cheeseburger9309/attendance-system-api

This is a Flask-based API for an attendance system. It allows users to upload images, detect faces, mark attendance, and train the face recognition model.

Attendance System API

This is a Flask-based API for an attendance system. It allows users to upload images, detect faces, mark attendance, and train the face recognition model.

Requirements

• Python 3.7 or higher
• OpenCV (cv2) library (opencv-contrib-python)
• Flask library
• NumPy library
• SQLite3 library

Installation

1. Clone the repository: git clone https://github.com/ganajayant/attendance-system-api.git

2. Navigate to the project directory: cd attendance-system-api

3. Install the required dependencies: pip install -r requirements.txt

4. Load Schema into Database python init_db.py

Run Command

To start the Flask application, use the following command:

• MacOS flask --app server run --debug
• Windows python -m flask --app server run --debug

API Endpoints

Postman Documentation

Upload Images and Add User

Endpoint: /upload

Method: POST

Request Body:

• images (multipart/form-data) - List of images to upload (maximum 10 images).
• rollno (form-data) - Roll number of the student.
• name (form-data) - Name of the student.
• email (form-data) - Email of the student.

Response:

• Success (200 OK):

{
    "message": "User added successfully & training started"
}

• Bad Request (400):
  • If no images are found:

  {
      "message": "No images found"
  }
  

  • If more than 10 images are uploaded:

  {
      "message": "Maximum 10 images are allowed"
  }
  

  • If the request data is invalid:

  {
      "message": "Invalid data"
  }
  

  • If the user already exists:

  {
      "message": "User already exists"
  }
  

• Internal Server Error (500):

  {
      "message": "Internal server error"
  }
  

Mark Attendance

Endpoint: /attendance

Method: POST

Request Body:

• file (multipart/form-data) - Image file for attendance marking.

Response:

• Success (200 OK):

{
    "message": "Attendance marked successfully"
}

• Not Found (404):
  • If no face is found in the uploaded image:

  {
      "message": "No face found"
  }
  

  • If the user is not found in the face recognition model:

  {
      "message": "User not found"
  }
  

  • If the student attendance is already marked for the current date:

  {
      "message": "Student attendance is marked"
  }
  

• Internal Server Error (500):

  {
      "message": "Internal server error"
  }
  

Please note that the API assumes the existence of the following directories:

uploads - for temporary image uploads

images - for storing user images

recognizer - for storing the trained face recognition model

Also, make sure to create an SQLite database file database.db in the db directory before running the application.