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tuan3w
416 Stars 130 Forks MIT License 12 Commits 1 Opened issues

Description

A visual search engine based on Elasticsearch and Tensorflow

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Visual Search

A visual search engine based on Elasticsearch and Tensorflow (now fully dockerized to run it in up-to-date development environments).

Visual search enging

Description

This repository contains code in

Python 2.7
and utilizes
Faster-RCNN
(with
VGG-16
as backbone) implemented in
Tensorflow 0.12.1
to extract features from images. An
Elasticsearch
instance is used to store feature vectors of the corresponding images, along with a plugin to compute distance between them.

TODO: Replace the outdated

Faster-RCNN
with a faster and more accurate model (suggestions or any collaboration is welcomed).

Requirements

The setup assumes you have a running installation of nvidia-docker and driver version 367.48 or above.

Setup Elasticsearch

First, we need to build the

Elasticsearch
plugin to compute distance between feature vectors. Make sure that you have Maven installed.
cd elasticsearch/es-plugin
mvn install

Next, we need to create a docker network so that all other containers can resolve the IP address of our

elasticsearch
instance.
docker network create vs_es_net

Finally, start the

elasticsearch
container. It will automatically add the plugin, create a named docker volume for persistent storage and connect the container to the network we just created:
cd ../ && docker-compose up -d

Index images

In order to populate the

elasticsearch
db with images, we need to first process them with a feature extractor (
Faster-RCNN
). The
indexer
services can do this for any image we place inside
visual_search/images
.

First we build a dockerized environment for the object detection model to run in:

cd visual_search && docker build --tag visual_search_env .

Here we use an earlier version implemented by @Endernewton. To get pre-trained model, you can visit release section, download and extract file

model.tar.gz
to
visual_search/models/
folder. Optionally, you can run:
mkdir models && cd models
curl https://github.com/tuan3w/visual_search/releases/download/v0.0.1/model.tar.gz
tar -xvf model.tar.gz

To index the desired images, copy the corresponding compose file to the proper directory and start the indexing service:

cd ../ && cp indexer/docker-compose.yml .
docker-compose up

Start server

Before starting the server, again copy the corresponding compose file (overwrite the one used for indexing data) into the proper directory and start the containerized

flask
server:
cp server/docker-compose.yml .
docker-compose up -d

Now, you can access the link

http://localhost:5000/static/index.html
to test the search engine.

LICENSE

MIT

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