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.
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
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