Commit

Author: carlthecoder123123

README.md

@@ -1,77 +1,113 @@
-# 🎭 Real-Time Emotion Detector from Camera Face Input
+# Realtime Emotion Detector 😃
 
-A deep learning-powered real-time emotion detection system that classifies facial expressions captured from a live webcam feed into seven distinct emotions.
+![Realtime Emotion Detector](https://img.shields.io/badge/Download%20Releases-%20%F0%9F%93%88-4CAF50?style=flat-square&logo=github)
 
----
+Welcome to the **Realtime Emotion Detector** repository! This project leverages deep learning techniques to analyze facial expressions captured through a webcam. It classifies these expressions into seven distinct emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise. Using a Convolutional Neural Network (CNN) model alongside OpenCV for real-time face detection, this system aims to provide a seamless experience in emotion recognition.
 
-## 📌 Project Overview
+## Table of Contents
 
-This application uses a Convolutional Neural Network (CNN) trained on facial expression data to detect and classify human emotions in real-time. The webcam captures a video stream, detects faces, and predicts the emotion being expressed—displaying it with a label overlay.
+- [Introduction](#introduction)
+- [Features](#features)
+- [Technologies Used](#technologies-used)
+- [Installation](#installation)
+- [Usage](#usage)
+- [How It Works](#how-it-works)
+- [Contributing](#contributing)
+- [License](#license)
+- [Acknowledgments](#acknowledgments)
 
-### 🔍 Detectable Emotions:
-- 😠 **Angry**
-- 🤢 **Disgust**
-- 😨 **Fear**
-- 😀 **Happy**
-- 😐 **Neutral**
-- 😢 **Sad**
-- 😲 **Surprise**
+## Introduction
 
----
+Understanding human emotions is crucial in various fields such as psychology, marketing, and human-computer interaction. The **Realtime Emotion Detector** offers a way to analyze facial expressions quickly and accurately. This project combines machine learning with computer vision to provide real-time feedback on emotional states.
 
-## 🧠 Features
+## Features
 
-- ✅ Real-time face detection using OpenCV
-- ✅ Emotion prediction using trained CNN model
-- ✅ Visual label overlay on detected faces
-- ✅ Clean preprocessing pipeline (grayscale, resize, normalize)
-- ✅ Customizable and extendable for more use-cases
+- **Real-time Detection**: Detects emotions in real-time using webcam input.
+- **Multi-Emotion Classification**: Classifies expressions into seven categories.
+- **User-Friendly Interface**: Easy to set up and use.
+- **Open Source**: Available for anyone to use, modify, and distribute.
 
----
+## Technologies Used
 
-## 🏗️ Model Architecture
+This project utilizes the following technologies:
 
-- 4 Convolutional Layers (ReLU activation + MaxPooling)
-- Dropout layers for regularization
-- Fully Connected Dense layers
-- Output layer with Softmax (7 classes)
+- **Python**: The primary programming language.
+- **TensorFlow**: For building and training the CNN model.
+- **Keras**: A high-level neural networks API.
+- **OpenCV**: For real-time face detection and image processing.
+- **NumPy**: For numerical operations.
+- **Matplotlib**: For data visualization.
+- **Jupyter Notebook**: For interactive coding and visualization.
 
----
+## Installation
 
-## 🧰 Tech Stack
+To get started with the **Realtime Emotion Detector**, follow these steps:
 
-| Tool | Purpose |
-|------|---------|
-| Python 3.10 | Core Programming Language |
-| TensorFlow / Keras | Deep Learning Framework |
-| OpenCV | Real-time Computer Vision |
-| NumPy | Numerical Computation |
-| Matplotlib | Visualization (optional) |
-| Jupyter Notebook | Model Training & Development |
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/carlthecoder123123/Realtime-emotion-detector.git
+   cd Realtime-emotion-detector
+   ```
 
----
+2. **Install Dependencies**:
+   Make sure you have Python installed. Then, run:
+   ```bash
+   pip install -r requirements.txt
+   ```
 
-## 📦 Dataset
+3. **Download the Model**:
+   You can download the pre-trained model from the [Releases section](https://github.com/carlthecoder123123/Realtime-emotion-detector/releases). Ensure to download and execute the necessary files.
 
-**FER-2013**: Facial Expression Recognition dataset  
-🔗 [Kaggle Link](https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset)
+## Usage
 
-- 48x48 grayscale images
-- 35,900 labeled facial images
-- 7 emotion classes
+To run the emotion detector, execute the following command in your terminal:
 
----
+```bash
+python emotion_detector.py
+```
 
-## 🚀 Installation & Setup
+This will launch the webcam feed and start detecting emotions in real-time. 
 
-```bash
-# Clone the repository
-git clone https://github.com/SusmoyNath/Realtime-emotion-detector.git
-cd Realtime-emotion-detector
+## How It Works
+
+The **Realtime Emotion Detector** works through the following steps:
+
+1. **Face Detection**: OpenCV captures the video feed and detects faces in real-time.
+2. **Preprocessing**: Detected faces are resized and normalized for the CNN model.
+3. **Emotion Classification**: The CNN model predicts the emotion based on the processed face.
+4. **Display Results**: The detected emotion is displayed on the screen.
+
+### Model Architecture
+
+The CNN model consists of several layers:
+
+- **Convolutional Layers**: Extract features from the input images.
+- **Pooling Layers**: Reduce the dimensionality of the feature maps.
+- **Fully Connected Layers**: Classify the features into the respective emotions.
+
+### Training the Model
+
+If you want to train your own model, you can use the training scripts provided in the `training` directory. Ensure you have a labeled dataset of facial expressions for effective training.
+
+## Contributing
+
+We welcome contributions! If you'd like to improve this project, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch (`git checkout -b feature-branch`).
+3. Make your changes.
+4. Commit your changes (`git commit -m 'Add some feature'`).
+5. Push to the branch (`git push origin feature-branch`).
+6. Open a Pull Request.
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
+
+## Acknowledgments
 
-# (Optional) Create a virtual environment
-python3 -m venv env
-source env/bin/activate  # or .\env\Scripts\activate on Windows
+- **OpenCV**: For providing powerful tools for image processing.
+- **TensorFlow and Keras**: For making deep learning accessible.
+- **NumPy and Matplotlib**: For numerical operations and data visualization.
 
-# Install dependencies
-pip install -r requirements.txt
+For more information and updates, visit the [Releases section](https://github.com/carlthecoder123123/Realtime-emotion-detector/releases).