22. December 2014       News, Books

No time to explain! Pay 5$ and grab a book from Packt-pub.

Pay 5$ and grab a book

The offer will end on January 6th 2015, hurry up!

 04. December 2014       OpenCV, Tutorials, Algorithms

How would you design an algorithm to process 40Mpx image? 100Mpx? What about gigapixel-sized panorams? Obviously, it should differs from those that are intended for 640x480 images. Here I want to present you implementation of the very simple but powerful approach called “Tile-based image processing”. I will show you how to make this using OpenCV.

Tile based image processing

 31. October 2014       Tutorials, Node.js

JavaScript. Do you like debug JavaScript code? I hate it. Literally. What what if you have to? In this post I’m going to show you how to simplify your life by automating unit testing of the JavaScript code for the browser.

To get things more interesting - let’s automate unit-testing of the image processing library called JSFeat. JSFeat provides a JavaScript implementation of the basic image processing operations that let you to process images in your browser and build sophisticated algorithms. It’s like OpenCV for web-browser.

The source code for this tutorial is available on my Github page: https://github.com/BloodAxe/jsfeat.

 01. October 2014       News, Computer Vision Digest

Third computer vision digest. Your monthly portion of news in computer vision for September 2014.

In this issue:

Previous issues:

 11. September 2014       Cloudcv, Node.js, Tutorials

During development of CloudCV I came to the problem on converting v8::Arguments to native C++ data types in my Node.js native module. If you are new to C++ and Node.js, I suggest you to read how to write C++ modules for Node.js and connecting OpenCV and Node.js first.

Mapping V8 data types to native C++ equivalents is trivial, but somewhat wordy. One should take the argument at given index, check whether it is defined, then check it’s type and finally cast to C++ type. This works fine while you have function that receive two or three arguments of trivial type (That can be mapped directly to built-in C++ types). What about strings? Arrays? Complex types like objects or function callback? You code will grow like and became hard-to-maintain pasta-code some day.

In this post I present my approach on solving this problem with a laconic way on describing what do you expect as input arguments.

 30. August 2014       News, Computer Vision Digest

Third computer vision digest. Your monthly portion of news in computer vision for August 2014.

In this issue:

Previous issues:

 16. August 2014       OpenCV, Tutorials

Eigen is a C++ template library for matrix and vector operations. It is highly optimized for numeric operations and support vectorization and use aligned memory allocators.

When it comes to matrix operations, Eigen is much faster than OpenCV. However, it can be situations when it is necessary to pass Eigen data to OpenCV functions.

In this post I will show how to map Eigen data to OpenCV with easy and efficient way. No copy, minimal overhead and maximum syntax sugar:

Simple case

Eigen::ArrayXXd img(480, 640);
cv::imshow("test", eigen2cv(img));

Proposed approach does not limited to continuous memory layout - it support expression and blocks as well. If given expression has to be evaluated - it will be evaluated into temporary dense storage and then mapped to OpenCV structure:


// Unsharp mask
Eigen::ArrayXXd img, blur;    
cv::GaussianBlur(eigen2cv(img), eigen2cv(blur));

cv::imshow("sharpened", eigen2cv(1.5 * img - 0.5 * blur));