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

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Med Future on 2 Nov 2022
Commented: Image Analyst on 9 Nov 2022
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

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.
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
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.
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
% 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
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?
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);
Image Analyst on 9 Nov 2022
Let's step back and ask WHY you want 1000 binary images, or even one binary image from your data? I see no need for it, and you haven't given any reason - you just said that you want that but with no justification. What do the original coordinates represent in reality?

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 CommentsShowHide 1 older comment
Walter Roberson on 2 Nov 2022
(CVT means Computer Vision Toolbox in this context, and IPT means Image Processing Toolbox)

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