The aim of this project ist to perform a face swap on a youtube video almost automatically.
The only step where a human is needed is the quality in step 1.5.
Siraj Raval explains that pretty good in his video:
https://www.youtube.com/watch?v=7XchCsYtYMQ&feature=youtu.be
I am using a desktop PC with one GTX1060 running ubuntu server 16.04.
Training the model for 100.000 epochs takes me about 30 hours.
sudo apt-get install ffmpeg x264 libx264-dev
sudo apt-get install xvfb
wget https://gist.githubusercontent.com/ziadoz/3e8ab7e944d02fe872c3454d17af31a5/raw/ff10e54f562c83672f0b1958a144c4b72c070158/install.sh
sudo sh ./install.sh
pip install -r requirements.txt
Scrape face images of two persons from google images.
python3 1_get_faces.py --name="angela merkel" --limit=500
python3 1_get_faces.py --name="taylor swift" --limit=500
Or scrape face images from youtube videos (e.g. interviews).
python3 1_get_faces_from_yt.py --url="https://www.youtube.com/watch?v=XtHwjrm4sMg" --start=30 --stop=200 --name="siraj raval" --limit=500
python3 1_get_faces_from_yt.py --url="https://www.youtube.com/watch?v=kwRM4PQdheE" --start=60 --stop=179 --name="kal penn" --limit=500
Have a look at the extracted face images in "data/faces/"! There will appear some missextractions, just delete the images that don't fit.
Train the faceswap model with the collected face images.
In this example Merkel's face will be inserted on Taylor Swift.
python3 2_train.py --src="angela merkel" --dst="taylor swift" --epochs=100000
You can download a pre trained model for Angela Swift here
Just place the "models" folder next to the code directory.
Perform facesqp on a youtube video.
The "--start" and "--stop" parameters define in seconds where to clip the video.
Set "--gif" to "True" if you want to export the generated video as gif file.
python3 3_youtube_face_swap.py --url="https://www.youtube.com/watch?v=XnbCSboujF4" --start=0 --stop=60 --gif=False
Donald Trump as Nicolas Cage:
Angela Merkel as Taylor Swift:
Video with better quality
Video with better quality
The first two exampels are trained with images scraped from google, that's why the swapped faces look a bit frozen.
The last one was trained using only two videos of interviews.
You can see that it can transfer facial expressions much better than the ones trained with static images.
Since I am more into audio processing, I would like to transfer the concept of face swapping on music signals.
If you have any suggestions, please let me know.
https://github.com/deepfakes/faceswap
https://github.com/rushilsrivastava/image-scrapers
https://gist.github.com/ziadoz/3e8ab7e944d02fe872c3454d17af31a5
Special thanks goes to Siraj Raval who inspired me to this project!
https://github.com/llSourcell/deepfakes