mtphotos/mt-photos-deepface

MT Photos人脸识别API

• 基于https://github.com/serengil/deepface%E5%AE%9E%E7%8E%B0%E7%9A%84%E4%BA%BA%E8%84%B8%E8%AF%86%E5%88%ABAPI
• 镜像代码仓库 https://github.com/MT-Photos/mt-photos-deepface

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 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
    }
  ]
}