## Wednesday, March 10, 2010

### Ultra simple incremental backups with rsync

Recently I bought an external hard drive for backups. While searching for the best way to do the backups I found rsync, which looks well suited for these tasks.
Then I found this webpage that provides some scripts to achieve circular snapshots.

However, most scripts on the web are extremely large and complex compared to what I need. So finally I made my own script that makes automated incremental backups, with no cycling but keeping a history file to identify each backup.

Here is the code:

This script will create backup directories called backup.xxxx, where xxxx is the backup number. This number is zero for the first backup done. Rsync hard-links the unchanged files to the previous backup which is a key method to save space.

With this script I am backuping a NTFS partition and that's why the option --modify-window=1 is needed in rsyncFlags. It is important to write sourceDir without a trailing forward slash!

In order to provide easy access to the last backup, a symbolic link called HEAD will point to the latest backup. Additionally, a file called backup-history holds the exact time and date where each backup was performed.

NOTE: I am not responsible for this script and it's correctness. There might be problems such as if there are backup.something folders or files in the backup directory where the script is called, and other bugs that may arise. This is a very simple and minimalistic script!

## Monday, March 1, 2010

### Merging Qt and Eigen

Again in ViBOT, image segmentation assignment, Matlab is really slow, wait minutes for results...

So I decided to try to use Qt for the GUI and OS abstraction layer together with Eigen which is another amazing template-based library for matrix manipulation. The important code to write was to link both libraries, taking advantage of Qt's amazing QImage class which is able to open several file formats and perform low-level pixel access. In a few words, I had to put all the image information contained in QImage into a Eigen's matrix.

Luckily, this task is very simple. Here there is some code:

#ifndef MIMG_H#define MIMG_HUSING_PART_OF_NAMESPACE_EIGEN#include <QImage>#include <Eigen/Core>#include <Eigen/Array>//general type, maybe float or double neededtypedef MatrixXf    MImgType;class MImg{public: //creates an all-black image MImg(unsigned int h, unsigned int w); //creates image from QImage MImg( const QImage &img ); MImgType    R,G,B;  //each component         //made public for faster access unsigned int    getHeight(); unsigned int    getWidth(); QImage *    toQImage();  //convert to QImage /**   Maximizes dynamic range of three channels   independently!  **/ void    maximizeIndependentDynamicRange();private: unsigned int mH,mW; //height, width};#endif // MIMG_H

#include "mimg.h"MImg::MImg(unsigned int h, unsigned int w){  R = MImgType::Zero(h,w);  G = MImgType::Zero(h,w);  B = MImgType::Zero(h,w);  mH = h;  mW = w;}MImg::MImg( const QImage &img ){  int w = img.width();  int h = img.height();  R = MImgType::Zero(h,w);  G = MImgType::Zero(h,w);  B = MImgType::Zero(h,w);   //now copy values..    for (int y=0; y < h; y++)        for (int x=0; x < w; x++)        {            QRgb color = img.pixel(x,y);            R(y,x) = qRed(color)/255.0;            G(y,x) = qGreen(color)/255.0;            B(y,x) = qBlue(color)/255.0;        }  return img;}void    MImg::maximizeIndependentDynamicRange(){  double  min, max;  min = R.minCoeff(); max = R.maxCoeff();  R = (R.cwise() - min) / (max - min);  min = G.minCoeff(); max = G.maxCoeff();  G = (G.cwise() - min) / (max - min);  min = B.minCoeff(); max = B.maxCoeff();  B = (B.cwise() - min) / (max - min);}unsigned int    MImg::getHeight() {  return mH;}unsigned int    MImg::getWidth() {  return mW;}

It is important to mention that this code only handles RGB and won't care about grayscale images or any other type of colour models. The advantage of having the image in this matrix form is that Eigen provides an easy syntax for matrix manipulation, along with many modules performing least squares, Cholesky, diagonalization, etc.