hcai-discover 1.0.4

DISCOVER is a lightweight server designed to create and manage machine learning jobs based on requests.

DISCOVER - A Modular Software Framework for Human Behavior Analysis

Overview

DISCOVER is an open-source software framework designed to facilitate computational-driven data exploration in human behavior analysis. This user-friendly and modular platform streamlines complex methodologies, enabling researchers across disciplines to engage in detailed behavioral analysis without extensive technical expertise.

Key Features

• Modularity: DISCOVER's modular architecture allows for easy integration of new features and customization.
• User-Friendliness: Intuitive interface simplifies the data exploration process, making it accessible to non-technical users.
• Flexibility: Supports a wide range of data types and analysis workflows.
• Scalability: Handles large datasets with ease.

Use Cases

• Interactive Semantic Content Exploration
• Visual Inspection
• Aided Annotation
• Multimodal Scene Search

Getting Started

DISCOVER provides a set of blueprints for exploratory data analysis, serving as a starting point for researchers to engage in detailed behavioral analysis.

Prerequesites

Before starting to install DISCOVER you need to install Python and FFMPEG. While other Python versions may work as well the module is only tested for the following versions:

• 3.9.x
• 3.10.x
• 3.11.x

You can download the current version of python for your system here.

Download the current version off FFMPEG binaries from here for your system and make sure to extract them to a place that is in your system path. It is recommended to setup a separate virtual environment to isolate the NOVA server installation from your system python installation. To do so, open a terminal at the directory where your virtual environment should be installed and paste the following command:

python -m venv discover-venv

You can then activate the virtual environment like this:

.\discover-venv\Scripts\activate

Setup

Install DISCOVER using pip like this:

pip install hcai-discover

Start the server

To start DISCOVER you just open a Terminal and type

discover

DISCOVER takes the following optional arguments as input:

--env: '' : Path to a dotenv file containing your server configuration

--host: 0.0.0.0 : The IP for the Server to listen

--port : 8080 : The port for the Server to be bound to

--cml_dir : cml : The cooperative machine learning directory containing DISCOVER modules (available at: https://github.com/hcmlab/discover-modules)

--data_dir : data : Directory where the data resides

--cache_dir : cache : Cache directory for Models and other downloadable content

--tmp_dir : tmp : Directory to store data for temporary usage

--log_dir : log : Directory to store logfiles.

--use_tls : Enable TLS/SSL for HTTPS connections (requires certificates)

Internally DISCOVER converts the input to environment variables with the following names:

DISCOVER_HOST, DISCOVER_PORT, DISCOVER_CML_DIR, DISCOVER_DATA_DIR, DISCOVER_CACHE_DIR, DISCOVER_TMP_DIR, DISCOVER_LOG_DIR, DISCOVER_USE_TLS

All variables can be either passed directly as commandline argument, set in a dotenv file or as system wide environment variables. During runtime the arguments will be prioritized in this order commandline arguments -> dotenv file -> environment variable -> default value.

If the server started successfully your console output should look like this:

Starting DISCOVER v1.0.0...
HOST: 0.0.0.0
PORT: 8080
DISCOVER_CML_DIR : cml
DISCOVER_DATA_DIR : data
DISCOVER_CACHE_DIR : cache
DISCOVER_TMP_DIR : tmp
DISCOVER_LOG_DIR : log
...done
DISCOVER HTTP server starting on 0.0.0.0:8080

Modules

DISCOVER modules contain the machine learning models and processing pipelines. You can get the official modules from:

https://github.com/hcmlab/discover-modules

Clone or download the modules repository and set the --cml_dir parameter to point to the modules directory.

You can find the full documentation of the project here.

Citation

If you use DISCOVER consider citing the following paper:

@article{schiller2024discover,
title={DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behaviour},
author={Schiller, Dominik and Hallmen, Tobias and Withanage Don, Daksitha and Andr{\'e}, Elisabeth and Baur, Tobias},
journal={arXiv e-prints},
pages={arXiv--2407},
year={2024}
}