face2face-demo

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 git@github.com:datitran/face2face-demo.git

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.

Output:

Two folders original and landmarks will be created.

3. Train Model

# Clone the repo from Christopher Hesse's pix2pix TensorFlow implementation
git clone https://github.com/affinelayer/pix2pix-tensorflow.git

# Move the original and landmarks folder into the pix2pix-tensorflow folder
mv face2face-demo/landmarks face2face-demo/original pix2pix-tensorflow/photos

# Go into the pix2pix-tensorflow folder
cd pix2pix-tensorflow/

# 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 --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.
• landmark-model is the facial landmark model that is used to detect the landmarks.
• tf-model is the frozen model file.

Example:

• Add example image here

Requirements

• Anaconda / Python 3.5
• TensorFlow 1.0
• OpenCV 3.0

Acknowledgments

Kudos to Christopher Hesse for his amazing pix2pix Tensorflow implementation and Gene Kogan for his inspirational workshop.

Copyright

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