Reading an image and prediction the emotion like sad, happy or so Setup Instructions Clone the Repository:
bash Copy code git clone https://github.com/your-username/emotion-detection.git cd emotion-detection Install Dependencies:
Python 3.x Required Python packages (opencv-python, numpy, keras) Run the Code:
Modify main.py or integrate the functions into your project. Example usage: python Copy code
classifier = loading_model('path/to/model.h5') face_classifier = cascade_clasify('path/to/haarcascade_frontalface_default.xml')
emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
detect_classify_emotion('path/to/image.jpg', classifier, face_classifier, emotion_labels) File Structure main.py: Example usage of the functions. emotion_detection.py: Contains the main functions for emotion detection and classification. utils.py: Utility functions (e.g., loading models, resizing images). haarcascade_frontalface_default.xml: Haar Cascade XML file for face detection. model.h5: Pre-trained CNN model for emotion classification. Contributing Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or a pull request.
License This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments This project utilizes Keras for deep learning-based emotion classification. OpenCV is used for face detection using Haar Cascade. Feel free to customize this README file further based on your specific project details, additional features, or any special instructions. Replace placeholders like your-username, path/to/, and add more detailed instructions if needed. This structure should provide a clear overview of your project for GitHub users visiting your repository.