ShrekNET

Based on https://github.com/datitran/face2face-demo

This is a pix2pix demo that learns from facial landmarks and translates this into a face. A webcam-enabled application is also provided that translates your face to the trained face in real-time.

Getting Started

1. Prepare Environment

# Clone this repo
git clone https://github.com/Heonozis/ShrekNET

# Create the conda environment from file (Mac OSX)
pip install -r requirements.txt

2. Generate Training Data

python generate_train_data.py --file angela_merkel_speech.mp4 --num 400 --landmark-model shape_predictor_68_face_landmarks.dat

Input:

• file is the name of the video file from which you want to create the data set.
• num is the number of train data to be created.
• landmark-model is the facial landmark model that is used to detect the landmarks. A pre-trained facial landmark model is provided here.

Output:

• Two folders original and landmarks will be created.

3. Train Model

# Resize original images
python tools/process.py \
  --input_dir photos/original \
  --operation resize \
  --output_dir photos/original_resized
  
# Resize landmark images
python tools/process.py \
  --input_dir photos/landmarks \
  --operation resize \
  --output_dir photos/landmarks_resized
  
# Combine both resized original and landmark images
python tools/process.py \
  --input_dir photos/landmarks_resized \
  --b_dir photos/original_resized \
  --operation combine \
  --output_dir photos/combined
  
# Split into train/val set
python tools/split.py \
  --dir photos/combined
  
# Train the model on the data
python pix2pix.py \
  --mode train \
  --output_dir face2face-model \
  --max_epochs 200 \
  --input_dir photos/combined/train \
  --which_direction AtoB

For more information around training, have a look at Christopher Hesse's pix2pix-tensorflow implementation.

4. Export Model

1. First, we need to reduce the trained model so that we can use an image tensor as input:

   python reduce_model.py --model-input face2face-model --model-output face2face-reduced-model
   

   Input:

   • model-input is the model folder to be imported.
   • model-output is the model (reduced) folder to be exported.

   Output:

   • It returns a reduced model with less weights file size than the original model.

2. Second, we freeze the reduced model to a single file.

   python freeze_model.py --model-folder face2face-reduced-model
   

   Input:

   • model-folder is the model folder of the reduced model.

   Output:

   • It returns a frozen model file frozen_model.pb in the model folder.

5. Run Demo

python run_webcam.py --source 0 --show 0 --landmark-model shape_predictor_68_face_landmarks.dat --tf-model face2face-reduced-model/frozen_model.pb

Input:

• source is the device index of the camera (default=0).
• show is an option to either display the normal input (0) or the facial landmark (1) alongside the generated image (default=0).
• landmark-model is the facial landmark model that is used to detect the landmarks.
• tf-model is the frozen model file.

Requirements

• Anaconda / Python 3.5
• TensorFlow 1.2
• OpenCV 3.0
• Dlib 19.4

Copyright

See LICENSE for details. Copyright (c) 2017 Dat Tran.