增加日志

app.py

@@ -3,6 +3,7 @@ import tempfile
 
 import face_recognition
 import requests
+import logging
 
 from flask import Flask, request, jsonify, json
 
@@ -59,14 +60,36 @@ def create_app():
             results = return_json(find)
             return results
         return jsonify({'error': 'An error occurred'}), 500
+    return app
 
 
 
+# 调用 setup_logging 函数创建并配置日志记录器
 
 
-    return app
+def setup_logging():
+    # 创建一个 Logger 对象
+    logger = logging.getLogger('sensitive-face-recognizer_logger')
+    # 设置日志级别，可根据需要调整为 DEBUG、INFO、WARNING、ERROR 或 CRITICAL
+    logger.setLevel(logging.INFO)
+
+    # 创建一个 Handler 对象，这里使用 StreamHandler 输出到控制台
+    console_handler = logging.StreamHandler()
+    # 创建一个文件处理器，输出到文件
+    file_handler = logging.FileHandler('app.log')
+
+    # 创建一个 Formatter 对象，设置日志格式
+    formatter = logging.Formatter('%(levelname)s - %(message)s')
+    console_handler.setFormatter(formatter)
+    file_handler.setFormatter(formatter)
 
+    # 将 Handler 添加到 Logger 中
+    logger.addHandler(console_handler)
+    logger.addHandler(file_handler)
 
+    return logger
+
+logger = setup_logging()
 
 def return_json(find):
     result = {
@@ -84,6 +107,7 @@ def return_json(find):
 
 
 
+
 # 全局变量存储已知人脸编码和名称
 known_face_encodings = []
 known_face_names = []
@@ -102,6 +126,12 @@ def load_known_faces(known_faces_dir):
             known_face_encodings.append(face_encoding)
             known_face_names.append(os.path.splitext(filename)[0])
 
+def get_face_similarity(known_face_encoding, face_encoding):
+    # 计算两张脸的面部编码之间的距离
+    face_distances = face_recognition.face_distance([known_face_encoding], face_encoding)
+    # 计算相似度，使用 1 - 距离作为相似度
+    similarity = 1 - face_distances
+    return similarity
 
 def find_faces_in_image(image_path):
     image = face_recognition.load_image_file(image_path)
@@ -111,12 +141,13 @@ def find_faces_in_image(image_path):
     found_faces = []
     for face_encoding in face_encodings:
         matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.35)
-
         if True in matches:
             first_match_index = matches.index(True)
             name = known_face_names[first_match_index]
             found_faces.append(name)
-
+            known_face_encoding = known_face_encodings[first_match_index]
+            similarity = get_face_similarity(known_face_encoding, face_encoding)
+            logger.info(f'image_name={name} similarity={similarity}')
     if found_faces:
         return True
     else: