praktische aspekte der informatik - graphics.tu-bs.de filepraktische aspekte der informatik moritz...

Praktische Aspekte

der InformatikMoritz Mühlhausen

Prof. Marcus Magnor

https://graphics.tu-bs.de/teaching/ws1718/padi/

MatlabPrototyping and designing new algorithms


Further Reading

Warning!The following slides are meant to give you

a very superficial introduction.

If you want to learn more, have a look at:http://www.mathworks.com/help/matlab/

http://www.cs.jhu.edu/CIRL/class/600.161/class2/shortmatlab.pdf

https://www.tu-braunschweig.de/it/service-interaktiv/software/liste#M


Outline

Introduction

Matrices

Plotting

Programming Language


Introduction

Benefits

• A large and widely used toolbox of numeric and

image functions.

• A great tool for prototyping

and developing new

algorithms.

• A lot of high-level features.

• You can link C/C++/cuda!

Drawbacks

• It can be slow.

• You need a license.

• Untyped variables.


Introduction


Outline

Introduction

Matrices

Plotting



Matrices

• Variables and Data Types will be very familiar to you. char, int8, uint8, … int64, uint64, single, double, …

• Matlab treats all variables as matrices.

Vectors are matrices with only one row/column.

Values are matrices with only one entry.

>> M = [1,2,3; 4,5,6; 7,8,9]

M =

1 2 3

4 5 6

7 8 9


Matrices

>> M = [1,2,3; 4,5,6; 7,8,9]

M =

1 2 3

4 5 6

7 8 9

>> M_sub = M(1:2,:)

M_sub =

1 2 3

4 5 6


Matrices

j:k is the same as [j,j+1,...,k] is empty if j > k

j:i:k is the same as [j,j+i,j+2i, ..,k] is empty if i > 0 and j > k or if i < 0 and j < k.

A(:,j) is the j-th column of A.

A(i,:) is the i-th row of A.

A(:,:) is the equivalent two-dimensional array. For matrices this is A.

A(j:k) is A(j), A(j+1),...,A(k).

A(:,j:k) is A(:,j), A(:,j+1),...,A(:,k).

A(:,:,k) is the k-th page of three-dimensional array A.

A(i,j,k,:) is a vector in a four-dimensional array A.

A(:) is all the elements of A, as a single column.On the left side of an assignment statement, A(:) fills A.


Matrices

X = M’ Transposed matrix M

X = inv(M) Inverse of an 𝑛 × 𝑛 matrix M

X = det(M) Determinant of M

[X,D] = eig(M) Eigenvalues and Eigenvectors of M

[r,c] = size(M) Size of M

[Q,R] = qr(M) QR decomposition of M


Outline

Introduction

Matrices

Plotting



Plotting

• Matlab can also quickly plot your data

>> X = [1,4,6,8];

>> Y = [7,3,5,2];

>> plot(X,Y)

• You can also plot a single vector,Matlab will just use it’s indices

>> V = [6,4,8,2];

>> plot(V)


Plotting

• Bar Charts

>> V = [75 91 105 123.5 131 189];

>> bar(V)

• Scatter Plots

>> X = rand(1,100);

>> Y = rand(1,100);

>> plot(X,Y,'*')


Outline

Introduction

Matrices

Plotting




Conditional

if a == b,

…

elseif a > b

…

else

…

end

For-Loop

for i = 1:8

for j = {1:4,8,15}

…

end

end

While-Loop

while i < N

…

end



You can often avoid simple loops…

for i = 1:10000

x(i) = sin(i);

end

… by doing something like this

n = 1:10000;

x = sin(n);



You can also create reusable scripts and functions

imageDenoiseTest.m

image = imread('padi.png'); % RGB image

redChannel = image(:,:,1); % extract the first channel

redChannel = wiener2(redChannel,[5,5]); % denoising function

disp('Image denoised!');

imshow(redChannel); % show the result

>> imageDenoiseTest

Image denoised!



You can also create reusable scripts and functions

functionTest.m

function [denoisedImage] = functionTest(imageName)

image = imread(imageName); % RGB image

denoisedImage = image(:,:,1); % extract the first channel

denoisedImage = wiener2(denoisedImage,[5,5]); % denoising function

disp('Image denoised!');

end

>> image = functionTest('padi.png')

Image denoised!

>> imshow(image)


Matlab

Introduction

Matrices

Plotting



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