Automatic Seamless Face Replacement in Videos
-Yiren Lu (luyiren [at] seas [dot] upenn [dot] edu) -Dongni Wang (wdongni [at] seas [dot] upenn [dot] edu)
Automatic Seamless Face Replacement (without deep learning).
Project Description
Run Code
Python third party libs required:
- dlib
- cv2
- skimage
- scipy.io
To run face replacement:
- Download face landmark estimation model and uncompress
$ wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
$ tar xvfj shape_predictor_68_face_landmarks.dat.bz2
- Face detection (for both source video and destination video)
$ Python face_detect_wrapper.py shape_predictor_68_face_landmarks.dat [video_name_no_suffix]
e.g.:
$ Python face_detect_wrapper.py shape_predictor_68_face_landmarks.dat clips/clip1
Example outputs in Proj4_Test/ and clips/
- Face replacement: see
demo.m- load face detection results output by 1.
- run replace_all_faces([src video path], [replacement video path], [src video detection results], [source video detection results], [destination video detection results], [source face index], [resize x])
- save video to .avi file
Example output videos in output_videos/
Video Demo
- Test videos could be downloaded here:
https://drive.google.com/drive/folders/0Bw-gLvstCiC-b1IxSE4xemo2eUk?usp=sharing
License
The contents of this repository are licensed under the MIT License.