This is the ML implementation of the features preented in our official Repo of Hackfest

• The repo contains two models:
  1. People Counter
  2. Face Recognition (API)

People Counter :

This models uses a pretrained mobilenet to classify objects as humans and non humans and gets the number of people entering the door or leaving the door.

The model is fed by a live camrea video stream (either from a local camera or a camrea publishing to a specific link (IOT) )

The live stream is breaken up into frames and the frames are used to clssify the objects as humans o and hence help to count the number of people inside the building.

Tech Stack : OpenCV , Cafee , PIL , Python , Esp32 library


Face Recognition :

This modeule is a fully functing API deployed on heroku.

This module uses cascade detector of Open-CV to generate encodings of the face.

The input image is converted to its encodings and are comparerd against the known encodings once a sucessful match is obtained the details of the student are returned to the calling application.

API Documentation: 

Methods : 
    Type |  Function  | Usage
         |            |
    Get  | reset      |  To reset the whole database and generate new encodings
   Post  |update      |  To add encoding of new user to the database
   Post  |predict     |  To recognize a face


Api Link : https://facereco23.herokuapp.com/

Teck Stack : Heroku , Open-Cv , Python , PIL , Flask