代码优化

app_main.py

@@ -224,22 +224,26 @@ async def lifespan(app: FastAPI):
     
     # 4. Day 21: 预加载新 AI 管道模型（避免首次使用时延迟）
     if USE_NEW_AI_PIPELINE:
-        async def _preload_models():
+        # Day 28: VAD 同步预加载，避免第一句话不识别
+        try:
+            print("[PRELOAD] 预加载 Silero VAD...")
+            from server_vad import get_vad_model
+            get_vad_model()  # 直接加载 VAD 模型
+            print("[PRELOAD] Silero VAD 预加载完成")
+        except Exception as e:
+            print(f"[PRELOAD] VAD 预加载失败: {e}")
+        
+        # SenseVoice 异步加载（不阻塞启动）
+        async def _preload_sensevoice():
             try:
-                print("[PRELOAD] 预加载 Silero VAD...")
-                from server_vad import get_server_vad
-                get_server_vad()  # 触发 VAD 模型加载
-                
                 print("[PRELOAD] 预加载 SenseVoice ASR...")
                 from sensevoice_asr import init_sensevoice
-                await init_sensevoice()  # 异步加载 ASR 模型
-                
+                await init_sensevoice()
                 print("[PRELOAD] 新 AI 管道模型预加载完成")
             except Exception as e:
-                print(f"[PRELOAD] 模型预加载失败: {e}")
+                print(f"[PRELOAD] SenseVoice 预加载失败: {e}")
         
-        # 后台预加载，不阻塞启动
-        asyncio.create_task(_preload_models())
+        asyncio.create_task(_preload_sensevoice())
     
     print("[LIFESPAN] 应用启动完成")
     
@@ -349,7 +353,9 @@ def load_navigation_models():
     # global yolo_seg_model, obstacle_detector (Moved to ctx)
 
     try:
-        seg_model_path = os.getenv("BLIND_PATH_MODEL", "model/yolo-seg.pt")
+        # 使用基于当前文件的绝对路径
+        default_seg_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", "yolo-seg.pt")
+        seg_model_path = os.getenv("BLIND_PATH_MODEL", default_seg_path)
         # Day 20: 优先使用 TensorRT 引擎
         seg_model_path = get_best_model_path(seg_model_path)
         #print(f"[NAVIGATION] 尝试加载模型: {seg_model_path}")
@@ -401,7 +407,8 @@ def load_navigation_models():
             print(f"[NAVIGATION] 请检查文件路径是否正确")
             
         # 【修改开始】使用 ObstacleDetectorClient 替代直接的 YOLO
-        obstacle_model_path = os.getenv("OBSTACLE_MODEL", "model/yoloe-11l-seg.pt")
+        default_obs_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", "yoloe-11l-seg.pt")
+        obstacle_model_path = os.getenv("OBSTACLE_MODEL", default_obs_path)
         # Day 20: 优先使用 TensorRT 引擎
         obstacle_model_path = get_best_model_path(obstacle_model_path)
         print(f"[NAVIGATION] 尝试加载障碍物检测模型: {obstacle_model_path}")
@@ -483,7 +490,10 @@ def load_indoor_model():
     from model_utils import is_tensorrt_engine # Imported here for usage
     
     try:
-        indoor_model_path = os.getenv("INDOOR_MODEL", "model/yolo11l-seg-indoor.engine")
+        # Day 28: 使用新训练的 14 类模型 (用户请求切换)
+        # 使用基于当前文件的绝对路径，确保在服务器任意目录启动都能找到模型
+        default_model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", "yolo11l-seg-indoor14.engine")
+        indoor_model_path = os.getenv("INDOOR_MODEL", default_model_path)
         # 优先使用 TensorRT 引擎
         indoor_model_path = get_best_model_path(indoor_model_path)
         print(f"[INDOOR] 尝试加载室内导盲模型: {indoor_model_path}")
@@ -751,7 +761,8 @@ async def start_ai_with_text_custom(user_text: str):
     if ctx.orchestrator:
         current_state = ctx.orchestrator.get_state()
         # 如果在导航模式或红绿灯检测模式（非CHAT模式）
-        if current_state not in ["CHAT", "IDLE"]:
+        # Day 28: 允许 INDOOR_NAV 模式下进行对话，但其他模式（盲道、过马路）依然严格屏蔽
+        if current_state not in ["CHAT", "IDLE", "INDOOR_NAV"]:
             # 检查是否是允许的对话触发词
             allowed_keywords = ["帮我看", "帮我看下", "帮我找", "找一下", "看看", "识别一下"]
             is_allowed_query = any(keyword in user_text for keyword in allowed_keywords)
@@ -759,7 +770,9 @@ async def start_ai_with_text_custom(user_text: str):
             # 检查是否是导航控制命令
             nav_control_keywords = ["开始过马路", "过马路结束", "开始导航", "盲道导航", "停止导航", "结束导航", 
                                    "检测红绿灯", "看红绿灯", "停止检测", "停止红绿灯",
-                                   "室内导航", "室内导盲"]  # 新增室内导航
+                                   "室内导航", "室内导盲", "四内导航", "思维导航", "失内导航", "时内导航",
+                                   "室类导航", "类导航",
+                                   "退出导航", "关闭导航", "别导了", "别念了", "停止", "导航"]  # Day 28: 增强停止命令识别 + 单独"导航"
             is_nav_control = any(keyword in user_text for keyword in nav_control_keywords)
             
             # 如果既不是允许的查询，也不是导航控制命令，则丢弃
@@ -843,7 +856,8 @@ async def start_ai_with_text_custom(user_text: str):
         return
     
     # 【修改】检查是否是导航相关命令 - 使用orchestrator控制
-    if "开始导航" in user_text or "盲道导航" in user_text or "帮我导航" in user_text:
+    # Day 28: 支持单独说"导航"作为盲道导航启动命令（防止因 AS R吞字变成聊天）
+    if "开始导航" in user_text or "盲道导航" in user_text or "帮我导航" in user_text or user_text.strip() == "导航":
         # 【新增】如果正在找物品，先停止
         if ctx.yolomedia_running:
             stop_yolomedia()
@@ -858,8 +872,11 @@ async def start_ai_with_text_custom(user_text: str):
             await ui_broadcast_final("[系统] 导航系统未就绪")
         return
     
-    # 【新增】检查是否是室内导航命令
-    if "室内导航" in user_text or "室内导盲" in user_text:
+    # 【新增】检查是否是室内导航命令（包含ASR误识别别名）
+    # Day 28: 添加更多同音误识别别名
+    indoor_nav_aliases = ["室内导航", "室内导盲", "四内导航", "思维导航", "失内导航", "时内导航",
+                          "室类导航", "类导航"]  # Day 28: 新增误识别
+    if any(alias in user_text for alias in indoor_nav_aliases):
         # 如果正在找物品，先停止
         if ctx.yolomedia_running:
             stop_yolomedia()
@@ -876,7 +893,8 @@ async def start_ai_with_text_custom(user_text: str):
     
     # 【修改】停止导航优先判断
     # 只要包含"停止导航"或"结束导航"，无论是否包含"室内"，都视为停止指令
-    if "停止导航" in user_text or "结束导航" in user_text:
+    stop_keywords = ["停止导航", "结束导航", "退出导航", "关闭导航", "别导了", "别念了", "停止"]
+    if any(k in user_text for k in stop_keywords):
         if ctx.orchestrator:
             ctx.orchestrator.stop_navigation()
             print(f"[NAVIGATION] 导航已停止，状态: {ctx.orchestrator.get_state()}")
@@ -1060,8 +1078,15 @@ async def start_ai_with_text(user_text: str):
             from audio_stream import stream_clients
             for sc in list(stream_clients):
                 if not sc.abort_event.is_set():
-                    try: sc.q.put_nowait(b"\x00"*BYTES_PER_20MS_16K)
-                    except Exception: pass
+                    # Day 28: 添加少量静音填充防止结尾爆音 (Pop noise fix)
+                    # 增加到 10 帧 (200ms) 以确保完全淡出
+                    try: 
+                        silence_frame = b'\x00' * 640  # 20ms silence (16k * 2 bytes * 0.02)
+                        for _ in range(10): # 200ms silence
+                            sc.q.put_nowait(silence_frame)
+                    except Exception: 
+                        pass
+                    
                     try: sc.q.put_nowait(None)
                     except Exception: pass
             
@@ -1128,8 +1153,9 @@ async def start_ai_with_text(user_text: str):
             from audio_stream import stream_clients
             for sc in list(stream_clients):
                 if not sc.abort_event.is_set():
-                    try: sc.q.put_nowait(b"\x00"*BYTES_PER_20MS_16K)
-                    except Exception: pass
+                    # Day 28: 移除静音填充包以消除杂音
+                    # try: sc.q.put_nowait(b"\x00"*BYTES_PER_20MS_16K)
+                    # except Exception: pass
                     try: sc.q.put_nowait(None)
                     except Exception: pass
 

asr_core.py

@@ -64,7 +64,9 @@ NAV_CONTROL_WHITELIST = [
     "停止导航", "结束导航", "停止检测", "停止红绿灯",
     "开始导航", "盲道导航", "开始过马路", "过马路结束",
     "帮我导航", "帮我过马路",
-    "室内导航", "室内导盲",  # Day 25: 新增室内导航命令
+    "室内导航", "室内导盲", "四内导航", "思维导航", "失内导航", "时内导航",  # Day 28: 室内导航 + 同音误识别
+    "室类导航", "类导航",  # Day 28: 新增误识别
+    "退出导航", "关闭导航", "别导了", "别念了", "停止",  # Day 28: 增强停止命令
 ]
 
 

audio_player.py

@@ -225,6 +225,14 @@ async def _broadcast_audio_optimized(pcm_data: bytes):
         
         # 注意：录制在 broadcast_pcm16_realtime 中统一完成，避免重复
 
+        # Day 28: 播放期间全局暂停 VAD，防止系统听到自己的声音
+        # 这对于没有回声消除(AEC)的系统至关重要，否则导航提示语音会触发 VAD
+        # 导致 VAD 误判为用户说话，从而一直占用识别通道
+        from server_vad import get_server_vad
+        vad = get_server_vad()
+        if vad:
+            vad.set_tts_playing(True)
+
         # 单次调用交给底层 pacing（20ms节拍在 broadcast_pcm16_realtime 内部实现）
         await broadcast_pcm16_realtime(full_audio)
 
@@ -232,6 +240,12 @@ async def _broadcast_audio_optimized(pcm_data: bytes):
     except Exception as e:
         print(f"[AUDIO] 广播音频失败: {e}")
     finally:
+        # 恢复 VAD 检测
+        from server_vad import get_server_vad
+        vad = get_server_vad()
+        if vad:
+            vad.set_tts_playing(False)
+
         # 清除播放标志
         with _playing_lock:
             _is_playing = False

audio_stream.py

@@ -102,6 +102,19 @@ async def hard_reset_audio(reason: str = ""):
     # 2) 取消当前AI任务
     await cancel_current_ai()
 
+    # Day 28: 强制重置 VAD TTS 状态，防止因任务取消导致计数器未归零（VAD 冻结）
+    try:
+        # Safe import to avoid circular dependency
+        import sys
+        if 'server_vad' in sys.modules:
+            server_vad = sys.modules['server_vad']
+            if hasattr(server_vad, 'get_server_vad'):
+                vad = server_vad.get_server_vad()
+                if vad:
+                    vad.reset_tts_state()
+    except Exception as e:
+        print(f"[HARD-RESET] 重置 VAD 状态失败: {e}")
+
     # 3) 日志
     if reason:
         print(f"[HARD-RESET] {reason}")

navigation_master.py

@@ -293,6 +293,38 @@ class NavigationMaster:
     def get_state(self) -> str:
         return self.state
     
+        # Day 28: 室内导航可视化绘制
+    def _draw_indoor_visualizations(self, image: np.ndarray, visualizations: list):
+        if not visualizations:
+            return
+
+        for viz in visualizations:
+            v_type = viz.get('type')
+            
+            if v_type == 'walkable_mask':
+                mask = viz.get('mask')
+                color_str = viz.get('color', 'rgba(0, 255, 0, 0.3)')
+                # 这里简单处理，只画绿色轮廓和半透明填充
+                if mask is not None:
+                     # 1. 绿色覆盖
+                    green_mask = np.zeros_like(image)
+                    green_mask[mask > 0] = [0, 255, 0] # BGR
+                    image[:] = cv2.addWeighted(image, 1.0, green_mask, 0.3, 0)
+                    
+                    # 2. 轮廓
+                    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                    cv2.drawContours(image, contours, -1, (0, 255, 0), 2)
+                    
+            elif v_type in ('obstacle', 'poi', 'person'):
+                center = viz.get('center')
+                label = viz.get('class_name_cn', '?')
+                if center:
+                    cx, cy = center
+                    color = (0, 0, 255) if v_type == 'obstacle' else (255, 255, 0)
+                    cv2.circle(image, (cx, cy), 5, color, -1)
+                    cv2.putText(image, label, (cx + 10, cy), cv2.FONT_HERSHEY_SIMPLEX, 
+                               0.6, color, 2, cv2.LINE_AA)
+
     def start_blind_path_navigation(self):
         """启动盲道导航模式"""
         self.state = BLINDPATH_NAV
@@ -330,8 +362,9 @@ class NavigationMaster:
         """启动室内导航模式（使用室内导盲模型）"""
         self.state = INDOOR_NAV
         self.cooldown_until = time.time() + self.COOLDOWN_SEC
-        if self.blind:
-            self.blind.reset()
+        # Day 28: 应该重置室内导航器，而不是盲道导航器
+        if self.indoor:
+            self.indoor.reset()
     
     def is_in_navigation_mode(self):
         """检查是否在导航模式（非对话模式）"""
@@ -481,18 +514,28 @@ class NavigationMaster:
         if self.state == INDOOR_NAV:
             # 优先使用室内导航器，如果没有则 fallback 到盲道导航器
             nav = self.indoor if self.indoor else self.blind
+            # Day 28: 添加警告日志
+            if self.indoor is None:
+                print("[NAV MASTER] 警告: 室内导航器未初始化，fallback 到盲道导航器！")
             try:
                 result = nav.process_frame(bgr)
             except Exception as e:
-                self.state = RECOVERY
+                # Day 28: 室内导航出错时，保持在室内模式，不要切到 RECOVERY (会导致自动切回盲道)
+                print(f"[INDOOR ERROR] 室内导航异常: {e}")
+                # self.state = RECOVERY  <-- 禁止切换!
                 ann_err = bgr.copy()
-                return OrchestratorResult(ann_err, self._say(now, ""), self.state, {"error": str(e)})
+                return OrchestratorResult(ann_err, self._say(now, ""), INDOOR_NAV, {"error": str(e)})
             
             ann = result.annotated_image if result.annotated_image is not None else bgr.copy()
             say = result.guidance_text or ""
             state_info = result.state_info if hasattr(result, 'state_info') else {}
             
-            return OrchestratorResult(ann, self._say(now, say), self.state, 
+            # Day 28: 绘制室内导航可视化
+            visualizations = result.visualizations if hasattr(result, 'visualizations') else []
+            self._draw_indoor_visualizations(ann, visualizations)
+            
+            # Day 28: 确保返回正确的状态 INDOOR_NAV
+            return OrchestratorResult(ann, self._say(now, say), INDOOR_NAV, 
                                       {"source": "indoor", "state_info": state_info})
 
         # 各状态处理

server_vad.py

@@ -96,7 +96,8 @@ class SileroVAD:
         self.speech_audio = bytearray()  # 存储语音音频
         
         # TTS 播放状态 - 播放期间暂停 VAD
-        self.tts_playing = False
+        # Day 28: 使用引用计数处理并发播放的情况
+        self.tts_playing_count = 0
         self.tts_end_time = 0  # TTS 结束时间
         self.tts_cooldown_ms = 500  # TTS 结束后等待 500ms 再开始检测
         
@@ -105,9 +106,9 @@ class SileroVAD:
         self.window_size = 5  # 滑动窗口大小
         self.frame_threshold = 3  # 至少多少帧语音才算开始说话
         
-        # Day 23: Pre-speech buffer (Lookback) to fix "cut-off" start of words
-        # 300ms lookback approx. (each chunk is 32ms) -> 10 chunks
-        self.pre_speech_buffer = collections.deque(maxlen=10)
+        # Day 23+28: Pre-speech buffer (Lookback) to fix "cut-off" start of words
+        # Day 28: 增加到 768ms (24 chunks) 以捕获 "室内导航" 等较长开头，防止 ASR 吞字
+        self.pre_speech_buffer = collections.deque(maxlen=24)
         
         print(f"[VAD] 初始化: threshold={threshold}, threshold_low={threshold_low}, "
               f"min_silence_ms={min_silence_ms}, min_speech_ms={min_speech_ms}")
@@ -120,29 +121,46 @@ class SileroVAD:
         self.last_speech_time = 0
         self.speech_start_time = 0
         self.voice_window.clear()
-        self.tts_playing = False
+        self.tts_playing_count = 0
         self.tts_end_time = 0
         if self.model:
             self.model.reset_states()
+        if hasattr(self, 'pre_speech_buffer'):
+            self.pre_speech_buffer.clear()
+
+    def reset_tts_state(self):
+        """强制重置 TTS 播放状态 (用于硬重置)"""
+        self.tts_playing_count = 0
+        print("[VAD] 强制重置 TTS 状态 (VAD 恢复)")
     
     def set_tts_playing(self, playing: bool):
-        """设置 TTS 播放状态"""
-        self.tts_playing = playing
-        if not playing:
-            # TTS 结束，记录时间
-            self.tts_end_time = time.time() * 1000
-            print("[VAD] TTS 结束，等待冷却期...")
+        """设置 TTS 播放状态 (引用计数)"""
+        if playing:
+            self.tts_playing_count += 1
+            if self.tts_playing_count == 1:
+                print("[VAD] TTS 开始播放，暂停 VAD 检测")
+                # TTS 开始播放时，如果正在录音则中断
+                if self.is_speaking:
+                    self.is_speaking = False
+                    self.speech_audio.clear()
+                    self.voice_window.clear()
+                    # Day 23: Clear lookback buffer
+                    if hasattr(self, 'pre_speech_buffer'):
+                        self.pre_speech_buffer.clear()
+                    # Day 28: 重置模型状态
+                    if self.model:
+                        self.model.reset_states()
+                    print("[VAD] TTS 播放打断语音录制")
         else:
-            print("[VAD] TTS 开始播放，暂停 VAD 检测")
-            # TTS 开始播放时，如果正在录音则中断
-            if self.is_speaking:
-                self.is_speaking = False
-                self.speech_audio.clear()
-                self.voice_window.clear()
-                # Day 23: Clear lookback buffer
-                if hasattr(self, 'pre_speech_buffer'):
-                    self.pre_speech_buffer.clear()
-                print("[VAD] TTS 播放打断语音录制")
+            if self.tts_playing_count > 0:
+                self.tts_playing_count -= 1
+                if self.tts_playing_count == 0:
+                    # TTS 结束，记录时间
+                    self.tts_end_time = time.time() * 1000
+                    print("[VAD] TTS 完全结束，等待冷却期...")
+            else:
+                # 已经是0了，忽略
+                pass
     
     def process(self, audio_bytes: bytes) -> dict:
         """
@@ -172,7 +190,7 @@ class SileroVAD:
         
         # TTS 播放期间，跳过 VAD 检测
         current_time = time.time() * 1000
-        if self.tts_playing:
+        if self.tts_playing_count > 0:
             return result
         
         # TTS 刚结束，等待冷却期

workflow_indoor.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 室内导航工作流 (Indoor Navigation Workflow)
-Day 26: 专为室内导盲模型 (yolo11l-seg-indoor) 设计
+Day 26: 专为室内导盲模型 (yolo11l-seg-indoor14) 设计
 
 类别映射 (14 classes from MIT Indoor):
 - 可行走区域: floor(0), corridor(1), sidewalk(2)
@@ -22,50 +22,46 @@ from collections import deque
 
 logger = logging.getLogger(__name__)
 
-# ========== 类别常量 ==========
-# 可行走区域
+# ========== 类别常量 (14类模型 - yolo11l-seg-indoor14) ==========
+# Day 28: 使用 14 类模型 (MIT Indoor Subset)
+
+# 可行走区域 (0-2)
 WALKABLE_CLASSES = {0, 1, 2}  # floor, corridor, sidewalk
 CLASS_FLOOR = 0
 CLASS_CORRIDOR = 1
 CLASS_SIDEWALK = 2
 
-# 静态障碍物 (家具 + 杂物)
-OBSTACLE_CLASSES = {3, 4, 5, 11, 12, 14, 15, 16, 17, 18, 19}
+# 静态障碍物 (3-5, 11-12)
+OBSTACLE_CLASSES = {3, 4, 5, 11, 12, 13} # window 只要是障碍物也算? window(13)是墙? 
+# Wait, Window is 13. Is window an obstacle? Usually yes (don't walk into it).
+# Cabinet 11, Trash 12.
 CLASS_CHAIR = 3
 CLASS_TABLE = 4
 CLASS_SOFA_BED = 5
 CLASS_CABINET = 11
 CLASS_TRASH_CAN = 12
-CLASS_CUP_BOTTLE = 14
-CLASS_BAG = 15
-CLASS_ELECTRONICS = 16
-CLASS_PLANT = 17
-CLASS_CLOCK = 18
-CLASS_OBSTACLE = 19
+CLASS_WINDOW = 13 # 窗户通常视为边界或障碍
+CLASS_WALL = 9 # Wall 9
 
-# 兴趣点
+# 兴趣点 (6-8)
 POI_CLASSES = {6, 7, 8}  # door, elevator, stairs
 CLASS_DOOR = 6
 CLASS_ELEVATOR = 7
 CLASS_STAIRS = 8
 
-# 边界
-BOUNDARY_CLASSES = {9, 10}  # wall, window
-CLASS_WALL = 9
-CLASS_WINDOW = 10
+# 动态障碍 (10)
+CLASS_PERSON = 10
 
-# 动态障碍
-CLASS_PERSON = 13
+# 边界
+BOUNDARY_CLASSES = {9, 13}  # wall(9), window(13)
 
 # 类别名称映射
 CLASS_NAMES = {
     0: 'floor', 1: 'corridor', 2: 'sidewalk',
     3: 'chair', 4: 'table', 5: 'sofa_bed',
     6: 'door', 7: 'elevator', 8: 'stairs',
-    9: 'wall', 10: 'window', 11: 'cabinet',
-    12: 'trash_can', 13: 'person', 14: 'cup_bottle',
-    15: 'bag', 16: 'electronics', 17: 'plant',
-    18: 'clock', 19: 'obstacle'
+    9: 'wall', 10: 'person', 11: 'cabinet',
+    12: 'trash_can', 13: 'window'
 }
 
 # 中文名称（用于语音）
@@ -73,22 +69,27 @@ CLASS_NAMES_CN = {
     0: '地面', 1: '走廊', 2: '人行道',
     3: '椅子', 4: '桌子', 5: '沙发',
     6: '门', 7: '电梯', 8: '楼梯',
-    9: '墙壁', 10: '窗户', 11: '柜子',
-    12: '垃圾桶', 13: '行人', 14: '杯子瓶子',
-    15: '包', 16: '电子设备', 17: '绿植',
-    18: '时钟', 19: '障碍物'
+    9: '墙壁', 10: '行人', 11: '柜子',
+    12: '垃圾桶', 13: '窗户'
 }
 
+# 物品类 (无)
+ITEM_CLASSES = set()
+
 # ========== 配置参数 ==========
-CONF_THRESHOLD = float(os.getenv('INDOOR_CONF_THRESHOLD', '0.25'))
-WALKABLE_MIN_AREA = int(os.getenv('INDOOR_WALKABLE_MIN_AREA', '3000'))
-OBSTACLE_MIN_AREA = int(os.getenv('INDOOR_OBSTACLE_MIN_AREA', '500'))
+# Day 28: 进一步降低阈值以提升木地板检测率
+# Day 28: 进一步降低阈值以提升木地板检测率
+CONF_THRESHOLD = float(os.getenv('INDOOR_CONF_THRESHOLD', '0.05'))  # 全局极低阈值，由后续逻辑二次过滤
+WALKABLE_MIN_AREA = int(os.getenv('INDOOR_WALKABLE_MIN_AREA', '50'))  # 极端降低最小面积以进行调试 (原 1000)
+OBSTACLE_MIN_AREA = int(os.getenv('INDOOR_OBSTACLE_MIN_AREA', '300'))
 
 # 语音间隔
 GUIDE_INTERVAL = float(os.getenv('INDOOR_GUIDE_INTERVAL', '3.0'))
 DIRECTION_INTERVAL = float(os.getenv('INDOOR_DIRECTION_INTERVAL', '2.5'))
 POI_INTERVAL = float(os.getenv('INDOOR_POI_INTERVAL', '5.0'))
 OBSTACLE_INTERVAL = float(os.getenv('INDOOR_OBSTACLE_INTERVAL', '2.0'))
+# Day 28: “未检测到可行走区域”播报间隔（8秒）
+NO_WALKABLE_INTERVAL = float(os.getenv('INDOOR_NO_WALKABLE_INTERVAL', '8.0'))
 
 # ========== 可视化颜色 (BGR) ==========
 VIS_COLORS = {
@@ -123,6 +124,10 @@ class IndoorNavigator:
         self.seg_model = seg_model
         self.device_id = device_id
         self.frame_counter = 0
+
+        # Day 28: 持久化缓冲参数
+        self.no_walkable_persistence_sec = 2.0
+        self.last_walkable_detected_time = 0
         
         # 语音节流
         self.last_guide_time = 0
@@ -138,11 +143,14 @@ class IndoorNavigator:
         
         # 缓存
         self.last_walkable_mask = None
+        self.last_valid_walkable_mask = None
+        self.last_no_walkable_time = 0
+        self.last_obstacles = []
         self.last_obstacles = []
         self.last_pois = []
         
-        # 灰度图（用于光流等）
-        self.prev_gray = None
+        # Day 28: 移除未使用的灰度图转换 (光流功能未启用)
+        # self.prev_gray = None
         
         # 日志间隔
         self.log_interval = int(os.getenv('AIGLASS_LOG_INTERVAL', '30'))
@@ -160,6 +168,9 @@ class IndoorNavigator:
         self.last_obstacle_time = 0
         self.last_guidance_text = ""
         self.last_direction_text = ""
+        self.last_valid_walkable_mask = None
+        self.last_no_walkable_time = 0  # Day 28: "未检测到可行走区域"节流
+        self.last_walkable_detected_time = 0
         self.last_walkable_mask = None
         self.last_obstacles = []
         self.last_pois = []
@@ -195,13 +206,27 @@ class IndoorNavigator:
             obstacles = self.last_obstacles
             pois = self.last_pois
         
-        # 生成导航引导
+        
+        # 3. 缓存有效的 mask (用于可视化防抖)
+        walkable_area = int(np.count_nonzero(walkable_mask)) if walkable_mask is not None else 0
+        if walkable_area > WALKABLE_MIN_AREA:
+             self.last_valid_walkable_mask = walkable_mask
+        
+        # 4. 生成导航引导
         if walkable_mask is not None:
             guidance_text = self._generate_guidance(walkable_mask, obstacles, pois, h, w, now)
-            
-            # 添加可视化
-            self._add_mask_visualization(walkable_mask, frame_visualizations, 
-                                        "walkable_mask", "rgba(0, 255, 0, 0.3)")
+       
+        # 5. 可视化 (带持久化防抖)
+        viz_mask = walkable_mask
+        
+        # 如果当前没有检测到路，但还在持久化时间内，使用缓存的 mask 进行可视化
+        if (viz_mask is None or walkable_area < WALKABLE_MIN_AREA) and \
+           (now - self.last_walkable_detected_time) < self.no_walkable_persistence_sec and \
+           self.last_valid_walkable_mask is not None:
+             viz_mask = self.last_valid_walkable_mask
+             
+        self._add_mask_visualization(viz_mask, frame_visualizations, 
+                                    "walkable_mask", "rgba(0, 255, 0, 0.3)")
         
         # 障碍物可视化
         for obs in obstacles:
@@ -213,7 +238,8 @@ class IndoorNavigator:
         
         # 日志
         if self.frame_counter % self.log_interval == 0:
-            walkable_area = int(walkable_mask.sum()) if walkable_mask is not None else 0
+            # Day 28: 修复面积计算 - 使用 count_nonzero 而不是 sum (mask 值是 0 或 255)
+            walkable_area = int(np.count_nonzero(walkable_mask)) if walkable_mask is not None else 0
             logger.info(f"[INDOOR] Frame={self.frame_counter} | 可行走面积={walkable_area} | "
                        f"障碍物={len(obstacles)} | 兴趣点={len(pois)}")
         
@@ -225,11 +251,12 @@ class IndoorNavigator:
             'pois_count': len(pois),
         }
         
-        # 更新灰度图
-        self.prev_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        # Day 28: 移除未使用的灰度图转换
+        # self.prev_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         
+        # Day 28: 避免每帧复制图像，直接传递原图像（下游如需可视化再复制）
         return IndoorResult(
-            annotated_image=image.copy(),
+            annotated_image=image,  # 不再 copy，节省内存/CPU
             guidance_text=guidance_text,
             state_info=state_info,
             visualizations=frame_visualizations
@@ -263,17 +290,41 @@ class IndoorNavigator:
                     cls_id = int(cls_id.item())
                     conf_val = float(conf.item())
                     
+                    # 过滤物品类 (默认不参与导航逻辑，避免刷屏)
+                    if cls_id in ITEM_CLASSES:
+                        continue
+                    
+                    # Day 28: 混合阈值策略
+                    # 地面类(WALKABLE)使用全局低阈值(0.05)以提高召回率
+                    # 障碍物(OBSTACLE/POI/BOUNDARY)使用较高阈值(0.25)以拒绝误报
+                    filter_threshold = 0.25
+                    if cls_id in WALKABLE_CLASSES:
+                        filter_threshold = 0.05
+                    
+                    if conf_val < filter_threshold:
+                        continue
+                         
                     # 调整 mask 尺寸
                     mask_resized = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
                     mask_bin = (mask_resized > 0.5).astype(np.uint8)
                     area = int(mask_bin.sum())
                     
-                    if area < 100:  # 过滤小碎片
+                    
+                    # Day 28: 调试日志 - 查看检测到的类别 (ALL detections)
+                    if area > 10:  # 几乎记录所有检测
+                        cls_name = CLASS_NAMES.get(cls_id, f'unknown_{cls_id}')
+                        logger.info(f"[INDOOR DEBUG] 检测到 {cls_name}(id={cls_id}) conf={conf_val:.2f} area={area}")
+                    
+                    if area < 50:  # 极端小的才过滤
                         continue
                     
                     # 可行走区域
                     if cls_id in WALKABLE_CLASSES and area > WALKABLE_MIN_AREA:
-                        walkable_mask = cv2.bitwise_or(walkable_mask, mask_bin * 255)
+                        # Day 28: 确保类型一致，避免 bitwise_or 失败
+                        mask_add = (mask_bin * 255).astype(np.uint8)
+                        walkable_mask = cv2.bitwise_or(walkable_mask, mask_add)
+                        if area > 10000:  # 调试：记录大面积添加
+                             logger.info(f"[INDOOR DEBUG] 添加可行走区域: class={cls_id} area={area} current_total={np.count_nonzero(walkable_mask)}")
                     
                     # 障碍物
                     elif cls_id in OBSTACLE_CLASSES or cls_id == CLASS_PERSON:
@@ -333,8 +384,15 @@ class IndoorNavigator:
             self.last_obstacle_time = now
             self.last_guidance_text = guidance_text
         elif direction_guidance:
+            # Day 28: "未检测到可行走区域" 降低播报频率
+            # Day 28: "未检测到可行走区域" 降低播报频率
+            if direction_guidance == "未检测到可行走区域":
+                # 首次检测到（last_no_walkable_time == 0）或者间隔已过8秒
+                if self.last_no_walkable_time == 0 or (now - self.last_no_walkable_time) > NO_WALKABLE_INTERVAL:
+                    guidance_text = direction_guidance
+                    self.last_no_walkable_time = now
             # 方向引导节流
-            if direction_guidance != self.last_direction_text:
+            elif direction_guidance != self.last_direction_text:
                 if (now - self.last_direction_time) > DIRECTION_INTERVAL:
                     guidance_text = direction_guidance
                     self.last_direction_time = now
@@ -351,13 +409,25 @@ class IndoorNavigator:
 
     def _compute_direction_guidance(self, walkable_mask, h, w):
         """计算方向引导"""
-        if walkable_mask is None or walkable_mask.sum() < WALKABLE_MIN_AREA:
+        # Day 28: 使用 count_nonzero 替代 sum (mask 值是 0 或 255)
+        walkable_area = np.count_nonzero(walkable_mask) if walkable_mask is not None else 0
+        now = time.time()
+        
+        if walkable_area < WALKABLE_MIN_AREA:
+            # 缓冲逻辑：如果最近才看到过路，不要立刻报错
+            if (now - self.last_walkable_detected_time) < self.no_walkable_persistence_sec:
+                return None # 保持沉默，或者返回 "保持直行" (更稳妥是沉默)
             return "未检测到可行走区域"
         
+        # 如果检测到了，更新时间戳
+        self.last_walkable_detected_time = now
+        
         # 分析下半部分（更近的区域）
         lower_half = walkable_mask[int(h * 0.5):, :]
         
-        if lower_half.sum() < 1000:
+        if np.count_nonzero(lower_half) < 1000:
+            if (now - self.last_walkable_detected_time) < self.no_walkable_persistence_sec:
+                 return None
             return "前方可行走区域较小，请小心"
         
         # 计算左中右分布

yoloe_backend.py

@@ -15,7 +15,9 @@ except Exception:
     from ultralytics import YOLO as _MODEL
 
 # Day 20: 优先使用 TensorRT 引擎
-DEFAULT_MODEL_PATH = get_best_model_path(os.getenv("YOLOE_MODEL_PATH", "model/yoloe-11l-seg.pt"))
+# Day 28: 使用基于当前文件的绝对路径
+_DEFAULT_YOLOE_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model", "yoloe-11l-seg.pt")
+DEFAULT_MODEL_PATH = get_best_model_path(os.getenv("YOLOE_MODEL_PATH", _DEFAULT_YOLOE_PATH))
 TRACKER_CFG        = os.getenv("YOLO_TRACKER_YAML", "bytetrack.yaml")
 
 class YoloEBackend: