This Python application utilizes Hugging Face's InsightFace library to swap faces between two images. It leverages pre-trained models for face detection and face swapping, allowing for easy experimentation.
Features:
Detects faces in two separate images.
Swaps the detected faces between the images.
Optionally displays the original and swapped images for visual verification.
Requirements:
Python (tested with version 3.x)
Hugging Face Transformers library
InsightFace library (installable via pip install insightface)
OpenCV-Python (for image processing)
matplotlib (for plotting)
NumPy (for numerical operations)
Installation:
Create a virtual environment (recommended) to isolate project dependencies.
Install the required libraries using pip:
Bash
pip install -r requirements.txt
Usa el código con precaución.
Note: Create a requirements.txt file in your project directory with the list of requirements mentioned above.
Usage:
Save two images (image1.jpg and image2.jpg) in the same directory as your script (app.py).
Run the script from the terminal:
Bash
python app.py
Usa el código con precaución.
Explanation of the Code (app.py):
The provided code defines functions for:
Initializing the FaceAnalysis application and face swapper model.
Reading images and detecting faces.
Swapping faces and optionally displaying the results.
Further Customization:
Modify the swap_n_show function to customize the plotting behavior or add functionalities like saving the swapped images.
Explore other functionalities offered by InsightFace for advanced face manipulation tasks.
Contributing:
Feel free to submit pull requests for bug fixes or improvements to this code.
License:
[Specify the license you want to use for your code, e.g., MIT License]
