Image generation with MATLAB

• Written by Yukiyasu Kamitani, April 16, 1999
• Modified by Mitsuaki Tsukamoto, November 15, 2016

Advantages of using MATLAB for image generation are:

• image as matrix: operation of images without "for" loop
• independent of platform
• mathematical transformation and analysis, combined with Image Processing Toolbox and Signal Processing Toolbox
• psychophysical experiment, combined with Psychophysics Toolbox (only for Mac)

Methods to generate 2D images as matrices using MATLAB are described here. 3D graphics is not covered.

Basics of image generation with MATLAB (Image Processing Toolbox required)

Basic image generation

White square (100x100 pix):

image = ones([100 100]);
imshow(image);

% By default, 0 -> black, 1 -> white.

Random-dot:

image = rand([100 100]);
imshow(image);

Sine grating:

image = 0.5 * sin([1:100]')*ones(1,100) + 0.5*ones([100 100]);
imshow(image);

Circle:

X = ones(100,1)*[-50:49]; Y = [-50:49]'*ones(1,100);
Z = X.^2 + Y.^2;
% mesh(Z);
image = zeros([100 100]);
image(find(Z <= 50^2)) = 1;
imshow(image);

Index color

Assign color by defining a color map (256x3).

%index  [ R     G      B]       (R, G, B = 0 - 1)
%    1  [ 0     0      0]
%    2  [ 0.1   0      1]
%               :
%               :
%   256 [ 0     1     0.5]

image = ones([100 100]);    % square composed of index "1"s
colormap = [0:1/255:1]' * ones(1,3);   % black (1)- white(256)
colormap(1,:) = [1 0 0]; % index "1" -> red
imshow(image, colormap);

Image analysis and processing (FFT and Convolution)

% Load jpeg files
orgImage = imread('Pixel.jpg', 'jpg');
figure(1); imshow(orgImage);

% 2D FFT
fftImage = fftshift(fft2(orgImage));   % 2d fft
ampImage= abs(fftImage);
figure(2); imshow(ampImage,  [0  10000 ]);

% Convolution (low-pass filtering)
filter = fspecial('gaussian',[10 10], 4);  % gaussian kernel
filterImage = conv2(orgImage, filter);     % convolution
figure(3); imshow(filterImage, [0 250]);

% 2D FFT of filtered image
fftFilterImage = fftshift(fft2(filterImage));
ampFilterImage= abs(fftFilterImage);
figure(4); imshow(ampFilterImage,  [0  10000 ]);

Yuki Kamitani's image tools

This repository contains Matlab functions to generate pattern images and basic figures. Here is a list of the functions and basic usage.

pattern

Sine grating:

imageMatrix = sinGrating(vhSize, cyclesPer100Pix, orientation, phase, mean, amplitude)

% Example
imshow(sinGrating([100 100], 5, 45, 0, 0.5, 0.5));

Concentric sine pattern:

imageMatrix = sinConcentric(vhSize, cyclePer100pix, phase, mean, amplitude)

% Example
imshow(sinConcentric([100 100], 10, 0, 0.5, 0.5));

Gabor patch:

imageMatrix = gabor(vhSize, cyclesPer100Pix,orientation, phase, sigma , mean, amplitude)

% Example
imshow(gabor([100 100], 8, 45, 0, 6 , 0.5, 0.5));

Random dots:

imageMatrix = randDot(vhSize, dotSize, dotCol, backCol, density);

% Example
imshow(randDot([100 100], 5, 1, 0, 0.3));

drawing

Filled oval:

imageMatrix = drawOvalFill(BaseIm, TopLeft_BotRight, foreground)

% Example
imshow(drawOvalFill(ones(150, 150), [50 50 90 90], 0.5));

Frame Oval:

imageMatrix = drawOvalFrame(BaseIm, TopLeft_BotRight, foreground, frameWidth)

% Example
imshow(drawOvalFrame(ones(150, 150), [50 50 90 90], 0.5, 3));

Filled Rectangle:

imageMatrix = drawRectFill(BaseIm, TopLeft_BotRight, foreground)

% Example
imshow(drawRectFill(zeros (100,100), [20 20 50 50], 1));

Frame rectangle:

imageMatrix = drawRectFrame(BaseIm, TopLeft_BotRight, foreground, frameWidth)

% Example
imshow(drawRectFrame(zeros (100,100), [20 20 50 50], 1,1));

Line:

imageMatrix = drawLines(BaseIm, startPos_endPos, foreground, PenSize)

% Example
imshow(drawLines(zeros(200,200), [100 100  180 140], 0.5, [2 2]));

Polygon:

imageMatrix = drawPolygon(BaseIm, dotPos, foreground)

%Example
imshow(drawPolygon(ones(100,100), [10 10 ; 90 30 ;  90 90], 0.5));

tool

contains subfunctions used in the functions in 'pattern' and 'drawing'

Examples

Neon color spreading:

radiusDisk = 35; % radius of disks
diskImage = drawOvalFill(zeros(200, 200), [50 50 50 50]+radiusDisk*[-1 -1 1 1], 1);
diskImage = drawOvalFill(diskImage, [150 50 150 50]+radiusDisk*[-1 -1 1 1], 1);
diskImage = drawOvalFill(diskImage, [50 150 50 150]+radiusDisk*[-1 -1 1 1], 1);
diskImage = drawOvalFill(diskImage, [150 150 150 150]+radiusDisk*[-1 -1 1 1], 1);

rectImage = drawRectFill(zeros(200,200), [40 40 160 160], 2);

neonImage = diskImage + rectImage + ones(200, 200);
% background: 1, pacman: 2, rectangle: 3, intersection: 4
colorMap = [0 0 0; 1 1 1; 0 0 0; 1 0 0];   % define color map for indices 1-4
imshow(neonImage, colorMap);

Random-dot stereogram

leftImage = randDot([200 200], 2, 1, 0, 0.5);
rightImage = leftImage;
smallRect =  randDot([70 70], 2, 1, 0, 0.5);  %square to be seen in depth

leftImage = imageOnImage(leftImage, smallRect, [100 100-10]);
rightImage = imageOnImage(rightImage, smallRect, [100 100+10]);

figure(1); imshow(leftImage);
figure(2); imshow(rightImage);

Psychophysics Toolbox

Image matrices can be used for presenting visual stimuli with Psychophysics Toolbox. Instead of imshow (imageMatrix):

SCREEN(windowPointer,'PutImage',imageMatrix);

Color map can be defined by:

SCREEN(windowPointer,'SetClut', colorMap);

Color values in color map of Psychophysics Toolbox are 0-255, not 0-1.

Psychophysics Toolbox Home