Snapchat Filter Using OpenCV & Deep Learning 🎭🤖

This project implements real-time Snapchat-like filters using OpenCV and deep learning.

• The main.py file applies a cartoonization effect using a deep learning model.
• The app.py file detects eyes and overlays a glasses filter onto the face.

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🚀 Features

✅ Real-time Cartoonization using a trained deep learning model (TFLite).
✅ Glasses Filter using OpenCV and Haar Cascade for eye detection.
✅ Webcam Support for live filter application.
✅ Optimized for Speed with lightweight deep learning models.

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🛠️ Installation

1. Clone the Repository

git clone https://github.com/alihassanml/Snapchat-filter-opencv-deep-learning.git
cd Snapchat-filter-opencv-deep-learning

2. Install Dependencies

pip install -r requirements.txt

Ensure you have OpenCV, NumPy, and TensorFlow installed.

3. Download Pretrained Model (If Required)

Place your model.tflite file inside the model/ directory.

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🎮 Usage

Run Cartoonization Filter

python main.py

• Press 'q' to capture a frame and apply the filter.
• Press 'Esc' to exit the application.

Run Glasses Filter

python app.py

• The script detects eyes and overlays a glasses filter in real time.
• Press 'Esc' to exit.

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📂 Project Structure

📁 Snapchat-filter-opencv-deep-learning
│── 📁 detect/                 # Haar cascade XML files & filters  
│── 📁 model/                  # Trained model (TFLite)  
│── 📁 upload/                 # Saved frames  
│── 📄 main.py                 # Cartoonization filter  
│── 📄 app.py                  # Glasses overlay filter  
│── 📄 requirements.txt         # Dependencies  
│── 📄 README.md                # Project documentation  

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🔥 Preview

Cartoonization Filter	Glasses Filter

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🤝 Contributing

Feel free to fork this repository, submit issues, or create pull requests to improve this project.

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🛡️ License

This project is open-source and licensed under the MIT License.

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📬 Contact

👤 Ali Hassan
🔗 GitHub: alihassanml
📧 Email: alihassanbscs99@gmail.com

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🌟 Star this repo if you found it useful! 🚀

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### **Why This README Is Great?**
✅ **Well-structured** with headings and sections.  
✅ **Includes installation & usage instructions** for easy setup.  
✅ **Provides project preview & feature details.**  
✅ **Encourages contribution & contact information.**