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Image Similarity Search with object detection

Overview

This demo uses the towhee pipelines to detect objects in images and extract feature vectors of images, and then uses Milvus to build an image similarity search system.

The following is the system diagram.

demo1

Data source

This demo uses the PASCAL VOC image set, which contains 17125 images with 20 categories: human; animals (birds, cats, cows, dogs, horses, sheep); transportation (planes, bikes, boats, buses, cars, motorcycles, trains); household (bottles, chairs, tables, pot plants, sofas, TVs)

Dataset size: ~ 2 GB.

Download: https://drive.google.com/file/d/1n_370-5Stk4t0uDV1QqvYkcvyV8rbw0O/view?usp=sharing

Note: You can also use your own images, and needs to be an object in the image. This demo supports images in formats of .jpg and .png.

How to deploy the system

1. Start Milvus and MySQL

As shown in the architecture diagram, the system will use Milvus to store and search the feature vector data, and Mysql is used to store the correspondence between the ids returned by Milvus and the image paths, then you need to start Milvus and Mysql first.

  • Start Milvus v2.0

    First, you are supposed to refer to the Install Milvus v2.0 for how to run Milvus docker.

Note the version of Milvus should be consistent with pymilvus version.

  • Start MySQL
$ docker run -p 3306:3306 -e MYSQL_ROOT_PASSWORD=123456 -d mysql:5.7

2. Start Server

The next step is to start the system server. It provides HTTP backend services, and there are two ways to start: running with Docker OR source code.

Option 1: Run server with Docker

  • Set parameters

Modify the parameters according to your own environment. Here listing some parameters that need to be set, for more information please refer to config.py.

Parameter Description example
DATAPATH1 The dictionary of the image path. /data/image_path
MILVUS_HOST The IP address of Milvus, you can get it by ifconfig. 172.16.20.10
MILVUS_PORT The port of Milvus. 19530
MYSQL_HOST The IP address of MySQL 172.16.20.10 
$ export DATAPATH1='/data/image_path'
$ export Milvus_HOST='172.16.20.10'
$ export Milvus_PORT='19530'
$ export Mysql_HOST='172.16.20.10'
  • Run Docker
$ docker run -d \
-v ${DATAPATH1}:${DATAPATH1} \
-p 5010:5010 \
-e "MILVUS_HOST=${Milvus_HOST}" \
-e "MILVUS_PORT=${Milvus_PORT}" \
-e "MYSQL_HOST=${Mysql_HOST}" \
milvusbootcamp/imgsearch-with-objdet:2.0

Note: -v ${DATAPATH1}:${DATAPATH1} means that you can mount the directory into the container. If needed, you can load the parent directory or more directories.

Option 2: Run source code

  • Install the Python packages
$ cd server
$ pip install -r requirements.txt
  • Set configuration
$ vim server/src/config.py

Please modify the parameters according to your own environment. Here listing some parameters that need to be set, for more information please refer to config.py.

Parameter Description Default setting
MILVUS_HOST milvus IP address 127.0.0.1
MILVUS_PORT milvus service port 19530
VECTOR_DIMENSION Dimensionality of the vectors 1000
MYSQL_HOST The IP address of Mysql. 127.0.0.1
MYSQL_PORT Port of Milvus. 3306
DEFAULT_TABLE The milvus and mysql default collection name. milvus_obj_det
  • Run the code
$ cd src
$ python main.py
  • API docs

Visit 127.0.0.1:5010/docs in your browser to use all the APIs.

fastapi

/data: get image by path

/progress: get load progress

/img/load: load images into milvus collection

/img/count: count rows in milvus collection

/img/drop: drop milvus collection & corresponding Mysql table

/img/search: search for most similar image emb in milvus collection and get image info by milvus id in Mysql

  • Code structure

If you are interested in our code or would like to contribute code, feel free to learn more about our code structure.

└───server
│   │   Dockerfile
│   │   requirements.txt
│   │   main.py  # File for starting the program.
│   │
│   └───src
│       │   config.py  # Configuration file.
│       │   encode.py  # Convert image/video/questions/... to embeddings.
│       │   milvus.py  # Connect to Milvus server and insert/drop/query vectors in Milvus.
│       │   mysql.py   # Connect to MySQL server, and add/delete/query IDs and object information.
│       │   
│       └───operations # Call methods in milvus.py and mysql.py to insert/query/delete objects.
│               │   insert.py
│               │   query.py
│               │   delete.py
│               │   count.py

3. Start Client

  • Start the front-end
# Modify API_URL to the IP address and port of the server.
$ export API_URL='http://172.16.20.10:5010'
$ docker run -d -p 8001:80 \
-e API_URL=${API_URL} \
milvusbootcamp/img-search-client:1.0

In this command, API_URL means the query service address.

  • How to use

Visit WEBCLIENT_IP:8001 in the browser to open the interface for reverse image search.

WEBCLIENT_IP specifies the IP address that runs pic-search-webclient docker.

arch

Enter the path of an image folder in the pic_search_webserver docker container with ${DATAPATH1}, then click + to load the pictures. The following screenshot shows the loading process:

Note: After clicking the Load button, it will take 1 to 2 seconds for the system to response. Please do not click again.

arch

The loading process may take several minutes. The following screenshot shows the interface with images loaded.

Only support jpg pictures.

Select an image to search.