+}
+
+.container {
+    max-width: 1800px;
+    margin: 0 auto;
+    padding: 20px;
+}
+
+/* 头部样式 */
+.header {
+    background: rgba(10, 25, 47, 0.9);
+    border-radius: 20px;
+    padding: 25px;
+    margin-bottom: 25px;
+    border: 2px solid rgba(0, 180, 216, 0.3);
+    box-shadow: 0 10px 30px rgba(0, 0, 0, 0.5);
+    backdrop-filter: blur(10px);
+}
+
+.header-content {
+    text-align: center;
+}
+
+.header h1 {
+    font-size: 2.8rem;
+    margin-bottom: 10px;
+    background: linear-gradient(90deg, #00b4d8, #0077b6);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    text-shadow: 0 2px 10px rgba(0, 180, 216, 0.3);
+}
+
+.subtitle {
+    color: #88ffdd;
+    font-size: 1.2rem;
+    margin-bottom: 15px;
+}
+
+.demo-badge {
+    display: inline-block;
+    background: linear-gradient(90deg, #00b894, #00cec9);
+    color: white;
+    padding: 8px 20px;
+    border-radius: 25px;
+    font-weight: bold;
+    box-shadow: 0 4px 15px rgba(0, 184, 148, 0.4);
+}
+
+/* 主内容区 */
+.main-content {
+    display: grid;
+    grid-template-columns: 2fr 1fr;
+    gap: 25px;
+    margin-bottom: 25px;
+}
+
+@media (max-width: 1200px) {
+    .main-content {
+        grid-template-columns: 1fr;
+    }
+}
+
+.section-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 20px;
+    padding-bottom: 15px;
+    border-bottom: 2px solid var(--primary-color);
+}
+
+.section-header h2 {
+    font-size: 1.8rem;
+    color: var(--primary-color);
+}
+
+.fps-indicator, .mode-indicator {
+    background: rgba(0, 0, 0, 0.3);
+    padding: 8px 15px;
+    border-radius: 15px;
+    font-weight: bold;
+    border: 1px solid var(--primary-color);
+}
+
+/* 视频区域 */
+.video-section {
+    background: rgba(10, 25, 47, 0.8);
+    border-radius: 20px;
+    padding: 25px;
+    border: 2px solid rgba(0, 180, 216, 0.2);
+    box-shadow: 0 10px 25px rgba(0, 0, 0, 0.4);
+}
+
+.video-container {
+    position: relative;
+    width: 100%;
+    border-radius: 15px;
+    overflow: hidden;
+    background: black;
+    margin-bottom: 20px;
+    border: 3px solid rgba(255, 255, 255, 0.1);
+}
+
+#video-feed {
+    width: 100%;
+    display: block;
+    transition: transform 0.3s;
+}
+
+#video-feed:hover {
+    transform: scale(1.01);
+}
+
+.video-overlay {
+    position: absolute;
+    top: 20px;
+    left: 20px;
+    right: 20px;
+    background: rgba(0, 0, 0, 0.7);
+    padding: 15px;
+    border-radius: 10px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    backdrop-filter: blur(5px);
+    border: 1px solid rgba(255, 255, 255, 0.1);
+}
+
+.detection-info {
+    display: flex;
+    flex-direction: column;
+    gap: 5px;
+}
+
+.detection-title {
+    font-size: 0.9rem;
+    color: #aaa;
+}
+
+.detection-result {
+    display: flex;
+    gap: 20px;
+    font-size: 1.2rem;
+    font-weight: bold;
+}
+
+#live-class {
+    color: #00ff88;
+}
+
+#live-confidence {
+    color: #ffcc00;
+}
+
+.btn-capture {
+    background: linear-gradient(90deg, #6c5ce7, #a29bfe);
+    color: white;
+    border: none;
+    padding: 10px 20px;
+    border-radius: 8px;
+    cursor: pointer;
+    font-weight: bold;
+    transition: all 0.3s;
+}
+
+.btn-capture:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(108, 92, 231, 0.4);
+}
+
+/* 检测面板 */
+.detection-panel {
+    background: rgba(20, 40, 80, 0.6);
+    padding: 20px;
+    border-radius: 15px;
+    border: 1px solid rgba(0, 150, 255, 0.3);
+}
+
+.confidence-meter {
+    margin: 20px 0;
+}
+
+.meter-label {
+    margin-bottom: 8px;
+    color: #88ffcc;
+}
+
+.meter-bar {
+    height: 20px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 10px;
+    overflow: hidden;
+    position: relative;
+}
+
+.meter-fill {
+    height: 100%;
+    background: linear-gradient(90deg, #ff0000, #ff9900, #00ff00);
+    border-radius: 10px;
+    transition: width 0.5s;
+}
+
+.meter-value {
+    text-align: center;
+    margin-top: 5px;
+    font-weight: bold;
+    font-size: 1.2rem;
+}
+
+.scene-controls {
+    margin-top: 25px;
+}
+
+.scene-controls h4 {
+    margin-bottom: 15px;
+    color: #00ccff;
+}
+
+.scene-buttons {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 10px;
+}
+
+.scene-btn {
+    padding: 12px;
+    background: rgba(255, 255, 255, 0.1);
+    border: 2px solid;
+    border-radius: 8px;
+    color: white;
+    cursor: pointer;
+    transition: all 0.3s;
+    font-size: 0.9rem;
+}
+
+.scene-btn:hover {
+    background: rgba(255, 255, 255, 0.2);
+    transform: translateY(-3px);
+}
+
+/* 控制区域 */
+.control-section {
+    background: rgba(10, 25, 47, 0.8);
+    border-radius: 20px;
+    padding: 25px;
+    border: 2px solid rgba(0, 180, 216, 0.2);
+    box-shadow: 0 10px 25px rgba(0, 0, 0, 0.4);
+}
+
+.status-grid {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 15px;
+    margin-bottom: 25px;
+}
+
+.status-card {
+    background: rgba(20, 40, 80, 0.6);
+    padding: 15px;
+    border-radius: 12px;
+    display: flex;
+    align-items: center;
+    gap: 15px;
+    border-left: 4px solid var(--primary-color);
+    transition: transform 0.3s;
+}
+
+.status-card:hover {
+    transform: translateY(-5px);
+    background: rgba(20, 40, 80, 0.8);
+}
+
+.status-icon {
+    font-size: 2rem;
+    color: var(--primary-color);
+}
+
+.status-content {
+    flex: 1;
+}
+
+.status-label {
+    font-size: 0.9rem;
+    color: #88ffcc;
+    margin-bottom: 5px;
+}
+
+.status-value {
+    font-size: 1.4rem;
+    font-weight: bold;
+    margin-bottom: 8px;
+}
+
+.status-bar {
+    height: 8px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 4px;
+    overflow: hidden;
+}
+
+.bar-fill {
+    height: 100%;
+    background: linear-gradient(90deg, #ff0000, #ff9900, #00ff00);
+    border-radius: 4px;
+    transition: width 0.5s;
+}
+
+/* 控制面板 */
+.control-panel {
+    margin: 25px 0;
+}
+
+.control-row {
+    display: flex;
+    gap: 15px;
+    margin-bottom: 15px;
+}
+
+.control-btn {
+    flex: 1;
+    padding: 20px 10px;
+    border: none;
+    border-radius: 12px;
+    color: white;
+    cursor: pointer;
+    transition: all 0.3s;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 8px;
+    font-size: 1rem;
+    font-weight: bold;
+}
+
+.control-btn i {
+    font-size: 1.8rem;
+}
+
+.btn-takeoff {
+    background: linear-gradient(145deg, #00b09b, #96c93d);
+}
+
+.btn-land {
+    background: linear-gradient(145deg, #2193b0, #6dd5ed);
+}
+
+.btn-emergency {
+    background: linear-gradient(145deg, #ff416c, #ff4b2b);
+}
+
+.btn-direction {
+    background: linear-gradient(145deg, #2a5298, #1e3c72);
+}
+
+.btn-action {
+    background: linear-gradient(145deg, #8a2387, #f27121);
+}
+
+.control-btn:hover {
+    transform: translateY(-5px) scale(1.05);
+    box-shadow: 0 10px 20px rgba(0, 0, 0, 0.3);
+}
+
+.control-btn:active {
+    transform: translateY(-2px);
+}
+
+/* 环境面板 */
+.environment-panel {
+    background: rgba(20, 40, 80, 0.6);
+    padding: 20px;
+    border-radius: 15px;
+    border: 1px solid rgba(0, 150, 255, 0.3);
+}
+
+.env-grid {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 15px;
+    margin-top: 15px;
+}
+
+.env-item {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    padding: 12px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 8px;
+}
+
+.env-item i {
+    color: var(--primary-color);
+    font-size: 1.2rem;
+}
+
+/* 底部区域 */
+.footer-section {
+    display: grid;
+    grid-template-columns: 2fr 1fr;
+    gap: 25px;
+    margin-bottom: 25px;
+}
+
+@media (max-width: 1200px) {
+    .footer-section {
+        grid-template-columns: 1fr;
+    }
+}
+
+.logs-panel, .system-info {
+    background: rgba(10, 25, 47, 0.8);
+    border-radius: 20px;
+    padding: 25px;
+    border: 2px solid rgba(0, 180, 216, 0.2);
+}
+
+.logs-container {
+    height: 200px;
+    overflow-y: auto;
+    margin-top: 15px;
+    background: rgba(0, 0, 0, 0.3);
+    border-radius: 10px;
+    padding: 15px;
+}
+
+.log-entry {
+    padding: 8px 12px;
+    margin-bottom: 8px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 6px;
+    border-left: 3px solid var(--primary-color);
+}
+
+.log-time {
+    color: #ffcc00;
+    font-weight: bold;
+    margin-right: 15px;
+}
+
+.log-message {
+    color: white;
+}
+
+.info-grid {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    gap: 15px;
+    margin: 20px 0;
+}
+
+.info-item {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    padding: 12px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 8px;
+}
+
+.info-item i {
+    color: var(--primary-color);
+}
+
+.system-controls {
+    display: flex;
+    gap: 10px;
+    margin-top: 20px;
+}
+
+.sys-btn {
+    flex: 1;
+    padding: 12px;
+    background: rgba(0, 150, 255, 0.3);
+    border: 1px solid rgba(0, 150, 255, 0.5);
+    border-radius: 8px;
+    color: white;
+    cursor: pointer;
+    transition: all 0.3s;
+}
+
+.sys-btn:hover {
+    background: rgba(0, 150, 255, 0.5);
+}
+
+/* 页脚 */
+.page-footer {
+    text-align: center;
+    padding: 25px;
+    border-top: 1px solid rgba(255, 255, 255, 0.1);
+    color: #88aaff;
+    background: rgba(0, 20, 40, 0.5);
+    border-radius: 15px;
+}
+
+/* 通知样式 */
+#notification-container {
+    position: fixed;
+    top: 20px;
+    right: 20px;
+    z-index: 1000;
+}
+
+.notification {
+    padding: 15px 25px;
+    margin-bottom: 10px;
+    border-radius: 10px;
+    color: white;
+    font-weight: bold;
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.3);
+    animation: slideIn 0.3s ease-out;
+    max-width: 300px;
+}
+
+.notification.success {
+    background: linear-gradient(90deg, #00b09b, #96c93d);
+}
+
+.notification.error {
+    background: linear-gradient(90deg, #ff416c, #ff4b2b);
+}
+
+.notification.info {
+    background: linear-gradient(90deg, #2193b0, #6dd5ed);
+}
+
+@keyframes slideIn {
+    from { transform: translateX(100%); opacity: 0; }
+    to { transform: translateX(0); opacity: 1; }
+}
+
+@keyframes slideOut {
+    from { transform: translateX(0); opacity: 1; }
+    to { transform: translateX(100%); opacity: 0; }
+}
+
+/* 滚动条样式 */
+::-webkit-scrollbar {
+    width: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb {
+    background: var(--primary-color);
+    border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: var(--secondary-color);
+}
+"""
+
+    css_path = "static/css/style.css"
+    with open(css_path, "w", encoding="utf-8") as f:
+        f.write(css_content)
+
+    # 创建JS文件
+    js_content = """// 无人机导航系统交互脚本
+document.addEventListener('DOMContentLoaded', function() {
+    // 全局变量
+    let updateInterval;
+    let logsInterval;
+
+    // 元素引用
+    const videoFeed = document.getElementById('video-feed');
+    const liveClass = document.getElementById('live-class');
+    const liveConfidence = document.getElementById('live-confidence');
+    const confidenceFill = document.getElementById('confidence-fill');
+    const confidenceValue = document.getElementById('confidence-value');
+    const modeIndicator = document.getElementById('mode-indicator');
+    const batteryValue = document.getElementById('battery-value');
+    const batteryFill = document.getElementById('battery-fill');
+    const altitudeValue = document.getElementById('altitude-value');
+    const speedValue = document.getElementById('speed-value');
+    const positionValue = document.getElementById('position-value');
+    const tempValue = document.getElementById('temp-value');
+    const windValue = document.getElementById('wind-value');
+    const gpsValue = document.getElementById('gps-value');
+    const emergencyValue = document.getElementById('emergency-value');
+    const systemTime = document.getElementById('system-time');
+    const logsContainer = document.getElementById('logs-container');
+    const lastUpdate = document.getElementById('last-update');
+
+    // 初始化函数
+    function init() {
+        console.log('🚀 无人机导航系统初始化...');
+
+        // 开始更新循环
+        startUpdateLoop();
+
+        // 绑定事件
+        bindEvents();
+
+        // 初始加载
+        updateDroneStatus();
+        updateFlightLog();
+        updateSystemInfo();
+
+        // 显示欢迎通知
+        showNotification('欢迎使用无人机导航演示系统！', 'info');
+    }
+
+    // 开始更新循环
+    function startUpdateLoop() {
+        // 更新无人机状态（每秒）
+        updateInterval = setInterval(updateDroneStatus, 1000);
+
+        // 更新飞行日志（每2秒）
+        logsInterval = setInterval(updateFlightLog, 2000);
+
+        // 更新系统时间（每秒）
+        setInterval(updateSystemTime, 1000);
+    }
+
+    // 绑定所有事件
+    function bindEvents() {
+        // 控制按钮
+        document.getElementById('takeoff-btn').addEventListener('click', () => sendCommand('takeoff'));
+        document.getElementById('land-btn').addEventListener('click', () => sendCommand('land'));
+        document.getElementById('emergency-btn').addEventListener('click', () => sendCommand('emergency_land'));
+        document.getElementById('hover-btn').addEventListener('click', () => sendCommand('hover'));
+        document.getElementById('charge-btn').addEventListener('click', () => sendCommand('charge'));
+        document.getElementById('auto-btn').addEventListener('click', () => sendCommand('auto_pilot'));
+        document.getElementById('home-btn').addEventListener('click', () => sendCommand('return_home'));
+
+        // 方向控制按钮
+        document.getElementById('forward-btn').addEventListener('click', () => showNotification('向前飞行', 'info'));
+        document.getElementById('backward-btn').addEventListener('click', () => showNotification('向后飞行', 'info'));
+        document.getElementById('left-btn').addEventListener('click', () => showNotification('向左转', 'info'));
+        document.getElementById('right-btn').addEventListener('click', () => showNotification('向右转', 'info'));
+
+        // 场景切换按钮
+        document.querySelectorAll('.scene-btn').forEach(btn => {
+            btn.addEventListener('click', function() {
+                const scene = this.getAttribute('data-scene');
+                simulateScene(scene);
+            });
+        });
+
+        // 截图按钮
+        document.getElementById('capture-btn').addEventListener('click', captureImage);
+
+        // 系统按钮
+        document.getElementById('refresh-btn').addEventListener('click', refreshAll);
+        document.getElementById('help-btn').addEventListener('click', showHelp);
+        document.getElementById('fullscreen-btn').addEventListener('click', toggleFullscreen);
+
+        // 视频点击全屏
+        videoFeed.addEventListener('click', function() {
+            if (this.requestFullscreen) {
+                this.requestFullscreen();
+            }
+        });
+    }
+
+    // 更新无人机状态
+    async function updateDroneStatus() {
+        try {
+            const response = await fetch('/drone_status');
+            const data = await response.json();
+
+            // 更新状态显示
+            liveClass.textContent = data.detected_class;
+            liveConfidence.textContent = (data.confidence * 100).toFixed(1) + '%';
+
+            confidenceFill.style.width = (data.confidence * 100) + '%';
+            confidenceValue.textContent = (data.confidence * 100).toFixed(1) + '%';
+
+            modeIndicator.textContent = data.mode;
+            batteryValue.textContent = data.battery.toFixed(1) + '%';
+            batteryFill.style.width = data.battery + '%';
+
+            altitudeValue.textContent = data.altitude.toFixed(1) + ' m';
+            speedValue.textContent = data.speed.toFixed(1) + ' m/s';
+            positionValue.textContent = `(${data.location.x.toFixed(1)}, ${data.location.y.toFixed(1)})`;
+
+            tempValue.textContent = data.temperature.toFixed(1) + '°C';
+            windValue.textContent = data.wind_speed.toFixed(1) + ' m/s';
+            gpsValue.textContent = data.gps_signal;
+
+            // 更新最后更新时间
+            if (data.timestamp) {
+                lastUpdate.textContent = data.timestamp;
+            }
+
+        } catch (error) {
+            console.error('更新状态失败:', error);
+        }
+    }
+
+    // 更新飞行日志
+    async function updateFlightLog() {
+        try {
+            const response = await fetch('/flight_log');
+            const logs = await response.json();
+
+            // 清空当前日志
+            logsContainer.innerHTML = '';
+
+            // 添加日志条目（最多显示10条）
+            const displayLogs = logs.slice(-10);
+
+            displayLogs.forEach(log => {
+                const logEntry = document.createElement('div');
+                logEntry.className = 'log-entry';
+
+                const timeSpan = document.createElement('span');
+                timeSpan.className = 'log-time';
+                timeSpan.textContent = log.timestamp;
+
+                const messageSpan = document.createElement('span');
+                messageSpan.className = 'log-message';
+                messageSpan.textContent = `${log.scene} → ${log.action}: ${log.message}`;
+
+                // 根据动作类型添加颜色
+                if (log.action.includes('紧急')) {
+                    messageSpan.style.color = '#ff5555';
+                } else if (log.action.includes('正常')) {
+                    messageSpan.style.color = '#55ff55';
+                }
+
+                logEntry.appendChild(timeSpan);
+                logEntry.appendChild(messageSpan);
+                logsContainer.appendChild(logEntry);
+            });
+
+            // 滚动到底部
+            logsContainer.scrollTop = logsContainer.scrollHeight;
+
+        } catch (error) {
+            console.error('更新日志失败:', error);
+        }
+    }
+
+    // 更新系统信息
+    async function updateSystemInfo() {
+        try {
+            const response = await fetch('/system_info');
+            const info = await response.json();
+
+            document.getElementById('system-mode').textContent = info.demo_mode ? '演示模式' : '实战模式';
+            document.getElementById('detection-accuracy').textContent = info.detection_accuracy;
+            document.getElementById('connection-status').textContent = '已连接';
+
+            // 更新紧急等级
+            emergencyValue.textContent = info.emergency_level;
+            if (info.emergency_level > 5) {
+                emergencyValue.style.color = '#ff5555';
+            } else {
+                emergencyValue.style.color = '#55ff55';
+            }
+
+        } catch (error) {
+            console.error('更新系统信息失败:', error);
+        }
+    }
+
+    // 更新系统时间
+    function updateSystemTime() {
+        const now = new Date();
+        const timeStr = now.toLocaleTimeString('zh-CN');
+        systemTime.textContent = timeStr;
+    }
+
+    // 发送控制命令
+    async function sendCommand(command) {
+        try {
+            const response = await fetch('/control', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ command: command })
+            });
+
+            const data = await response.json();
+
+            if (data.success) {
+                showNotification(data.message, 'success');
+                console.log('命令成功:', data.message);
+            } else {
+                showNotification(data.message, 'error');
+                console.error('命令失败:', data.message);
+            }
+
+        } catch (error) {
+            showNotification('网络连接错误', 'error');
+            console.error('请求失败:', error);
+        }
+    }
+
+    // 模拟场景切换
+    async function simulateScene(scene) {
+        try {
+            const response = await fetch('/simulate_scene', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ scene: scene })
+            });
+
+            const data = await response.json();
+
+            if (data.success) {
+                showNotification(`已切换到 ${scene} 场景`, 'info');
+            } else {
+                showNotification(data.message, 'error');
+            }
+
+        } catch (error) {
+            console.error('场景切换失败:', error);
+        }
+    }
+
+    // 捕获图像
+    async function captureImage() {
+        try {
+            const response = await fetch('/capture_image');
+            const blob = await response.blob();
+
+            // 创建下载链接
+            const url = window.URL.createObjectURL(blob);
+            const a = document.createElement('a');
+            a.href = url;
+            a.download = `drone_capture_${new Date().getTime()}.png`;
+            document.body.appendChild(a);
+            a.click();
+            document.body.removeChild(a);
+            window.URL.revokeObjectURL(url);
+
+            showNotification('图像已保存', 'success');
+
+        } catch (error) {
+            showNotification('截图失败', 'error');
+            console.error('截图失败:', error);
+        }
+    }
+
+    // 刷新所有数据
+    function refreshAll() {
+        updateDroneStatus();
+        updateFlightLog();
+        updateSystemInfo();
+        showNotification('系统状态已刷新', 'info');
+    }
+
+    // 显示帮助信息
+    function showHelp() {
+        const helpMessage = `
+无人机导航系统使用说明：
+
+🎮 控制功能：
+• 起飞/降落 - 控制无人机起降
+• 紧急降落 - 立即安全降落
+• 自动驾驶 - 启用自动飞行模式
+• 返航 - 返回起始位置
+
+🌍 场景模拟：
+• 点击场景按钮可手动切换环境
+• 系统会自动模拟环境变化
+• 每个环境都有独特的视觉特征
+
+📊 状态监控：
+• 实时显示无人机状态
+• 环境检测结果
+• 飞行日志记录
+
+💡 提示：
+• 点击视频可全屏显示
+• 使用截图功能保存当前画面
+• 系统使用虚拟数据演示
+        `;
+
+        alert(helpMessage);
+    }
+
+    // 切换全屏
+    function toggleFullscreen() {
+        const elem = document.documentElement;
+
+        if (!document.fullscreenElement) {
+            if (elem.requestFullscreen) {
+                elem.requestFullscreen();
+            }
+        } else {
+            if (document.exitFullscreen) {
+                document.exitFullscreen();
+            }
+        }
+    }
+
+    // 显示通知
+    function showNotification(message, type = 'info') {
+        const container = document.getElementById('notification-container');
+
+        const notification = document.createElement('div');
+        notification.className = `notification ${type}`;
+        notification.textContent = message;
+
+        container.appendChild(notification);
+
+        // 3秒后移除
+        setTimeout(() => {
+            notification.style.animation = 'slideOut 0.3s ease-out';
+            setTimeout(() => {
+                if (notification.parentNode) {
+                    notification.parentNode.removeChild(notification);
+                }
+            }, 300);
+        }, 3000);
+    }
+
+    // 键盘快捷键
+    document.addEventListener('keydown', function(event) {
+        switch(event.key) {
+            case ' ':
+                // 空格键 - 起飞/降落切换
+                const mode = modeIndicator.textContent;
+                if (mode === 'LANDED') {
+                    sendCommand('takeoff');
+                } else if (mode === 'FLYING') {
+                    sendCommand('land');
+                }
+                event.preventDefault();
+                break;
+
+            case 'Escape':
+                // ESC键 - 紧急降落
+                sendCommand('emergency_land');
+                break;
+
+            case 'h':
+                // H键 - 返航
+                sendCommand('return_home');
+                break;
+
+            case 'c':
+                // C键 - 截图
+                captureImage();
+                break;
+        }
+    });
+
+    // 页面卸载时清理
+    window.addEventListener('beforeunload', function() {
+        if (updateInterval) clearInterval(updateInterval);
+        if (logsInterval) clearInterval(logsInterval);
+    });
+
+    // 初始化应用
+    init();
+});
+"""
+
+    js_path = "static/js/main.js"
+    with open(js_path, "w", encoding="utf-8") as f:
+        f.write(js_content)
+
+    print(f"✅ 已创建静态文件: {css_path}, {js_path}")
\ No newline at end of file

From 64752edae9a37b68638200d9383114b1a24bd84d Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Mon, 22 Dec 2025 10:21:11 +0800
Subject: [PATCH 02/11] =?UTF-8?q?=E6=97=A0=E4=BA=BA=E6=9C=BA=E5=AF=BC?=
 =?UTF-8?q?=E8=88=AA=E7=B3=BB=E7=BB=9F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

完善了readme
---
 src/UAV_navigation_system/README.md | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/UAV_navigation_system/README.md b/src/UAV_navigation_system/README.md
index 1a062b6c3f..476a6cfaf3 100644
--- a/src/UAV_navigation_system/README.md
+++ b/src/UAV_navigation_system/README.md
@@ -1,9 +1,15 @@
 ## 自主无人机导航系统
 
 ### 项目简介
-使用深度学习对周围环境进行分类，并根据视觉输入做出导航决策
+该项目实现了一种利用深度学习的实时无人机视觉系统，用于对周围环境进行分类，并基于视觉输入做出导航决策。无人机能检测森林、火灾、城市、动物、车辆和水域等场景，并执行特定的导航响应。
 
 ### 运行步骤
 在Android 手机上安装 DroidCam 并将其连接到与计算机相同的 Wi-Fi。
 打开应用程序并记下显示的 Wi-Fi IP（例如 ）。192.168.147.233
 在代码（函数）中，更新 ：main()VIDEO_SOURCE
+
+### 安装说明：
+在你的安卓手机上安装DroidCam，并连接到与电脑相同的Wi-Fi。
+打开应用，记录显示的Wi-Fi IP（例如，）。192.168.147.233
+在代码（函数）中，更新：main()VIDEO_SOURCE
+VIDEO_SOURCE = "http://192.168.147.233:4747/video"

From a20a3d7ca0f3013f553d294ab447467d3c8c69b1 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Wed, 24 Dec 2025 16:10:00 +0800
Subject: [PATCH 03/11] =?UTF-8?q?=E6=97=A0=E4=BA=BA=E6=9C=BA=E5=AF=BC?=
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代码添加了注释
---
 src/UAV_navigation_system/pytorch_model.py | 267 ++++++++++++++-------
 1 file changed, 186 insertions(+), 81 deletions(-)

diff --git a/src/UAV_navigation_system/pytorch_model.py b/src/UAV_navigation_system/pytorch_model.py
index dc52bf40fa..315993751c 100644
--- a/src/UAV_navigation_system/pytorch_model.py
+++ b/src/UAV_navigation_system/pytorch_model.py
@@ -1,75 +1,104 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
 """
 PyTorch模型工具类
-用于加载和运行PyTorch模型
+用于加载和运行无人机场景分类的PyTorch模型
+核心功能：
+1. 支持自定义CNN/ResNet18/MobileNetV2三种模型架构加载
+2. 实现图像预处理、单张/批量图像预测
+3. 适配CPU/GPU设备，自动检测硬件环境
 """
 
-import torch
-import torch.nn as nn
-import torchvision.transforms as transforms
-from PIL import Image
-import numpy as np
-import cv2
-import os
+# 导入核心依赖库
+import torch  # PyTorch核心库，用于模型构建和推理
+import torch.nn as nn  # 神经网络层模块
+import torchvision.transforms as transforms  # 图像预处理工具
+from PIL import Image  # PIL库，用于图像读取和格式转换
+import numpy as np  # 数值计算库，处理图像数组
+import cv2  # OpenCV库，处理视频/图像数据
+import os  # 文件路径操作库
 
 
 class PyTorchDroneModel:
-    """PyTorch无人机视觉模型类"""
+    """
+    PyTorch无人机视觉场景分类模型类
+    封装模型加载、图像预处理、场景分类预测等核心功能
+    支持的场景类别：Forest(森林)、Fire(火灾)、City(城市)、Animal(动物)、Vehicle(车辆)、Water(水域)
+    """
 
     def __init__(self, model_path=None, device=None):
+        """
+        初始化模型类
+
+        Args:
+            model_path (str, optional): 预训练模型权重文件路径，默认None
+            device (torch.device, optional): 模型运行设备（cpu/cuda），默认自动检测
+        """
+        # 模型实例初始化
         self.model = None
+        # 运行设备初始化
         self.device = None
+        # 场景分类类别名称（与训练时标签对应）
         self.class_names = ['Forest', 'Fire', 'City', 'Animal', 'Vehicle', 'Water']
+        # 模型输入图像尺寸（需与训练时一致）
         self.img_size = (224, 224)
 
-        # 设置设备
+        # 自动检测/指定运行设备
         if device is None:
             self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         else:
             self.device = device
-
         print(f"✅ 使用设备: {self.device}")
 
-        # 图像预处理变换
+        # 定义图像预处理流水线（与训练时预处理逻辑一致）
         self.transform = transforms.Compose([
-            transforms.Resize(self.img_size),
-            transforms.ToTensor(),
+            transforms.Resize(self.img_size),  # 调整图像尺寸
+            transforms.ToTensor(),  # 转换为Tensor（0-1归一化）
+            # 标准化（使用ImageNet均值/标准差，适配预训练模型）
             transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
         ])
 
-        # 如果提供了模型路径，自动加载
+        # 如果传入有效模型路径，自动加载模型
         if model_path and os.path.exists(model_path):
             self.load_model(model_path)
 
     def define_model_architecture(self):
-        """定义PyTorch模型架构（需要与训练时一致）"""
+        """
+        定义自定义CNN模型架构（需与训练时的模型结构完全一致）
+        适用于无人机场景分类的轻量级卷积神经网络
+
+        Returns:
+            nn.Module: 自定义CNN模型实例
+        """
 
         class DroneCNN(nn.Module):
+            """内部自定义CNN模型类"""
             def __init__(self, num_classes=6):
                 super(DroneCNN, self).__init__()
+                # 特征提取层（4层卷积+批归一化+激活+池化+dropout）
                 self.features = nn.Sequential(
-                    # 第一层卷积
+                    # 第一层卷积：3通道输入→32通道输出，3×3卷积核，填充1
                     nn.Conv2d(3, 32, kernel_size=3, padding=1),
-                    nn.BatchNorm2d(32),
-                    nn.ReLU(inplace=True),
-                    nn.MaxPool2d(kernel_size=2, stride=2),
-                    nn.Dropout(0.25),
+                    nn.BatchNorm2d(32),  # 批归一化，加速训练，防止过拟合
+                    nn.ReLU(inplace=True),  # ReLU激活函数，inplace=True节省内存
+                    nn.MaxPool2d(kernel_size=2, stride=2),  # 2×2最大池化，步长2
+                    nn.Dropout(0.25),  # Dropout层，随机丢弃25%神经元，防止过拟合
 
-                    # 第二层卷积
+                    # 第二层卷积：32通道→64通道
                     nn.Conv2d(32, 64, kernel_size=3, padding=1),
                     nn.BatchNorm2d(64),
                     nn.ReLU(inplace=True),
                     nn.MaxPool2d(kernel_size=2, stride=2),
                     nn.Dropout(0.25),
 
-                    # 第三层卷积
+                    # 第三层卷积：64通道→128通道
                     nn.Conv2d(64, 128, kernel_size=3, padding=1),
                     nn.BatchNorm2d(128),
                     nn.ReLU(inplace=True),
                     nn.MaxPool2d(kernel_size=2, stride=2),
                     nn.Dropout(0.25),
 
-                    # 第四层卷积
+                    # 第四层卷积：128通道→256通道
                     nn.Conv2d(128, 256, kernel_size=3, padding=1),
                     nn.BatchNorm2d(256),
                     nn.ReLU(inplace=True),
@@ -77,127 +106,179 @@ def __init__(self, num_classes=6):
                     nn.Dropout(0.25),
                 )
 
+                # 分类器层（全连接层）
                 self.classifier = nn.Sequential(
-                    nn.Flatten(),
-                    nn.Linear(256 * 14 * 14, 512),  # 224/2/2/2/2 = 14
+                    nn.Flatten(),  # 展平特征图：256×14×14 → 256×14×14
+                    # 全连接层1：特征展平后→512维隐藏层（224/2^4=14，4次池化后尺寸）
+                    nn.Linear(256 * 14 * 14, 512),
                     nn.ReLU(inplace=True),
-                    nn.Dropout(0.5),
-                    nn.Linear(512, num_classes)
+                    nn.Dropout(0.5),  # 丢弃50%神经元，防止过拟合
+                    nn.Linear(512, num_classes)  # 输出层：512→分类类别数
                 )
 
             def forward(self, x):
-                x = self.features(x)
-                x = self.classifier(x)
+                """前向传播逻辑"""
+                x = self.features(x)  # 特征提取
+                x = self.classifier(x)  # 分类预测
                 return x
 
+        # 返回自定义模型实例（分类类别数与场景类别数一致）
         return DroneCNN(num_classes=len(self.class_names))
 
     def load_resnet18_model(self):
-        """加载预训练的ResNet18模型"""
+        """
+        加载预训练的ResNet18模型并适配自定义分类任务
+        修改最后一层全连接层，适配无人机6类场景分类
+
+        Returns:
+            nn.Module: 适配后的ResNet18模型实例
+        """
         from torchvision import models
 
+        # 加载ResNet18骨架（不加载ImageNet预训练权重，避免与自定义任务冲突）
         model = models.resnet18(pretrained=False)
+        # 获取最后一层全连接层的输入特征数
         num_features = model.fc.in_features
+        # 替换最后一层全连接层：原1000类→自定义6类
         model.fc = nn.Linear(num_features, len(self.class_names))
 
         return model
 
     def load_mobilenetv2_model(self):
-        """加载预训练的MobileNetV2模型"""
+        """
+        加载预训练的MobileNetV2模型并适配自定义分类任务
+        轻量级模型，适配无人机嵌入式设备
+
+        Returns:
+            nn.Module: 适配后的MobileNetV2模型实例
+        """
         from torchvision import models
 
+        # 加载MobileNetV2骨架（不加载预训练权重）
         model = models.mobilenet_v2(pretrained=False)
+        # 替换分类器最后一层：原1000类→自定义6类
         model.classifier[1] = nn.Linear(model.classifier[1].in_features, len(self.class_names))
 
         return model
 
     def load_model(self, model_path, model_type='custom'):
-        """加载PyTorch模型"""
+        """
+        加载预训练PyTorch模型权重
+
+        Args:
+            model_path (str): 模型权重文件路径（.pth/.pt）
+            model_type (str): 模型架构类型，可选['custom', 'resnet18', 'mobilenet']
+
+        Returns:
+            bool: 加载成功返回True，失败返回False
+        """
         print(f"🔄 正在加载PyTorch模型: {model_path}")
 
         try:
-            # 根据类型创建模型架构
+            # 根据模型类型创建对应架构的模型实例
             if model_type == 'resnet18':
                 self.model = self.load_resnet18_model()
             elif model_type == 'mobilenet':
                 self.model = self.load_mobilenetv2_model()
-            else:
+            else:  # 默认加载自定义CNN
                 self.model = self.define_model_architecture()
 
-            # 加载模型权重
+            # 加载模型权重文件（兼容多种保存格式）
             checkpoint = torch.load(model_path, map_location=self.device)
 
             if isinstance(checkpoint, dict):
-                # 如果保存的是检查点字典
+                # 情况1：保存的是检查点字典（包含state_dict/优化器参数等）
                 if 'model_state_dict' in checkpoint:
                     self.model.load_state_dict(checkpoint['model_state_dict'])
                 elif 'state_dict' in checkpoint:
                     self.model.load_state_dict(checkpoint['state_dict'])
                 else:
-                    # 尝试直接加载
+                    # 情况2：字典仅包含模型权重
                     self.model.load_state_dict(checkpoint)
             else:
-                # 如果保存的是模型本身
+                # 情况3：直接保存的模型实例
                 self.model = checkpoint
 
-            # 移动到设备
+            # 将模型移动到指定设备（CPU/GPU）
             self.model = self.model.to(self.device)
 
-            # 设置为评估模式
+            # 设置模型为评估模式（禁用Dropout/BatchNorm的训练行为）
             self.model.eval()
 
+            # 打印加载成功信息
             print(f"✅ PyTorch模型加载成功")
             print(f"📊 模型结构: {self.model.__class__.__name__}")
-            print(f"📊 参数数量: {sum(p.numel() for p in self.model.parameters()):,}")
+            # 计算并打印模型总参数数量
+            total_params = sum(p.numel() for p in self.model.parameters())
+            print(f"📊 参数数量: {total_params:,}")
 
             return True
 
         except Exception as e:
+            # 捕获加载过程中的所有异常
             print(f"❌ 模型加载失败: {e}")
             self.model = None
             return False
 
     def preprocess_image(self, image):
-        """预处理图像以供PyTorch模型使用"""
-        # 转换OpenCV BGR图像为PIL RGB图像
+        """
+        图像预处理：将输入图像转换为模型可接受的Tensor格式
+
+        Args:
+            image (np.ndarray/PIL.Image): 输入图像（OpenCV格式(BGR)或PIL格式(RGB)）
+
+        Returns:
+            torch.Tensor: 预处理后的4维Tensor (batch_size, channels, height, width)
+        """
+        # 处理OpenCV格式图像（BGR→RGB，转换为PIL图像）
         if isinstance(image, np.ndarray):
-            # OpenCV图像 (BGR) -> PIL图像 (RGB)
-            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            pil_image = Image.fromarray(image_rgb)
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # BGR→RGB转换
+            pil_image = Image.fromarray(image_rgb)  # 数组→PIL图像
         else:
+            # 直接使用PIL图像
             pil_image = image
 
-        # 应用变换
+        # 应用预处理流水线
         tensor = self.transform(pil_image)
 
-        # 添加批次维度
+        # 添加批次维度（模型要求批量输入，单张图像batch_size=1）
         tensor = tensor.unsqueeze(0)
 
-        # 移动到设备
+        # 将Tensor移动到指定设备
         tensor = tensor.to(self.device)
 
         return tensor
 
     def predict(self, image):
-        """对图像进行预测"""
+        """
+        单张图像场景分类预测
+
+        Args:
+            image (np.ndarray/PIL.Image): 输入图像（OpenCV/PIL格式）
+
+        Returns:
+            tuple: (预测类别名称, 置信度)，预测失败返回(None, 0)
+        """
+        # 检查模型是否加载
         if self.model is None:
-            print("⚠️  模型未加载")
+            print("⚠️  模型未加载，无法预测")
             return None, 0
 
         try:
-            # 预处理图像
+            # 图像预处理
             input_tensor = self.preprocess_image(image)
 
-            # 禁用梯度计算
+            # 禁用梯度计算（推理阶段无需计算梯度，提升速度，节省内存）
             with torch.no_grad():
-                # 前向传播
+                # 模型前向传播，获取预测logits
                 outputs = self.model(input_tensor)
 
-                # 获取预测结果
+                # 计算softmax概率（将logits转换为0-1的概率分布）
                 probabilities = torch.nn.functional.softmax(outputs, dim=1)
+                # 获取最大概率的类别索引和置信度
                 confidence, predicted = torch.max(probabilities, 1)
 
-                # 转换为Python标量
+                # 转换为Python标量（从Tensor→数值）
                 class_idx = predicted.item()
                 confidence_value = confidence.item()
 
@@ -205,42 +286,52 @@ def predict(self, image):
                 if 0 <= class_idx < len(self.class_names):
                     class_name = self.class_names[class_idx]
                 else:
-                    class_name = f"Class_{class_idx}"
+                    class_name = f"Class_{class_idx}"  # 未知类别兜底
 
                 return class_name, confidence_value
 
         except Exception as e:
+            # 捕获预测过程中的异常
             print(f"❌ 预测失败: {e}")
             return None, 0
 
     def predict_batch(self, images):
-        """批量预测图像"""
+        """
+        批量图像场景分类预测（提升批量处理效率）
+
+        Args:
+            images (list): 图像列表，每个元素为np.ndarray/PIL.Image格式
+
+        Returns:
+            tuple: (预测类别名称列表, 置信度列表)，失败返回([], [])
+        """
         if self.model is None:
+            print("⚠️  模型未加载，无法批量预测")
             return [], []
 
         try:
-            # 预处理所有图像
+            # 预处理所有图像，生成Tensor列表
             tensors = []
             for img in images:
                 tensor = self.preprocess_image(img)
                 tensors.append(tensor)
 
-            # 堆叠为批次
+            # 堆叠为批量Tensor（batch_size=N）
             batch = torch.cat(tensors, dim=0)
             batch = batch.to(self.device)
 
-            # 预测
+            # 推理阶段禁用梯度计算
             with torch.no_grad():
                 outputs = self.model(batch)
                 probabilities = torch.nn.functional.softmax(outputs, dim=1)
                 confidences, predicted = torch.max(probabilities, 1)
 
-            # 转换结果
+            # 解析批量预测结果
             results = []
             conf_values = []
-
             for i in range(len(images)):
                 class_idx = predicted[i].item()
+                # 映射类别索引到名称
                 if 0 <= class_idx < len(self.class_names):
                     class_name = self.class_names[class_idx]
                 else:
@@ -256,42 +347,56 @@ def predict_batch(self, images):
             return [], []
 
 
-# 模型工厂函数
 def load_pytorch_model(model_path, model_type='custom'):
-    """加载PyTorch模型的便捷函数"""
+    """
+    加载PyTorch模型的便捷工厂函数
+
+    Args:
+        model_path (str): 模型权重文件路径
+        model_type (str): 模型架构类型，可选['custom', 'resnet18', 'mobilenet']
+
+    Returns:
+        PyTorchDroneModel: 模型实例（加载成功）/None（加载失败）
+    """
     model = PyTorchDroneModel()
     success = model.load_model(model_path, model_type)
     return model if success else None
 
 
-# 测试函数
 def test_model():
-    """测试模型加载和预测"""
-    print("🧪 测试PyTorch模型...")
+    """
+    测试函数：验证模型架构创建、加载等核心功能
+    无需实际权重文件，仅测试模型结构完整性
+    """
+    print("🧪 开始测试PyTorch模型工具类...")
 
-    # 创建一个测试图像
+    # 创建随机测试图像（224×224×3，模拟RGB图像）
     test_image = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
 
-    # 加载模型
+    # 初始化模型实例
     model = PyTorchDroneModel()
 
-    # 测试自定义模型
-    print("\n1. 测试自定义模型架构...")
+    # 测试1：自定义CNN模型架构创建
+    print("\n1. 测试自定义CNN模型架构...")
     custom_model = model.define_model_architecture()
-    print(f"✅ 自定义模型创建成功，参数数量: {sum(p.numel() for p in custom_model.parameters()):,}")
+    total_params = sum(p.numel() for p in custom_model.parameters())
+    print(f"✅ 自定义模型创建成功，参数数量: {total_params:,}")
 
-    # 测试ResNet18
-    print("\n2. 测试ResNet18架构...")
+    # 测试2：ResNet18模型架构加载
+    print("\n2. 测试ResNet18模型架构...")
     resnet_model = model.load_resnet18_model()
-    print(f"✅ ResNet18模型创建成功，参数数量: {sum(p.numel() for p in resnet_model.parameters()):,}")
+    total_params = sum(p.numel() for p in resnet_model.parameters())
+    print(f"✅ ResNet18模型创建成功，参数数量: {total_params:,}")
 
-    # 测试MobileNetV2
-    print("\n3. 测试MobileNetV2架构...")
+    # 测试3：MobileNetV2模型架构加载
+    print("\n3. 测试MobileNetV2模型架构...")
     mobilenet_model = model.load_mobilenetv2_model()
-    print(f"✅ MobileNetV2模型创建成功，参数数量: {sum(p.numel() for p in mobilenet_model.parameters()):,}")
+    total_params = sum(p.numel() for p in mobilenet_model.parameters())
+    print(f"✅ MobileNetV2模型创建成功，参数数量: {total_params:,}")
 
-    print("\n🧪 测试完成！")
+    print("\n🧪 所有测试完成！")
 
 
+# 主函数：仅在直接运行脚本时执行测试
 if __name__ == "__main__":
     test_model()
\ No newline at end of file

From d410ad58b8f0667f134b9671bcb5b3f424582655 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Wed, 24 Dec 2025 20:01:38 +0800
Subject: [PATCH 04/11] =?UTF-8?q?=E6=97=A0=E4=BA=BA=E6=9C=BA=E5=AF=BC?=
 =?UTF-8?q?=E8=88=AA=E7=B3=BB=E7=BB=9F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

添加了代码注释
---
 src/UAV_navigation_system/pytorch_model.py | 26 ++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/src/UAV_navigation_system/pytorch_model.py b/src/UAV_navigation_system/pytorch_model.py
index 315993751c..636540156b 100644
--- a/src/UAV_navigation_system/pytorch_model.py
+++ b/src/UAV_navigation_system/pytorch_model.py
@@ -3,6 +3,32 @@
 """
 PyTorch模型工具类
 用于加载和运行无人机场景分类的PyTorch模型
+"""
+
+# 临时修复：在导入 torch 前尝试处理 typing_extensions 问题
+try:
+    import typing_extensions
+    # 检查 TypeIs 是否可用，如果不可用则模拟一个
+    if not hasattr(typing_extensions, 'TypeIs'):
+        typing_extensions.TypeIs = type(lambda: None)
+except ImportError:
+    pass
+
+# 导入核心依赖库
+import torch  # PyTorch核心库，用于模型构建和推理
+import torch.nn as nn  # 神经网络层模块
+import torchvision.transforms as transforms  # 图像预处理工具
+from PIL import Image  # PIL库，用于图像读取和格式转换
+import numpy as np  # 数值计算库，处理图像数组
+import cv2  # OpenCV库，处理视频/图像数据
+import os  # 文件路径操作库
+
+# ... 其余代码保持不变 ...
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+PyTorch模型工具类
+用于加载和运行无人机场景分类的PyTorch模型
 核心功能：
 1. 支持自定义CNN/ResNet18/MobileNetV2三种模型架构加载
 2. 实现图像预处理、单张/批量图像预测

From 720f56a9fb4516a120b6284e2ce57ba2ebb8c2ec Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Wed, 24 Dec 2025 21:42:28 +0800
Subject: [PATCH 05/11] =?UTF-8?q?=E6=97=A0=E4=BA=BA=E6=9C=BA=E5=AF=BC?=
 =?UTF-8?q?=E8=88=AA?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

添加了导航系统的初代码
---
 src/UAV_navigation_system/drone_main.py | 212 ++++++++++++++++++++++++
 1 file changed, 212 insertions(+)
 create mode 100644 src/UAV_navigation_system/drone_main.py

diff --git a/src/UAV_navigation_system/drone_main.py b/src/UAV_navigation_system/drone_main.py
new file mode 100644
index 0000000000..64aced148d
--- /dev/null
+++ b/src/UAV_navigation_system/drone_main.py
@@ -0,0 +1,212 @@
+"""
+无人机视觉导航系统 - 简化版
+可以立即运行测试
+"""
+
+import os
+import sys
+import time
+import cv2
+import numpy as np
+
+# 添加项目路径，让Python能找到src模块
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+
+def ensure_directories():
+    """确保所有目录都存在"""
+    dirs = ['data/images', 'data/videos', 'data/logs', 'data/config']
+    for d in dirs:
+        os.makedirs(d, exist_ok=True)
+        print(f"✓ 目录已就绪: {d}")
+
+
+class SimpleDroneCamera:
+    """简单的无人机摄像头类"""
+
+    def __init__(self, camera_id=0):
+        self.camera_id = camera_id
+        self.cap = None
+
+    def open(self):
+        """打开摄像头"""
+        print(f"尝试打开摄像头 {self.camera_id}...")
+        self.cap = cv2.VideoCapture(self.camera_id)
+
+        if not self.cap.isOpened():
+            print("⚠️  无法打开物理摄像头，使用模拟模式")
+            return False
+        else:
+            print("✓ 摄像头已连接")
+            return True
+
+    def read_frame(self):
+        """读取一帧"""
+        if self.cap and self.cap.isOpened():
+            ret, frame = self.cap.read()
+            if ret:
+                return frame
+
+        # 如果没有摄像头或读取失败，返回模拟图像
+        return self.simulate_frame()
+
+    def simulate_frame(self):
+        """生成模拟图像"""
+        width, height = 640, 480
+        frame = np.zeros((height, width, 3), dtype=np.uint8)
+
+        # 添加一些图形
+        cv2.putText(frame, "无人机模拟视图", (50, 50),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.rectangle(frame, (100, 100), (300, 300), (255, 0, 0), 2)
+        cv2.circle(frame, (400, 200), 50, (0, 0, 255), -1)
+
+        return frame
+
+    def release(self):
+        """释放摄像头"""
+        if self.cap:
+            self.cap.release()
+            print("摄像头已释放")
+
+
+def analyze_scene_simple(frame):
+    """简单场景分析（基于颜色）"""
+    if frame is None:
+        return "未知", 0.5
+
+    # 转换为HSV
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+
+    # 检测绿色（植被）
+    green_mask = cv2.inRange(hsv, (35, 40, 40), (85, 255, 255))
+    green_pct = np.sum(green_mask > 0) / green_mask.size
+
+    # 检测蓝色（水域）
+    blue_mask = cv2.inRange(hsv, (100, 40, 40), (140, 255, 255))
+    blue_pct = np.sum(blue_mask > 0) / blue_mask.size
+
+    # 判断场景
+    if green_pct > 0.3:
+        return "森林/草地", green_pct
+    elif blue_pct > 0.2:
+        return "水域", blue_pct
+    else:
+        return "城市/建筑", max(green_pct, blue_pct)
+
+
+def get_decision(scene_type, confidence):
+    """根据场景类型做出决策"""
+    decisions = {
+        "森林/草地": "✓ 安全区域，继续飞行",
+        "水域": "⚠️  接近水域，提高飞行高度",
+        "城市/建筑": "⚠️  城市区域，降低速度并避让",
+        "未知": "? 无法识别，保持警戒"
+    }
+    return decisions.get(scene_type, "保持当前状态")
+
+
+def main():
+    """主函数"""
+    print("=" * 50)
+    print("无人机视觉导航系统")
+    print("版本: 1.0.0")
+    print("按 'q' 键退出，按 's' 键保存图像")
+    print("=" * 50)
+
+    # 确保目录存在
+    ensure_directories()
+
+    # 创建无人机摄像头
+    drone_cam = SimpleDroneCamera(camera_id=0)
+    drone_cam.open()
+
+    # 初始化状态
+    battery = 100
+    flight_time = 0
+    frame_count = 0
+    start_time = time.time()
+
+    print("\n开始飞行...")
+
+    while True:
+        # 读取帧
+        frame = drone_cam.read_frame()
+        frame_count += 1
+
+        # 分析场景
+        scene_type, confidence = analyze_scene_simple(frame)
+
+        # 获取决策
+        decision = get_decision(scene_type, confidence)
+
+        # 更新状态
+        flight_time = time.time() - start_time
+        battery = max(0, battery - 0.05)  # 慢慢消耗电池
+
+        # 在图像上显示信息
+        info_lines = [
+            f"场景: {scene_type} ({confidence:.1%})",
+            f"决策: {decision}",
+            f"飞行时间: {flight_time:.1f}s",
+            f"电池: {battery:.1f}%",
+            f"帧数: {frame_count}"
+        ]
+
+        y_offset = 30
+        for line in info_lines:
+            cv2.putText(frame, line, (10, y_offset),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
+            y_offset += 25
+
+        # 显示图像
+        cv2.imshow('无人机视觉导航', frame)
+
+        # 检查按键
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord('q'):  # 按 q 退出
+            break
+        elif key == ord('s'):  # 按 s 保存图像
+            filename = f"data/images/capture_{frame_count}.jpg"
+            cv2.imwrite(filename, frame)
+            print(f"✓ 保存图像: {filename}")
+
+        # 检查电池
+        if battery <= 0:
+            print("\n⚠️  电池耗尽！紧急降落...")
+            break
+
+        # 限制运行时间（可选）
+        if flight_time > 60:  # 60秒后自动停止
+            print("\n⏰ 飞行时间到，安全降落...")
+            break
+
+    # 清理
+    drone_cam.release()
+    cv2.destroyAllWindows()
+
+    # 显示统计信息
+    print("\n" + "=" * 50)
+    print("飞行统计:")
+    print(f"- 总飞行时间: {flight_time:.1f} 秒")
+    print(f"- 处理帧数: {frame_count}")
+    print(f"- 平均帧率: {frame_count / flight_time:.1f} FPS" if flight_time > 0 else "- 平均帧率: N/A")
+    print(f"- 最终电池: {battery:.1f}%")
+    print("=" * 50)
+
+    print("\n飞行结束！")
+    return 0
+
+
+if __name__ == "__main__":
+    try:
+        exit_code = main()
+    except KeyboardInterrupt:
+        print("\n程序被用户中断")
+        exit_code = 0
+    except Exception as e:
+        print(f"\n程序出错: {e}")
+        exit_code = 1
+
+    input("\n按 Enter 键退出...")
+    sys.exit(exit_code)
\ No newline at end of file

From 3bc591a785094e6d9d7e5143538856f9ac8b7583 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Fri, 26 Dec 2025 15:25:06 +0800
Subject: [PATCH 06/11] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=B3=A8=E9=87=8A?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

给文件添加注释
---
 .../drone_nav_with_vision.py                  | 154 +++++++++++++-----
 1 file changed, 115 insertions(+), 39 deletions(-)

diff --git a/src/UAV_navigation_system/drone_nav_with_vision.py b/src/UAV_navigation_system/drone_nav_with_vision.py
index cfd02296cd..6e77d16970 100644
--- a/src/UAV_navigation_system/drone_nav_with_vision.py
+++ b/src/UAV_navigation_system/drone_nav_with_vision.py
@@ -1,63 +1,122 @@
-import cv2
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array
-import random  # For simulating low battery and emergency conditions
-import time  # For the timeout condition
-
-# DroneBattery Class to manage battery
+# Import necessary libraries
+import cv2  # OpenCV for computer vision tasks (video capture, image processing)
+import numpy as np  # NumPy for numerical operations and array manipulation
+from tensorflow.keras.models import load_model  # Load pre-trained Keras model
+from tensorflow.keras.preprocessing.image import img_to_array  # Convert image to array for model input
+import random  # For simulating random emergency conditions (low battery, etc.)
+import time  # For time-related operations (timeouts, sleep)
+
+
+# DroneBattery Class to manage battery state and operations
 class DroneBattery:
+    """Class to simulate and manage drone battery operations"""
+
     def __init__(self, max_capacity=100, current_charge=100):
+        """
+        Initialize battery parameters
+        Args:
+            max_capacity: Maximum battery capacity (default 100%)
+            current_charge: Current battery charge level (default 100%)
+        """
         self.max_capacity = max_capacity
         self.current_charge = current_charge
-        
+
     def display_battery_status(self):
+        """Display current battery percentage"""
         print(f"Battery Status: {self.current_charge}%")
-        
+
     def charge_battery(self, charge_rate=10):
+        """
+        Simulate battery charging process
+        Args:
+            charge_rate: Percentage to charge per iteration (default 10%)
+        """
         while self.current_charge < self.max_capacity:
             self.current_charge += charge_rate
             if self.current_charge > self.max_capacity:
                 self.current_charge = self.max_capacity
             print(f"Charging... {self.current_charge}%")
-            time.sleep(1)
+            time.sleep(1)  # Simulate time delay for charging
         print("Battery fully charged!")
-        
+
     def discharge_battery(self, discharge_rate=10):
+        """
+        Simulate battery discharging process
+        Args:
+            discharge_rate: Percentage to discharge per iteration (default 10%)
+        """
         while self.current_charge > 0:
             self.current_charge -= discharge_rate
             if self.current_charge < 0:
                 self.current_charge = 0
             print(f"Discharging... {self.current_charge}%")
-            time.sleep(1)
+            time.sleep(1)  # Simulate time delay for discharging
         print("Battery completely drained!")
-    
+
     def is_battery_low(self):
+        """
+        Check if battery level is critically low
+        Returns:
+            Boolean: True if battery < 20%, False otherwise
+        """
         return self.current_charge < 20
 
-# ✅ Load and compile model
+
+# Load and compile the pre-trained machine learning model
 print("📦 Loading model...")
+# Load model from specified file path
 model = load_model(r"C:\Users\hp\DroneNavVision\data\best_model.h5")
-model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])  # ✅ Compile step added
+# Compile model with optimizer and loss function for inference
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
 
+# Define class names corresponding to model output categories
 class_names = ['Animal', 'City', 'Fire', 'Forest', 'Vehicle', 'Water']
 
+
 def check_emergency():
+    """
+    Simulate random emergency condition checks
+    Returns:
+        String: Randomly selected emergency condition
+    """
     emergency_conditions = ['low_battery', 'emergency']
     return random.choice(emergency_conditions)
 
-# Handle low battery condition
+
 def handle_low_battery(drone_battery):
+    """
+    Handle low battery emergency procedure
+    Args:
+        drone_battery: DroneBattery instance to manage charging
+    """
     print("🔋 Low battery! Returning to base.")
-    drone_battery.charge_battery(charge_rate=15)
-    exit()
+    drone_battery.charge_battery(charge_rate=15)  # Fast charge at 15% per second
+    exit()  # Exit program after charging
+
 
 def preprocess_frame(frame):
+    """
+    Preprocess video frame for model input
+    Args:
+        frame: Raw image frame from camera
+    Returns:
+        numpy array: Preprocessed image ready for model prediction
+    """
+    # Resize frame to match model input dimensions (128x128)
     resized = cv2.resize(frame, (128, 128))
+    # Normalize pixel values to [0, 1] and convert to array
     img_array = img_to_array(resized) / 255.0
+    # Add batch dimension (1, 128, 128, 3)
     return np.expand_dims(img_array, axis=0)
 
+
 def decide_navigation(predicted_class):
+    """
+    Determine navigation action based on detected class
+    Args:
+        predicted_class: String of detected object/environment class
+    """
+    # Define navigation strategies for each detected class
     if predicted_class == 'Fire':
         print("🔥 Fire detected! Navigate away.")
     elif predicted_class == 'Animal':
@@ -73,62 +132,79 @@ def decide_navigation(predicted_class):
     else:
         print("✅ Clear path. Continue normal navigation.")
 
+
 def main():
+    """
+    Main function to run drone vision system
+    Handles video capture, frame processing, prediction, and navigation decisions
+    """
     print("🚁 Starting the drone vision process...")
-    start_time = time.time()
-    
-    # Use the correct video source based on your setup
-    VIDEO_SOURCE = "http://192.168.1.3:4747/video"  # Or set your IP stream (e.g., "rtsp://<IP_ADDRESS>/stream")
+    start_time = time.time()  # Record start time for timeout condition
 
-    cap = cv2.VideoCapture(VIDEO_SOURCE, cv2.CAP_FFMPEG)
-    cap.set(cv2.CAP_PROP_BUFFERSIZE, 10)  # Increase buffer size
-    cap.set(cv2.CAP_PROP_FPS, 30)  # Set the desired FPS (adjust as needed)
+    # Video source configuration - adjust based on drone camera setup
+    # Options: RTSP stream, IP camera, or local camera
+    VIDEO_SOURCE = "http://192.168.1.3:4747/video"  # IP camera stream URL
 
+    # Initialize video capture with FFMPEG backend for streaming
+    cap = cv2.VideoCapture(VIDEO_SOURCE, cv2.CAP_FFMPEG)
+    # Configure video capture properties
+    cap.set(cv2.CAP_PROP_BUFFERSIZE, 10)  # Increase buffer size for smoother streaming
+    cap.set(cv2.CAP_PROP_FPS, 30)  # Set frame rate to 30 FPS
 
-    # Check if video stream is opened successfully
+    # Validate video stream connection
     if not cap.isOpened():
         print("❌ Failed to open video source. Check connection or URL.")
         return
     else:
         print("✅ Video source opened successfully.")
 
-
+    # Initialize drone battery management
     drone_battery = DroneBattery()
 
+    # Main processing loop
     while True:
         print("📸 Processing frame...")
-        
-        # Check if battery is low
+
+        # Emergency condition check: Low battery
         if drone_battery.is_battery_low():
             handle_low_battery(drone_battery)
 
+        # Timeout condition: Stop after 5 minutes (300 seconds)
         elapsed_time = time.time() - start_time
         if elapsed_time > 300:
             print("⏰ Timeout reached! Stopping the drone.")
             break
 
+        # Capture frame from video stream
         ret, frame = cap.read()
-        if not ret:
+        if not ret:  # Check if frame capture was successful
             print("❌ Failed to capture frame.")
             break
 
-        processed = preprocess_frame(frame)
-        pred = model.predict(processed)
-        predicted_class = class_names[np.argmax(pred)]
-        confidence = np.max(pred) * 100
+        # Process frame and make prediction
+        processed = preprocess_frame(frame)  # Preprocess for model
+        pred = model.predict(processed)  # Run model inference
+        predicted_class = class_names[np.argmax(pred)]  # Get class with highest probability
+        confidence = np.max(pred) * 100  # Convert confidence to percentage
 
+        # Display prediction on video feed
         cv2.putText(frame, f"{predicted_class} ({confidence:.2f}%)", (10, 30),
                     cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-        cv2.imshow("Drone Vision Feed", frame)
+        cv2.imshow("Drone Vision Feed", frame)  # Show annotated video
 
+        # Make navigation decision based on prediction
         decide_navigation(predicted_class)
 
+        # Exit condition: Press 'q' to quit
         if cv2.waitKey(1) & 0xFF == ord('q'):
             print("🛑 Manual stop initiated by the user.")
             break
 
-    cap.release()
-    cv2.destroyAllWindows()
+    # Cleanup resources
+    cap.release()  # Release video capture device
+    cv2.destroyAllWindows()  # Close all OpenCV windows
+
 
+# Standard Python idiom to run main function when script is executed directly
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file

From e55f1beadbda5e7e26dee230497d1718ed151ef0 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Fri, 26 Dec 2025 16:01:09 +0800
Subject: [PATCH 07/11] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=B3=A8=E9=87=8A?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

代码添加注释
---
 src/UAV_navigation_system/drone_main.py | 140 ++++++++++++++----------
 1 file changed, 83 insertions(+), 57 deletions(-)

diff --git a/src/UAV_navigation_system/drone_main.py b/src/UAV_navigation_system/drone_main.py
index 64aced148d..5b0afa90d7 100644
--- a/src/UAV_navigation_system/drone_main.py
+++ b/src/UAV_navigation_system/drone_main.py
@@ -9,53 +9,64 @@
 import cv2
 import numpy as np
 
-# 添加项目路径，让Python能找到src模块
+# 添加项目路径，让Python能找到src模块（如果需要）
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 
 
 def ensure_directories():
-    """确保所有目录都存在"""
+    """确保所有目录都存在（用于存储数据）"""
+    # 定义需要创建的目录列表
     dirs = ['data/images', 'data/videos', 'data/logs', 'data/config']
     for d in dirs:
+        # 如果目录不存在则创建
         os.makedirs(d, exist_ok=True)
         print(f"✓ 目录已就绪: {d}")
 
 
 class SimpleDroneCamera:
-    """简单的无人机摄像头类"""
+    """简单的无人机摄像头类 - 模拟或真实摄像头"""
 
     def __init__(self, camera_id=0):
+        """
+        初始化摄像头
+        Args:
+            camera_id: 摄像头ID，0通常表示默认摄像头
+        """
         self.camera_id = camera_id
-        self.cap = None
+        self.cap = None  # OpenCV视频捕获对象
 
     def open(self):
         """打开摄像头"""
         print(f"尝试打开摄像头 {self.camera_id}...")
+        # 尝试打开指定ID的摄像头
         self.cap = cv2.VideoCapture(self.camera_id)
 
+        # 检查摄像头是否成功打开
         if not self.cap.isOpened():
             print("⚠️  无法打开物理摄像头，使用模拟模式")
-            return False
+            return False  # 打开失败
         else:
             print("✓ 摄像头已连接")
-            return True
+            return True  # 打开成功
 
     def read_frame(self):
-        """读取一帧"""
+        """读取一帧图像"""
+        # 如果有摄像头且已打开，尝试读取帧
         if self.cap and self.cap.isOpened():
             ret, frame = self.cap.read()
-            if ret:
+            if ret:  # 读取成功
                 return frame
 
         # 如果没有摄像头或读取失败，返回模拟图像
         return self.simulate_frame()
 
     def simulate_frame(self):
-        """生成模拟图像"""
+        """生成模拟图像（在没有摄像头时使用）"""
+        # 创建黑色背景图像
         width, height = 640, 480
         frame = np.zeros((height, width, 3), dtype=np.uint8)
 
-        # 添加一些图形
+        # 在图像上添加一些图形作为模拟内容
         cv2.putText(frame, "无人机模拟视图", (50, 50),
                     cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
         cv2.rectangle(frame, (100, 100), (300, 300), (255, 0, 0), 2)
@@ -64,87 +75,94 @@ def simulate_frame(self):
         return frame
 
     def release(self):
-        """释放摄像头"""
+        """释放摄像头资源"""
         if self.cap:
             self.cap.release()
             print("摄像头已释放")
 
 
 def analyze_scene_simple(frame):
-    """简单场景分析（基于颜色）"""
+    """简单场景分析（基于颜色特征）"""
+    # 检查输入帧是否有效
     if frame is None:
         return "未知", 0.5
 
-    # 转换为HSV
+    # 将BGR颜色空间转换为HSV（色调、饱和度、明度）便于颜色分析
     hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
 
-    # 检测绿色（植被）
+    # 检测绿色（植被）范围
     green_mask = cv2.inRange(hsv, (35, 40, 40), (85, 255, 255))
+    # 计算绿色像素占比
     green_pct = np.sum(green_mask > 0) / green_mask.size
 
-    # 检测蓝色（水域）
+    # 检测蓝色（水域）范围
     blue_mask = cv2.inRange(hsv, (100, 40, 40), (140, 255, 255))
+    # 计算蓝色像素占比
     blue_pct = np.sum(blue_mask > 0) / blue_mask.size
 
-    # 判断场景
-    if green_pct > 0.3:
+    # 根据颜色占比判断场景类型
+    if green_pct > 0.3:  # 绿色占比超过30%认为是森林/草地
         return "森林/草地", green_pct
-    elif blue_pct > 0.2:
+    elif blue_pct > 0.2:  # 蓝色占比超过20%认为是水域
         return "水域", blue_pct
-    else:
+    else:  # 其他情况认为是城市/建筑区域
         return "城市/建筑", max(green_pct, blue_pct)
 
 
 def get_decision(scene_type, confidence):
-    """根据场景类型做出决策"""
+    """根据场景类型做出飞行决策"""
+    # 决策映射表：不同场景类型对应的飞行决策
     decisions = {
         "森林/草地": "✓ 安全区域，继续飞行",
         "水域": "⚠️  接近水域，提高飞行高度",
         "城市/建筑": "⚠️  城市区域，降低速度并避让",
         "未知": "? 无法识别，保持警戒"
     }
+    # 获取对应决策，如果没有匹配则返回默认决策
     return decisions.get(scene_type, "保持当前状态")
 
 
 def main():
-    """主函数"""
+    """主函数 - 无人机视觉导航系统主循环"""
+    # 打印程序标题和说明
     print("=" * 50)
     print("无人机视觉导航系统")
     print("版本: 1.0.0")
     print("按 'q' 键退出，按 's' 键保存图像")
     print("=" * 50)
 
-    # 确保目录存在
+    # 确保所需目录存在
     ensure_directories()
 
-    # 创建无人机摄像头
+    # 创建无人机摄像头对象
     drone_cam = SimpleDroneCamera(camera_id=0)
-    drone_cam.open()
+    drone_cam.open()  # 打开摄像头
 
-    # 初始化状态
-    battery = 100
-    flight_time = 0
-    frame_count = 0
-    start_time = time.time()
+    # 初始化飞行状态变量
+    battery = 100  # 电池电量（百分比）
+    flight_time = 0  # 飞行时间（秒）
+    frame_count = 0  # 已处理帧数
+    start_time = time.time()  # 飞行开始时间
 
     print("\n开始飞行...")
 
+    # 主循环：处理每一帧图像
     while True:
-        # 读取帧
+        # 1. 读取当前帧
         frame = drone_cam.read_frame()
-        frame_count += 1
+        frame_count += 1  # 帧数计数器递增
 
-        # 分析场景
+        # 2. 分析场景类型
         scene_type, confidence = analyze_scene_simple(frame)
 
-        # 获取决策
+        # 3. 根据场景类型做出决策
         decision = get_decision(scene_type, confidence)
 
-        # 更新状态
-        flight_time = time.time() - start_time
-        battery = max(0, battery - 0.05)  # 慢慢消耗电池
+        # 4. 更新飞行状态
+        flight_time = time.time() - start_time  # 计算已飞行时间
+        battery = max(0, battery - 0.05)  # 模拟电池消耗（每帧减少0.05%）
 
-        # 在图像上显示信息
+        # 5. 在图像上显示状态信息
         info_lines = [
             f"场景: {scene_type} ({confidence:.1%})",
             f"决策: {decision}",
@@ -153,60 +171,68 @@ def main():
             f"帧数: {frame_count}"
         ]
 
-        y_offset = 30
+        # 逐行绘制状态信息到图像上
+        y_offset = 30  # 文字起始y坐标
         for line in info_lines:
             cv2.putText(frame, line, (10, y_offset),
                         cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
-            y_offset += 25
+            y_offset += 25  # 每行文字间隔25像素
 
-        # 显示图像
+        # 6. 显示处理后的图像
         cv2.imshow('无人机视觉导航', frame)
 
-        # 检查按键
-        key = cv2.waitKey(1) & 0xFF
-        if key == ord('q'):  # 按 q 退出
+        # 7. 检查键盘输入
+        key = cv2.waitKey(1) & 0xFF  # 等待1毫秒并获取按键
+        if key == ord('q'):  # 按 'q' 键退出程序
+            print("\n用户请求退出...")
             break
-        elif key == ord('s'):  # 按 s 保存图像
+        elif key == ord('s'):  # 按 's' 键保存当前图像
             filename = f"data/images/capture_{frame_count}.jpg"
             cv2.imwrite(filename, frame)
             print(f"✓ 保存图像: {filename}")
 
-        # 检查电池
+        # 8. 检查电池电量（安全条件）
         if battery <= 0:
             print("\n⚠️  电池耗尽！紧急降落...")
             break
 
-        # 限制运行时间（可选）
-        if flight_time > 60:  # 60秒后自动停止
+        # 9. 检查飞行时间限制（可选安全限制）
+        if flight_time > 60:  # 运行60秒后自动停止（演示用途）
             print("\n⏰ 飞行时间到，安全降落...")
             break
 
-    # 清理
-    drone_cam.release()
-    cv2.destroyAllWindows()
+    # 10. 清理资源
+    drone_cam.release()  # 释放摄像头
+    cv2.destroyAllWindows()  # 关闭所有OpenCV窗口
 
-    # 显示统计信息
+    # 11. 打印飞行统计信息
     print("\n" + "=" * 50)
     print("飞行统计:")
     print(f"- 总飞行时间: {flight_time:.1f} 秒")
     print(f"- 处理帧数: {frame_count}")
-    print(f"- 平均帧率: {frame_count / flight_time:.1f} FPS" if flight_time > 0 else "- 平均帧率: N/A")
+    # 计算平均帧率（FPS）
+    if flight_time > 0:
+        print(f"- 平均帧率: {frame_count / flight_time:.1f} FPS")
+    else:
+        print("- 平均帧率: N/A")
     print(f"- 最终电池: {battery:.1f}%")
     print("=" * 50)
 
     print("\n飞行结束！")
-    return 0
+    return 0  # 返回成功状态码
 
 
+# Python标准入口点
 if __name__ == "__main__":
     try:
-        exit_code = main()
-    except KeyboardInterrupt:
+        exit_code = main()  # 运行主函数
+    except KeyboardInterrupt:  # 处理Ctrl+C中断
         print("\n程序被用户中断")
         exit_code = 0
-    except Exception as e:
+    except Exception as e:  # 处理其他异常
         print(f"\n程序出错: {e}")
         exit_code = 1
 
+    # 等待用户确认退出（便于查看输出结果）
     input("\n按 Enter 键退出...")
-    sys.exit(exit_code)
\ No newline at end of file
+    sys.exit(exit_code)  # 退出程序
\ No newline at end of file

From 68040a4bef0172e63ca991d93f8a1f200af12f8b Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Sat, 27 Dec 2025 14:55:56 +0800
Subject: [PATCH 08/11] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86=E7=9B=AE?=
 =?UTF-8?q?=E5=BD=95=E6=96=87=E4=BB=B6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

添加了文件
---
 src/UAV_navigation_system/mulu.test | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 src/UAV_navigation_system/mulu.test

diff --git a/src/UAV_navigation_system/mulu.test b/src/UAV_navigation_system/mulu.test
new file mode 100644
index 0000000000..2af639ca9c
--- /dev/null
+++ b/src/UAV_navigation_system/mulu.test
@@ -0,0 +1,17 @@
+Drone_Vision_Navigation/
+├── main.py                    # 主程序
+├── config.yaml               # 配置文件
+├── requirements.txt          # 依赖包
+├── data/
+│   ├── captured/            # 捕获的图像
+│   ├── training/           # 训练数据
+│   └── models/             # 模型文件（best_model.hs）
+├── scripts/
+│   ├── droidcam_stream.py  # DroidCam集成
+│   ├── battery_manager.py  # 电池管理
+│   └── emergency_handler.py # 紧急处理
+├── utils/
+│   ├── image_processor.py  # 图像处理
+│   ├── classifier.py       # 分类器
+│   └── navigation_logic.py # 导航逻辑
+└── logs/                   # 系统日志
\ No newline at end of file

From 00288e9fff04ee49b265cc079d8985a8b24ce081 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Sat, 27 Dec 2025 17:15:22 +0800
Subject: [PATCH 09/11] =?UTF-8?q?=E6=93=8D=E4=BD=9C=E9=94=AE=E4=BD=8D?=
 =?UTF-8?q?=E6=96=87=E4=BB=B6=E8=A1=A5=E5=85=85?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

文件补充
---
 src/UAV_navigation_system/caozuo | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 src/UAV_navigation_system/caozuo

diff --git a/src/UAV_navigation_system/caozuo b/src/UAV_navigation_system/caozuo
new file mode 100644
index 0000000000..0ddfb63988
--- /dev/null
+++ b/src/UAV_navigation_system/caozuo
@@ -0,0 +1,9 @@
+F1 - 显示帮助
+F - 切换到第一人称视角
+B - 第三人称视角
+R - 开始/停止录制
+退格键 - 重置无人机位置
+方向键 - 控制无人机移动
+q - 退出程序
+s - 保存当前快照
+p - 暂停/继续
\ No newline at end of file

From 424fbd0c31d0d1dbe948369e3569d52dddb6d2c6 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Sat, 27 Dec 2025 17:55:40 +0800
Subject: [PATCH 10/11] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86=E6=93=8D?=
 =?UTF-8?q?=E4=BD=9C=E6=8C=89=E9=94=AE=E8=A1=A5=E5=85=85=E7=9A=84=E6=96=87?=
 =?UTF-8?q?=E4=BB=B6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

操作按键补充
---
 src/UAV_navigation_system/{caozuo => caozuo.test} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename src/UAV_navigation_system/{caozuo => caozuo.test} (100%)

diff --git a/src/UAV_navigation_system/caozuo b/src/UAV_navigation_system/caozuo.test
similarity index 100%
rename from src/UAV_navigation_system/caozuo
rename to src/UAV_navigation_system/caozuo.test

From fa2917ad91afeea638c6135bb4f9a8d8f7df4b75 Mon Sep 17 00:00:00 2001
From: 725921 <3407246504@qq.com>
Date: Tue, 30 Dec 2025 17:57:45 +0800
Subject: [PATCH 11/11] =?UTF-8?q?=E6=97=A0=E4=BA=BA=E6=9C=BA?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

添加了主程序代码 和检测airsim的程序  还有错误直接执行的bat
---
 src/UAV_navigation_system/drone_main.py   | 981 +++++++++++++++++-----
 src/UAV_navigation_system/start_fixed.bat |  56 ++
 src/UAV_navigation_system/test_airsim.py  |  10 +
 3 files changed, 822 insertions(+), 225 deletions(-)
 create mode 100644 src/UAV_navigation_system/start_fixed.bat
 create mode 100644 src/UAV_navigation_system/test_airsim.py

diff --git a/src/UAV_navigation_system/drone_main.py b/src/UAV_navigation_system/drone_main.py
index 5b0afa90d7..4b2481d88f 100644
--- a/src/UAV_navigation_system/drone_main.py
+++ b/src/UAV_navigation_system/drone_main.py
@@ -1,238 +1,769 @@
+# drone_vision_system_en_fixed.py
 """
-无人机视觉导航系统 - 简化版
-可以立即运行测试
+Drone Vision Navigation System
+Optimized for Abandoned Park Environment
+Pure English interface to avoid encoding issues
+Fixed KeyboardInterrupt handling
 """
 
-import os
-import sys
-import time
+import airsim
 import cv2
 import numpy as np
-
-# 添加项目路径，让Python能找到src模块（如果需要）
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-
-
-def ensure_directories():
-    """确保所有目录都存在（用于存储数据）"""
-    # 定义需要创建的目录列表
-    dirs = ['data/images', 'data/videos', 'data/logs', 'data/config']
-    for d in dirs:
-        # 如果目录不存在则创建
-        os.makedirs(d, exist_ok=True)
-        print(f"✓ 目录已就绪: {d}")
-
-
-class SimpleDroneCamera:
-    """简单的无人机摄像头类 - 模拟或真实摄像头"""
-
-    def __init__(self, camera_id=0):
-        """
-        初始化摄像头
-        Args:
-            camera_id: 摄像头ID，0通常表示默认摄像头
+import time
+import json
+import os
+import sys
+from datetime import datetime
+import random
+
+class DroneVisionSystem:
+    """Drone vision navigation system"""
+    
+    def __init__(self):
+        """Initialize system"""
+        self.clear_screen()
+        print("=" * 70)
+        print("DRONE VISION NAVIGATION SYSTEM v2.0")
+        print("=" * 70)
+        print("Optimized for Abandoned Park Environment")
+        print("-" * 70)
+        
+        # Try to connect to simulator
+        try:
+            print("Connecting to AirSim simulator...")
+            self.client = airsim.MultirotorClient()
+            self.client.confirmConnection()
+            print("✓ Connection successful!")
+        except Exception as e:
+            print(f"✗ Connection failed: {str(e)}")
+            print("\nPlease ensure:")
+            print("1. AbandonedPark.exe is running")
+            print("2. Simulator is fully loaded")
+            print("3. Switched to drone mode if needed")
+            self.client = None
+        
+        # System status
+        self.running = False
+        self.flying = False
+        self.battery = 100.0
+        self.current_env = "Unknown"
+        self.env_confidence = 0.0
+        self.emergency = False
+        
+        # Create directories
+        self.create_folders()
+        
+        # Initialize classifier
+        self.classifier = EnvironmentClassifier()
+        
+        print("System initialization complete!")
+        print("=" * 70)
+    
+    def clear_screen(self):
+        """Clear console screen"""
+        if sys.platform == 'win32':
+            os.system('cls')
+        else:
+            os.system('clear')
+    
+    def create_folders(self):
+        """Create project folders"""
+        folders = ['data/images', 'data/logs', 'models', 'debug']
+        for folder in folders:
+            os.makedirs(folder, exist_ok=True)
+    
+    def log(self, message):
+        """Log message"""
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        log_msg = f"[{timestamp}] {message}"
+        
+        # Print to console
+        print(log_msg)
+        
+        # Save to file
+        log_file = f"data/logs/drone_{datetime.now().strftime('%Y%m%d')}.log"
+        with open(log_file, 'a') as f:
+            f.write(log_msg + "\n")
+    
+    def show_status(self):
+        """Display system status"""
+        status_text = f"""
+Current Status:
+  Flight Status: {'Flying' if self.flying else 'Landed'}
+  Environment: {self.current_env} ({self.env_confidence:.1%})
+  Battery: {self.battery:.1f}%
+  Emergency Mode: {'Yes' if self.emergency else 'No'}
+  System Running: {'Yes' if self.running else 'No'}
         """
-        self.camera_id = camera_id
-        self.cap = None  # OpenCV视频捕获对象
-
-    def open(self):
-        """打开摄像头"""
-        print(f"尝试打开摄像头 {self.camera_id}...")
-        # 尝试打开指定ID的摄像头
-        self.cap = cv2.VideoCapture(self.camera_id)
-
-        # 检查摄像头是否成功打开
-        if not self.cap.isOpened():
-            print("⚠️  无法打开物理摄像头，使用模拟模式")
-            return False  # 打开失败
+        print(status_text)
+    
+    def takeoff(self, height=15):
+        """Take off to specified height"""
+        if not self.client:
+            print("Not connected to simulator!")
+            return False
+        
+        try:
+            self.log(f"Taking off to {height} meters...")
+            
+            # Unlock drone
+            self.client.enableApiControl(True)
+            self.client.armDisarm(True)
+            time.sleep(1)
+            
+            # Take off
+            self.client.takeoffAsync().join()
+            time.sleep(2)
+            
+            # Ascend to specified height
+            self.client.moveToZAsync(-height, 3).join()
+            
+            self.flying = True
+            self.log("Takeoff successful!")
+            return True
+            
+        except Exception as e:
+            self.log(f"Takeoff failed: {str(e)}")
+            return False
+    
+    def land(self):
+        """Land the drone"""
+        try:
+            self.log("Landing...")
+            self.client.landAsync().join()
+            time.sleep(2)
+            
+            # Lock drone
+            self.client.armDisarm(False)
+            self.client.enableApiControl(False)
+            
+            self.flying = False
+            self.log("Landing successful!")
+            return True
+            
+        except Exception as e:
+            self.log(f"Landing failed: {str(e)}")
+            return False
+    
+    def capture_image(self):
+        """Capture image from drone camera"""
+        try:
+            responses = self.client.simGetImages([
+                airsim.ImageRequest("0", airsim.ImageType.Scene, False, False)
+            ])
+            
+            if responses:
+                response = responses[0]
+                img1d = np.frombuffer(response.image_data_uint8, dtype=np.uint8)
+                img_rgb = img1d.reshape(response.height, response.width, 3)
+                
+                # Convert to BGR format
+                img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
+                return img_bgr
+                
+        except Exception as e:
+            self.log(f"Image capture failed: {str(e)}")
+        
+        return None
+    
+    def analyze_environment(self, image):
+        """Analyze environment using classifier"""
+        return self.classifier.classify(image)
+    
+    def update_battery(self):
+        """Update battery status"""
+        if self.flying:
+            self.battery -= 0.05  # Flying consumption
         else:
-            print("✓ 摄像头已连接")
-            return True  # 打开成功
-
-    def read_frame(self):
-        """读取一帧图像"""
-        # 如果有摄像头且已打开，尝试读取帧
-        if self.cap and self.cap.isOpened():
-            ret, frame = self.cap.read()
-            if ret:  # 读取成功
-                return frame
-
-        # 如果没有摄像头或读取失败，返回模拟图像
-        return self.simulate_frame()
-
-    def simulate_frame(self):
-        """生成模拟图像（在没有摄像头时使用）"""
-        # 创建黑色背景图像
-        width, height = 640, 480
-        frame = np.zeros((height, width, 3), dtype=np.uint8)
-
-        # 在图像上添加一些图形作为模拟内容
-        cv2.putText(frame, "无人机模拟视图", (50, 50),
-                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-        cv2.rectangle(frame, (100, 100), (300, 300), (255, 0, 0), 2)
-        cv2.circle(frame, (400, 200), 50, (0, 0, 255), -1)
-
-        return frame
-
-    def release(self):
-        """释放摄像头资源"""
-        if self.cap:
-            self.cap.release()
-            print("摄像头已释放")
-
-
-def analyze_scene_simple(frame):
-    """简单场景分析（基于颜色特征）"""
-    # 检查输入帧是否有效
-    if frame is None:
-        return "未知", 0.5
-
-    # 将BGR颜色空间转换为HSV（色调、饱和度、明度）便于颜色分析
-    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-
-    # 检测绿色（植被）范围
-    green_mask = cv2.inRange(hsv, (35, 40, 40), (85, 255, 255))
-    # 计算绿色像素占比
-    green_pct = np.sum(green_mask > 0) / green_mask.size
-
-    # 检测蓝色（水域）范围
-    blue_mask = cv2.inRange(hsv, (100, 40, 40), (140, 255, 255))
-    # 计算蓝色像素占比
-    blue_pct = np.sum(blue_mask > 0) / blue_mask.size
-
-    # 根据颜色占比判断场景类型
-    if green_pct > 0.3:  # 绿色占比超过30%认为是森林/草地
-        return "森林/草地", green_pct
-    elif blue_pct > 0.2:  # 蓝色占比超过20%认为是水域
-        return "水域", blue_pct
-    else:  # 其他情况认为是城市/建筑区域
-        return "城市/建筑", max(green_pct, blue_pct)
-
-
-def get_decision(scene_type, confidence):
-    """根据场景类型做出飞行决策"""
-    # 决策映射表：不同场景类型对应的飞行决策
-    decisions = {
-        "森林/草地": "✓ 安全区域，继续飞行",
-        "水域": "⚠️  接近水域，提高飞行高度",
-        "城市/建筑": "⚠️  城市区域，降低速度并避让",
-        "未知": "? 无法识别，保持警戒"
-    }
-    # 获取对应决策，如果没有匹配则返回默认决策
-    return decisions.get(scene_type, "保持当前状态")
-
-
-def main():
-    """主函数 - 无人机视觉导航系统主循环"""
-    # 打印程序标题和说明
-    print("=" * 50)
-    print("无人机视觉导航系统")
-    print("版本: 1.0.0")
-    print("按 'q' 键退出，按 's' 键保存图像")
-    print("=" * 50)
-
-    # 确保所需目录存在
-    ensure_directories()
-
-    # 创建无人机摄像头对象
-    drone_cam = SimpleDroneCamera(camera_id=0)
-    drone_cam.open()  # 打开摄像头
-
-    # 初始化飞行状态变量
-    battery = 100  # 电池电量（百分比）
-    flight_time = 0  # 飞行时间（秒）
-    frame_count = 0  # 已处理帧数
-    start_time = time.time()  # 飞行开始时间
-
-    print("\n开始飞行...")
-
-    # 主循环：处理每一帧图像
-    while True:
-        # 1. 读取当前帧
-        frame = drone_cam.read_frame()
-        frame_count += 1  # 帧数计数器递增
-
-        # 2. 分析场景类型
-        scene_type, confidence = analyze_scene_simple(frame)
-
-        # 3. 根据场景类型做出决策
-        decision = get_decision(scene_type, confidence)
-
-        # 4. 更新飞行状态
-        flight_time = time.time() - start_time  # 计算已飞行时间
-        battery = max(0, battery - 0.05)  # 模拟电池消耗（每帧减少0.05%）
-
-        # 5. 在图像上显示状态信息
-        info_lines = [
-            f"场景: {scene_type} ({confidence:.1%})",
-            f"决策: {decision}",
-            f"飞行时间: {flight_time:.1f}s",
-            f"电池: {battery:.1f}%",
-            f"帧数: {frame_count}"
+            self.battery -= 0.01  # Standby consumption
+        
+        if self.battery < 0:
+            self.battery = 0
+        
+        # Check low battery
+        if self.battery < 20 and not self.emergency:
+            self.log(f"Warning: Low battery ({self.battery:.1f}%)")
+            if self.battery < 10:
+                self.log("Critical: Very low battery!")
+                self.emergency = True
+    
+    def save_image_data(self, image, environment, confidence):
+        """Save image data"""
+        if image is None:
+            return
+        
+        # Generate filename
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+        filename = f"data/images/img_{timestamp}_{environment}.jpg"
+        
+        # Save image
+        cv2.imwrite(filename, image)
+        
+        # Record to JSON
+        data = {
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
+            "filename": filename,
+            "environment": environment,
+            "confidence": float(confidence),
+            "battery": float(self.battery),
+            "flying": self.flying
+        }
+        
+        json_file = "data/images/images_log.json"
+        
+        try:
+            # Read existing data
+            if os.path.exists(json_file):
+                with open(json_file, 'r') as f:
+                    all_data = json.load(f)
+            else:
+                all_data = []
+            
+            # Add new data
+            all_data.append(data)
+            
+            # Keep only recent 1000 records
+            if len(all_data) > 1000:
+                all_data = all_data[-1000:]
+            
+            # Save
+            with open(json_file, 'w') as f:
+                json.dump(all_data, f, indent=2)
+                
+        except Exception as e:
+            self.log(f"Data save failed: {str(e)}")
+    
+    def navigate(self, environment, confidence):
+        """Navigate based on environment"""
+        if not self.flying:
+            return
+        
+        # Choose action based on environment
+        actions = {
+            "Ruins": self.navigate_ruins,
+            "Building": self.navigate_building,
+            "Forest": self.navigate_forest,
+            "Road": self.navigate_road,
+            "Sky": self.navigate_sky,
+            "Water": self.navigate_water,
+            "Fire": self.navigate_fire,
+            "Animal": self.navigate_animal,
+            "Vehicle": self.navigate_vehicle
+        }
+        
+        # Execute corresponding navigation function
+        action_func = actions.get(environment, self.navigate_default)
+        action_func(confidence)
+    
+    def navigate_ruins(self, confidence):
+        """Navigate in ruins"""
+        self.log("Exploring ruins...")
+        # Move slowly to avoid collisions
+        self.client.moveByVelocityAsync(2, 0, 0, 2).join()
+        time.sleep(1)
+    
+    def navigate_building(self, confidence):
+        """Navigate around buildings"""
+        self.log("Avoiding building...")
+        # Rise to avoid collision
+        self.client.moveByVelocityAsync(0, 0, -1, 1).join()
+        # Move right
+        self.client.moveByVelocityAsync(0, 2, 0, 2).join()
+    
+    def navigate_forest(self, confidence):
+        """Navigate through forest"""
+        self.log("Moving through forest...")
+        # Rise slightly and move slowly
+        self.client.moveByVelocityAsync(1.5, 0, -0.5, 2).join()
+    
+    def navigate_road(self, confidence):
+        """Navigate along road"""
+        self.log("Following road...")
+        # Move at normal speed
+        self.client.moveByVelocityAsync(3, 0, 0, 3).join()
+    
+    def navigate_sky(self, confidence):
+        """Navigate in open sky"""
+        self.log("Open sky, normal flight...")
+        # Move faster
+        self.client.moveByVelocityAsync(4, 0, 0, 3).join()
+    
+    def navigate_water(self, confidence):
+        """Avoid water"""
+        self.log("Avoiding water area...")
+        # Rise immediately
+        self.client.moveByVelocityAsync(0, 0, -2, 1).join()
+        # Move backward
+        self.client.moveByVelocityAsync(-2, 0, 0, 2).join()
+    
+    def navigate_fire(self, confidence):
+        """Navigate fire emergency"""
+        self.log("Fire detected! Emergency response...")
+        self.emergency = True
+        # Emergency ascent
+        self.client.moveToZAsync(-30, 5).join()
+        # Send alert
+        self.log("Fire alert!")
+    
+    def navigate_animal(self, confidence):
+        """Navigate around animals"""
+        self.log("Animal detected, keeping distance...")
+        # Hover and observe
+        self.client.hoverAsync().join()
+        time.sleep(3)
+        # Move back slowly
+        self.client.moveByVelocityAsync(-1, 0, 0, 2).join()
+    
+    def navigate_vehicle(self, confidence):
+        """Navigate around vehicles"""
+        self.log("Vehicle detected, following...")
+        # Follow at distance
+        self.client.moveByVelocityAsync(2, 0, 0, 2).join()
+    
+    def navigate_default(self, confidence):
+        """Default navigation"""
+        self.log("Unknown environment, conservative exploration...")
+        # Move slowly
+        self.client.moveByVelocityAsync(1, 0, 0, 2).join()
+    
+    def display_image(self, image, environment, confidence):
+        """Display image with information"""
+        if image is None:
+            return
+        
+        # Create display image
+        display_img = image.copy()
+        height, width = display_img.shape[:2]
+        
+        # Add environment info
+        env_text = f"Env: {environment}"
+        conf_text = f"Conf: {confidence:.1%}"
+        bat_text = f"Battery: {self.battery:.1f}%"
+        
+        # Set text color
+        color = (0, 255, 0)  # Green
+        if confidence < 0.6:
+            color = (0, 255, 255)  # Yellow
+        if confidence < 0.4:
+            color = (0, 0, 255)  # Red
+        
+        # Add text to image
+        cv2.putText(display_img, env_text, (20, 40),
+                   cv2.FONT_HERSHEY_SIMPLEX, 1.2, color, 3)
+        cv2.putText(display_img, conf_text, (20, 80),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
+        cv2.putText(display_img, bat_text, (20, 110),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
+        
+        # Add timestamp
+        timestamp = datetime.now().strftime("%H:%M:%S")
+        cv2.putText(display_img, timestamp, (width - 150, 40),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
+        
+        # Show image
+        cv2.imshow('Drone Vision System', display_img)
+    
+    def run_mission(self, mission_time=300):
+        """Run main mission loop"""
+        if not self.client:
+            print("Cannot run: Not connected to simulator")
+            return
+        
+        self.clear_screen()
+        print("=" * 70)
+        print("STARTING DRONE MISSION")
+        print(f"Mission time: {mission_time} seconds")
+        print("=" * 70)
+        print("CONTROLS:")
+        print("  Q - Quit program")
+        print("  L - Manual landing")
+        print("  R - Return home")
+        print("  S - Save current image")
+        print("  P - Pause/Resume")
+        print("-" * 70)
+        
+        # Take off
+        if not self.takeoff():
+            return
+        
+        self.running = True
+        start_time = time.time()
+        frame_count = 0
+        last_env = "Unknown"
+        
+        try:
+            while self.running and (time.time() - start_time) < mission_time:
+                frame_count += 1
+                
+                # Update battery
+                self.update_battery()
+                
+                # Check emergency
+                if self.emergency and self.flying:
+                    self.log("Emergency! Returning home...")
+                    self.client.moveToPositionAsync(0, 0, -20, 5).join()
+                    self.land()
+                    break
+                
+                # Capture image
+                image = self.capture_image()
+                
+                if image is not None:
+                    # Analyze environment
+                    try:
+                        environment, confidence = self.analyze_environment(image)
+                        last_env = environment
+                        self.current_env = environment
+                        self.env_confidence = confidence
+                    except Exception as e:
+                        self.log(f"Environment analysis failed: {str(e)}")
+                        environment, confidence = "Unknown", 0.0
+                        self.current_env = "Unknown"
+                        self.env_confidence = 0.0
+                    
+                    # Save data
+                    if confidence > 0.7 or frame_count % 5 == 0:
+                        self.save_image_data(image, environment, confidence)
+                    
+                    # Display image
+                    self.display_image(image, environment, confidence)
+                    
+                    # Navigation decision
+                    try:
+                        self.navigate(environment, confidence)
+                    except Exception as e:
+                        self.log(f"Navigation error: {str(e)}")
+                
+                # Show status (每20帧更新一次，减少屏幕闪烁)
+                if frame_count % 20 == 0:
+                    elapsed = int(time.time() - start_time)
+                    remaining = mission_time - elapsed
+                    self.clear_screen()
+                    print(f"MISSION IN PROGRESS...")
+                    print(f"Elapsed: {elapsed} sec | Remaining: {remaining} sec")
+                    print(f"Environment: {last_env} ({self.env_confidence:.1%})")
+                    print(f"Battery: {self.battery:.1f}%")
+                    print(f"Frames processed: {frame_count}")
+                    print("-" * 40)
+                    print("CONTROLS: Q-Quit, L-Land, R-Return, S-Save, P-Pause")
+                
+                # Check keyboard input
+                key = cv2.waitKey(30) & 0xFF
+                if key == ord('q'):
+                    self.log("User quit")
+                    break
+                elif key == ord('l'):
+                    self.land()
+                    break
+                elif key == ord('r'):
+                    self.log("Manual return home")
+                    self.client.moveToPositionAsync(0, 0, -20, 5).join()
+                    self.land()
+                    break
+                elif key == ord('s'):
+                    if image is not None:
+                        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                        filename = f"debug/snapshot_{timestamp}.jpg"
+                        cv2.imwrite(filename, image)
+                        self.log(f"Saved snapshot: {filename}")
+                elif key == ord('p'):
+                    self.running = not self.running
+                    status = "Paused" if not self.running else "Resumed"
+                    self.log(f"Mission {status}")
+                
+                # Small delay to control processing speed
+                time.sleep(0.1)  # 从0.5秒减少到0.1秒，提高响应性
+            
+            # Mission complete
+            self.log("Mission complete!")
+            
+        except KeyboardInterrupt:
+            self.log("Mission interrupted by user (Ctrl+C)")
+            print("\nMission interrupted. Landing drone...")
+        except Exception as e:
+            self.log(f"Runtime error: {str(e)}")
+        finally:
+            # Cleanup
+            if self.flying:
+                try:
+                    self.land()
+                except:
+                    pass
+            cv2.destroyAllWindows()
+            try:
+                self.generate_report()
+            except:
+                pass
+    
+    def generate_report(self):
+        """Generate mission report"""
+        report = {
+            "mission": "Drone Vision Navigation",
+            "date": datetime.now().strftime("%Y-%m-%d"),
+            "time": datetime.now().strftime("%H:%M:%S"),
+            "summary": {
+                "battery_final": round(self.battery, 1),
+                "environment_detected": self.current_env,
+                "emergency_activated": self.emergency
+            },
+            "files": {
+                "images": "data/images/",
+                "logs": "data/logs/",
+                "debug": "debug/"
+            }
+        }
+        
+        report_file = f"data/logs/report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+        
+        try:
+            with open(report_file, 'w') as f:
+                json.dump(report, f, indent=2)
+            self.log(f"Report saved: {report_file}")
+        except Exception as e:
+            self.log(f"Report save failed: {str(e)}")
+
+
+class EnvironmentClassifier:
+    """Environment classifier for abandoned park"""
+    
+    def __init__(self):
+        self.environments = [
+            "Ruins", "Building", "Forest", "Road",
+            "Sky", "Water", "Fire", "Animal", "Vehicle"
         ]
+        
+        # Feature weights for abandoned park
+        self.weights = {
+            "Ruins": 0.35,  # Highest probability for ruins
+            "Building": 0.20,
+            "Forest": 0.15,
+            "Road": 0.10,
+            "Sky": 0.08,
+            "Water": 0.05,  # Lower water weight
+            "Fire": 0.02,
+            "Animal": 0.03,
+            "Vehicle": 0.02
+        }
+    
+    def classify(self, image):
+        """Classify image"""
+        if image is None:
+            return "Unknown", 0.0
+        
+        # Extract features
+        features = self.extract_features(image)
+        
+        # Rule-based classification
+        env, conf = self.rule_based(features)
+        
+        # If rules unclear, use weighted random
+        if env == "Unknown":
+            env, conf = self.weighted_random(features)
+        
+        return env, conf
+    
+    def extract_features(self, image):
+        """Extract image features"""
+        features = {}
+        
+        height, width = image.shape[:2]
+        
+        # Color features
+        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        
+        # Blue regions
+        blue_low = np.array([100, 50, 50])
+        blue_high = np.array([130, 255, 255])
+        blue_mask = cv2.inRange(hsv, blue_low, blue_high)
+        features['blue_ratio'] = np.sum(blue_mask > 0) / (height * width)
+        
+        # Green regions
+        green_low = np.array([40, 50, 50])
+        green_high = np.array([80, 255, 255])  # 修复：添加 np.
+        green_mask = cv2.inRange(hsv, green_low, green_high)
+        features['green_ratio'] = np.sum(green_mask > 0) / (height * width)
+        
+        # Red regions
+        red_low1 = np.array([0, 50, 50])
+        red_high1 = np.array([10, 255, 255])
+        red_low2 = np.array([170, 50, 50])
+        red_high2 = np.array([180, 255, 255])
+        red_mask1 = cv2.inRange(hsv, red_low1, red_high1)
+        red_mask2 = cv2.inRange(hsv, red_low2, red_high2)
+        red_mask = cv2.bitwise_or(red_mask1, red_mask2)
+        features['red_ratio'] = np.sum(red_mask > 0) / (height * width)
+        
+        # Texture features
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        edges = cv2.Canny(gray, 50, 150)
+        features['edge_density'] = np.sum(edges > 0) / (height * width)
+        features['gray_variance'] = np.var(gray)
+        features['brightness'] = np.mean(gray)
+        
+        # Sky detection
+        if height > 10:
+            top_blue = np.sum(blue_mask[:height//3, :] > 0) / (width * height//3)
+            bottom_blue = np.sum(blue_mask[2*height//3:, :] > 0) / (width * height//3)
+            features['sky_ratio'] = top_blue
+            features['is_sky'] = top_blue > 0.3 and top_blue > bottom_blue * 2
+        
+        return features
+    
+    def rule_based(self, features):
+        """Rule-based classification"""
+        blue = features.get('blue_ratio', 0)
+        green = features.get('green_ratio', 0)
+        red = features.get('red_ratio', 0)
+        edges = features.get('edge_density', 0)
+        variance = features.get('gray_variance', 0)
+        bright = features.get('brightness', 0)
+        is_sky = features.get('is_sky', False)
+        
+        # Rules for abandoned park
+        if edges > 0.06 and variance > 1000:
+            return "Ruins", 0.85
+        
+        if green > 0.25:
+            return "Forest", 0.80
+        
+        if blue > 0.3:
+            if is_sky:
+                return "Sky", 0.75
+            else:
+                return "Water", 0.65
+        
+        if edges < 0.03 and 100 < bright < 180:
+            return "Road", 0.70
+        
+        if red > 0.15:
+            if bright > 180:
+                return "Fire", 0.60
+            else:
+                return "Ruins", 0.65
+        
+        if edges > 0.04 and bright < 120:
+            return "Building", 0.75
+        
+        return "Unknown", 0.0
+    
+    def weighted_random(self, features):
+        """Weighted random selection"""
+        # Adjust weights based on features
+        adj_weights = self.weights.copy()
+        
+        blue = features.get('blue_ratio', 0)
+        green = features.get('green_ratio', 0)
+        edges = features.get('edge_density', 0)
+        
+        # Adjust based on features
+        if blue > 0.2:
+            adj_weights["Sky"] *= 1.5
+            if blue > 0.3:
+                adj_weights["Water"] *= 0.5  # Reduce water weight
+        
+        if green > 0.15:
+            adj_weights["Forest"] *= 2.0
+        
+        if edges > 0.05:
+            adj_weights["Ruins"] *= 1.8
+        
+        # Normalize
+        total = sum(adj_weights.values())
+        if total > 0:
+            probs = [adj_weights[env]/total for env in self.environments]
+        else:
+            probs = [1/len(self.environments)] * len(self.environments)
+        
+        # Random selection
+        env_idx = random.choices(range(len(self.environments)), weights=probs)[0]
+        env = self.environments[env_idx]
+        
+        # Calculate confidence
+        base_conf = 0.6
+        
+        # Increase confidence based on features
+        if env == "Ruins" and edges > 0.04:
+            base_conf += 0.15
+        elif env == "Forest" and green > 0.15:
+            base_conf += 0.1
+        elif env == "Sky" and blue > 0.2:
+            base_conf += 0.1
+        
+        conf = min(base_conf, 0.9)
+        
+        return env, conf
+
+
+def safe_input(prompt):
+    """Safe input function with KeyboardInterrupt handling"""
+    try:
+        return input(prompt).strip()
+    except KeyboardInterrupt:
+        print("\n\nOperation cancelled by user (Ctrl+C)")
+        print("Exiting program...")
+        sys.exit(0)
 
-        # 逐行绘制状态信息到图像上
-        y_offset = 30  # 文字起始y坐标
-        for line in info_lines:
-            cv2.putText(frame, line, (10, y_offset),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
-            y_offset += 25  # 每行文字间隔25像素
-
-        # 6. 显示处理后的图像
-        cv2.imshow('无人机视觉导航', frame)
-
-        # 7. 检查键盘输入
-        key = cv2.waitKey(1) & 0xFF  # 等待1毫秒并获取按键
-        if key == ord('q'):  # 按 'q' 键退出程序
-            print("\n用户请求退出...")
-            break
-        elif key == ord('s'):  # 按 's' 键保存当前图像
-            filename = f"data/images/capture_{frame_count}.jpg"
-            cv2.imwrite(filename, frame)
-            print(f"✓ 保存图像: {filename}")
-
-        # 8. 检查电池电量（安全条件）
-        if battery <= 0:
-            print("\n⚠️  电池耗尽！紧急降落...")
-            break
-
-        # 9. 检查飞行时间限制（可选安全限制）
-        if flight_time > 60:  # 运行60秒后自动停止（演示用途）
-            print("\n⏰ 飞行时间到，安全降落...")
-            break
-
-    # 10. 清理资源
-    drone_cam.release()  # 释放摄像头
-    cv2.destroyAllWindows()  # 关闭所有OpenCV窗口
-
-    # 11. 打印飞行统计信息
-    print("\n" + "=" * 50)
-    print("飞行统计:")
-    print(f"- 总飞行时间: {flight_time:.1f} 秒")
-    print(f"- 处理帧数: {frame_count}")
-    # 计算平均帧率（FPS）
-    if flight_time > 0:
-        print(f"- 平均帧率: {frame_count / flight_time:.1f} FPS")
-    else:
-        print("- 平均帧率: N/A")
-    print(f"- 最终电池: {battery:.1f}%")
-    print("=" * 50)
 
-    print("\n飞行结束！")
-    return 0  # 返回成功状态码
+def main():
+    """Main function with improved exception handling"""
+    try:
+        print("DRONE VISION NAVIGATION SYSTEM")
+        print("-" * 50)
+        
+        # Create system
+        drone = DroneVisionSystem()
+        
+        if drone.client is None:
+            print("Cannot connect to simulator, exiting")
+            return
+        
+        # User configuration
+        print("\nMISSION CONFIGURATION:")
+        print("1. Test mode (60 seconds)")
+        print("2. Short mission (5 minutes)")
+        print("3. Long mission (10 minutes)")
+        print("4. Custom time")
+        
+        choice = safe_input("\nSelect mode (1-4): ")
+        
+        if choice == '1':
+            mission_time = 60
+        elif choice == '2':
+            mission_time = 300
+        elif choice == '3':
+            mission_time = 600
+        elif choice == '4':
+            try:
+                time_input = safe_input("Enter mission time (seconds): ")
+                mission_time = int(time_input)
+            except ValueError:
+                mission_time = 300
+                print(f"Invalid input, using default: {mission_time} seconds")
+        else:
+            mission_time = 300
+            print(f"Using default: {mission_time} seconds")
+        
+        print(f"\nMISSION SETTINGS:")
+        print(f"  Time: {mission_time} seconds ({mission_time/60:.1f} minutes)")
+        print(f"  Image saving: Auto")
+        print(f"  Logging: Enabled")
+        
+        confirm = safe_input("\nStart mission? (y/n): ").lower()
+        
+        if confirm == 'y':
+            drone.run_mission(mission_time)
+        else:
+            print("Mission cancelled")
+        
+        print("\nProgram ended gracefully")
+        
+    except KeyboardInterrupt:
+        print("\n\nProgram interrupted by user (Ctrl+C)")
+        print("Exiting...")
+    except Exception as e:
+        print(f"\nUnexpected error: {str(e)}")
+        print("Please check your setup and try again.")
 
 
-# Python标准入口点
 if __name__ == "__main__":
-    try:
-        exit_code = main()  # 运行主函数
-    except KeyboardInterrupt:  # 处理Ctrl+C中断
-        print("\n程序被用户中断")
-        exit_code = 0
-    except Exception as e:  # 处理其他异常
-        print(f"\n程序出错: {e}")
-        exit_code = 1
-
-    # 等待用户确认退出（便于查看输出结果）
-    input("\n按 Enter 键退出...")
-    sys.exit(exit_code)  # 退出程序
\ No newline at end of file
+    main()
\ No newline at end of file
diff --git a/src/UAV_navigation_system/start_fixed.bat b/src/UAV_navigation_system/start_fixed.bat
new file mode 100644
index 0000000000..58693749b2
--- /dev/null
+++ b/src/UAV_navigation_system/start_fixed.bat
@@ -0,0 +1,56 @@
+@echo off
+cls
+
+echo ============================================
+echo    DRONE VISION NAVIGATION SYSTEM
+echo ============================================
+echo    FIXED VERSION - KeyboardInterrupt Handled
+echo ============================================
+echo.
+
+echo Step 1: Checking simulator...
+tasklist | findstr "AbandonedPark.exe" > nul
+if errorlevel 1 (
+    echo Simulator not running!
+    echo Please run AbandonedPark.exe manually
+    echo Waiting 20 seconds...
+    timeout /t 20 /nobreak > nul
+) else (
+    echo ✓ Simulator is running
+)
+
+echo.
+echo Step 2: Activating Python environment...
+if exist "venv\Scripts\activate.bat" (
+    call venv\Scripts\activate.bat
+    echo ✓ Virtual environment activated
+) else (
+    echo Creating virtual environment...
+    python -m venv venv
+    call venv\Scripts\activate.bat
+    echo Installing dependencies...
+    pip install airsim opencv-python numpy
+)
+
+echo.
+echo Step 3: Checking Python packages...
+python -c "import airsim; print('✓ AirSim installed')" 2>nul
+if errorlevel 1 (
+    echo Installing AirSim...
+    pip install airsim
+)
+
+python -c "import cv2; print('✓ OpenCV installed')" 2>nul
+if errorlevel 1 (
+    echo Installing OpenCV...
+    pip install opencv-python
+)
+
+echo.
+echo Step 4: Starting drone system...
+echo Note: Press Ctrl+C at any time to safely exit
+echo.
+python drone_vision_system_en_fixed.py
+
+echo.
+pause
\ No newline at end of file
diff --git a/src/UAV_navigation_system/test_airsim.py b/src/UAV_navigation_system/test_airsim.py
new file mode 100644
index 0000000000..8a23a4dd4b
--- /dev/null
+++ b/src/UAV_navigation_system/test_airsim.py
@@ -0,0 +1,10 @@
+import airsim
+import sys
+
+print("Python 版本:", sys.version)
+print("AirSim 版本:", airsim.__version__)
+print("AirSim 导入成功！")
+
+# 检查客户端是否可以创建
+client = airsim.MultirotorClient()
+print("客户端创建成功")
\ No newline at end of file