# 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:

- `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](http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2).

Output:

- Two folders `original` and `landmarks` will be created.

If you want to download my dataset, here is also the [video file](https://dl.dropboxusercontent.com/s/2g04onlkmkq9c69/angela_merkel_speech.mp4) that I used and the generated [training dataset](https://dl.dropboxusercontent.com/s/pfm8b0yogmum63w/dataset.zip) (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](https://github.com/affinelayer/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.
    
I have uploaded a pre-trained frozen model [here](https://dl.dropboxusercontent.com/s/rzfaoeb3e2ta343/face2face_model_epoch_200.zip). 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:

- `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.

Example:

![example](example.gif)

## Requirements
- [Anaconda / Python 3.5](https://www.continuum.io/downloads)
- [TensorFlow 1.2](https://www.tensorflow.org/)
- [OpenCV 3.0](http://opencv.org/)
- [Dlib 19.4](http://dlib.net/)

## Acknowledgments
Kudos to [Christopher Hesse](https://github.com/christopherhesse) for his amazing pix2pix TensorFlow implementation and [Gene Kogan](http://genekogan.com/) for his inspirational workshop. 

## Copyright

See [LICENSE](LICENSE) for details.
Copyright (c) 2017 [Dat Tran](http://www.dat-tran.com/).