The Emotion Detector project is a machine learning-based application that analyzes facial expressions in images or videos to detect and classify human emotions such as happiness, sadness, anger, surprise, fear, and neutrality.
In today's digital age, understanding human emotions from visual cues has become increasingly important in various domains such as marketing, healthcare, and human-computer interaction. The Emotion Detector project aims to provide a tool that can accurately recognize and classify emotions from facial expressions in images or videos.
- Real-time emotion detection from live camera feed or video files.
- Support for detecting multiple faces and emotions simultaneously.
- Pre-trained deep learning models for robust emotion classification.
- User-friendly interface for easy interaction and visualization of results.
- Customizable parameters for fine-tuning the detection process.
To use the Emotion Detector project, follow these steps:
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Clone the repository: git clone https://github.com/your-username/emotion-detector.git
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Navigate to the project directory: cd emotion-detector
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Install the required dependencies: pip install -r requirements.txt
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Run the Emotion Detector application: python emotion_detector.py
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Follow the on-screen instructions to select input source (camera or video file) and start the emotion detection process.
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View the real-time emotion predictions and corresponding visualizations.
The Emotion Detector project utilizes the following technologies and frameworks:
- Python: The core programming language used for development.
- OpenCV: An open-source computer vision and machine learning software library for image and video processing tasks.
- TensorFlow: An open-source machine learning framework for building and training deep neural networks.
- Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.
- NumPy: A fundamental package for scientific computing with Python, used for numerical operations and array manipulation.
- Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.
- scikit-learn: A machine learning library in Python, used for data preprocessing and evaluation of machine learning models.
These technologies are combined to create a powerful and efficient Emotion Detector application that can accurately classify human emotions from facial expressions.
Contributions to the Emotion Detector project are welcome and appreciated! If you would like to contribute, please follow these guidelines:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and ensure they adhere to the project's coding style.
- Test your changes thoroughly.
- Submit a pull request, describing the changes you made and why they are necessary.
The Emotion Detector project is licensed under the MIT License.