GNOME3 Fallback mode 'fix'

I switched to archlinux recently. I was used to Gnome2, and Gnome3 is quite of a disappointment in the sense that it involves re-learning how to do things, when software is supposed to adapt to us. Anyway, I don't plan to start a discussion on that, googling already reveals many 'gnome3 sucks' posts which provide lot of detail on the 'why'.

In summary, I tried KDE4, but didn't feel comfortable either. Finally I ended up in GNOME3 fallback mode, whose default theme is disappointing as well. I found this theme which makes it look friendlier http://gnome-look.org/content/show.php?content=145210 , but had some problems in nautilus and background font color. So I modified it and uploaded it here.

Now my desktop reminds me of Gnome2 and sometimes I believe it is even friendlier and better looking ;)

ITK and C++11

Dacap made me aware of the new C++11 standard and the supported functionality in GCC 4.6.1. Right after that, I downloaded GCC 4.6.1 and compiled it to try it with the Insight Tool-kit (ITK), particularly because of all those super-templated types in ITK that make coding longer than what it should take, mostly because of the amount of typedefs and re-typing of class names. One of the 'wonders' of C++11 is the auto keyword, which happens to be close to a salvation angel for ITK users.

My main concern was that C++11's implementation in GCC is still quite new, such that incompatibilities may appear almost instantly; and that was the case with a particular GCC extension that -std=c++0x was considering invalid. Therefore, when compiling ITK-based code you may find 'constexpr' errors if you enable c++0x support, particulary in the file vnl.h. Fortunately, a patch has already been created and published here and can be applied directly to the 4.6.1 release source code.

To compile it with Ubuntu you can follow the instructions given here. In my case I didn't want to overwrite the previous gcc compiler in my system, nor I hadn't administrative rights, so I used make DESTDIR=/wherever/you/want install . This way I just had to modify the PATH environment variable to point to the gcc 4.6.1 binaries and tell CMake to use g++-4.6 as the compiler rather than the standard gcc one.

The patch mentioned earlier doesn't disable the constexpr error by itself; for that to be effective one must use the -fpermissive compiler flag. Some other compatibility problems are described here.

Moreover, it is very likely to run on another error regarding an include file missing. To obtain a successful build of ITK my CXX flags were set to: -std=c++0x -fpermissive -include cstddef

UPDATE: a better practice is to compile GMP, MPFR and other libs as static before compiling GCC, so that they are statically linked and the generated compiler binaries can be executed in other machines that may not have those specific libraries installed. Instructions for compiling these libraries and GCC can be found here. My particular configure line for GCC 4.6.1 is the following:

configure --prefix=/usr \

--enable-languages=c,c++,fortran \

--enable-threads=posix \

--enable-tls \

--enable-libgomp \

--enable-lto \

--disable-nls \

--disable-checking \

--disable-multilib \

--with-gmp=/tmp/gcc \

--with-mpfr=/tmp/gcc \

--with-mpc=/tmp/gcc \

--with-libelf=/tmp/gcc \

--with-fpmath=sse

Linux: Fixing resolution problem on external monitor

I recently got a Dell L502x laptop. In order to connect it to an external VGA monitor I am using the mini-Displayport (a la Apple).

The problem is that Linux (Ubuntu Natty) does not always figure out the right display modes for this particular monitor. Unfortunately, cvt nor gtf generate the correct modelines and I am stuck with a lower resolution.

The trick was to dual-boot Windows :S and use PowerStrip to extract the proper modelines, as explained here http://www.x.org/wiki/FAQVideoModes

A bunch of very useful information can also be found here: https://wiki.ubuntu.com/X/Config/Resolution

Excellent good-looking plots

When doing research one usually doesn't care about the aesthetics of plots or the visualized results, as long as they are clear enough to be interpreted. However, when making presentations or in publications, nice plots have an interesting impact and, even though they do not change how good results are, they still make things look more professional.

I have been usin Matlab for a while now. It is one of those softwares that you usually hate, especially if you come from a better-structured programming background. Leaving execution speed out of the equation, Matlab sucks in many ways but there a few pros that build up its popularity, such as extremely easy and straightforward debugging and the available toolboxes and functions on Matlab Central. However, it is very easy to find blogs like Abandon Matlab, where some posts really make the point about leaving Matlab forever and finding a better and more appropriate alternative.

Anyways, enough of Matlab hate. The point is that I was looking for nice plots and I remembered about that amazing piece of software called Mathematica. I will not discuss the differences between Mathematica and Matlab, but just say that they were made for different purposes. However, take a look at the following plots generated with Matlab and Mathematica respectively, from the same data (click to see the real image since blogger is automatically introducing some JPEG artifacts):

Matlab	Mathematica
load data.mat; stem(x,y); hold on; xlabel('x','Interpreter','LaTex'); ylabel('f(x)','Interpreter','LaTex');	data = Import["test.mat", "LabeledData"]; ListPlot[ Transpose[Flatten[{"x" /. data, "y" /. data}, 1]], Filling -> Axis, AxesLabel -> {x, f[x]} ]

The difference is easy to grasp by just looking at the plots: Mathematica does a great job, while Matlab looks just ok. Something I usually do to make Matlab plots is to apply a grid with grid on, but the plot still looks not as professional as with Mathematica. Obviously there is space for cheating here; maybe if you look at how much Mathematica code is needed you may say that I haven't been fair enough. However, most of the code in the Mathematica snippet is needed because the data is read from a Matlab MAT file.

In my opinion, the most amazing and simple detail that Mathematica uses and Matlab does not is antialiasing. It is a very subtle detail but it makes plots look softer and, somehow, more human and easier for our eyes to watch. There have been some attempts such as this script. However, that script is an smart user attempt to generate a plot with antialiasing but the antialiasing is simulated by resizing the plot, which still doesn't look as good as Mathematica's output (images not shown here but you can try it on your own).

Beyond that, there are some problems when exporting plots from Matlab. First, exporting to PDF generates a PDF file that contains a whole page such as A4 and the plot in the middle with huge white spaces around. This is very annoying when working with pdflatex and the PDF generated by Matlab must be cropped. Moreover, even if exporting to PNG, the final image file does not look exactly as what is seen on the computer screen. This can lead to an endless 'fight' with Matlab's exporting options, sometimes without success. All of this doesn't seem to happen to Mathematica, at least considering what I have tried so far. PDF files look great and have the exact size of the plot and can be inserted straight away into latex documents.

I don't want to go into much detail in this post since it could take a long time to discuss plot customization options in Matlab and Mathematica. My experience shows that Matlab works well to show the data properly and it is still interpretable, but it lacks the quality of softwares such as Mathematica or Matplotlib (this last one is another very nice plotting tool, free of charge). For more examples on the plotting power of Mathematica see here, and for Matplotlib here.

Nice Linux PDF manipulation utilities

I found myself writing many reports with LaTex lately. Using pdflatex has it advantages, but things can get quite annoying when one wants to insert a figure which was generated in Matlab or any other program.

Specially, MATLab does not do a nice job when exporting PDFs and leaves a whole blank area (the page itself actually) which is not desirable if we want to put a figure in a latex document. Fortunately, there is a linux program called pdfcrop that does the job correctly (not 100% trustable, but 90% of the times I get good results).

Another useful program is pdfimages, which extracts images from a pdf file. The pdfimages output is generally in huge pgm files, so it's better to convert it to something like png which results in smaller files, still usable with pdflatex.

Finally, pdfjoin and pdf90 allows one to join several files into a single one and rotate pages respectively. The Ubuntu package for these two is called pdfjam.

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:
<br />#!/bin/bash<br />##############################################<br />## This script should be called from the dir<br />## where the backup will be made to<br />###############################################<br /><br /># directory to backup<br />sourceDir=/media/something<br />rsyncFlags='--modify-window=1 -a --verbose --delete '<br /><br />#find if we have a backup already<br />find backup.0 -maxdepth 0 > /dev/null;<br /><br />if (( $? != 0 )); then {<br /> echo 'First Backup';<br /> nextBackup='0';<br /> firstBackup=1;<br />} else<br />{<br /> # find last backup<br /> lastBackup=`find backup.* -maxdepth 0 | sed 's/backup.//' | sort -n -r | head -1`;<br /><br /> echo 'Resuming backup' $lastBackup '...';<br /><br /> # +1 for next one<br /> nextBackup=$(( $lastBackup + 1 ));<br /> echo $nextBackup;<br /> firstBackup=0;<br />}<br />fi<br /><br />lastBackup=backup.$lastBackup;<br />nextBackup=backup.$nextBackup;<br /><br />if (( $firstBackup == 1 )); then {<br /> rsync $rsyncFlags $sourceDir/ $nextBackup/;<br /> R=$?;<br />} else<br />{<br /> rsync $rsyncFlags --link-dest=../$lastBackup $sourceDir/ $nextBackup/;<br /> R=$?;<br />} fi<br /><br />if (( $R != 0 )); then {<br /> echo '=======> ERROR DURING BACKUP!!';<br /> exit $R;<br />} fi<br /><br /># make link to last backup<br />rm -f HEAD;<br />ln -s -f $nextBackup HEAD<br />if (( $? != 0 )); then {<br /> echo '=======> ERROR Creating link!!';<br /> exit $?;<br />} fi<br /><br /># save history<br />echo -e $nextBackup '\t\t' `date -R` >> backup-history;<br />if (( $? != 0 )); then {<br /> echo '=======> ERROR Saving History!!';<br /> exit $?;<br />} fi<br /><br />echo ' ';<br />echo '------------- BACKUP DONE ----------------';<br />echo ' ';<br />

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!

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_H

USING_PART_OF_NAMESPACE_EIGEN

#include <QImage>

#include <Eigen/Core>
#include <Eigen/Array>

//general type, maybe float or double needed
typedef 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.