Smart Attendance System

The Smart Attendance System automates attendance calculation during lectures using a video uploaded through a user-friendly interface. The system processes the video frames with OpenCV, identifies students using pre-trained face encodings, and calculates attendance based on a threshold of 75% frame presence. It ensures concurrency with threading and provides results in a downloadable Excel format.

Index:

• Smart Attendance System
• Project Details
  • Aim
  • Methodology
  • Features
  • Tech Stack
• Steps to run
• Extra Measures
  • General Instructions
  • Folders
  • Template Rendering Errors
• Contributions
• License
• Contact

🎯 Project Details:

Aim:

To automate attendance calculation efficiently using face recognition, ensuring accuracy and convenience for faculty and students.

Methodology:

• Video is pre-processed and uploaded from the frontend and sent to a Flask server.
• Frames are processed using OpenCV for face recognition.
• Captured face Locations are compared against pre-saved encodings using face distance and face encodings to identify individuals.
• Face encodings are a one-time process completed during registration of new student.
• Attendance is calculated based on frame presence with a threshold of 75%.
• Results are rendered on a webpage and made downloadable in Excel format.

Features:

• Upload video for attendance processing.
• Real-time face recognition with concurrency using threading, locking for consistent read-write operations.
• Faster operation and time-saving due to parallel processing.
• Threshold-based attendance marking for customizable and accurate results.
• Faculty access to detailed results and downloadable attendance records.
• Web-based interface for ease of use.

Tech Stack:

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• Libraries: OpenCV, face_recognition, threading
• Data Formats: JSON, Excel
• Tools: Virtual environment (venv), Python's standard libraries

🚀 Steps to run:

1. Clone the repository:

   git clone --depth 1 https://github.com/Bbs1412/SmartAttendanceSystem

2. Navigate to the project directory:

   cd SmartAttendanceSystem

3. Create a virtual environment:

   python -m venv venv

4. Activate the virtual environment:

   • Windows

     venv\Scripts\activate

   • Linux

     source venv/bin/activate

5. Install the required packages:

   pip install -r requirements.txt

6. Set the environment variables (Optional):

   • Open the .env file and update the environment variables if needed or keep the default values

7. Adding the images:

   • Create a folder named Pics in the project directory

     mkdir Pics

   • Add the images of the people you want to recognize in the Pics folder.

8. Train the models:

   • Open the face_train.py file
   • Scroll down (~ line 73) to update the people list
   • Add all the people you want to recognize in the people list by calling:

     Person(
         reg='registration_number',
         name='Name',
         image='person_name.jpg',      # Image should be in the 'Pics' folder
         display_name='Display Name',  # optional
         pickle_name='person_name.pkl' # optional
     )

   • Run the face_train.py file

     python face_train.py

9. Run the app:

   python app.py

10. Open the browser and go to the following URL (default):

    http://localhost:5000

ℹ️ Extra Measures:

General Instructions:

• Using Conda for environment creation may help ease the process and might make it faster.
• If some errors occurs:
  1. First try deleting the __pycache__ folder
  2. Restart the server and run the application again.

Folders:

• Although the code includes conditions to dynamically check and resolve issues, if an error occurs or for re-verification, please check the following folders:

  Folder	Detail	Checking
  assets/	Should contain all the css, js, images, etc.	Cloned from repository
  Templates/	Should contain all the html files	Cloned from repository
  Pics/	Should contain all the images of people to be recognized	Must be done by user
  Excels/	Empty initially	Dynamically Checked in app.py
  Uploads/	Empty initially	Dynamically Checked in image_processor.py
  Jsons/	Empty initially	Dynamically Checked in face_train.py
  Models/	Empty initially	Dynamically Checked in face_train.py
  .env	(File) Should contain all the environment variables	Cloned from repository

• In case you have changed the env variables, check folders accordingly

Template Rendering Errors:

• Templates/ might give some error on linux, check the 'T' being either capital or small
• Or else, Set the Templates folder path in the app.py file manually:

  app = Flask(__name__, template_folder='Templates')

🤝 Contributions:

Any contributions or suggestions are welcome!

📜 License:

• This project is licensed under the GNU General Public License v3.0
• See the LICENSE file for details.
• You can use the code with proper credits to the author.

📧 Contact:

• Email - [email protected]