Jacek's Blog

Software Engineering Consultant

# Constructing Parameterized Matrices with GNU Octave

February 6, 2023

This week I was dealing with image processing and linear algebra, and I needed a quick derivation of a specific kind of matrix. With GNU Octave to the rescue, this cost me only a few minutes! This article represents my notes from this little journey from the mathematical derivation of the matrix and the symbolic solution that I then ported to C++.

In a C++ project where I use the OpenCV library to undistort pictures, I needed a function that creates shearing matrices for specific shearing factors. The following illustration shows the needed transformation: Example projection: The B’/B’’ and D’/D’’ points are the projections for values of N smaller/larger 1.0

Given a picture that is W pixels wide and H pixels high, all points on the main diagonal line shall remain untouched by the transformation. For a shearing factor N < 1.0, all other points should be pulled towards the main diagonal line, along the direction of the other diagonal line (which goes from point B to D). The same applies for a factor N > 1.0 but in this case, the points are pushed away from the main diagonal line instead of being pulled towards it.

## Defining the Equations

So we are looking for a Matrix $$M_N$$ for which the following qualitative example projections hold:

\forall N \in \Reals \qquad \begin{align} M_N A &= A \\ M_N C &= C \\ M_N B &= \begin{cases} B' & \text{if }N < 1 \\ B & \text{if } N = 1 \\ B'' & \text{if }N > 1 \end{cases} \\ M_N D &= \begin{cases} D' & \text{if }N < 1 \\ D & \text{if } N = 1 \qquad \\ D'' & \text{if }N > 1 \end{cases} \end{align}

I’m not good at memorizing standard matrices, but most geometric transformations can be calculated using eigenvalues and eigenvectors: From the image and the example projections, we can derive the following generic eigenvalue- and eigenvector-based equations, where $$x_1$$ is the main diagonal and $$x_2$$ is the other diagonal:

$x_1 = \frac{C - A}{|C - A|} = \frac{1}{\sqrt{W^2 + H^2}} \begin{pmatrix} W \\ H \end{pmatrix} , \quad \lambda_1 = 1$

$x_2 = \frac{B - D}{|B - D|} = \frac{1}{\sqrt{W^2 + H^2}} \begin{pmatrix} W \\ -H \end{pmatrix} , \quad \lambda_2 = N$

$M x_1 = \lambda_1 x_1 = x_1 , \quad M x_2 = \lambda_2 x_2 = N x_2$

The idea is now to create a system of equations that we could solve on paper or automatically using the computer: From two multiplications of the matrix with a vector each, we can form one equation where we multiply the matrix $$M$$ with another matrix that is composed of the two input vectors, which then equals another matrix that is composed of the two output vectors.:

\begin{align} M \begin{pmatrix} x_1 & x_2 \end{pmatrix} &= \begin{pmatrix} \lambda_1 x_1 & \lambda_2 x_2 \end{pmatrix} \\ M X &= X_\lambda \end{align}

Libraries for Linear Algebra (e.g. numpy, MATLAB, and GNU Octave) have functions for solving linear systems of equations. They all work with a matrix equation like $$AX = B$$: Calling $$\text{solve}(A, B)$$ returns the solution of $$X$$. At this point, we can’t just run $$\text{solve}(X, X_\lambda)$$ because $$M$$ and $$X$$ first need to be swapped to fit the input format.

Using known transposition properties, we can quickly bring our system of equations to the right form by transposing the matrices:

\begin{align} ( M X )^\intercal &= X_\lambda^\intercal \\ X^\intercal M^\intercal &= X_\lambda^\intercal \\ M^\intercal &= \mathrm{solve}(X^\intercal, X_\lambda^\intercal) \\ M &= \mathrm{solve}(X^\intercal, X_\lambda^\intercal)^\intercal \end{align}

…and now we can finally solve the system automatically.

## Solving the Equations

Back in my university days, I used the proprietary MATLAB suite to do such tasks with just very few lines of code, which students got for free. I am not a student at a university any longer, so when choosing software, I generally first have a look if there is free software before choosing anything proprietary. In hindsight, I also find it a great pity that my university (RWTH Aachen in Germany) played a part in luring its students into a comfortable dependency on very expensive proprietary tools (apart from that it is a super awesome university). The free solutions available at the same time would not have been less suitable for educational purposes.

Luckily, GNU Octave exists and it can do many many things just like MATLAB. What I also like about GNU Octave:

• It’s a much smaller package (with a package manager and a package ecosystem, so you can choose what you need)
• You can run it without being connected to an annoying license server
• It runs in the shell so you don’t have to launch a big IDE for everything (Maybe that has changed in the last 10 years, but I doubt it)

So let’s write a very short script that calculates our parametrized Matrix $$M$$:

# filename: octave-script.m

syms W H N

x1 = [W; H]
x2 = [W;-H]

M = transpose(transpose([x1,x2]) \ transpose([x1,N * x2]))

In my later program, I wanted not only N to be an input parameter, but also the image dimensions W and H, so we have three symbols in this program. I also left out the division by the length of the main vectors $$x_1$$ and $$x_2$$, because they would cancel out in the resulting system of equations anyway (try it out to see yourself).

I typically don’t have special tools like this installed on my system, because NixOS makes it so easy to install project-specific tools in project-specific shells. In the nixpkgs ecosystem, octave is a package that can be preloaded with the user’s package selection from the octave ecosystem. The program at hand only needs the symbolic package. I just added the myOctave symbol from the following code to the nix flake of my C++ project.

It is also possible to run a shell with just octave, without a project around it, by running this script through nix-shell:

let pkgs = import <nixpkgs> { };
myOctave = pkgs.octave.passthru.withPackages (p: with p; [
symbolic
]);

in pkgs.mkShell { nativeBuildInputs = [ myOctave ]; }

Let’s see it in action:

$nix-shell octave-shell.nix [nix-shell:~]$ octave octave-script.m
...

M = (sym 2×2 matrix)

⎡  N   1    W⋅(1 - N)⎤
⎢  ─ + ─    ─────────⎥
⎢  2   2       2⋅H   ⎥
⎢                    ⎥
⎢H⋅(1 - N)    N   1  ⎥
⎢─────────    ─ + ─  ⎥
⎣   2⋅W       2   2  ⎦

Voilà, that’s the matrix that we needed!

It’s very rewarding to see how the matrix collapses to an identity matrix if we define $$N = 0$$.

## Using the Matrix in C++

We can now take this solution and simply implement it 1:1 in our target language, which is C++ in this case. The matrix type that is used in OpenCV is the cv::Mat type, which can be constructed like this:

cv::Mat shearingMatrix(const cv::Size &s, double n) {
const float w = s.width, h = s.height;

return cv::Mat_<float>(3, 3) <<
n / 2.0 + 0.5,           (1.0 - n) / 2.0 * w / h, 0,
(1.0 - n) / 2.0 * h / w, n / 2.0 + 0.5,           0,
0,                       0,                       1;
}

## Summary

GNU Octave is fun to use once it’s installed. I only use it in such rare cases that I don’t install it regularly on my systems. It was however very easy to integrate into the project-specific shell. This way everyone who takes part in the development of this repository can also execute the Octave scripts that have been used to derive some of the C++ code. If any upfront requirement changes that led to the design of specific code, it’s easy to change even the most complicated snippets.

If you happened to like this article or need some help with Nix/NixOS, also have a look at my corporate Nix & NixOS Trainings and Consulting Services.