This is a pytorch based toolkit that can be used for face replacement. It is used for research and learning.
This toolkit allows you to use pytorch to perform face replacement. It offers simple code that can grab two videos and replace the faces from one person in video A into the faces from another person in video B.
I built this toolkit while learning pytorch. I did not build my own face predictor to detect the faces, for that I used codeniko's landmarks_81 predictor.
https://github.com/codeniko/shape_predictor_81_face_landmarks
In this toolkit I built pytorch scripts that cut videos into list of images that can be used for training to "learn" two people's face landamrks. Then can replace one persons face into the target person's face respecting face orientation and facial expression. It does this by on each image replacing the target person's face with the best approximate face it finds from the source person's face. After all the images of the target video are independently face-replaced, there is a pytorch script that puts all the images together into a video.
Below is a sample result:
from:
The individual image results often look belivable. If lightning and color contrasts are alike that's also helpful, since this face replacement merely finds the best face to replace but doesn't adjust any other contrast or colors. For video is not good at all because there is often too much variation from one face to the next and thus the face movements do not look natural.
I used a Mac OS so to install dlib in Mac OS, I used these fantastic instructions: https://www.learnopencv.com/install-dlib-on-macos/
This environment will support running the face landmark script that I leveraged, create_landmark_dataset_81.py
workon facecourse-py3
Install PyAV for torchvision io video. Used instructions in: https://github.com/mikeboers/PyAV#installation
There may be a bug in torchvision related to the audio code. Since I didn't use audio, I overwrote that file to not do audio and avoid the error. I did (but you may not need to):
sed -i 's/if container.streams.audio:/if False:/g' /Users/USER_NAME/anaconda2/lib/python2.7/site-packages/torchvision/io/video.py
To extract the images labeled lexicographically in order run
python video_read.py -f data/videos/vid.mp4 -o data/faces -p faces_name -m 100000 -r 10000
Be sure to set the arguments for both videos depending on the results. Particularly different videos may have different number of points per frame.
To learn the face landmarks you need to enter the dlib-enabled python environment. You need to create landmarks for both sets of faces. So open a separate terminal and run:
workon facecourse-py3
python create_landmark_dataset_81.py -i data/faces_person_1
Once the landamark csv files are created, it's time to run the face replacer script:
python replace_faces.py -s data/faces/person_a/ -t data/faces/person_b/ -o output/dir/ -f result
The resulting images will be created in the output/dir/ with names like, result_100000, result_100001, etc depending on your args.
Once the resulting images are done you could put them together in a video:
python write_video.py -s input_dir -o output_dir -f video_name
This will create a video like video_name.mp4. As you would see the individual images may look better than the resulting video.
The other scripts not referenced here so far (like crop_face and show_landmarks) are here just for fun and are not needed for the face replacement.