March 11, 2017 | Tam Nguyen DeepDetect Client Rails Example Requirements Ubuntu 14.04 or Ubuntu 16.04 Rails 5+ Ruby 2.2+ Redis Install DeepDetect Install dependencies: sudo apt-get install build-essential libgoogle-glog-dev libgflags-dev libeigen3-dev libopencv-dev libcppnetlib-dev libboost-dev libboost-iostreams-dev libcurlpp-dev libcurl4-openssl-dev protobuf-compiler libopenblas-dev libhdf5-dev libprotobuf-dev libleveldb-dev libsnappy-dev liblmdb-dev libutfcpp-dev cmake libgoogle-perftools-dev unzip Clone & build deepdetect: cd && mkdir Projects && git clone git@github.com:beniz/deepdetect.git cd deepdetect && mkdir build cd build cmake .. make Start the server: cd build/main ./dede DeepDetect [ commit 73d4e638498d51254862572fe577a21ab8de2ef1 ] Running DeepDetect HTTP server on localhost:8080 Read more at https://github.com/beniz/deepdetect Clone models that are trained by me: cd && mkdir Models wget http://ntam.me/downloads/person_yes_no.tar.gz tar xvf person_yes_no.tar.gz Type cmd pwd at folder Models to get absolute path Example with my absolute path model /home/tamnguyen/Models How to run git clone git@github.com:ntamvl/deepdetect-client-rails-example.git bundle install # edit config/database.yml then run rails db:create && rails db:migrate && rails db:seed # edit deepdetect config at config/deepdetect.json # update model_path like this { "model_path": "/home/tamnguyen/Models" } # run on development rails s -b 0.0.0.0 # run on production as daemonize bundler exec puma -C config/puma.rb -e production -d Usage Predict an image by model - Endpoint: `POST /v1/predict` - Header: - Authorization: Authentication token - Params: - image_url: string, required - model: string, required How to use curl -X POST --header 'Content-Type: application/x-www-form-urlencoded' --header 'Accept: application/json' --header 'Authorization: Token token=TtDKqIuz50GyNpl7z8tMtQtt' -d 'image_url=https%3A%2F%2Fscontent.fsgn1-1.fna.fbcdn.net%2Fv%2Ft1.0-9%2F1239414_10201956642662189_587041755_n.jpg%3Foh%3D9e9be2c2b9318abac66abde163a88399%26oe%3D592CF192&model=person_yes_no' 'http://127.0.0.1:3000/v1/predict.json' OR use API docs at http://localhost:3000/docs Architecture Diagram I will upload more models when I have free time 😀 😛 you can get more models at http://ntam.me/downloads/ Share this:Click to share on Twitter (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Google+ (Opens in new window) Related