This is supplementary code for the blog post here
The main motivation of this work is to use Variational Autoencoder model to embed unseen faces into the latent space of pre-trained single actor-centric face expressions. The datasets used in described experiments are based on youtube videos passed through openface feature extraction utility
short demo video available here
- python v2.7.6
- numpy v1.11.1
- scipy v0.13.3
- h5py v2.6.0
- Pillow v2.3.0
- progressbar v2.3
- argparse v1.2.1
- tensorflow 0.9.0
- prettytensor 0.6.2
for help try
$ python vae.py -h
usage: vae.py [-h] {train,sample,reconstruct} ...
positional arguments:
{train,sample,reconstruct}
train train VAE model [vae.py train -h]
sample sample from existing model [vae.py sample -h]
reconstruct reconstruct images based on existing model [vae.py
reconstruct -h]
optional arguments:
-h, --help show this help message and exitThe tool implements 3 high level commands:
- train
$ python vae.py train -h
vagrant@vagrant-ubuntu-trusty-64:~/tflow/VAE_OOP$ python vae.py train -h
usage: vae.py train [-h] [--hdf5-dataset-name] [--batch-size] [--epochs]
[--learning-rate] [--latent-dim] [--input-width]
[--input-height]
input output
positional arguments:
input Path of HDF5 data file
output Output tf model dir
optional arguments:
-h, --help show this help message and exit
--hdf5-dataset-name Name of dataset in hdf5
--batch-size Batch size
--epochs Number of epochs to run
--learning-rate Learning rate
--latent-dim latent variable dimensionality
--input-width Width of input images
--input-height Height of input imagesthe most important detail here is format of input data. Input is expected to be HDF5 file containing dataset that is 4 dimensional tensor of size:
nr_of_objects x width x height x 3
the output directory by default will be populated with tensorflow saved model + metadata files
- sample
when your model is fully trained you can use sample command to draw points from latent space and walk from one to another randomly
$ python vae.py sample -h
usage: vae.py sample [-h] [--latent-dim] [--input-width] [--input-height]
[--output--dir]
input
positional arguments:
input Path of tensorflow model dir
optional arguments:
-h, --help show this help message and exit
--latent-dim latent variable dimensionality
--input-width Width of input images
--input-height Height of input images
--output--dir Output dir where png files are stored- reconstruct
If you want to use existing model to reconstruct input images use reconstruct command
$ python vae.py reconstruct -h
usage: vae.py reconstruct [-h] [--latent-dim] [--input-width] [--input-height]
input inputdir output
positional arguments:
input Path of tensorflow model dir
inputdir Path directory where input images are stored
output Output directory where reconstructions will be stored
optional arguments:
-h, --help show this help message and exit
--latent-dim latent variable dimensionality
--input-width Width of model input images
--input-height Height of model input images