Esper: a framework for end-to-end video analytics
Esper is a development environment that provides out-of-the-box integration for a variety of tools useful for analyzing videos, including:
- Scanner: high-performance parallel processing of videos
- Rekall: data representation and operations for spatiotemporal data in Python
- VGrid: visualizing metadata on videos using Python and Javascript
- Frameserver: dynamically extracting frames from a video
- Django: managing SQL models and serving web pages
- Spark: parallel analysis of metadata
Esper is kind of like Ruby on Rails but for video analytics. It provides a skeleton of a data science workbench that you can customize for your particular project, along with a set of useful utility functions, models, preinstalled dependencies, and so on.
Setup
First, install Docker CE, Python 3.5, jq, and pip. If you're on Ubuntu, you can install Python/pip/jq as follows:
sudo apt-get install python3 python3-pip jq
Ensure that you have Docker version >= 17.12, which you can check by running:
$ docker --version
Docker version 17.12.0-ce, build c97c6d6
Note: If you have a GPU and are running on Linux, then install nvidia-docker2. Set
device = "gpu-9.1-cudnn7inconfig/local.toml.
Next, you need to install Esper. You can do this through pip, or see the developer instructions below for installing from source.
pip3 install esper-video
Finally you need to set up your Esper project.
mkdir my_project
cd my_project
wget https://raw.githubusercontent.com/scanner-research/esper/master/config/local.toml
esper -c local.toml
docker-compose up -d
TODO: next steps
Esper developers
If you're developing the Esper core platform or otherwise want to stay up to date with our dependencies, then you should clone and link our submodules.
git clone --recursive https://github.com/scanner-research/esper
TODO: remaining instructions