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faceit_live3

This is an update to http://github.com/faceit_live using first order model by Aliaksandr Siarohin to generate the images. This model only requires a single image, so no training is needed and things are much easier.

Setup

Requirements

This has been tested on Ubuntu 18.04 with a Titan RTX/X GPU. You will need the following to make it work:

Linux host OS
NVidia fast GPU (GTX 1080, GTX 1080i, Titan, etc ...)
Fast Desktop CPU (Quad Core or more)
NVidia CUDA 10 and cuDNN 7 libraries installed
Webcam

Setup Host System

To use the fake webcam feature to enter conferences with our stream we need to insert the v4l2loopback kernel module in order to create /dev/video1. Follow the install instructions at (https://github.com/umlaeute/v4l2loopback), then let's setup our fake webcam:

$ git clone https://github.com/umlaeute/v4l2loopback.git
$ make && sudo make install
$ sudo depmod -a
$ sudo modprobe v4l2loopback devices=1
$ sudo modprobe v4l2loopback exclusive_caps=1 card_label="faceit_live" video_nr=1
$ v4l2-ctl -d /dev/video1 -c timeout=1000

v4l2loopback-ctl set-timeout-image caio.png /dev/video1

Change the video_nr above in case you already have a webcam running on /dev/video1

To check if things are working, try running an mp4 to generate a video the /dev/video1 (replace ale.mp4 with your own video).

$ ffmpeg -re -i media/ale.mp4 -f v4l2 /dev/video1 -loop 10

And view it

$ ffplay -f v4l2 /dev/video1

On Ubuntu 18, I had to make a minor change to the source code of v4l2loopback.c to get loopback working. In case the above doesn't work, you can try this change before running make :

# v4l2loopback.c
from
#if LINUX_VERSION_CODE >= KERNEL_VERSION(2, 6, 29)

to
#if LINUX_VERSION_CODE >= KERNEL_VERSION(3,7,0)

You can also inspect your /dev/video* devices:

$ v4l2-ctl --list-devices
$ v4l2-ctl --list-formats -d /dev/video1

If you have more than one GPU, you might need to set some environment variables:

# specify which display to use for rendering
$ export DISPLAY=:1

# which CUDA DEVICE to use (run nvidia-smi to discover the ID)
$ export CUDA_VISIBLE_DEVICES = 0

Clone this repository

Don't forget to use the --recurse-submodules parameter to checkout all dependencies.

$ git clone --recurse-submodules https://github.com/alew3/faceit_live3.git /local_path/

Create an Anaconda environment and install requirments

$ conda create -n faceit_live3 python=3.8
$ source activate faceit_live3
$ conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.1 -c pytorch
$ pip install -r requirements.txt

Download 'vox-adv-cpk.pth.tar' to /models folder

You can find it at: google-drive or yandex-disk.

Usage

Put in the ./media/ directory the images in jpg/png you want to play with.

Run the program

$ python faceit_live.py

Parameters

--webcam # the videoid of the Webcam e.g. 0 if /dev/video0 (default is 0)
--image # the face to use for transformations, put the files inside media (by default it loads the first image in the folder)
--streamto # the /dev/video number to stream to (default is 1)

Example

$ python faceit_live.py --webcam 0 --stream 1 --image oliver.jpg

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