DeepFace【部署 03】轻量级人脸识别和面部属性分析框架deepface在Linux环境下服务部署(conda虚拟环境+docker)

简介: DeepFace【部署 03】轻量级人脸识别和面部属性分析框架deepface在Linux环境下服务部署(conda虚拟环境+docker)

1.使用虚拟环境[810ms]

1.1 环境部署

Anaconda的安装步骤这里不再介绍,直接开始使用。

# 1.创建虚拟环境
conda create -n deepface python=3.9.18
# 2.激活虚拟环境
conda activate deepface
# 3.安装deepface
pip install deepface -i https://pypi.tuna.tsinghua.edu.cn/simple

以下操作在虚拟环境deepface下执行:

# 1.安装mesa-libGL.x86_64
yum install mesa-libGL.x86_64
# 防止报错
ImportError: libGL.so.1: cannot open shared object file: No such file or directory
# 2.安装deprecated
pip install deprecated==1.2.13
# 防止报错
ModuleNotFoundError: No module named 'deprecated'

使用yum install mesa-libGL.x86_64命令会在Linux系统中安装mesa-libGL包。这个包包含了Mesa 3D图形库的运行时库和DRI驱动。安装mesa-libGL包后,系统将能够支持OpenGL,这是一种用于渲染2D和3D矢量图形的跨语言、跨平台的应用程序编程接口(API)。

1.2 服务启动

DeepFace serves an API as well. You can clone [/api](https://github.com/serengil/deepface/tree/master/api) folder and run the api via gunicorn server. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.

cd scripts
./service.sh

Linux系统使用这个命令是前台启动,实际的启动用的是shell脚本,内容如下:

#!/bin/bash
nohup python -u ./api/api.py > ./deepfacelog.out 2>&1 &

Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Default service endpoints will be http://localhost:5000/verify for face recognition, http://localhost:detector_backend for facial attribute analysis, and http://localhost:5000/represent for vector representation. You can pass input images as exact image paths on your environment, base64 encoded strings or images on web. Here, you can find a postman project to find out how these methods should be called.

这里仅贴出如何传递base64进行接口调用:

{
    "img_path": "data:image/,image_base64_str"
}

仅看一下base64相关源码:

def load_image(img):
    # The image is a base64 string
    if img.startswith("data:image/"):
        return loadBase64Img(img)
def loadBase64Img(uri):
    encoded_data = uri.split(",")[1]
    nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return img

2.使用Docker[680ms]

You can deploy the deepface api on a kubernetes cluster with docker. The following shell script will serve deepface on localhost:5000. You need to re-configure the Dockerfile if you want to change the port. Then, even if you do not have a development environment, you will be able to consume deepface services such as verify and analyze. You can also access the inside of the docker image to run deepface related commands. Please follow the instructions in the shell script.

修改Dockerfile,调整镜像库:

# base image
FROM python:3.8
LABEL org.opencontainers.image.source https://github.com/serengil/deepface
# -----------------------------------
# create required folder
RUN mkdir /app
RUN mkdir /app/deepface
# -----------------------------------
# Copy required files from repo into image
COPY ./deepface /app/deepface
COPY ./api/app.py /app/
COPY ./api/routes.py /app/
COPY ./api/service.py /app/
COPY ./requirements.txt /app/
COPY ./setup.py /app/
COPY ./README.md /app/
# -----------------------------------
# switch to application directory
WORKDIR /app
# -----------------------------------
# update image os
RUN apt-get update
RUN apt-get install ffmpeg libsm6 libxext6 -y
# -----------------------------------
# if you will use gpu, then you should install tensorflow-gpu package
# RUN pip install --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org tensorflow-gpu
# -----------------------------------
# install deepface from pypi release (might be out-of-the-date)
RUN pip install deepface -i https://pypi.tuna.tsinghua.edu.cn/simple
# -----------------------------------
# environment variables
ENV PYTHONUNBUFFERED=1
# -----------------------------------
# run the app (re-configure port if necessary)
EXPOSE 5000
CMD ["gunicorn", "--workers=1", "--timeout=3600", "--bind=0.0.0.0:5000", "app:create_app()"]

官网启动命令:

cd scripts
./dockerize.sh

报错:

unable to prepare context: unable to evaluate symlinks in Dockerfile path: lstat /home/deepface/scripts/Dockerfile: no such file or directory
Unable to find image 'deepface:latest' locally
docker: Error response from daemon: pull access denied for deepface, repository does not exist or may require 'docker login': denied: requested access to the resource is denied.
See 'docker run --help'.

解决【不要 cd scripts】原因是执行脚本的文件夹要跟构建镜像使用的Dockerfile同级:

./scripts/dockerize.sh
# 这个过程一共有两个步骤:1是构建镜像;2是启动容器。构建镜像的速度取决于网速【时间可能会比较久】

分解步骤:

# 构建镜像
docker build -t deepface_image .
# 创建模型文件夹【并将下载好的模型文件上传】
mkdir -p /root/.deepface/weights/
# 启动容器
docker run --name deepface --privileged=true --restart=always --net="host" -v /root/.deepface/weights/:/root/.deepface/weights/ -d deepface_image
目录
相关文章
|
14天前
|
存储 测试技术 Linux
【Docker项目实战】使用Docker部署bender个人仪表板
【4月更文挑战第2天】使用Docker部署bender个人仪表板
27 1
|
19天前
|
JSON Kubernetes Linux
Linux环境签发CA证书和K8s需要的证书
Linux环境签发CA证书和K8s需要的证书
22 0
|
6天前
|
存储 算法 Linux
【实战项目】网络编程:在Linux环境下基于opencv和socket的人脸识别系统--C++实现
【实战项目】网络编程:在Linux环境下基于opencv和socket的人脸识别系统--C++实现
17 6
|
15天前
|
前端开发 应用服务中间件 nginx
Nginx配置详解Docker部署Nginx使用Nginx部署vue前端项目
Nginx配置详解Docker部署Nginx使用Nginx部署vue前端项目
73 0
|
4天前
|
测试技术 Linux 数据安全/隐私保护
【Docker项目实战】使用Docker部署Seatsurfing预订座位系统
【4月更文挑战第12天】使用Docker部署Seatsurfing预订座位系统
30 3
|
7天前
|
JavaScript 前端开发 Docker
全栈开发实战:结合Python、Vue和Docker进行部署
【4月更文挑战第10天】本文介绍了如何使用Python、Vue.js和Docker进行全栈开发和部署。Python搭配Flask创建后端API,Vue.js构建前端界面,Docker负责应用的容器化部署。通过编写Dockerfile,将Python应用构建成Docker镜像并运行,前端部分使用Vue CLI创建项目并与后端交互。最后,通过Nginx和另一个Dockerfile部署前端应用。这种组合提升了开发效率,保证了应用的可维护性和扩展性,适合不同规模的企业使用。
|
11天前
|
测试技术 Linux 数据安全/隐私保护
【Docker项目实战】使用Docker部署PicoShare共享文件平台
【4月更文挑战第5天】使用Docker部署PicoShare共享文件平台
46 4
|
15天前
|
人工智能 Ubuntu 机器人
【ubuntu】 Linux(ubuntu)创建python的虚拟环境
【ubuntu】 Linux(ubuntu)创建python的虚拟环境
|
16天前
|
测试技术 Linux Docker
Docker部署RPG网页小游戏
Docker部署RPG网页小游戏
43 2
|
19天前
|
Linux Shell 开发工具
CentOS8中Docker安装及部署
CentOS8中Docker安装及部署
65 0