优化性能；解决图像反转问题

face_rec.py

@@ -57,6 +57,10 @@ class FaceRecognitionSystem:
         # 识别记录(防止重复识别)
         self.recognition_history = {}  # {person_id: last_recognition_time}
 
+        # 定期清理机制
+        self.last_cleanup_time = time.time()
+        self.cleanup_interval = 60  # 清理间隔(秒)
+
         # 显示相关变量
         self.current_display_frame = None
         self.last_detection_result = None  # 最后的检测结果
@@ -107,7 +111,7 @@ class FaceRecognitionSystem:
         self.stream_retry_cooldown = 10  # 重试冷却时间(秒)
 
         # 添加异步推流队列和线程
-        self.stream_frame_queue = queue.Queue(maxsize=10)  # 限制队列大小防止内存溢出
+        self.stream_frame_queue = queue.Queue(maxsize=30)  # 增加到30帧缓冲
         self.stream_thread = None
         self.stream_thread_running = False
 
@@ -119,6 +123,9 @@ class FaceRecognitionSystem:
         self.camera_retry_cooldown = 3  # 重试冷却时间(秒)
         self.camera_max_failures = 5  # 触发重新初始化的失败次数阈值
 
+        # 缓存屏幕尺寸，避免每帧重新获取
+        self._cache_screen_size()
+
         # 二维码显示相关
         self.qrcode_image_path = self.config.get('qrcode', {}).get('image_path', 'qrcode.png')
         self.qrcode_display_duration = self.config.get('qrcode', {}).get('display_duration', 10)
@@ -139,6 +146,22 @@ class FaceRecognitionSystem:
         self.stderr_thread = None
         self.stderr_thread_running = False
 
+        # 缓存屏幕尺寸，避免每帧重新获取
+        self._cache_screen_size()
+
+    def _cache_screen_size(self):
+        """缓存屏幕尺寸，提高性能"""
+        self.screen_width = 1920
+        self.screen_height = 1080
+        try:
+            import screeninfo
+            screen = screeninfo.get_monitors()[0]
+            self.screen_width = screen.width
+            self.screen_height = screen.height
+            self.logger.info(f"屏幕尺寸已缓存: {self.screen_width}x{self.screen_height}")
+        except Exception as e:
+            self.logger.debug(f"无法获取屏幕尺寸，使用默认尺寸: {e}")
+
     def _force_cleanup_ffmpeg(self):
         """强制清理所有FFmpeg相关资源 - 防止多进程推流"""
         self.logger.info("=== 开始清理FFmpeg资源 ===")
@@ -361,17 +384,8 @@ class FaceRecognitionSystem:
                 # 在视频帧上绘制机器人状态
                 frame_with_status = self._draw_robot_status_on_video(frame)
 
-                # 获取屏幕尺寸并拉伸视频帧到全屏
-                try:
-                    import screeninfo
-                    screen = screeninfo.get_monitors()[0]
-                    screen_width, screen_height = screen.width, screen.height
-                    
-                    # 拉伸视频帧到屏幕尺寸
-                    frame_with_status = cv2.resize(frame_with_status, (screen_width, screen_height), interpolation=cv2.INTER_LINEAR)
-                except:
-                    # 如果获取屏幕信息失败，使用默认尺寸
-                    frame_with_status = cv2.resize(frame_with_status, (1920, 1080), interpolation=cv2.INTER_LINEAR)
+                # 使用缓存的屏幕尺寸拉伸视频帧到全屏
+                frame_with_status = cv2.resize(frame_with_status, (self.screen_width, self.screen_height), interpolation=cv2.INTER_LINEAR)
 
                 # 显示视频帧
                 cv2.imshow(self.video_window_name, frame_with_status)
@@ -678,20 +692,11 @@ class FaceRecognitionSystem:
             # 创建二维码窗口
             cv2.namedWindow(self.qrcode_window_name, cv2.WINDOW_NORMAL | cv2.WINDOW_GUI_NORMAL)
 
-            qr_height, qr_width = qr_image.shape[:2]
-            try:
-                import screeninfo
-                screen = screeninfo.get_monitors()[0]
-                canvas_width = int(screen.width)
-                canvas_height = int(screen.height)
-                x_pos = 0
-                y_pos = 0
-            except Exception as e:
-                self.logger.warning(f"无法获取屏幕信息,使用默认尺寸: {e}")
-                canvas_width = max(qr_width * 2, 1920)
-                canvas_height = max(qr_height, 1080)
-                x_pos = 0
-                y_pos = 0
+            # 使用缓存的屏幕尺寸
+            canvas_width = self.screen_width
+            canvas_height = self.screen_height
+            x_pos = 0
+            y_pos = 0
 
             rendered = self._build_qrcode_instruction_canvas(qr_image, (canvas_width, canvas_height))
             if rendered is None:
@@ -1098,8 +1103,8 @@ class FaceRecognitionSystem:
                     if self.ffmpeg_process.stdin and not self.ffmpeg_process.stdin.closed:
                         # 简单的写入，依赖较小的缓冲区和flush_packets
                         self.ffmpeg_process.stdin.write(frame_bytes)
-                        # 关键：定期flush避免缓冲区堆积
-                        if total_frames % 30 == 0:  # 每30帧flush一次
+                        # 每5帧flush一次，避免缓冲区堆积
+                        if total_frames % 5 == 0:
                             self.ffmpeg_process.stdin.flush()
 
                         consecutive_failures = 0
@@ -1372,6 +1377,28 @@ class FaceRecognitionSystem:
 
         return elapsed >= cooldown
 
+    def cleanup_expired_records(self):
+        """清理过期的识别记录，防止内存无限增长"""
+        current_time = time.time()
+        if current_time - self.last_cleanup_time < self.cleanup_interval:
+            return
+
+        self.last_cleanup_time = current_time
+
+        # 清理超过1小时的记录
+        expiry_seconds = 3600
+        expired_keys = []
+        for person_id, last_time in self.recognition_history.items():
+            if isinstance(last_time, datetime):
+                elapsed = (datetime.now() - last_time).total_seconds()
+                if elapsed > expiry_seconds:
+                    expired_keys.append(person_id)
+
+        if expired_keys:
+            for key in expired_keys:
+                del self.recognition_history[key]
+            self.logger.debug(f"清理了 {len(expired_keys)} 条过期识别记录")
+
     def cv2_add_chinese_text(self, img: np.ndarray, text: str, position: Tuple[int, int],
                              font: ImageFont.FreeTypeFont, text_color: Tuple[int, int, int]) -> np.ndarray:
         """在OpenCV图像上添加中文文本"""
@@ -1698,6 +1725,9 @@ class FaceRecognitionSystem:
 
         try:
             while True:
+                # 定期清理过期记录，防止内存无限增长
+                self.cleanup_expired_records()
+
                 # 检查机器人角色并显示/隐藏视频
                 self._check_role_and_display_video()
 
@@ -1879,19 +1909,13 @@ class FaceRecognitionSystem:
 
                 # 绘制信息
                 display_frame = self.draw_info_on_frame(frame)
+
+                # 使用缓存的屏幕尺寸拉伸视频帧到全屏
+                display_frame = cv2.resize(display_frame, (self.screen_width, self.screen_height), interpolation=cv2.INTER_LINEAR)
                 
-                # 获取屏幕尺寸并拉伸视频帧到全屏
-                try:
-                    import screeninfo
-                    screen = screeninfo.get_monitors()[0]
-                    screen_width, screen_height = screen.width, screen.height
-                    
-                    # 拉伸视频帧到屏幕尺寸
-                    display_frame = cv2.resize(display_frame, (screen_width, screen_height), interpolation=cv2.INTER_LINEAR)
-                except:
-                    # 如果获取屏幕信息失败，使用默认尺寸
-                    display_frame = cv2.resize(display_frame, (1920, 1080), interpolation=cv2.INTER_LINEAR)
-                
+                # 镜像翻转：使画面与实际场景一致（取消默认的左右镜像）
+                display_frame = cv2.flip(display_frame, 1)
+
                 cv2.imshow(window_name, display_frame)
 
                 # 检查二维码显示是否超时