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 = [email protected]
- 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...'