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Check out the documentation for more information.
Pokémon Doppelganger
Description:
"Pokémon Doppelganger" is an innovative application that employs facial recognition technology and machine learning to find the Pokémon species most closely resembling a user's facial features. By analyzing facial embeddings extracted from user images and comparing them with pre-computed embeddings of Pokémon species, the system identifies the closest match using cosine similarity.
Features:
-> Facial Feature Extraction: Utilizes a pre-trained VGG16-based model to extract facial embeddings from user-provided images.
-> Cosine Similarity: Computes cosine similarity between user embeddings and pre-computed embeddings of Pokémon species to find the closest match.
-> Dynamic Data Loading: Loads Pokémon embeddings from a CSV file, enabling easy updates and scalability.
-> Error Handling: Handles scenarios where no faces are detected in the input image gracefully, providing informative messages to the user.
Installation:
- Clone the repository: git clone [https://github.com/LaKsHiT013/Pokemon-Doppelganger]
- nstall the required Python packages: pip install -r requirements.txt
- Ensure proper setup of OpenCV and TensorFlow libraries for image processing and machine learning functionalities.
Usage:
- Run the pokemon_doppelganger.py script.
- Provide the path to the image containing your face when prompted.
- The system will analyze the image, extract facial features, compare them with Pokémon embeddings, and output the most similar Pokémon species.
Disclaimer:
This project is developed for educational and entertainment purposes only. It is not affiliated with or endorsed by The Pokémon Company.