After a long hiatus I’m back with an update. Recently I’ve been upgrading the Structure-from-Motion Toy Library (https://github.com/royshil/SfM-Toy-Library/) to OpenCV 3.x from OpenCV 2.4.x.
Years ago I wanted to implement PTAM. I was young and naïve 🙂
Well I got a few moments to spare on a recent sleepless night, and I set out to implement the basic bootstrapping step of initializing a map with a planar object – no known markers needed, and then tracking it for augmented reality purposes.
You already know I love libQGLViewer. So here a snippet on how to do AR in a QGLViewer widget. It only requires a couple of tweaks/overloads to the plain vanilla widget setup (using the matrices properly, disable the mouse binding) and it works.
The major problems I recognize with getting a working AR from OpenCV’s intrinsic and extrinsic camera parameters are their translation to OpenGL. I saw a whole lot of solutions online, and I contributed from my own experience a while back, so I want to reiterate here again in the context of libQGLViewer, with a couple extra tips.
Just sharing a simple recipe for a video stabilizer in OpenCV based on goodFeaturesToTrack() and calcOpticalFlowPyrLK().
Well… it’s a bit more than 20 lines, but it is short. And it doesn’t work for every kind of video (although the results are funny anyway! :).
So lately I’m into Optical Music Recognition (OMR), and a central part of that is doing staff line removal. That is when you get rid of the staff lines that obscure the musical symbols to make recognition much easier. There are a lot of ways to do it, but I’m going to share with you how I did it (fairly easily) with Hidden Markov Models (HMMs), which will also teach us a good lesson on this wonderfully useful approach.
OMR has been around for ages, and if you’re interested in learning about it [Fornes 2014] and [Rebelo 2012] are good summary articles.
The matter of Staff Line Removal has occupied dozens of researchers for as long as OMR exists; [Dalitz 2008] give a good overview. Basically the goal is to remove the staff lines that obscure the musical symbols, so they would be easier to recognize.
But, the staff lines are connected to the symbols, so simply removing them will cut up the symbols and make them hardly recognizable.
So let’s see how we could do this with HMMs.
I came across an extremely simple color balancing algorithm here. And I thought I’ll quickly transcode it to OpenCV.
Here’s the gist:
For those of you still interested, I’ve made the move to using Qt and QGLViewer in the SfM-Toy-Lib project. Getting rid of PCL dependency (I think it’s a bloated library), and burying FLTK long in the past, I feel much better about it now.
Get it on github: https://github.com/royshil/SfM-Toy-Library
I wish to report of a number of tweaks and additions to the hand silhouette tracker I posted a while back. First is the ability for it to “snap” to the object using a simple Active Snake method, another is a more advanced resampling technique (the older tracker always resampled after every frame), and of a number of optimizations to increase the speed (tracker now runs at real-time on a single core).
For those of you using OpenCV that are looking to upgrade from OpenNI 1.x to the new OpenNI 2.x, here’s a bit of code to make life a tiny bit easier. It simply wraps the OpenNI 2.x APIs to expose a simple frame grabber for OpenCV.