https://github.com/qanastek/winenot
WineNot is an easy to use app to let you find a wine according to it's label. One step, Just can it !
https://github.com/qanastek/winenot
android flask flutter ios levenshtein-distance object-detection opencv python rails ruby ruby-on-rails sqlite tensorflow tesseract tesseract-ocr
Last synced: 3 months ago
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WineNot is an easy to use app to let you find a wine according to it's label. One step, Just can it !
- Host: GitHub
- URL: https://github.com/qanastek/winenot
- Owner: qanastek
- Created: 2020-10-13T10:07:55.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-02T23:12:11.000Z (over 5 years ago)
- Last Synced: 2025-10-23T07:58:36.480Z (8 months ago)
- Topics: android, flask, flutter, ios, levenshtein-distance, object-detection, opencv, python, rails, ruby, ruby-on-rails, sqlite, tensorflow, tesseract, tesseract-ocr
- Language: Dart
- Homepage:
- Size: 25.9 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
WineNot is an easy to use app to let you find a wine according to it's label. One step, Just can it !
## Architecture
## Dependencies
- API
- `pip install -r requirements.txt`
- `sudo apt-get install tesseract-ocr-fra`
- APP
- [Install the Dart and Flutter SDK on your machine](https://flutter.dev/docs/get-started/install)
- Enable the same extensions in Android Studio
- Backoffice
- [Follow the starting guide of Rails](https://guides.rubyonrails.org/getting_started.html)
## Run the project
- Start the Rails server first
- `cd Backoffice`
- `sh run.sh`
- Open your port 3000 to the in/out request on your firewall
- Then start the Flask server
- `cd API`
- `sh run.sh`
- Open your port 5000 to the in/out request on your firewall
- Finally, compile the Flutter app on your mobile.
- Go to AndroidStudio
- Run the project
## Docker
- Run the docker process: `sudo service docker start`
- Compile the docker by going in the API or Backoffice folder and use the command `docker build .`
- Then go to your kubernetes dashboard
## Postman
- You can find a Postman collection in the `postman` directory in a way to show you the differents endpoints
## JMeter
- You can find a JMeter collection in the `JMeter` directory in a way to make a load testing
## Tensorflow Model
- You can find the tensorflow model in the `LabelExtractorTensorflow` directory
- In a way to make it works on your machine you need to change the differents path in the `LabelExtractorTensorflow/TF2_Model/LicencePlatesFR-TF2/workspace/training_demo/exported-models/my_mobilenet_model/pipeline.config` file at the lines:
- 165, 175, 177, 185 and 189