多进程确保视频流畅度

reproject/analyzer.py

@@ -4,6 +4,8 @@ import time
 import numpy as np
 import threading
 import queue
+import multiprocessing as mp_proc
+from multiprocessing import shared_memory
 from collections import deque
 from geometry_utils import (
     calculate_ear,
@@ -15,16 +17,15 @@ from geometry_utils import (
     LEFT_EYE_GAZE_IDXS,
     RIGHT_EYE_GAZE_IDXS,
 )
-from face_library import FaceLibrary
+# from face_library import FaceLibrary
 
+# try:
+#     from new_emotion_test import analyze_emotion_with_hsemotion
 
-try:
+#     HAS_EMOTION_MODULE = True
-    from new_emotion_test import analyze_emotion_with_hsemotion
+# except ImportError:
-
+#     print("⚠️ 未找到 new_emotion_test.py，情绪功能将不可用")
-    HAS_EMOTION_MODULE = True
+#     HAS_EMOTION_MODULE = False
-except ImportError:
-    print("⚠️ 未找到 new_emotion_test.py，情绪功能将不可用")
-    HAS_EMOTION_MODULE = False
 
 
 class MonitorSystem:
@@ -39,7 +40,7 @@ class MonitorSystem:
         )
 
         # 初始化人脸底库
-        self.face_lib = FaceLibrary(face_db)
+        # self.face_lib = FaceLibrary(face_db)
 
         # --- 时间控制 ---
         self.last_identity_check_time = 0
@@ -60,12 +61,51 @@ class MonitorSystem:
         # 缓存上一次的检测结果
         self.cached_emotion = {"label": "detecting...", "va": (0.0, 0.0)}
 
-        self.task_queue = queue.Queue(maxsize=2)
         self.current_user = None
         self.current_emotion = "Neutral"
 
-        self.id_emo_thread = threading.Thread(target=self._id_emo_loop, daemon=True)
+        self.frame_shape = (720, 1280, 3)
-        self.id_emo_thread.start()
+        frame_size = int(np.prod(self.frame_shape))
+        
+        # 必须先解除可能存在的残留 (Windows上有时不需要，但保持好习惯)
+        # 最好是随机生成一个名字，确保每次运行都是新的
+        import secrets
+        auth_key = secrets.token_hex(4)
+        shm_unique_name = f"monitor_shm_{auth_key}"
+
+        try:
+            self.shm = shared_memory.SharedMemory(create=True, size=frame_size, name=shm_unique_name)
+        except FileExistsError:
+            # 如果真的点背碰上了，就 connect 这一块
+            self.shm = shared_memory.SharedMemory(name=shm_unique_name)
+        
+        print(f"[Main] 共享内存已创建: {self.shm.name} (Size: {frame_size} bytes)")
+
+        # 本地 numpy 包装器
+        self.shared_frame_array = np.ndarray(
+            self.frame_shape, dtype=np.uint8, buffer=self.shm.buf
+        )
+        # 初始化为全黑，避免噪音
+        self.shared_frame_array.fill(0)
+
+        # 跨进程队列
+        self.task_queue = mp_proc.Queue(maxsize=2) 
+        self.result_queue = mp_proc.Queue(maxsize=2) # 1就够了，最新的覆盖
+
+        # 3. 启动进程
+        # Windows下传参，只传名字字符串是安全的
+        self.worker_proc = mp_proc.Process(
+            target=background_worker_process,
+            args=(
+                self.shm.name,
+                self.frame_shape,
+                self.task_queue,
+                self.result_queue,
+                face_db,
+            ),
+        )
+        self.worker_proc.daemon = True
+        self.worker_proc.start()
 
     def _get_smoothed_value(self, history, current_val):
         """内部函数：计算滑动平均值"""
@@ -78,7 +118,20 @@ class MonitorSystem:
         """
         输入 BGR 图像，返回分析结果字典
         """
+        # 强制检查分辨率，如果不匹配则 Resize (对应 __init__ 中硬编码的 1280x720)
+        # 这一步至关重要，否则后台进程读到的全是黑屏
+        target_h, target_w = self.frame_shape[:2]
+        if frame.shape[:2] != (target_h, target_w):
+            frame = cv2.resize(frame, (target_w, target_h))
+
         h, w = frame.shape[:2]
+        # 现在肯定匹配了，放心写入
+        try:
+            self.shared_frame_array[:] = frame[:]
+        except Exception:
+            # 极端情况：数组形状不匹配 (比如通道数变了)
+            pass
+
         rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
         results = self.face_mesh.process(rgb_frame)
@@ -174,7 +227,9 @@ class MonitorSystem:
         left = face_loc[3]
         scale_factor = 10
         small_bg = cv2.resize(
-            frame, (w // scale_factor, h // scale_factor), interpolation=cv2.INTER_LINEAR
+            frame,
+            (w // scale_factor, h // scale_factor),
+            interpolation=cv2.INTER_LINEAR,
         )
         # 使用 INTER_NEAREST 马赛克效果
         # 使用 INTER_LINEAR 毛玻璃模糊效果
@@ -189,23 +244,27 @@ class MonitorSystem:
         # --- 身份识别 ---
         if now - self.last_identity_check_time > self.IDENTITY_CHECK_INTERVAL:
             sface_loc = (
-                int(min(ys) * h), int(max(xs) * w),
+                int(min(ys) * h),
-                int(max(ys) * h), int(min(xs) * w)
+                int(max(xs) * w),
+                int(max(ys) * h),
+                int(min(xs) * w),
             )
             spad = 20
-            sface_loc = (max(0, sface_loc[0]-spad), min(w, sface_loc[1]+spad),
+            sface_loc = (
-                        min(h, sface_loc[2]+spad), max(0, sface_loc[3]-spad))
+                max(0, sface_loc[0] - spad),
+                min(w, sface_loc[1] + spad),
+                min(h, sface_loc[2] + spad),
+                max(0, sface_loc[3] - spad),
+            )
 
             if self.task_queue.full():
                 self.task_queue.get()
-            self.task_queue.put((rgb_frame.copy(), sface_loc, 0))
+            self.task_queue.put((sface_loc, 0))
 
             self.last_identity_check_time = now
 
-        analysis_data["identity"] = self.current_user
-
         # --- 情绪识别 ---
-        if HAS_EMOTION_MODULE and (
+        if (
             now - self.last_emotion_check_time > self.EMOTION_CHECK_INTERVAL
         ):
 
@@ -227,38 +286,115 @@ class MonitorSystem:
 
             if self.task_queue.full():
                 self.task_queue.get()
-            self.task_queue.put((frame.copy(), face_loc, 1))
+            self.task_queue.put((face_loc, 1))
 
             self.last_emotion_check_time = now
 
+        while not self.result_queue.empty():
+            type_, data = self.result_queue.get()
+            if type_ == "identity":
+                self.current_user = data
+            elif type_ == "emotion":
+                self.cached_emotion["label"] = data.get("emotion", "unknown")
+                self.cached_emotion["va"] = data.get("vaVal", (0.0, 0.0))
+        
+        analysis_data["identity"] = self.current_user
         analysis_data["emotion_label"] = self.cached_emotion["label"]
         analysis_data["emotion_va"] = self.cached_emotion["va"]
 
         return analysis_data
 
-    def _id_emo_loop(self):
+    # def _id_emo_loop(self):
+    #     while True:
+    #         try:
+    #             frame, face_loc, task_type = self.task_queue.get()
+    #             if task_type == 0:
+    #                 match_result = self.face_lib.identify(frame, face_location=face_loc)
+    #                 if match_result:
+    #                     self.current_user = match_result["info"]
+    #             elif task_type == 1 and HAS_EMOTION_MODULE:
+    #                 face_crop = frame[
+    #                     face_loc[0] : face_loc[2], face_loc[3] : face_loc[1]
+    #                 ]
+
+    #                 if face_crop.size > 0:
+    #                     try:
+    #                         emo_results = analyze_emotion_with_hsemotion(face_crop)
+
+    #                         if emo_results:
+    #                             top_res = emo_results[0]
+    #                             self.cached_emotion["label"] = top_res.get(
+    #                                 "emotion", "unknown"
+    #                             )
+    #                             self.cached_emotion["va"] = top_res.get(
+    #                                 "vaVal", (0.0, 0.0)
+    #                             )
+
+    #                     except Exception as e:
+    #                         print(f"情绪分析出错: {e}")
+    #         except Exception as e:
+    #             print(f"线程处理出错: {e}")
+
+
+def background_worker_process(
+    shm_name,  # 共享内存的名字
+    frame_shape,  # 图像大小 (h, w, 3)
+    task_queue,  # 任务队列 (主 -> 从)
+    result_queue,  # 结果队列 (从 -> 主)
+    face_db_data,  # 把人脸库数据传过去初始化
+):
+    existing_shm = shared_memory.SharedMemory(name=shm_name)
+    # 创建 numpy 数组视图，无需复制数据
+    shared_frame = np.ndarray(frame_shape, dtype=np.uint8, buffer=existing_shm.buf)
+
+    print("[Worker] 正在加载模型...")
+    from face_library import FaceLibrary
+
+    face_lib = FaceLibrary(face_db_data)
+
+    try:
+        from new_emotion_test import analyze_emotion_with_hsemotion
+
+        has_emo = True
+    except:
+        has_emo = False
+    print("[Worker] 模型加载完毕")
+
     while True:
         try:
-                frame, face_loc, task_type = self.task_queue.get()
+            # 阻塞等待任务
-                if task_type == 0:
+            # task_info = (task_type, face_loc)
-                    match_result = self.face_lib.identify(frame, face_location=face_loc)
+            face_loc, task_type = task_queue.get()
-                    if match_result:
-                        self.current_user = match_result["info"]
-                elif task_type == 1 and HAS_EMOTION_MODULE:
-                    face_crop = frame[face_loc[0]:face_loc[2], face_loc[3]:face_loc[1]]
 
-                    if face_crop.size > 0:
+            # 注意：这里读取的是共享内存里的图，不需要传图！
-                        try:
+            # 切片操作也是零拷贝
-                            emo_results = analyze_emotion_with_hsemotion(face_crop)
+            # 为了安全，这里 copy 一份出来处理，避免主进程修改
+            # 但实际上如果主进程只写新帧，这里读旧帧也问题不大
+            # 为了绝对安全和解耦，我们假定主进程已经写入了对应的帧
 
-                            if emo_results:
+            # (实战技巧：通常我们会用一个信号量或多块共享内存来实现乒乓缓存)
-                                top_res = emo_results[0]
+            # 简化版：我们直接从 shared_frame 读。
-                                self.cached_emotion["label"] = top_res.get(
+            # 由于主进程跑得快，可能SharedMemory里已经是下一帧了。
-                                    "emotion", "unknown"
+            # 但对于识别身份来说，差一两帧根本没区别！这才是优化的精髓。
-                                )
+
-                                self.cached_emotion["va"] = top_res.get("vaVal", (0.0, 0.0))
+            current_frame_view = shared_frame.copy()  # .copy() 如果你怕读写冲突
+
+            if task_type == 0:  # Identity
+                # RGB转换
+                rgb = cv2.cvtColor(current_frame_view, cv2.COLOR_BGR2RGB)
+                res = face_lib.identify(rgb, face_location=face_loc)
+                if res:
+                    result_queue.put(("identity", res["info"]))
+
+            elif task_type == 1 and has_emo:  # Emotion
+                # BGR 直接切
+                roi = current_frame_view[
+                    face_loc[0] : face_loc[2], face_loc[3] : face_loc[1]
+                ]
+                if roi.size > 0:
+                    emo_res = analyze_emotion_with_hsemotion(roi)
+                    if emo_res:
+                        result_queue.put(("emotion", emo_res[0]))
 
         except Exception as e:
-                            print(f"情绪分析出错: {e}")
+            print(f"[Worker Error] {e}")
-            except Exception as e:
-                print(f"线程处理出错: {e}")

reproject/main.py

@@ -92,7 +92,7 @@ def capture_thread():
     """
     采集线程：优化了分发逻辑，对视频流进行降频处理
     """
-    cap = cv2.VideoCapture(0)
+    cap = cv2.VideoCapture(1)
     cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
     cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
 
@@ -244,8 +244,9 @@ def video_stream_thread():
     发送线程：优化了 Socket 设置和压缩参数
     """
     print(f"[Video] 准备连接服务器 {SERVER_HOST}:{SERVER_PORT} ...")
-    server = WebRTCServer(fps=60)
+    server = WebRTCServer(60, 5, "ws://10.128.50.6:5000")
     server.start()
+    print("[Video] WebRTC 服务器启动完成")
     fourcc = cv2.VideoWriter_fourcc(*'avc1')
     # jetson-nvenc 编码器
     # bitrate = 1000000  # 1 Mbps
@@ -268,7 +269,9 @@ def video_stream_thread():
         try:
             frame = video_queue.get(timeout=1)
             # small_frame = cv2.resize(apply_soft_roi(frame), (1280, 720))
+            # print("[Video] 获取一帧视频")
             server.provide_frame(frame)
+            # print("[Video] 已提供给 WebRTC 服务器")
             # out1.write(frame)
             # out2.write(frame)
         except queue.Empty:

reproject/webrtc_server.py

@@ -17,7 +17,7 @@ from aiortc.mediastreams import VIDEO_CLOCK_RATE, VIDEO_TIME_BASE
 
 
 class WebRTCServer:
-    def __init__(self, fps):
+    def __init__(self, fps, seat, server="ws://localhost:5000"):
         self.pcs = set()
         self.fps = fps
         self.frameContainer = [None]
@@ -31,7 +31,8 @@ class WebRTCServer:
         )
         self._rtc_thread.start()
 
-        self.server = "ws://10.128.50.6:5000"
+        self.server = server
+        self.seat = seat
 
     def _start_background_loop(self, loop):
         asyncio.set_event_loop(loop)
@@ -43,8 +44,7 @@ class WebRTCServer:
         @sio.event
         async def connect():
             print("已连接到中心信令服务器")
-            # TODO 注册自己为设备
+            await sio.emit("checkin", {"seat_id": self.seat})
-            await sio.emit("checkin", {"device_id": "cam_001"})
 
         @sio.event
         async def offer(data):