FaceSwap - A Face Swapping Application

Table of Contents

• Description
• Tech Stack
• Installation
• Key Features

Description

FaceSwap is an application designed for swapping faces in images using deep learning and image processing techniques. The application provides both sequential and multithreading processing for optimized performance. The user interface is built with Tkinter and includes a demo UI to visualize the face-swapping process.

The application leverages OpenCV and Dlib for face detection and alignment, ensuring precise and seamless face swaps. Additionally, it supports parallel processing with multithreading to handle multiple image pairs efficiently.

Tech Stack

• Programming Language: Python
• Libraries: OpenCV, Dlib, NumPy, Tkinter
• Parallel Processing: ThreadPoolExecutor for multithreading
• UI Framework: Tkinter

Installation

Follow these steps to set up the project locally:

1. Clone the repository

git clone https://github.com/swiftmg0d/FaceSwapApplication.git

2. Navigate to the project directory

cd FaceSwapApplication

3. Install dependencies

Ensure you have Python installed, then install the required libraries:

pip install opencv-python dlib numpy

4. Download the required Dlib model

The application requires the shape_predictor_68_face_landmarks.dat file for facial landmark detection. Download it from Dlib's official website and place it in the assets/ directory.

5. Run the application

To start the FaceSwap application, navigate to the faceswap directory:

cd demo/faceswap

To run the application, click on:

demo_application.exe

Key Features

• Face Detection: Uses Dlib's pre-trained model for detecting and aligning faces.
• Face Landmarking: Extracts key facial points for seamless face blending.
• Sequential & Multithreading: Supports both single-threaded and multithreaded processing for efficient face swapping.
• User Interface: Built with Tkinter, offering an easy-to-use demo UI.
• Parallel Processing: Uses ThreadPoolExecutor to process multiple face swaps simultaneously.
• Error Handling: Robust exception handling for missing faces or processing errors.
• High-Quality Output: Optimized blending and color correction for realistic results.

Parallel and Sequential Processing Code

The parallel and sequential processing code are located in:

source-code/faceswap/

• single.py: Contains the sequential face-swapping logic.
• multi.py: Contains the multithreading implementation for faster processing.

This application is ideal for fun projects, research, and image editing. Enjoy seamless and efficient face swapping with FaceSwap!