From 7e7a967eb41a9433eda85dce0a6532fc4adbdc89 Mon Sep 17 00:00:00 2001
From: hubian <908234780@qq.com>
Date: Thu, 16 Apr 2026 18:59:40 +0800
Subject: [PATCH] =?UTF-8?q?fix:=20=E4=BD=BF=E7=94=A8=20MediaPipe=20?=
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---
 person_manager.py | 47 +++++++++++++++++++++++++++++++++++------------
 1 file changed, 35 insertions(+), 12 deletions(-)

diff --git a/person_manager.py b/person_manager.py
index 06c7d09..50a1a95 100644
--- a/person_manager.py
+++ b/person_manager.py
@@ -150,7 +150,9 @@ class PersonManager:
         return faces
     
     def extract_face_encoding(self, image, face_bbox):
-        """提取人脸特征
+        """提取人脸特征（用于识别是否为同一个人）
+        
+        使用 MediaPipe 的人脸关键点作为特征，不依赖 dlib
         
         Args:
             image: 图片
@@ -177,25 +179,46 @@ class PersonManager:
         # 提取人脸区域
         face_image = image[y:y+h, x:x+w]
         
+        # 方法1：使用 face_recognition（如果安装了）
         if HAS_FACE_REC:
-            # 使用 face_recognition 库
-            rgb_face = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
-            encodings = face_recognition.face_encodings(rgb_face)
-            
-            if len(encodings) > 0:
-                return encodings[0]
+            try:
+                rgb_face = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
+                encodings = face_recognition.face_encodings(rgb_face)
+                if len(encodings) > 0:
+                    return encodings[0]
+            except:
+                pass
         
-        # 简单特征：使用颜色直方图作为特征
-        # 将人脸缩放到固定大小
+        # 方法2：使用 MediaPipe 人脸关键点（推荐）
+        if HAS_MEDIAPIPE:
+            try:
+                mp_face_mesh = mp.solutions.face_mesh
+                face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1)
+                
+                rgb_face = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
+                results = face_mesh.process(rgb_face)
+                
+                if results.multi_face_landmarks:
+                    # 提取关键点坐标作为特征
+                    landmarks = results.multi_face_landmarks[0]
+                    features = []
+                    
+                    for landmark in landmarks.landmark:
+                        features.extend([landmark.x, landmark.y, landmark.z])
+                    
+                    face_mesh.close()
+                    return np.array(features)
+            except:
+                pass
+        
+        # 方法3：使用颜色直方图（最简单，备用）
         face_resized = cv2.resize(face_image, (64, 64))
-        
-        # 计算 HSV 直方图
         hsv = cv2.cvtColor(face_resized, cv2.COLOR_BGR2HSV)
+        
         hist_h = cv2.calcHist([hsv], [0], None, [16], [0, 180])
         hist_s = cv2.calcHist([hsv], [1], None, [16], [0, 256])
         hist_v = cv2.calcHist([hsv], [2], None, [16], [0, 256])
         
-        # 合并特征
         feature = np.concatenate([
             cv2.normalize(hist_h, hist_h).flatten(),
             cv2.normalize(hist_s, hist_s).flatten(),