darkk-pvt/avmod

AVmod is a Audiovisual modulator developed with Deep Fake

AVmod

AVmod is a Audiovisual modulator developed as Project for Third year

• AVmod uses LSGAN and cyclic GAN to achieve the functionality of face swaping and voice modulation

Contributors

• Himanshu
• Jai
• Karan
• Sagar

Description

• MTCNN_face_detection.ipynb

  • File-1
  • Responsible for frame extraction, face detection/alignment on input video.
  • Detected faces are saved in ./faces/raw_face for non-aligned result and ./faces/aligned_faces for aligned results.
  • Crude eyes binary masks saved in ./faces/binary_mask_eye.

• prep_binary_masks.ipynb

  • File-2
  • For datapreprocessing.
  • Create binary masks using aligned_faces and save results in ./binary_masks/faceA_eyes and ./binary_masks/faceB_eyes folder.
  • Require face_alignment package.

• train.ipynb

  • File-3
  • Used for model training.
  • Require additional training images generated through prep_binary_masks.ipynb
  • Save models in ./model directory.
  • Save backup models in ./model/backup_iter{iteration_num}.

• video_conversion.ipynb

  • File-4
  • Used for video conversion based on training done in train.ipynb
  • Use five-points landmarks for face alignment.

Training Data

• Pick images that are stored in ./facesA/aligned_faces and ./facesB/aligned_faces for each target.
• Resizing of image will be performed to make images 256x256 for training.
• Training will happen for 40000 iterations (default) can be increased to 80000 and more according to requirement.

Requirements

• python 3.6.4
• tensorflow r1.15.2
• keras r2.1.5
• opencv
• keras_vggface
• moviepy
• face_alignment
• pathlib
  

Guide for Installations of Dependencies

• Create a new Virtual environment with python v3.6.4
• Run command -

pip install -r requirements.txt

Todo

• Functionality for voice modulation
• Increase face swapping area
• Binary Mask for mouth
• Interface for easy access to training and conversion
• GPU support for py files (CUDA)

Acknowledgment

Code borrowed from tjwei and keras-contrib. The generative network is adopted from CycleGAN. Weights and scripts of MTCNN are from FaceNet.