25. December 2014
This post convers very specific but important topic about writing memory-efficient code. I will show you how to collect and analyze memory allocations that happens in OpenCV.
When it comes to writing efficient code we usually care about CPU-efficiency. However there are many times, when memory-efficiency is more important. A limited amount of RAM is not so rare as one can think. On iOS and Android there are a strict memory usage restrictions, and of your app uses more memory than allowed your app can get killed by the system. Embedded hardware systems used in IoT, Raspberri Pi and others also have very limited amount of RAM. So you should be very careful when porting code from desktop with gigabytes of memory to mobile platform.
04. December 2014
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.
31. October 2014
01. October 2014
Third computer vision digest. Your monthly portion of news in computer vision for September 2014.
In this issue:
- Real-time face 3D model reconstruction
- Image color correction and contrast enhancement
- Robust Optimization Techniques in Computer Vision
- Computer Vision Digest (May 2014)
- Computer Vision Digest (June 2014)
- Computer Vision Digest (August 2014)
11. September 2014
During development of CloudCV I came to the problem on converting
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
Third computer vision digest. Your monthly portion of news in computer vision for August 2014.
In this issue:
- Free Photo Editing Software Lets You Manipulate Objects in 3D
- Real-Time Digital Makeup with Projection Mapping
- Video stabilization through 3D scene recovery
- Using OpenCV, Python and Template Matching to play “Where’s Waldo?”
- OpenCV 3.0 alpha is out
16. August 2014
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:
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));