FACSvatar | Open Source Modular Framework From Face to FACS

by NumesSanguis Python Version: v0.3.4-alpha-release License: LGPL-3.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | FACSvatar Summary

FACSvatar is a Python library. FACSvatar has no bugs, it has no vulnerabilities, it has a Weak Copyleft License and it has low support. However FACSvatar build file is not available. You can download it from GitHub.

Affective computing and avatar animation both share that a person's facial expression contains useful information. Up until now, these fields use different processes to obtain and use these data. FACSvatar combines both purposes in a single framework. Empower your Embodied Conversational Agents (ECAs)!. This interoperability is possible, because FACSvatar uses the Facial Action Coding System (FACS) by Paul Ekman as an intermediate data representation. FACS describes facial expressions in terms of muscle groups, called Action Units (AUs). By giving these AUs a value between 0-1, we can describe the contractions / relaxation of facial muscles.

Support

Quality

Security

License

Reuse

Support

FACSvatar has a low active ecosystem.

It has 355 star(s) with 91 fork(s). There are 25 watchers for this library.

It had no major release in the last 12 months.

There are 23 open issues and 12 have been closed. On average issues are closed in 20 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FACSvatar is v0.3.4-alpha-release

Quality

FACSvatar has 0 bugs and 0 code smells.

Security

FACSvatar has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

FACSvatar code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FACSvatar is licensed under the LGPL-3.0 License. This license is Weak Copyleft.

Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

Reuse

FACSvatar releases are available to install and integrate.

FACSvatar has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

FACSvatar saves you 12715 person hours of effort in developing the same functionality from scratch.

It has 25597 lines of code, 96 functions and 35 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed FACSvatar and discovered the below as its top functions. This is intended to give you an instant insight into FACSvatar implemented functionality, and help decide if they suit your requirements.

Handle modal event
Find MBLab object
Rotate head bones
Updates the pump
Gather all csv files in csv folder
Remove columns from the main dataframe
Search for csv files matching csv
Clean the cleaned data
Publish a function to Kafka
Store data to csv
Return the time in seconds since epoch
Publish data
Generate messages from csv group
Create OpenFaceMessage from csv group
Sub - pub sub - pub sub - function
Deep facs
Calculate trailing Moving Average
Smooth 2d array
Start all async functions
Load data from JSON files
Publish the FACS message
Set parameters from the router
Set face configuration
Subscribe to data
Blend all the messages in the subsets
Calculate trailing moving average

Get all kandi verified functions for this library.

FACSvatar Key Features

No Key Features are available at this moment for FACSvatar.

FACSvatar Examples and Code Snippets

No Code Snippets are available at this moment for FACSvatar.

Community Discussions

No Community Discussions are available at this moment for FACSvatar.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install FACSvatar

FACSvatar is tested on Ubuntu and Windows, but should work on MacOS. More detailed quickstart.
Downloads - Go to the release page of this GitHub repo and download: openface_2.1.0_zeromq.zip Unzip and execute download_models.sh or .ps1 to download trained models Windows 7 / 8 / 10 Home: unity_FACSvatar_standalone_docker-ip.zip Windows 10 Pro / Enterprise / Education: unity_FACSvatar_standalone.zip Windows / Linux / Mac: Unity3D editor (documentation) Source code (zip / tar.gz) or download this repository with: git clone https://github.com/NumesSanguis/FACSvatar.git Press the green Clone or Download button on this page --> Download ZIP
Docker Install - Let's you execute applications without worrying about OS or programming language. General Docker instructions Docker Toolbox for Windows 7/8/10 Home Docker for Windows 10 Pro, Enterprise or Education Ubuntu: Docker and docker-compose and sudo usermod -a -G docker $USER
Docker Modules - Open a terminal (W7/8: cmd.exe / W10: PowerShell) and navigate to folder FACSvatar/modules, then execute: docker-compose pull (Downloads FACSvatar Docker containers) docker-compose up (Starts downloaded Docker containers)
Facial Animation with Unity3D - Navigate inside folder unity_FACSvatar_standalone(_docker-ip) and Double-click unity_FACSvatar.exe / Press play button in Unity3D editor
See the quickstart video:.