Particles explained using Gifs!!

Over the past few months, I have been reading, understanding and implementing a number of existing algorithms in Computer Vision domain. Implementing particles and particle based algorithms have really had me excited and almost on the edge of my seat. One may ask what makes particles so interesting?? Let me try to get the concept through.

Particles, just like most existing algorithms in computer science, are inspired by nature. Have you ever seen a beam of sunlight coming through a window and illuminate a bunch of floating particles (impossible in London though I have seen it before)? When you see these tiny particles, you notice that they are suspended in air and that it's very difficult to predict their motion unless you disturb the surrounding air. This simple concept is vital for many computer algorithms that model motion/dynamics of an object.

Particles, along with their randomness, can be simulated inside a computer program. The simplest of such algorithm is called Random Walk, where a particle is modelled with its current position/state alone and a random displacement/jump determines its next position in time. Here I have shown one Random Walk particle:

A Single Random Walk Particle

Matlab script for checking and deleting folders

Just putting this simple but extremely useful matlab script for my future self and anyone trying to handle folders using matlab. This script checks all the sub directory within the starting directory and then deletes the one that do not satisfy a given criteria. In my case this was the number of image samples within a folder.

% script for deleting folders with less than a certain number of files
close all
clear all
clc

% count the number of png files
D = dir(' ');


numFoldersOrFiles = size(D, 1);

thresholdFiles = 30;

% skipping the first two which are just . and ..
for i = 3: numFoldersOrFiles
    
    if D(i).isdir
        
        Ds = dir([D(i).name '\*.png']);
        numFiles = size(Ds, 1) / 3;
        if numFiles < thresholdFiles
            
            rmdir(D(i).name, 's');
            
        end
    end
end


% all done :)

Particle Filtering - Survival of the fittest

I recently studied dynamic system models such as Kalman and Particle Filters.
For Kalman Filter I followed a Matlab demo that can be found here.

In this demo, the simple problem of tracking a ball is addressed using a Kalman Filter. The input sequence is of a ball, which is travelling at varying velocity and which is occluded in some frames by a box. I think this is a great example to demonstrate the power of dynamic system  models, especially the occluded frames can be used to test how good a dynamic model is. Here is the actual sequence:

As you can see the ball goes underneath the box and comes out of the other end. If our dynamic model is accurate it will be able to predict the state of the ball even when it is not visible, and should match the position when the ball comes out.

Designing an algorithm - from ideas to code

I had always been interested in solving sudoku puzzles, partly because there are too many combinations that make each Sudoku unique. Since my work involves writing and using programming in different scenarios, I thought why not try using my skills on Sudoku. So there I was on a London Underground train to Barbican - looking at a Sudoku puzzle at the back of a morning newspaper, wondering how I can write an algorithm to solve it. I figured out a few simple tricks that I have always used in algorithm design. Here I explain what thoughts I had while designing my very own Sudoku solver and how I transformed those ideas into a working prototype.

First of all lets have a look at a typical Sudoku puzzle and some basic rules:

Sudoku Puzzle

Yes - it has got everything to do with numbers!! lots of numbers!

A Sudoku puzzle typically has 81 boxes where each box can have a number between 1 to 9. However, all these boxes follow some rules that make it all interesting. You may have noticed 3x3 squares grouping the number boxes. A correct solution of Sudoku ensures no repetition of numbers from 1 to 9 inside each of the 3x3 squares, in each horizontal line and each vertical line. When solving a Sudoku puzzle, this is exactly where I look for a solution, and exactly where my thought process starts for my Sudoku solver algorithm.

Long Exposure Shots with a GoPro and Matlab

I recently got  a GoPro. You know to get cool selfies, videos and all :D I am very much impressed by all the cool things you can do with it but was specifically impressed by the fact that one can create a time lapse video.

After giving a couple of tries to time lapse videos, I wanted to go beyond. I had always seen photographers make a long exposure shots by using specific DSLR cameras. I wanted to create just that using the only camera I had, a GoPro. However I had something much more than the camera, I knew how to write a code that deals with a number of images (I am a Computer Vision Engineer).

What if I told you, you can use OpenCV code with Matlab mex!!

Matlab is probably one of the best tools for quickly prototyping and testing your research ideas. As quick and flexible it is, sometimes Matlab code can consume a lot of execution time. This is specifically a big hurdle when multiple experiments need to be run. A real-time execution alternative is to implement Matlab compatible C++ code and compile it with mex-compiler. While this works most of the time, it is well known that quickly implementing ideas in C++ is not possible.

Executing Matlab scripts on different Operating Systems

Just a quick post about making matlab scripts run on different OS.

Writing a matlab code that works on both Windows and Linux is a little challenging, especially when accessing the disk both OS use a slightly different syntax for filesystem.

One solution to this is using computer string to check the OS. Once checked you can use if condition statements to execute relevant code on each system.

The script for this is pretty straight forward and is listed below:

%compile everything
if strcmpi(computer,'PCWIN') |strcmpi(computer,'PCWIN64')
   compile_windows
else
   compile_linux
end

Saving Numpy arrays to Matlab compatible files

Working in Python but want to use you data in Matlab too.

A simple function call can do this. Here is the code:

import scipy.io as spio
spio.savemat('saveSymmetricPose.mat', 
             dict(matlabVarName = pythonVarName, matlabVarName1 = pythonVarName1))

Here savemat from scipy.io stores a number of arrays to a Mat file, that can be easily opened in Matlab. Hope this could help someone.

Source: converting-numpy-arrays-to-matlab-and-vice-versa

Compiling OpenCV-3.0 with Matlab Support

A big uppercase HELLO to everyone!  I am back and after a long time (yet again) I am going to write a tutorial. The thing I am able to achieve here is awesome for us computer vision researchers. Yes! you heard it correct, exciting stuff.

I have been using OpenCV for quite sometime now. As good as it is for real-time computer vision applications, it can also be time consuming when it comes to exploring and implementing new research designs. Matlab on the other hand has always been flexible and a quick work around to achieve my research goals. The only problem, though, with matlab is that it is not real-time or even worse is that if you plan to implement code in OpenCV for real-time application, you would have to write the algorithms all over again as the usage of Matlab toolboxes is different than using the same methods in OpenCV.

Now comes the fun part, what if you can access OpenCV function calls within Matlab code? What if you can have easily transferable code from Matlab to C++?  This is all possible now with the OpenCV 3.0 Dev including matlab mex wrappers, which really is a good big step in the right direction. So lets start compiling the code.