A visual search engine based on Elasticsearch and Tensorflow
$ cd visual_search
$ pip install -r requirements.txt
- Setup Elasticsearch
The easiest way to setup is using Docker with Docker Compose. With docker-compose
everything you have to do is so simple:
cd visual_search/elasticsearch
docker-compose up -d
- Install elasticsearch plugin
We need to build Elasticsearch plugin to compute distance between feature vectors. Make sure that you have Maven installed.
$ cd visual_search/es-plugin
$ mvn install
$ cd target/release
// create simple server to serve plugin
$ python -m 'SimpleHTTPServer' &
//install plugin
//go back to elasticsearch folder
$ cd ../../..
$ docker exec -it elasticsearch_elasticsearch_1 elasticsearch-plugin install http://localhost:8000/esplugin-0.0.1.zip
$ docker-compose restart
- Index preparation
curl -XPUT http://localhost:9200/im_data -d @schema_es.json
- Setup faster r-cnn
I used earlier faster r-cnn
version implemented by @Endernewton for object detection. To get pretrained model, please visit release section, download and extract file model.tar.gz
to visual_search/models
folder.
You also need to build faster r-cnn library by running following commands:
$ cd visual_search/lib
$ make
To index data, just run command:
$ python index_es.py --input [image dir]
For full comamnd options, please run:
$ python index_es.py --help
Before starting the server, you must to update IMGS_PATH
variable in visual_search/server.py
to the location of folder where images are stored.
cd visual_search
python server.py
Now, you can access the link http://localhost:5000/static/index.html
to test the search engine.