Set up Amazon Web Services – Part 2
Home Run into the Cloud
DIY Python scripts run in container environments on Amazon's Lambda service – this snapshot example deploys an AI program for motion analysis in video surveillance recordings.
After some initial steps in a previous article [1] to set up an AWS account, an S3 storage server with a static web server, and the first Lambda function, I'll now show you how to set up an API server on Amazon to track down interesting scenes in videos from a surveillance camera.
The Lambda function triggered either by a web request from the browser or a command-line tool like curl
retrieves a video from the web, runs it through an artificial intelligence (AI) algorithm implemented by the OpenCV library, generates a motion profile, and returns the URL of a contact sheet generated as a JPEG with all the interesting movements from the recording (Figures 1 and 2).
Sandbox Games
Unlike Amazon's EC2 instances with their full-blooded (albeit virtual) Linux servers, the Lambda Service [2] provides only a containerized environment. Inside a container, Node.js, Python, or Java programs run in a sandbox, which Amazon pushes around at will between physical servers, eventually going as far as putting the container to sleep in case of inactivity – just to conjure it up again when next accessed. Leaving data on the virtual disk of the container and hoping to find it still there next time would thus result in an unstable application. Instead, Lambda functions communicate with AWS offerings such as S3 storage or the Dynamo database to secure data and are otherwise "stateless."
Developers can upload things that an application cannot describe in a Python script to the (as rumor has it) CentOS-based containers as ZIP files (Figure 3).
A Lambda function that uses artificial intelligence capabilities from the OpenCV library, like the example, needs to compile the required binaries or libraries up front in a Unix environment similar to the Lambda container, package and upload the results, and call it with the Python script at run time. Existing Python bindings to shared libraries are used here, or the Python script calls precompiled binaries as external processes.
Lean and Mean
To prevent the AI program [3] from using too much compute time after installation in the Amazon cloud – and thus also using up money after exceeding the "free tier" quota – the improved code [4] (updated in Listing 1 from the previous article) no longer looks for movements in every frame (i.e., 50 times a second) but hops through the movie in increments of half a second in line 99. After a frame with detected motion, line 96 even skips forward two seconds. To accomplish this, vid.grab()
called in line 50 no longer painstakingly decodes the frame in a complex process, as vid.read()
did previously, but discards it to retrieve the next one.
Listing 1
max-movement-lk.cpp
Whereas the first version [3] only printed the number of seconds into the video in which the algorithm detected motion, to subsequently use MPlayer to extract the frames as JPEG files, lines 92 to 94 now use the imwrite()
image processing functions included with OpenCV to write detected frames immediately as 000x.jpg
to the virtual disk. A second run and the shenanigans for installing MPlayer in the Lambda container are thus no longer required.
Based on these JPEG images, another Python script, mk-montage.py
, then produces a contact sheet, also in .jpg
format, with the help of the ImageMagick library. The Lambda program puts this file into Amazon's S3 cloud storage, and then sends a link to the file to the calling client.
RAM Is Money
How does a Python programmer now pick up a document from the web? A first approach would be the read()
method provided by urlopen()
, which then sends all the bytes it has obtained to a local file through write()
. But, this would mean that a potentially large video file would be completely read into memory before Python finally starts writing it to disk.
The ample supply of RAM needed for this costs money on Amazon. To avoid this, the urlretrieve()
method from the urllib
module used in Listing 2 can buffer smaller data chunks – in a hopefully more or less intelligent way.
Listing 2
vimo.py
Buy this article as PDF
(incl. VAT)
Buy Linux Magazine
Subscribe to our Linux Newsletters
Find Linux and Open Source Jobs
Subscribe to our ADMIN Newsletters
Support Our Work
Linux Magazine content is made possible with support from readers like you. Please consider contributing when you’ve found an article to be beneficial.
News
-
TUXEDO Computers Unveils Linux Laptop Featuring AMD Ryzen CPU
This latest release is the first laptop to include the new CPU from Ryzen and Linux preinstalled.
-
XZ Gets the All-Clear
The back door xz vulnerability has been officially reverted for Fedora 40 and versions 38 and 39 were never affected.
-
Canonical Collaborates with Qualcomm on New Venture
This new joint effort is geared toward bringing Ubuntu and Ubuntu Core to Qualcomm-powered devices.
-
Kodi 21.0 Open-Source Entertainment Hub Released
After a year of development, the award-winning Kodi cross-platform, media center software is now available with many new additions and improvements.
-
Linux Usage Increases in Two Key Areas
If market share is your thing, you'll be happy to know that Linux is on the rise in two areas that, if they keep climbing, could have serious meaning for Linux's future.
-
Vulnerability Discovered in xz Libraries
An urgent alert for Fedora 40 has been posted and users should pay attention.
-
Canonical Bumps LTS Support to 12 years
If you're worried that your Ubuntu LTS release won't be supported long enough to last, Canonical has a surprise for you in the form of 12 years of security coverage.
-
Fedora 40 Beta Released Soon
With the official release of Fedora 40 coming in April, it's almost time to download the beta and see what's new.
-
New Pentesting Distribution to Compete with Kali Linux
SnoopGod is now available for your testing needs
-
Juno Computers Launches Another Linux Laptop
If you're looking for a powerhouse laptop that runs Ubuntu, the Juno Computers Neptune 17 v6 should be on your radar.