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Smile-CNN is a real-time face and smile detection project made using Flask, Tailwind CSS, OpenCV, AJAX and a trained CNN. It was done during the ML internship conducted by IIT Bhubaneswar.

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Smile-CNN

Realtime Face and Smile Detection

Webpage Screenshot


This project was done in the Machine Learning Internship conducted by Indian Institute of Technology Bhubaneswar.



Table of Contents

  1. Features
  2. Getting Started
    1. Prerequisites
    2. Installation
  3. Running the Flask App
  4. Usage
  5. Contributing
  6. License



Features

  • Real-time face detection using haarcascade
  • Real-time smile detection using Convolution Neural Networks (CNN)
  • Built with Flask web framework and Tailwind CSS for styling
  • AJAX GET XMLHttpRequest for dynamic real-time updates
  • OpenCV for webcam access and image processing
  • Custom CNN model for smile predictions trained on a dataset

Getting Started

Prerequisites

  • Python 3.x
  • Virtualenv (optional, but recommended)

Installation

  1. Clone the GitHub repository:
    git clone https://github.com/IshanMehta115/Smile-CNN.git
  2. (Optional) Set up a virtual environment (recommended):
    virtualenv venv
  3. Install the required dependencies:
    pip install -r requirements.txt

Running the Flask App

Execute the following command the run the app: python app.py
Visit http://localhost:5000 in your web browser to access the application.

Usage

  • Allow the application to access your webcam when prompted.
  • The application will detect your face and predict whether you are smiling or not in real-time.
  • Enjoy the interactive real-time smile detection experience!

Contributing

I welcome contributions from the open-source community! If you have any suggestions, improvements, or bug fixes, please feel free to create a pull request.

License

This project is licensed under the MIT License.

About

Smile-CNN is a real-time face and smile detection project made using Flask, Tailwind CSS, OpenCV, AJAX and a trained CNN. It was done during the ML internship conducted by IIT Bhubaneswar.

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