Archive for the ‘OpenCV’ Category

Intro to Image Processing

Sunday, November 13th, 2016

Intro to Image Processing by Eric Schles.

A short but useful introduction to some, emphasis on some, of the capabilities of OpenCV.

Understanding image processing will make you a better consumer and producer of digital imagery.

To its great surprise, the “press” recently re-discovered government isn’t to be trusted.

The same is true for the “press.”

Develop your capability to judge images offered by any source.

How to analyze 100 million images for $624

Tuesday, December 10th, 2013

How to analyze 100 million images for $624 by Pete Warden.

From the post:

Jetpac is building a modern version of Yelp, using big data rather than user reviews. People are taking more than a billion photos every single day, and many of these are shared publicly on social networks. We analyze these pictures to discover what they can tell us about bars, restaurants, hotels, and other venues around the world — spotting hipster favorites by the number of mustaches, for example.

[photo omitted]

Treating large numbers of photos as data, rather than just content to display to the user, is a pretty new idea. Traditionally it’s been prohibitively expensive to store and process image data, and not many developers are familiar with both modern big data techniques and computer vision. That meant we had to cut a path through some thick underbrush to get a system working, but the good news is that the free-falling price of commodity servers makes running it incredibly cheap.

I use m1.xlarge servers on Amazon EC2, which are beefy enough to process two million Instagram-sized photos a day, and only cost $12.48! I’ve used some open source frameworks to distribute the work in a completely scalable way, so this works out to $624 for a 50-machine cluster that can process 100 million pictures in 24 hours. That’s just 0.000624 cents per photo! (I seriously do not have enough exclamation points for how mind-blowingly exciting this is.)
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There are a couple of other components that are necessary to reach the same results as Pete.

Seek HIPI for processing photos on Hadoop and OpenCV and the rest of Pete’s article for some very helpful tips.

Face detection using Python and OpenCV

Saturday, November 17th, 2012

Face detection using Python and OpenCV by Paolo D’Incau.

From the post:

Most of the posts you will find in this blog are Erlang related (of course they are!), but sometimes I like writing also about my experiences at University of Trento as I am doing right now. During the last couple of years I have attended many courses about Computer Vision and Digital Signal Processing so today I would like to show you something about it.

In this post I will write about making some code for face detection purposes using python and OpenCV. This post will have no code, actually you can just grab my original code from here (the files needed are faces.py and haarcascade_frontalface_alt.xml).

Face detection is a computer technology that determines the locations and sizes of human faces in images or video. It detects facial features and ignores anything else, such as buildings, trees and bodies.

I can imagine any number of topic map applications that could use or be enhanced by face detection capabilities.