Hi,
I wanted to put up a quick note on how to use Kalman Filters in OpenCV 2.2 with the C++ API, because all I could find online was using the old C API. Plus the kalman.cpp example that ships with OpenCV is kind of crappy and really doesn’t explain how to use the Kalman Filter.
I’m no expert on Kalman filters though, this is just a quick hack I got going as a test for a project. It worked, so I’m posting the results.
Category: Recommended
Ever wanted to try and download an mp3 file at your workplace, but couldn’t because corporate firewall policy was to block every url ending with the .mp3 prefix?
I’m a fan of Last.fm online radio, and I have a habit of marking every good song that I hear as a “loved track”. Over the years I got quite a list, and so I decided to turn it into my jogging playlist. But for that, I need all the songs downloaded to my computer so I can put them on my mobile. While Last.fm does link to Amazon for downloading all the loved songs for pay, I’m going to walk the fine moral line here and suggest how you can download every song from existing free YouTube videos.
If it really bothers you, think of it as if I created a YouTube playlist and now I’m using my data plan to stream the songs off YT itself..
Moral issues resolved, we can move on to the scripting.
Update (4/27/12): youtube-dl.py has moved: https://github.com/rg3/youtube-dl/, and also added a very neat –extract-audio option so you can get the songs in audio right away (it basically does a conversion in a second step).
Hi
I’d like to share a quick tip on rotating video files.
I’m always frustrated with taking videos with my phone. Single handedly it’s easiest to do it when the phone is upright and not in landscape mode. But the files are always saved in landscape mode, which makes them rotated when you watch.
Although there are plenty of GUI software to do it, using the command line is faster and can also be batched!
Hi
I believe that every builder-hacker should have their own little Swiss-army-knife server that just does everything they need, but as a webservice. You can basically do anything as a service nowadays: image/audio/video manipulation, mock-cloud data storage, offload heavy computation, and so on.
Tornado, the lightweight Python webserver is perfect for this, and since so many of the projects these days have Python binding (see python-tesseract), it should be a breeze to integrate them with minimal work.
Let’s see how it’s done
Hi
Just wanted to share a thing I made – a simple 2D hand pose estimator, using a skeleton model fitting. Basically there has been a crap load of work on hand pose estimation, but I was inspired by this ancient work. The problem is setting out to find a good solution, and everything is very hard to understand and implement. In such cases I like to be inspired by a method, and just set out with my own implementation. This way, I understand whats going on, simplify it, and share it with you!
Anyway, let’s get down to business.
Edit (6/5/2014): Also see some of my other work on hand gesture recognition using smart contours and particle filters
Android + Yourmuze.fm + Dolphin Browser HD + XiiaLive = WIN
It’s been a while since I’ve posted anything in the blog… Sorry for that… very busy times. I had a lot of ideas of what my “comeback post” should be about, but I knew I had to share one of my relatively recent discoveries that made my smartphone online-radio listening experience a whole lot better
If you don’t know yourmuze.fm, this might be the time to get to know it. It’s a free service that has a LOT of worldwide radio stations available as an online stream for usage with most of the smartphones.
In order to start using it you need to register for free via your desktop computer, and add the stations you like. Later on, you can surf to the mobile version of the service by mobile web and listen to the stations you selected.
So far so good… I like it. But how about multitasking?
Hi,
I’ll present a quick and simple implementation of image recoloring, in fact more like color transfer between images, using OpenCV in C++ environment. The basis of the algorithm is learning the source color distribution with a GMM using EM, and then applying changes to the target color distribution. It’s fairly easy to implement with OpenCV, as all the “tools” are built in.
I was inspired by Lior Shapira’s work that was presented in Eurographics 09 about image appearance manipulation, and a work about recoloring for the colorblind by Huang et al presented at ICASSP 09. Both works deal with color manipulation using Gaussian Mixture Models.
Update 5/28/2015: Adrien contributed code that works with OpenCV v3! Thanks! https://gist.github.com/adriweb/815c1ac34a0929292db7
Let’s see how it’s done!
This is a tutorial on using Graph-Cuts and Gaussian-Mixture-Models for image segmentation with OpenCV in C++ environment.
Update 10/30/2017: See a new implementation of this method using OpenCV-Python, PyMaxflow, SLIC superpixels, Delaunay and other tricks.
Been wokring on my masters thesis for a while now, and the path of my work came across image segmentation. Naturally I became interested in Max-Flow Graph Cuts algorithms, being the “hottest fish in the fish-market” right now if the fish market was the image segmentation scene.
So I went looking for a CPP implementation of graphcut, only to find out that OpenCV already implemented it in v2.0 as part of their GrabCut impl. But I wanted to explore a bit, so I found this implementation by Olga Vexler, which is build upon Kolmogorov’s framework for max-flow algorithms. I was also inspired by Shai Bagon’s usage example of this implementation for Matlab.
Let’s jump in…
Update: check out my new post about this https://www.morethantechnical.com/2012/10/17/head-pose-estimation-with-opencv-opengl-revisited-w-code/
Hi
Just wanted to share a small thing I did with OpenCV – Head Pose Estimation (sometimes known as Gaze Direction Estimation). Many people try to achieve this and there are a ton of papers covering it, including a recent overview of almost all known methods.
I implemented a very quick & dirty solution based on OpenCV’s internal methods that produced surprising results (I expected it to fail), so I decided to share. It is based on 3D-2D point correspondence and then fitting of the points to the 3D model. OpenCV provides a magical method – solvePnP – that does this, given some calibration parameters that I completely disregarded.
Here’s how it’s done