A visual search engine(for go) based on Elasticsearch, and A Tensorflow implementation of faster RCNN detection.

Requirements

There are serveral python2.7 libraries you must to install before building the search engine.

• elasticsearch==5.2.0

• Tensorflow==0.12.1

• Flask

• opencv-python

• easydict

• cython

• numpy

• Or

pip install -r requirements.txt

• Then

cd visual_search/lib & make

Setup

• Setup Elasticsearch5

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

• Building 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
cd ../elasticsearch
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. You can fetch pre-trained model here.

Indexing images to elasticsearch

export WEIGHT_PATH=...
export MODEL_PATH=...
export INPUT=..
cd visual_search
python index_es.py --weight $WEIGHT_PATH --model_path $MODEL_PATH --input $INPUT

Example

sudo python index_es.py --weight ./models/imagenet_weights/vgg16.weights --model_path ./models/faster_rcnn_models/coco_2014_train+coco_2014_valminusminival/default/vgg16_faster_rcnn_iter_490000.ckpt --input ../KGSoutput/2002-01-01-9.png

Start server

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

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

Have fun =))

Issue

export CFLAGS="-I /usr/local/lib/python3.5/dist-packages/numpy/core/include $CFLAGS"

tensorflow/tensorflow#251

References

https://blog.algorithmia.com/deep-dive-into-object-detection-with-open-images-using-tensorflow/