VladKha / face_detector

Website for face detection from image (using HOG + SVM framework)

Face detector

Website for face detection from image.

Built using mine object_detector project (see more on it's Github page), trained on faces datasets. Tools for detection are inside object_detector package located in project root.

Note 1: site may need some spin up time if nobody has accessed it for a certain period.

Note 2: detection can take some time (few seconds) depending on image size because of the slow algorithm (HOG + SVM framework).

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development purposes. There are 2 ways of running the project for development of deployment: usual (not dockerized) and dockerized.

Usual	Dockerized
1. Install RabbitMQ	1. Install Docker and Docker-Compose
2. Copy environment file env.example and rename it to .env	2. Copy environment file env.example and rename it to .env
3. In .env file set DJANGO_SECRET_KEY and CELERY_BROKER_URL	3. In .env file set DJANGO_SECRET_KEY
4. Execute in command line pip install -r requirements.txt	4. Start docker engine
5. Start RabbitMQ	5. Execute in command line sh up.sh which will start-up all the containers (first time can take some time to boot up because of the Docker images downloading and building)
6. Start Celery worker celery -A config worker -l	6. Access website on http://0.0.0.0:8000/
7. Start Django server python manage.py runserver 0.0.0.0:8000	To shutdown dockerized application - execute in command line sh down.sh
8. Access website on http://0.0.0.0:8000/

Deployment into Heroku

    heroku login

    heroku create <app_name>

    # config environment vars
    heroku config:set DJANGO_SETTINGS_MODULE=config.settings.production
    heroku config:set DJANGO_SECRET_KEY=<YOURS_DJANGO_SECRET_KEY>
    heroku config:set CELERY_BROKER_URL=<YOURS_CELERY_BROKER_URL>

    git push heroku master

    heroku ps:scale web=1 worker=1

    heroku open

Built With

• Python 3.6
• Django 2 - back-end web framework
• Bootstrap 4 - front-end framework for site design
• a little of jQuery and Ajax
• Celery - asynchronous task queue/job queue based on distributed message passing
• RabbitMQ 3.7 - message broker
• Docker - containerization
• docker-compose - tool for defining and running multi-container Docker applications

License

This project is licensed under the MIT License - see the LICENSE file for details