# How to create a Binary image from two columns of raw data

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Hello, I have the following dataset, which consists of two columns. I have also attached a scatter plot of the dataset, where first column is on x axis and 2nd column is on y axis.

I want to create a Binary image for the dataset.

How can i do it in MATLAB

##### 0 Comments

### Answers (3)

DGM
on 2 Nov 2022

This is similar to the prior answer, but in this case, we need to deal with scaling both x and y data.

load matlab.mat

outsize = [500 500];

% rescale data to fit width, generate indices

dlen = size(pdw,1);

x0 = pdw(:,1);

y0 = pdw(:,2);

x0 = (outsize(2)-1)*normalize(x0,'range') + 1;

xidx = 1:outsize(2);

yidx = interp1(x0,y0,xidx);

yidx = outsize(1) - (outsize(1)-1)*normalize(yidx,'range');

% display a dummy image to fix geometry

imshow(false(outsize))

% create ROI object and configure

ROI = images.roi.Polyline(gca);

ROI.Position = [xidx(:) yidx(:)];

% convert to logical mask

outpict = createMask(ROI);

imshow(outpict)

Note that strokes are drawn across any periods where there are no samples being taken.

The same configuration of xidx,yidx would work with the rudimentary non-aa polyline example given in that same thread.

##### 12 Comments

DGM
on 9 Nov 2022

If you knew what the original range of y had been, you might recover the y-data with a resolution determined by the height of the image. All the actual x-data will be lost. It's merely one dot per sample. Any jumps in x are not represented by the image.

Of course, I don't know why you would be wanting to recover the data from the image. Even if the issue with the nonuniform x-data weren't the case, you'd still be losing information in converting it to a raster image.

% you have an output image

inpict = imread('superwidepict.png');

% and a limited-precision stored reference to the original data range

y0range = [1.41351e-05 0.00327854];

% you can recover the y-position of each dot

[yidx,~] = find(inpict);

% you can rescale to data units

yrec = y0range(2) - rescale(yidx,0,range(y0range));

% compare to the original data

load matlab.mat

y0 = pdw(:,2);

% compare original and recovered data

xl = [1830 3196]; % look at a closeup

yl = [0.0061 0.2693]*1E-3;

subplot(2,1,1)

plot(y0); xlim(xl); ylim(yl)

subplot(2,1,2)

plot(yrec); xlim(xl); ylim(yl)

% plot the error

plot(y0-yrec)

Image Analyst
on 2 Nov 2022

What do you want the size of this image to be in pixels? How many rows and columns?

s = load('matlab1.mat')

xy = s.pdw;

x = xy(:, 1);

y = xy(:, 2);

subplot(2, 1, 1);

plot(x, y, 'b.', 'MarkerSize', 10);

grid on;

% Define the size of the image you want

rows = 512;

columns = 1500;

% Rescale data

x = rescale(x, 1, columns);

y = rescale(y, 1, rows);

binaryImage = false(rows, columns);

for k = 1 : length(x)

row = rows - round(y(k)) + 1;

col = round(x(k));

binaryImage(row, col) = true;

end

subplot(2, 1, 2);

imshow(binaryImage);

##### 6 Comments

Image Analyst
on 9 Nov 2022

Walter Roberson
on 2 Nov 2022

filename = 'matlab.mat';

datastruct = load(filename, 'pdw');

pdw = datastruct.pdw;

targetsize = [930 1860]; %rows, columns

margin = 5;

scaled_x = rescale(pdw(:,1), margin+1, targetsize(2)-margin);

scaled_y = rescale(pdw(:,2), margin+1, targetsize(1)-margin);

canvas = zeros(targetsize(1), targetsize(2), 3, 'uint8');

r = 3;

xyr = [scaled_x, scaled_y, r * ones(size(scaled_x))];

canvas = insertShape(canvas, 'circle', xyr, 'Color','white');

binary = canvas(:,:,1) > 0;

%verify

imshow(flipud(binary))

Except for the imshow() at the end, none of this requires the graphics system or creating any files.

##### 2 Comments

Walter Roberson
on 2 Nov 2022

(CVT means Computer Vision Toolbox in this context, and IPT means Image Processing Toolbox)

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