Style transfer models and photobooth infrastructure.
The style transfer model is modified from PyTorch/examples
and is located under the
fast_neural_style directory along with the original
license and readme.
For 'quick out of the box' localhost setup:
- Use paperspace! (DL box is has versions that are too new, https://myselfhimanshu.github.io/posts/setting_paperspace_dl/) - USE ML IN A BOX - ./setup.sh (may run into some directory issues, data directory should be on base of DetectronPytorch) - Load appropriate env vars: - SENDGRID_API_KEY - FROM_EMAIL = firstname.lastname@example.org - CLOUD_BUCKET (new one should be made per event, just the bucket name) - Export service key json filename: - (have file in backend/) - export GOOGLE_APPLICATION_CREDENTIALS=<key.json> - https://cloud.google.com/storage/docs/reference/libraries#client-libraries-install-cpp (export/load into a file) - https://cloud.google.com/storage/docs/reference/libraries#client-libraries-install-cpp - conda install -c pytorch=0.4.1 - open port, include port in address (test server up using simple get) - sudo ufw status verbose - sudo ufw enable - sudo ufw allow <port>/tcp - python server.py - Startup the backend server, link the proper address in frontend
- Clone this repo and nfc-badge-server on serving laptop - Use nvm to get node, npm i, npm run build - Start nfc-badge-server and follow appropriate instructions - if registered, starting the server will output nfc id - Load env vars: - REGISTRATION_API_KEY (find in admin panel) - REGISTRATION_URL (graphql for registration, make sure it's non-redirect) - CHECKIN_API_KEY [?] - CHECKIN_URL (non redirect) - Use python3/pip3 to install everything - python3 app.py - access via localhost to avoid insecure origins on chrome - Ready camera should log 'ready to capture...'