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https://github.com/bbs1412/smartattendancesystem

Smart Attendance System to automate the attendace calculation in lectures using Python
https://github.com/bbs1412/smartattendancesystem

attendance-monitoring attendance-system face-recognition opencv parallel-computing python

Last synced: 7 months ago
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Smart Attendance System to automate the attendace calculation in lectures using Python

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README 

          
# 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](#smart-attendance-system)

- [Project Details](#-project-details)

    - [Aim](#aim)

    - [Methodology](#methodology)

    - [Features](#features)

    - [Tech Stack](#tech-stack)

- [Steps to run](#-steps-to-run)

- [Extra Measures](#ℹī¸-extra-measures)

    - [General Instructions](#general-instructions)

    - [Folders](#folders)

    - [Template Rendering Errors](#template-rendering-errors)

- [Contributions](#-contributions)

- [License](#-license)

- [Contact](#-contact)



## đŸŽ¯ 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.

+ A distributed computing implementation of this project is here: [![Distributed Version Repository](https://img.shields.io/badge/Bbs1412/-DistributedAttendanceSystem-ff8800.svg?logo=github)](https://github.com/Bbs1412/DistributedAttendanceSystem)



### Features:

+ Web-based interface for ease of use.

  ![Home Page - Dark Mode](Docs/home_dark.png)

+ Record and Upload video for attendance processing.

  ![Recording](Docs/recording.png)

+ Proper asynchronous handling ensures seamless request processing and smooth UI transitions during face recognition.

  ![Processing](Docs/processing.png)

+ 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.

  ![Results](Docs/result_dark.png)

+ Results are provided in downloadable Excel format.

  ![Excel](Docs/excel.png)

+ Light and Dark mode for better accessibility.

  ![Home Page - Light Mode](Docs/home_light.png)

  ![Results - Light Mode](Docs/result_light.png)



### 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:

    ```bash

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

    ```

    

1. Navigate to the project directory:

    ```bash

    cd SmartAttendanceSystem

    ```



1. Create a virtual environment:

    ```bash

    python -m venv venv

    ```



1. Activate the virtual environment:

    - Windows

        ```bash

        venv\Scripts\activate

        ```

    - Linux

        ```bash

        source venv/bin/activate

        ```



1. Install the required packages:

    ```bash

    pip install -r requirements.txt

    ```



1. Set the environment variables (Optional):

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



1. Adding the images:

    - Create a folder named `Pics` in the project directory

        ```bash

        mkdir Pics

        ```

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

    

1. 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:

        ```Python

        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

        ```bash

        python face_train.py

        ```



1. Run the app:

    ```bash

    python app.py

    ```



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

    ```URL

    http://localhost:5000

    ```



## ℹī¸ Extra Measures:



### General Instructions:

- If some errors occurs:

    1. First try deleting the `__pycache__` folder

    1. Restart the server and run the application again.

- Using Conda for environment creation may help ease the process and might make it faster.



### 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:

    ```Python

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

    ```



## 🤝 Contributions:

   Any contributions or suggestions are welcome! 



## 📜 License: 

[![Code-License](https://img.shields.io/badge/License%20-GNU%20--%20GPL%20v3.0-blue.svg?logo=GNU)](https://www.gnu.org/licenses/gpl-3.0)

- This project is licensed under the `GNU General Public License v3.0`

- See the [LICENSE](LICENSE) file for details.

- You can use the code with proper credits to the author.



## 📧 Contact:

- **Email -** [bhushanbsongire@gmail.com](mailto:bhushanbsongire@gmail.com)