20-lines AR in OpenCV [w/code]


Just wanted to share a bit of code using OpenCV's camera extrinsic parameters recovery, camera position and rotation - solvePnP (or it's C counterpart cvFindExtrinsicCameraParams2). I wanted to get a simple planar object surface recovery for augmented reality, but without using any of the AR libraries, rather dig into some OpenCV and OpenGL code.
This can serve as a primer, or tutorial on how to use OpenCV with OpenGL for AR.

Update 2/16/2015: I wrote another post on OpenCV-OpenGL AR, this time using the fine QGLViewer - a very convenient Qt OpenGL widget.

The program is just a straightforward optical flow based tracking, fed manually with four points which are the planar object's corners, and solving camera-pose every frame. Plain vanilla AR.

Well the whole cpp file is ~350 lines, but there will only be 20 or less interesting lines... Actually much less. Let's see what's up
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Image Recoloring using Gaussian Mixture Model and Expectation Maximization [OpenCV, w/Code]

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!
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Iterative Closest Point (ICP) for 2D curves with OpenCV [w/ code]

ICP - Iterative closest point, is a very trivial algorithm for matching object templates to noisy data. It's also super easy to program, so it's good material for a tutorial. The goal is to take a known set of points (usually defining a curve or object exterior) and register it, as good as possible, to a set of other points, usually a larger and noisy set in which we would like to find the object. The basic algorithm is described very briefly in wikipedia, but there are a ton of papers on the subject.

I'll take you through the steps of programming it with OpenCV.
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Bust out your own graphcut based image segmentation with OpenCV [w/ code]

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...
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Quick and Easy Head Pose Estimation with OpenCV [w/ code]

Update: check out my new post about this http://www.morethantechnical.com/2012/10/17/head-pose-estimation-with-opencv-opengl-revisited-w-code/

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

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Implementing PTAM: stereo, tracking and pose estimation for AR with OpenCV [w/ code]


Been working hard at a project for school the past month, implementing one of the more interesting works I've seen in the AR arena: Parallel Tracking and Mapping (PTAM) [PDF]. This is a work by George Klein [homepage] and David Murray from Oxford university, presented in ISMAR 2007.

When I first saw it on youtube [link] I immediately saw the immense potential - mobile markerless augmented reality. I thought I should get to know this work a bit more closely, so I chose to implement it as a part of advanced computer vision course, given by Dr. Lior Wolf [link] at TAU.

The work is very extensive, and clearly is a result of deep research in the field, so I set to achieve a few selected features: Stereo initialization, Tracking, and small map upkeeping. I chose not to implement relocalization and full map handling.

This post is kind of a tutorial for 3D reconstruction with OpenCV 2.0. I will show practical use of the functions in cvtriangulation.cpp, which are not documented and in fact incomplete. Furthermore I'll show how to easily combine OpenCV and OpenGL for 3D augmentations, a thing which is only briefly described in the docs or online.

Here are the step I took and things I learned in the process of implementing the work.

Update: A nice patch by yazor fixes the video mismatching - thanks! and also a nice application by Zentium called "iKat" is doing some kick-ass mobile markerless augmented reality.
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SmartHome - Embedded computing course project

In the past few weeks I have been working hard at a few projects for end-of-term at Uni. One of the projects is what I called "SmartHome", for Embedded computing [link] course, is a home monitoring [link] application. In the course the students were given an LPC2148 arm7-MCU (NXP) based education board, implemented by Embedded Artists [link]. My partner Gil and I decided to work with ZigBee extension modules [link] to enable remote communication.

Here are the steps we took to bring this project to life.
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