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
# Create the conda environment from file (Mac OSX)
conda env create -f environment.yml
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:
fileis the name of the video file from which you want to create the data set.numis the number of train data to be created.landmark-modelis the facial landmark model that is used to detect the landmarks. A pre-trained facial landmark model is provided here.
Output:
- Two folders
originalandlandmarkswill be created.
If you want to download my dataset, here is also the video file that I used and the generated training dataset (400 images already split into training and validation).
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
-
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-modelInput:
model-inputis the model folder to be imported.model-outputis the model (reduced) folder to be exported.
Output:
- It returns a reduced model with less weights file size than the original model.
-
Second, we freeze the reduced model to a single file.
python freeze_model.py --model-folder face2face-reduced-modelInput:
model-folderis the model folder of the reduced model.
Output:
- It returns a frozen model file
frozen_model.pbin the model folder.
I have uploaded a pre-trained frozen model here. This model is trained on 400 images with epoch 200.
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:
sourceis the device index of the camera (default=0).showis an option to either display the normal input (0) or the facial landmark (1) alongside the generated image (default=0).landmark-modelis the facial landmark model that is used to detect the landmarks.tf-modelis the frozen model file.
Example:
Requirements
Acknowledgments
Kudos to Christopher Hesse for his amazing pix2pix TensorFlow implementation and Gene Kogan for his inspirational workshop.