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README.md

@@ -1,161 +1 @@
-# MT Photos人脸识别API
 
-- 基于[serengil/deepface](https://github.com/serengil/deepface)实现的人脸识别API
-
-## 模型选择
-
-### 人脸检测模型
-
-默认的人脸检测模型为retinaface，虽然计算耗时较长，但是最准确；
-
-可通过环境变量 `DETECTOR_BACKEND`来自定义检测模型；
-
-```python
-backends = [
-    'opencv',
-    'ssd',
-    'dlib',
-    'mtcnn',
-    'retinaface',
-    'mediapipe',
-    'yolov8',
-    'yunet',
-    'fastmtcnn',
-]
-detector_backend = os.getenv("DETECTOR_BACKEND", "retinaface")
-```
-
-### 人脸特征提取模型
-
-默认的人脸检测模型为Facenet512
-
-可通过环境变量 `RECOGNITION_MODEL`来自定义特征提取模型；
-
-```python
-models = [
-    "VGG-Face",
-    "Facenet",
-    "Facenet512",
-    "OpenFace",
-    "DeepFace",
-    "DeepID",
-    "ArcFace",
-    "Dlib",
-    "SFace",
-    "GhostFaceNet",
-]
-recognition_model = os.getenv("RECOGNITION_MODEL", "Facenet512")
-```
-
-## 预训练模型
-
-如果容器内下载模型很慢，可以增加 /models 目录映射
-```
--v /host_path:/models
-```
-
-然后将对应的预训练模型放到容器内的`/models/.deepface/weights/`下；比如`/models/.deepface/weights/retinaface.h5`
-
-### 预训练模型下载地址
-
-https://github.com/serengil/deepface_models/releases/tag/v1.0
-
-
-## 安装方法
-
-- 下载镜像
-
-```
-docker pull mtphotos/mt-photos-deepface:latest
-```
-
-- 创建容器
-
-```
-docker run -i -p 8066:8066 -e API_AUTH_KEY=mt_photos_ai_extra --name mt-photos-deepface --restart="unless-stopped" mtphotos/mt-photos-deepface:latest
-```
-
-
-
-## 打包docker镜像
-
-```bash
-docker build  . -t mt-photos-deepface:latest
-```
-
-### 运行docker容器
-
-```bash
-docker run -i -p 8066:8066 -e API_AUTH_KEY=mt_photos_ai_extra --name mt-photos-deepface --restart="unless-stopped" mt-photos-deepface:latest
-```
-
-### 下载源码本地运行
-
-- 安装python **3.8版本**
-- 在文件夹下执行`pip install -r requirements.txt`
-- 复制`.env.example`生成`.env`文件，然后修改`.env`文件内的API_AUTH_KEY
-- 执行 `python server.py` ，启动服务
-
-看到以下日志，则说明服务已经启动成功
-```bash
-INFO:     Started server process [27336]
-INFO:     Waiting for application startup.
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://0.0.0.0:8066 (Press CTRL+C to quit)
-```
-
-
-## API
-
-### /check
-
-检测服务是否可用，及api-key是否正确
-
-```bash
-curl --location --request POST 'http://127.0.0.1:8000/check' \
---header 'api-key: api_key'
-```
-
-**response:**
-
-```json
-{
-  "result": "pass"
-}
-```
-
-### /represent
-
-```bash
-curl --location --request POST 'http://127.0.0.1:8000/represent' \
---header 'api-key: api_key' \
---form 'file=@"/path_to_file/test.jpg"'
-```
-
-**response:**
-
-- detector_backend : 人脸检测模型
-- recognition_model : 人脸特征提取模型
-- result : 识别到的结果
-
-### 返回数据示例
-```json
-{
-  "detector_backend": "retinaface",
-  "recognition_model": "Facenet512",
-  "result": [
-    {
-      "embedding": [ 0.5760641694068909,... 512位向量 ],
-      "facial_area": {
-        "x": 212,
-        "y": 112,
-        "w": 179,
-        "h": 250,
-        "left_eye": [ 271, 201 ],
-        "right_eye": [ 354, 205 ]
-      },
-      "face_confidence": 1.0
-    }
-  ]
-}
-```

server.py

@@ -18,6 +18,13 @@
 api_auth_key = os.getenv("API_AUTH_KEY", "mt_photos_ai_extra")
 http_port = int(os.getenv("HTTP_PORT", "8066"))
 
+# 获取级联分类器文件路径
+cascade_path = os.getenv("CASCADE_PATH", "models/haarcascade_frontalface_default.xml")
+# 加载级联分类器
+face_cascade = cv2.CascadeClassifier(cascade_path)
+# 确保face_cascade已正确加载
+if face_cascade.empty():
+    raise FileNotFoundError(f"未找到级联分类器文件，请检查CASCADE_PATH环境变量: {cascade_path}")
 
 inactive_task = None