A set of tools and utils to operate face swapping and explore creative usage of derived artifacts.
Install via (requires python > 3.4)
python setup.py [develop]
Notice that the project depends on another repository of mine for reusable utils and modules. This dependency should be installed via the previous command, but if you encounter any problem you can run
git clone https://github.com/5agado/data-science-learning.git
cd data-science-learning
pip install .
Configuration settings and models location should be specified in models.cfg.
scripts.sh provides a simplified interface to various operations and setup based on video conversion. Requires a workdir containing a video file (i.e. .gif, .webm, mp4) and requires ffmpeg to be installed on the machine.
Init dir (required step). Creates required folders
./scripts.sh -w ~/workdir/path -i
Extract faces (relies on params set in models.cfg)
./scripts.sh -w ~/workdir/path -e
Swap faces and create video (relies on params set in models.cfg)
./scripts.sh -w ~/workdir/path -s
As an alternative to the calling scripts you can rely directly on Python commands installed during initial setup.
Faces Extract
extract -i dir/to/video -o output/dir/faces
Faces Swap
deep_swap -i dir/to/video -o output/video/path -A -model_name base_gan -model_version v0
You can train a new model by using train.py. See train.py -h for more info.
Otherwise I would suggest to rely on Google Colaboratory, which provides a free GPU environment accessible via Jupyter. See my related notebook which installs all the required dependencies and then provides basic interface to the training functions.
This code is colossally based on shaoanlu repo and work. I tried to give it a bit more structure, and add comments and pointers for new comers to better understand what exactly is going on.
Other useful references for code and techniques for pre and post-processing phases:
- Face Swap using OpenCV
- How to install dlib
- Detect eyes, nose, lips, and jaw with dlib, OpenCV, and Python
- Face Alignment with OpenCV and Python
You can also build a Docker image (complete of all requirement code and utils) via
docker build -t faceswap .
Start image and exec bash
docker run -it faceswap bash
- align face -> feed to generator -> detect face in generated image -> align back using landmarks
- log some stats (frames w w/o faces, faces size, conversion size, execution time of operations)
Released under version 2.0 of the Apache License.