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reproject/analyzer.py

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+import cv2
+import mediapipe as mp
+import time
+import numpy as np
+from collections import deque
+from geometry_utils import calculate_ear, calculate_mar_simple, estimate_head_pose, LEFT_EYE, RIGHT_EYE
+from face_library import FaceLibrary
+
+
+try:
+    from new_emotion_test import analyze_emotion_with_hsemotion
+    HAS_EMOTION_MODULE = True
+except ImportError:
+    print("⚠️ 未找到 new_emotion_test.py，情绪功能将不可用")
+    HAS_EMOTION_MODULE = False
+
+class MonitorSystem:
+    def __init__(self, face_db):
+        # 初始化 MediaPipe
+        self.mp_face_mesh = mp.solutions.face_mesh
+        self.face_mesh = self.mp_face_mesh.FaceMesh(
+            max_num_faces=1,
+            refine_landmarks=True,
+            min_detection_confidence=0.5,
+            min_tracking_confidence=0.5
+        )
+        
+        # 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  
+        
+        self.last_emotion_check_time = 0
+        self.EMOTION_CHECK_INTERVAL = 3.0   
+
+        # --- 历史数据 ---
+        self.HISTORY_LEN = 5
+        self.ear_history = deque(maxlen=self.HISTORY_LEN)
+        self.mar_history = deque(maxlen=self.HISTORY_LEN)
+        
+        # 缓存上一次的检测结果 
+        self.cached_emotion = {
+            "label": "detecting...", 
+            "va": (0.0, 0.0)
+        }
+
+    def _get_smoothed_value(self, history, current_val):
+        """内部函数：计算滑动平均值"""
+        history.append(current_val)
+        if len(history) == 0:
+            return current_val
+        return sum(history) / len(history)
+
+    def process_frame(self, frame):
+        """
+        输入 BGR 图像，返回分析结果字典
+        """
+        h, w = frame.shape[:2]
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        
+        results = self.face_mesh.process(rgb_frame)
+        
+        analysis_data = {
+            "has_face": False,
+            "ear": 0.0, 
+            "mar": 0.0,
+            "pose": (0, 0, 0),
+            "identity": self.current_user,
+            "emotion_label": self.cached_emotion["label"],
+            "emotion_va": self.cached_emotion["va"]
+        }
+
+        if not results.multi_face_landmarks:
+            self.ear_history.clear()
+            self.mar_history.clear()
+            return analysis_data
+
+        analysis_data["has_face"] = True
+        landmarks = results.multi_face_landmarks[0].landmark
+        
+        # 计算 EAR 
+        left_ear = calculate_ear([landmarks[i] for i in LEFT_EYE], w, h)
+        right_ear = calculate_ear([landmarks[i] for i in RIGHT_EYE], w, h)
+        raw_ear = (left_ear + right_ear) / 2.0
+        
+        # 计算 MAR
+        top = np.array([landmarks[13].x * w, landmarks[13].y * h])
+        bottom = np.array([landmarks[14].x * w, landmarks[14].y * h])
+        left = np.array([landmarks[78].x * w, landmarks[78].y * h])
+        right = np.array([landmarks[308].x * w, landmarks[308].y * h])
+        raw_mar = calculate_mar_simple(top, bottom, left, right)
+
+        # --- 使用 History 进行数据平滑 ---
+        smoothed_ear = self._get_smoothed_value(self.ear_history, raw_ear)
+        smoothed_mar = self._get_smoothed_value(self.mar_history, raw_mar)
+
+        # 计算头部姿态
+        pitch, yaw, roll = estimate_head_pose(landmarks, w, h)
+
+        analysis_data.update({
+            "ear": round(smoothed_ear, 4),
+            "mar": round(smoothed_mar, 4),
+            "pose": (int(pitch), int(yaw), int(roll))
+        })
+
+        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))
+
+            match_result = self.face_lib.identify(rgb_frame, face_location=face_loc)
+            if match_result:
+                self.current_user = match_result["info"]
+            self.last_identity_check_time = now
+        
+        analysis_data["identity"] = self.current_user
+
+        # --- 情绪识别 ---
+        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)
+                
+                pad_x = int((x_max - x_min) * 0.2)
+                pad_y = int((y_max - y_min) * 0.2)
+                
+                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}")
+            
+            self.last_emotion_check_time = now
+
+        analysis_data["emotion_label"] = self.cached_emotion["label"]
+        analysis_data["emotion_va"] = self.cached_emotion["va"]
+
+        return analysis_data