EVA (Exploratory Video Analytics)

What is EVA?

EVA is a visual data management system (think MySQL for videos). It supports a declarative language similar to SQL and a wide range of commonly used computer vision models.

What does EVA do?

• EVA enables querying of visual data in user facing applications by providing a simple SQL-like interface for a wide range of commonly used computer vision models.

• EVA improves throughput by introducing sampling, filtering, and caching techniques.

• EVA improves accuracy by introducing state-of-the-art model specialization and selection algorithms.

Table of Contents

• Installation
• Client Testing
• Docker
• Development
• Architecture

Installation

Installation of EVA involves setting a virtual environment using miniconda and configuring git hooks.

1. Clone the repository

git clone https://github.com/georgia-tech-db/eva.git

2. Install the dependencies.

sh script/install/before_install.sh
export PATH="$HOME/miniconda/bin:$PATH"
sh script/install/install.sh

3. Connect mysql user root with normal account and no password

sudo mysql -u root
> SELECT User,Host FROM mysql.user;
> DROP USER 'root'@'localhost';
> CREATE USER 'root'@'%' IDENTIFIED BY '';
> GRANT ALL PRIVILEGES ON *.* TO 'root'@'%' WITH GRANT OPTION;
> FLUSH PRIVILEGES;
mysql -u root

refer to askubuntu

4. Install docker and docker-compose. Please refer to official doc.

Client Testing

1. Set up the server and client

• Launch EVA database Server: python eva.py

• Launch CLI: python eva_client.py

2. Run the LOAD command in the client terminal: (may take a while)

LOAD DATA INFILE 'data/ua_detrac/ua_detrac.mp4' INTO MyVideo;

3. Below is a basic query that should work on the client

SELECT id, data FROM MyVideo WHERE id < 5;

Docker

1. Standup EVA testing for CPU/GPU hardware.

docker-compose -f docker-compose.yml eva-test-[cpu/gpu] up

Development

1. Install git hooks in your .git/ directory. [optional, but recommended]

conda activate eva
pre-commit install

2. Ensure that all the unit test cases (including the ones you have added) run succesfully and the coding style conventions are followed.

bash script/test/test.sh

Architecture

EVA consists of four core components:

• EVAQL Query Parser
• Query Optimizer
• Query Execution Engine (Filters + Deep Learning Models)
• Distributed Storage Engine

Contributing

To file a bug or request a feature, please file a GitHub issue. Pull requests are welcome.

Contributors

See the people page for the full listing of contributors.

License

Copyright (c) 2018-2020 Georgia Tech Database Group Licensed under the Apache License.