Download the model and place it in the models directory

• Link 1: https://github.com/mymagicpower/AIAS/releases/download/apps/face_feature.zip
• Link 2: https://github.com/mymagicpower/AIAS/releases/download/apps/ultranet.zip

Introduction to face big data search

• Person image high-precision search: Face feature extraction (use face feature model to extract 512-dimensional features) First, do face detection, face key point extraction, and face alignment

####M ain features -The bottom layer uses feature vector similarity search -Millisecond level search for billions of data on a single server -Near real-time search, support distributed deployment -Insert, delete, search, update, etc. can be performed on data at any time -Support online user management and server performance monitoring, support limiting single user login

Function

-People search: Upload a portrait picture for search -Data management: Provide image compression package (zip format) upload, portrait feature extraction

1. Front-end deployment

1.1 Run directly:

npm run dev

1.2 Build dist installation package:

npm run build:prod

1.3 Deploy and run nginx (mac environment)

cd /usr/local/etc/nginx/
vi /usr/local/etc/nginx/nginx.conf
#Edit nginx.conf

server {
listen 8080;
server_name localhost;

location / {
root/Users/calvin/face_search/dist/;
index index.html index.htm;
}
......

#Reload configuration:
sudo nginx -s reload

#After deploying the application, restart:
cd /usr/local/Cellar/nginx/1.19.6/bin

#Fast stop
sudo nginx -s stop

#Start
sudo nginx

2. Backend jar deployment

2.1 Environmental requirements:

-System JDK 1.8+ -Need to install redis -Need to install MySQL database

2.2 Running the program:

java -jar face-search-1.0.jar

3. Backend vector engine deployment

3.1 Environmental requirements:

-Need to install docker operating environment, Mac environment can use Docker Desktop

3.2 Pull Milvus vector engine image (used to calculate feature vector similarity)

Download the milvus-standalone-docker-compose.yml configuration file and save it as docker-compose.yml

Standalone Installation Document
Configure Milvus

Please refer to the latest version of the official website

• Milvus vector engine reference link Milvus vector engine official website
  Milvus vector engine Github

# example - v2.2.4
wget https://github.com/milvus-io/milvus/releases/download/v2.2.4/milvus-standalone-docker-compose.yml -O docker-compose.yml

3.3 Start Docker container

sudo docker-compose up -d

3.4 Edit the vector engine connection configuration information

• application.yml
• Edit the vector engine connection IP address 127.0.0.1 to the host IP of the container as needed

#####################向量引擎 ###############################
search:
  host: 127.0.0.1
  port: 19530
  indexFileSize: 1024 # maximum size (in MB) of each index file
  nprobe: 256
  nlist: 16384
  dimension: 512 #dimension of each vector
  collectionName: face_search #collection name

##4. Open the browser -Enter the address: http://localhost:8089

• Upload pictures 1). Click the upload button to upload the zip package. 2). Click the Extract Face Features button.

• People search

• Reset the Milvus vector engine (clear data):

me.calvin.modules.search.tools.MilvusInit.java