Facial Emotion Detection

This project is a Python-based application that captures video from the webcam and detects facial emotions using a pre-trained model from the fer package. Detected emotions are displayed on the video feed with bounding boxes and confidence scores.

Table of Contents

• Features
• Prerequisites
• Installation
• Running the Application
• Usage
• Notes

Features

• Real-time facial emotion detection using a webcam.
• Displays bounding boxes around detected faces with the dominant emotion and confidence score.

Prerequisites

• Python 3.7+
• OpenCV
• fer package (Facial Expression Recognition)

Installation

1. Clone the repository:

   git clone https://github.com/hija-happy/facial-emotion-detection-using-opencv.git
   cd facial-emotion-detection-using-opencv

2. Create a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install the required libraries:

   pip install opencv-python-headless numpy fer

Running the Application

1. Ensure your webcam is connected and working.
2. Run the application:

   python emotion_detection.py

Usage

• The application will open a video feed window from your webcam.
• It will detect faces in real-time and draw bounding boxes around them.
• The detected emotion and its confidence score will be displayed above each face.
• Press the 'q' key to exit the application.

Notes

• Ensure that your webcam is properly connected and configured.
• The application uses the fer package for emotion detection and OpenCV for video capture and processing.
• TensorFlow is a dependency of the fer package, even though it is not directly used in this application. It is required for the emotion detection functionality.