修改线程架构

reproject/analyzer.py

@@ -2,6 +2,8 @@ import cv2
 import mediapipe as mp
 import time
 import numpy as np
+import threading
+import queue
 from collections import deque
 from geometry_utils import (
     calculate_ear,
@@ -39,9 +41,6 @@ class MonitorSystem:
         # 初始化人脸底库
         self.face_lib = FaceLibrary(face_db)
 
-        # 状态变量
-        self.current_user = None
-
         # --- 时间控制 ---
         self.last_identity_check_time = 0
         self.IDENTITY_CHECK_INTERVAL = 2.0
@@ -61,6 +60,13 @@ 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.id_emo_thread.start()
+
     def _get_smoothed_value(self, history, current_val):
         """内部函数：计算滑动平均值"""
         history.append(current_val)
@@ -182,20 +188,18 @@ class MonitorSystem:
         now = time.time()
         # --- 身份识别 ---
         if now - self.last_identity_check_time > self.IDENTITY_CHECK_INTERVAL:
-            # xs = [l.x for l in landmarks]
-            # ys = [l.y for l in landmarks]
-            # # 计算人脸框
-            # face_loc = (
-            #     int(min(ys) * h), int(max(xs) * w),
-            #     int(max(ys) * h), int(min(xs) * w)
-            # )
-            # pad = 20
-            # face_loc = (max(0, face_loc[0]-pad), min(w, face_loc[1]+pad),
-            #             min(h, face_loc[2]+pad), max(0, face_loc[3]-pad))
+            sface_loc = (
+                int(min(ys) * h), 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),
+                        min(h, sface_loc[2]+spad), max(0, sface_loc[3]-spad))
 
-            match_result = self.face_lib.identify(rgb_frame, face_location=face_loc)
-            if match_result:
-                self.current_user = match_result["info"]
+            if self.task_queue.full():
+                self.task_queue.get()
+            self.task_queue.put((rgb_frame.copy(), sface_loc, 0))
+            
             self.last_identity_check_time = now
 
         analysis_data["identity"] = self.current_user
@@ -204,40 +208,26 @@ class MonitorSystem:
         if HAS_EMOTION_MODULE and (
             now - self.last_emotion_check_time > self.EMOTION_CHECK_INTERVAL
         ):
-            if results.multi_face_landmarks:
-                landmarks = results.multi_face_landmarks[0].landmark
-                xs = [l.x for l in landmarks]
-                ys = [l.y for l in landmarks]
 
-                # 计算裁剪坐标
-                x_min = int(min(xs) * w)
-                x_max = int(max(xs) * w)
-                y_min = int(min(ys) * h)
-                y_max = int(max(ys) * h)
+            # 计算裁剪坐标
+            x_min = int(min(xs) * w)
+            x_max = int(max(xs) * w)
+            y_min = int(min(ys) * h)
+            y_max = int(max(ys) * h)
 
-                pad_x = int((x_max - x_min) * 0.2)
-                pad_y = int((y_max - y_min) * 0.2)
+            pad_x = int((x_max - x_min) * 0.1)
+            pad_y = int((y_max - y_min) * 0.1)
 
-                x_min = max(0, x_min - pad_x)
-                x_max = min(w, x_max + pad_x)
-                y_min = max(0, y_min - pad_y)
-                y_max = min(h, y_max + pad_y)
-
-                face_crop = frame[y_min:y_max, x_min:x_max]
-
-                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}")
+            x_min = max(0, x_min - pad_x)
+            x_max = min(w, x_max + pad_x)
+            y_min = max(0, y_min - pad_y)
+            y_max = min(h, y_max + pad_y)
+            
+            face_loc = (y_min, x_max, y_max, x_min)
+            
+            if self.task_queue.full():
+                self.task_queue.get()
+            self.task_queue.put((frame.copy(), face_loc, 1))
 
             self.last_emotion_check_time = now
 
@@ -245,3 +235,30 @@ class MonitorSystem:
         analysis_data["emotion_va"] = self.cached_emotion["va"]
 
         return analysis_data
+    
+    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}")