The project would require the use of OpenCV’s python module on an Intel Galileo. However, as I quickly found out full support for OpenCV was not available on Intel Galileo’s default Linux image. The solution was to build a clanton-full Linux image (the shipped version is a stripped down clanton-tiny) with OpenCV support included. However, I also quickly found out that creating the full build required a little more attention than pressing the “full build please” button. With that in mind I’ve put together a list of the steps I took to create the clanton-full image.
Tag Archives: OpenCV
Today’s post is a followup to a previous (extremely popular) article on detecting barcodes in images using Python and OpenCV.
In the previous post we explored how to detect and find barcodes in images. But today we are going to refactor the code to detect barcodes in video.
Whether you are tagging and categorizing your personal images, searching for stock photos for your company website, or simply trying to find the right image for your next epic blog post, trying to use text and keywords to describe something that is inherently visual is a real pain.
I faced this pain myself last Tuesday as I was going through some old family photo albums there were scanned and digitized nine years ago.
You see, I was looking for a bunch of photos that were taken along the beaches of Hawaii with my family. I opened up iPhoto, and slowly made my way through the photographs. It was a painstaking process. The meta-information for each JPEG contained incorrect dates. The photos were not organized in folders like I remembered — I simply couldn’t find the beach photos that I was desperately searching for.
Perhaps by luck, I stumbled across one of the beach photographs. It was a beautiful, almost surreal beach shot. Puffy white clouds in the sky. Crystal clear ocean water, lapping at the golden sands. You could literally feel the breeze on your skin and smell the ocean air.
In a recent article I showed how you can use webrtc, canvas and websockets together to create a face detection application whose frontend runs completely in the browser, without the need for plugins. In that article I used a Jetty based backend to handle the image analysis using OpenCV through the JavaCV wrapper.
When I almost finished the article, I noticed that websockets is also supported from Play 2.0. I really like developping in Play and in Scala so as an experiment I rewrote the backend part from a Jetty/Java/JavaCV stack to a Play2.0/Scala/JavaCV stack. If you want to do this for yourself, make sure you start with the frontend code from here. Since the frontend code hasn’t changed except the location where the websockets are listening.