Enlarging models in MATLAB - matlab

Im trying to create a project in MATLAB where i project a heat map matrix on to a cylinder in MATLAB but i'm bumping in to all kinds of issues.
Im using MATLAB version R2019b(Latest version).
Now my first issue id like to adress is is the modells being absolutely tiny even when i zoom in to the max.
Is there any way to make these models larger or to display them in a separate window ??
Also my second question is the regarding the scaling. As you can see the scale on the Z-axis of the cylinder goes to 512. any way to get a larger more detailed image where i can see each point on that scale?
clear vars
filename =
['/Users/gomer/Documents/Datavisualisering/Projekt/data/day/filenames.txt'];
%This line simply gives us a table of all filenames in this file.
T = readtable(filename);
tsize = size(T);
%extracts the content of the table as a categorical array.
%%
%
% {rownumber,variablenumber}. {100,1} = row 100, variable 1.
%converts the categorical array into a string array.
%joins the string array across the column.
%string(T{100:105,1}); implies from row 100 to row 105 and use variable 1.
%This line simply adds the name of the file at row 100 to the path of the
%file. Hnece we get a full filepath.
filename = strcat('/Users/gomer/Documents/Datavisualisering/Projekt/data/day/', string(T{100,1}));
map100 = getHeatMap(filename);
%%
%
filename = strcat('/Users/gomer/Documents/Datavisualisering/Projekt/data/day/', string(T{1000,1}));
map1000 = getHeatMap(filename);
%creates a image
k=imshow(map100);
%creates a colormap.
%gca returns the current axes (or standalone visualization) in the current figure.
%hence the command just works top down and affects last image displayed.
colormap(gca, 'jet');
k=imshow(map1000);
colormap(gca, "jet");
function heat = getHeatMap(filename)
%Returns a struct with info about the file.
s = dir(filename);
%Opens a file for reading, hence the 'r'.
fin=fopen(filename,'r');
%A = fread(fileID,sizeA,precision)
%reads file data into an array, A, with dimensions, sizeA, and positions the file pointer after the last value read.
%fread populates A in column order.
I=fread(fin,s.bytes,'uint8=>uint8');
%The uint16 function converts a Input array, specified as a scalar, vector, matrix, or multidimensional array.
%Converts the values in to type uint16 and creates a Matrix with the values.
%Looks more like we are just calculating the size of values to be
%deducted from matrix size (no idea why).
w = uint16(I(1))+256*uint16(I(2));
h = uint16(I(3))+256*uint16(I(4));
skip = s.bytes - w*h + 1;
IN = I(skip:1:s.bytes);
%reshape(A,sz) reshapes A using the size vector, sz, to define size(B). For example, reshape(A,[2,3]) reshapes A into a 2-by-3 matrix.
%sz must contain at least 2 elements, and prod(sz) must be the same as numel(A).
%single(X) converts the values in X to single precision.
Z=single(reshape(IN,w,h));
%Interpolate the values.
%telling the system which points to interpolate in between.
Z=griddedInterpolant(Z');
y_range = linspace(1.0,single(h),360);
x_range = linspace(1.0,single(w),512);
%Used to apecify the query points (points in between which we want
%interpolation) as vectors (this is to get a more continues image).
%specifies the query points as grid vectors.
%Use this syntax to conserve memory when you want to query a large grid of points.
heat = uint8(Z({y_range, x_range}));
end
Thank you

Resizing Figure Window
Copying your script to a .m and running it instead of a live .mlx file will automatically create all the figures in a separate window by default. To adjust the size of the figures the following the position property can be modified. To adjust the scale functions xticks(), yticks() and zticks() can be used. These three scale functions take in an array representing all the line markers along the respective axis/scale.
Test Plot Script:
X_Position = 10;
Y_Position = 10;
Width = 1000;
Height = 500;
%Configuring the figure settings%
figure('Position', [X_Position Y_Position Width Height])
%Test plot (replace with your plot)%
Data = [1 2 3 4 5 6];
plot(Data);
Plotting Heatmaps on a Cylindrical Surfaces
Method 1: Using warp() Function
Haven't delved into which orientation the data should be set as but this is an option to use the warp() function to wrap the heatmap around a cylinder. They're most likely other 3D plotting options if specific points are of interest. The points of the cylinder to be plotted are generated using the cylinder() function which returns the xyz-coordinates of the cylinder's wireframe. The fourth input argument into the warp() function serves and a colormap in this case it is proportional to the heatmap values.
load('HeatMapMatrix.mat');
%Setting up the figure%
clf;
close all;
figure('Position', [10 10 1000 500])
%Creating the cylinder%
Cylinder_Radius = 360;
Cylinder_Height = 512;
[X,Y,Z] = cylinder(Cylinder_Radius,Cylinder_Height-1);
%Warping the heatmap along the cylinder%
subplot(1,2,1); warp(X,Y,Cylinder_Height.*Z,map100');
colormap(gca,'jet');
subplot(1,2,2); warp(X,Y,Cylinder_Height.*Z,map100);
colormap(gca,'jet');
Method 2: Plotting All Points and Using surf() Function:
In this case, the coordinates for a cylinder are generated by first finding the coordinates of the circumference of the cylinder. This is done by using the sin() and cos() relationships:
X-Points = Radius × cos(𝜃)
Y-Points = Radius × sin(𝜃)
This results in the xy-coordinates of the cylinder's circle. These xy-coordinates need to be duplicated using repmat() to be later used for the varying heights. The process can be described best with a diagram as follows:
Four matrices above are created to plot each Heat Data point corresponding to an xyz-coordinate. The x and y coordinates are repeated in every row of matrices X-Points and Y_Points since those are constant for the repeating circles. The columns of the matrix Z-Points are also duplicates of each other since the heights should be constant for each row corresponding to each circle.
load('HeatMapMatrix.mat');
Radius = 20;
Number_Of_Data_Points = 360;
Theta = linspace(0,2*pi,Number_Of_Data_Points);
%The xy values according to radius and number of points%
X_Points = Radius*cos(Theta);
Y_Points = Radius*sin(Theta);
map100 = rot90(map100);
Sample_Range = 255 - 0;
Temperature_Range = 450 - 50;
Multiplier = Temperature_Range/Sample_Range;
map100 = map100.*Multiplier + 50;
Height = 512;
X_Points = repmat(X_Points,Height,1);
Y_Points = repmat(Y_Points,Height,1);
Z_Points = (1:512)';
Z_Points = repmat(Z_Points,1,Number_Of_Data_Points);
clf;
close;
figure('Position', [10 10 800 500])
Offset = 200;
subplot(1,3,1:2); Surface = surf(Y_Points,X_Points,Z_Points,'Cdata',map100);
title("3D Heatmap Plot");
zlabel("Height");
shading interp
colorbar
% direction = [0 1 0];
% rotate(Surface,direction,90)
Maximum_Value = 450;
Minimum_Value = 50;
caxis([Minimum_Value Maximum_Value]);
subplot(1,3,3); imshow(rot90(rot90(map100)));
colormap(gca, 'default');
title("Flat Heatmap Plot");
caxis([Minimum_Value Maximum_Value]);
colorbar;
Ran using MATLAB R2019b

Related

Why is my bilinear interpolation vastly different from the in-built matlab function?

I've been working on bilinear interpolation based on wiki example in matlab. I followed the example to the T, but when comparing the outputs from my function and the in-built matlab function, the results are vastly different and I can't figure out why or how that happens.
Using inbuilt matlab function:
Result of my function below:
function T = bilinear(X,h,w)
%pre-allocating the output size
T = uint8(zeros(h,w));
%padding the original image with 0 so i don't go out of bounds
X = padarray(X,[2,2],'both');
%calculating dimension ratios
hr = h/size(X,1);
wr = w/size(X,2);
for row = 3:h-3
for col = 3:w-3
%for calculating equivalent position on the original image
o_row = ceil(row/hr);
o_col = ceil(col/wr);
%getting the intensity values from horizontal neighbors
Q12=X(o_row+1,o_col-1);
Q22=X(o_row+1,o_col+1);
Q11=X(o_row-1,o_col-1);
Q21=X(o_row-1,o_col+1);
%calculating the relative positions to the enlarged image
y2=round((o_row-1)*hr);
y=round(o_row*hr);
y1=round((o_row+1)*hr);
x1=round((o_col-1)*wr);
x=round(o_col*wr);
x2=round((o_col+1)*wr);
%interpolating on 2 first axis and the result between them
R1=((x2-x)/(x2-x1))*Q11+((x-x1)/(x2-x1))*Q21;
R2=((x2-x)/(x2-x1))*Q12+((x-x1)/(x2-x1))*Q22;
P=round(((y2-y)/(y2-y1))*R1+((y-y1)/(y2-y1))*R2);
T(row,col) = P;
T = uint8(T);
end
end
end
The arguments passed to the function are step4 = bilinear(Igray,1668,1836); (scale factor of 3).
You are finding the pixel nearest to the point you want to interpolate, then find 4 of this pixel’s neighbors and interpolate between them:
o_row = ceil(row/hr);
o_col = ceil(col/wr);
Q12=X(o_row+1,o_col-1);
Q22=X(o_row+1,o_col+1);
Q11=X(o_row-1,o_col-1);
Q21=X(o_row-1,o_col+1);
Instead, find the 4 pixels nearest the point you want to interpolate:
o_row = ceil(row/hr);
o_col = ceil(col/wr);
Q12=X(o_row,o_col-1);
Q22=X(o_row,o_col);
Q11=X(o_row-1,o_col-1);
Q21=X(o_row-1,o_col);
The same pixel’s coordinates then need to be used when computing distances. The easiest way to do that is to separate out the floating-point coordinates of the output pixel ((row,col)) in the input image (o_row,o_col), and the location of the nearest pixel in the input image (fo_row,fo_col). Then, the distances are simply d_row = o_row - fo_row and 1-d_row, etc.
This is how I would write this function:
function T = bilinear(X,h,w)
% Pre-allocating the output size
T = zeros(h,w,'uint8'); % Create the matrix in the right type, rather than cast !!
% Calculating dimension ratios
hr = h/size(X,1); % Not with the padded sizes!!
wr = w/size(X,2);
% Padding the original image with 0 so I don't go out of bounds
pad = 2;
X = padarray(X,[pad,pad],'both');
% Loop
for col = 1:w % Looping over the row in the inner loop is faster!!
for row = 1:h
% For calculating equivalent position on the original image
o_row = row/hr;
o_col = col/wr;
fo_row = floor(o_row); % Code is simpler when using floor here !!
fo_col = floor(o_col);
% Getting the intensity values from horizontal neighbors
Q11 = double(X(fo_row +pad, fo_col +pad)); % Indexing taking padding into account !!
Q21 = double(X(fo_row+1+pad, fo_col +pad)); % Casting to double might not be necessary, but MATLAB does weird things with integer computation !!
Q12 = double(X(fo_row +pad, fo_col+1+pad));
Q22 = double(X(fo_row+1+pad, fo_col+1+pad));
% Calculating the relative positions to the enlarged image
d_row = o_row - fo_row;
d_col = o_col - fo_col;
% Interpolating on 2 first axis and the result between them
R1 = (1-d_row)*Q11 + d_row*Q21;
R2 = (1-d_row)*Q12 + d_row*Q22;
T(row,col) = round((1-d_col)*R1 + d_col*R2);
end
end
end

In Matlab, how to draw lines from the curve to specific xaxis position?

I have a spectral data (1000 variables on xaxis, and peak intensities as y) and a list of peaks of interest at various specific x locations (a matrix called Peak) which I obtained from a function I made. Here, I would like to draw a line from the maximum value of each peaks to the xaxis - or, eventually, place a vertical arrow above each peaks but I read it is quite troublesome, so just a vertical line is welcome. However, using the following code, I get "Error using line Value must be a vector of numeric type". Any thoughts?
X = spectra;
[Peak,intensity]=PeakDetection(X);
nrow = length(Peak);
Peak2=Peak; % to put inside the real xaxis value
plot(xaxis,X);
hold on
for i = 1 : nbrow
Peak2(:,i) = round(xaxis(:,i)); % to get the real xaxis value and round it
xline = Peak2(:,i);
line('XData',xline,'YData',X,'Color','red','LineWidth',2);
end
hold off
Simple annotation:
Here is a simple way to annotate the peaks:
plot(x,y,x_peak,y_peak+0.1,'v','MarkerFaceColor','r');
where x and y is your data, and x_peak and y_peak is the coordinates of the peaks you want to annotate. The add of 0.1 is just for a better placing of the annotation and should be calibrated for your data.
For example (with some arbitrary data):
x = 1:1000;
y = sin(0.01*x).*cos(0.05*x);
[y_peak,x_peak] = PeakDetection(y); % this is just a sketch based on your code...
plot(x,y,x_peak,y_peak+0.1,'v','MarkerFaceColor','r');
the result:
Line annotation:
This is just a little bit more complicated because we need 4 values for each line. Again, assuming x_peak and y_peak as before:
plot(x,y);
hold on
ax = gca;
ymin = ax.YLim(1);
plot([x_peak;x_peak],[ymin*ones(1,numel(y_peak));y_peak],'r')
% you could write instead:
% line([x_peak;x_peak],[ymin*ones(1,numel(y_peak));y_peak],'Color','r')
% but I prefer the PLOT function.
hold off
and the result:
Arrow annotation:
If you really want those arrows, then you need to first convert the peak location to the normalized figure units. Here how to do that:
plot(x,y);
ylim([-1.5 1.5]) % only for a better look of the arrows
peaks = [x_peak.' y_peak.'];
ax = gca;
% This prat converts the axis unites to the figure normalized unites
% AX is a handle to the figure
% PEAKS is a n-by-2 matrix, where the first column is the x values and the
% second is the y values
pos = ax.Position;
% NORMPEAKS is a matrix in the same size of PEAKS, but with all the values
% converted to normalized units
normpx = pos(3)*((peaks(:,1)-ax.XLim(1))./range(ax.XLim))+ pos(1);
normpy = pos(4)*((peaks(:,2)-ax.YLim(1))./range(ax.YLim))+ pos(2);
normpeaks = [normpx normpy];
for k = 1:size(normpeaks,1)
annotation('arrow',[normpeaks(k,1) normpeaks(k,1)],...
[normpeaks(k,2)+0.1 normpeaks(k,2)],...
'Color','red','LineWidth',2)
end
and the result:

Pixel subtraction

I am working on code that select set of pixels randomly from gray images, then comparing the intensity of each 2 pixels by subtracting the intensity of pixel in one location from another one in different location.
I have code do random selection, but I am not sure of this code and I do not know how to do pixels subtraction?
thank you in advance..
{
N = 100; % number of random pixels
im = imread('image.bmp');
[nRow,nCol,c] = size(im);
randRow = randi(nRow,[N,1]);
randCol = randi(nCol,[N,1]);
subplot(2,1,1)
imagesc(im(randRow,randCol,:))
subplot(2,1,2)
imagesc(im)
}
Parag basically gave you the answer. In order to achieve this vectorized, you need to use sub2ind. However, what I would do is generate two sets of rows and columns. The reason why is because you need one set for the first set of pixels and another set for the next set of pixels so you can subtract the two sets of intensities. Therefore, do something like this:
N = 100; % number of random pixels
im = imread('image.bmp');
[nRow,nCol,c] = size(im);
%// Generate two sets of locations
randRow1 = randi(nRow,[N,1]);
randCol1 = randi(nCol,[N,1]);
randRow2 = randi(nRow,[N,1]);
randCol2 = randi(nCol,[N,1]);
%// Convert each 2D location into a single linear index
%// for vectorization, then subtract
locs1 = sub2ind([nRow, nCol], randRow1, randCol1);
locs2 = sub2ind([nRow, nCol], randRow2, randCol2);
im_subtract = im(locs1) - im(locs2);
subplot(2,1,1)
imagesc(im_subtract);
subplot(2,1,2)
imagesc(im);
However, the above code only assumes that your image is grayscale. If you want to do this for colour, you'll have to do a bit more work. You need to access each channel and subtract on a channel basis. The linear indices that were defined above are just for a single channel. As such, you'll need to offset by nRow*nCol for each channel if you want to access the same corresponding locations in the next channels. As such, I would use sub2ind in combination with bsxfun to properly generate the right values for vectorizing the subtraction. This requires just a slight modification to the above code. Therefore:
N = 100; % number of random pixels
im = imread('image.bmp');
[nRow,nCol,c] = size(im);
%// Generate two sets of locations
randRow1 = randi(nRow,[N,1]);
randCol1 = randi(nCol,[N,1]);
randRow2 = randi(nRow,[N,1]);
randCol2 = randi(nCol,[N,1]);
%// Convert each 2D location into a single linear index
%// for vectorization
locs1 = sub2ind([nRow, nCol], randRow1, randCol1);
locs2 = sub2ind([nRow, nCol], randRow2, randCol2);
%// Extend to as many channels as we have
skip_ind = permute(0:nRow*nCol:(c-1)*nRow*nCol, [1 3 2]);
%// Create 3D linear indices
locs1 = bsxfun(#plus, locs1, skip_ind);
locs2 = bsxfun(#plus, locs2, skip_ind);
%// Now subtract the locations
im_subtract = im(locs1) - im(locs2);
subplot(2,1,1)
imagesc(im_subtract);
subplot(2,1,2)
imagesc(im);

Matlab - Trying to use vectors with grid coordinates and value at each point for a color plot

I'm trying to make a color plot in matlab using output data from another program. What I have are 3 vectors indicating the x-position, y-yposition (both in milliarcseconds, since this represents an image of the surroundings of a black hole), and value (which will be assigned a color) of every point in the desired image. I apparently can't use pcolor, because the values which indicate the color of each "pixel" are not in a matrix, and I don't know a way other than meshgrid to create a matrix out of the vectors, which didn't work due to the size of the vectors.
Thanks in advance for any help, I may not be able to reply immediately.
If we make no assumptions about the arrangement of the x,y coordinates (i.e. non-monotonic) and the sparsity of the data samples, the best way to get a nice image out of your vectors is to use TriScatteredInterp. Here is an example:
% samplesToGrid.m
function [vi,xi,yi] = samplesToGrid(x,y,v)
F = TriScatteredInterp(x,y,v);
[yi,xi] = ndgrid(min(y(:)):max(y(:)), min(x(:)):max(x(:)));
vi = F(xi,yi);
Here's an example of taking 500 "pixel" samples on a 100x100 grid and building a full image:
% exampleSparsePeakSamples.m
x = randi(100,[500 1]); y = randi(100,[500 1]);
v = exp(-(x-50).^2/50) .* exp(-(y-50).^2/50) + 1e-2*randn(size(x));
vi = samplesToGrid(x,y,v);
imagesc(vi); axis image
Gordon's answer will work if the coordinates are integer-valued, but the image will be spare.
You can assign your values to a matrix based on the x and y coordinates and then use imagesc (or a similar function).
% Assuming the X and Y coords start at 1
max_x = max(Xcoords);
max_y = max(Ycoords);
data = nan(max_y, max_x); % Note the order of y and x
indexes = sub2ind(size(data), max_y, max_x);
data(indexes) = Values;
imagesc(data); % note that NaN values will be colored with the minimum colormap value

How can thin horizontal lines be added between each row in a grayscale image?

I need to create an nth-order Hadamard matrix, row double it, within each row randomly permute the elements of the matrix, and then display it. So far, I have accomplished all of these things. What I end up with when I imshow(matrix) is a nice picture of black and white boxes. But I haven't figured out how to insert a fine line to divide each row. I can create something like the first image on the left, but not the image on the right (these are Figures 1 and 2 from this paper)
Any help or comments would be thoroughly appreciated.
I've found using vector approaches (e.g., patch and rectangle) for this sort of problem unnecessarily challenging. I think that it's more straightforward to build a new image. This avoids floating-point rounding issues and other things that crop up with vector graphics. My solution below relies on some functions in the Image Processing Toolbox, but is simple and fast:
% Create data similarly to #TryHard
H = hadamard(48);
C = (1+[H;-H])/2;
rng(0); % Set seed
C(:) = C(randperm(numel(C))); % For demo, just permute all values, not rows
% Scale image and lines
scl = 10; % Amount to vertically scale each row
pad = 2; % Number of pixels to add between each row
C = imresize(C,scl,'nearest');
C = blockproc(C,[scl size(C,2)],#(x)[x.data;zeros(pad,size(C,2))]);
C = C(1:end-pad,:); % Remove last line added
% Dispay image
imshow(C)
This results in an image like this
The scl and pad parameters can be easily adjusted to obtain different sizes and relative sizes. You can call imresize(...,'nearest') again after adding the lines to further scale the image if desired. The blocproc line could potentially be made more efficient with various options (see the help). It could also be replaced by calls to im2col and col2im, which possibly could be faster, if messier.
I did not try the code, but I think that something like that should work:
sizeOfACube = 6;
numberOfRows = 47;
RGB = imread('image.png');
RGB = imresize(A, [(numRows+numberOfRows) numCols]);
for i=1:1:NumberOfRows
RGB(i*6,:,:) = 0;
end
imagesc(RGB);
imwrite(RGB,'newImage.png');
with:
sizeOfAcube the size of one cube on the QRcode.
numRows and numCols the number of Rows and Column of the original image.
One solution is to use patches, for instance as follows:
% set up example array
xl = 24; yl = xl;
[X Y] = find(hadamard(xl)==1);
% generate figure
figure, hold on
for ii=1:length(X)
patch(X(ii) + [0 0 1 1],Y(ii) + [0.1 0.9 0.9 0.1],[1 1 1],'Edgecolor',[1 1 1])
end
axis([0 xl+1 0 yl+1])
axis('square')
The patch command patch(x,y, color) accepts the vertices of the polygon element as x and y. In this example you can modify the term [0.1 0.9 0.9 0.1] to set the thickness of the bounding black line.
This generates
Edited
For the particular instance provided by the OP:
H=Hadamard(48); %# now to row-double the matrix
A=(1+H)/2;
B=(1-H)/2;
C=[A; B]; %# the code below randomly permutes elements within the rows of the matrix
[nRows,nCols] = size(C);
[junk,idx] = sort(rand(nRows,nCols),2); %# convert column indices into linear indices
idx = (idx-1)*nRows + ndgrid(1:nRows,1:nCols); %# rearrange whatever matrix
E = C;
E(:) = E(idx);
[X Y] = find(logical(E));
xl = length(X);
yl = length(Y);
figure, hold on
for ii=1:xl
rectangle('Position',[X(ii) Y(ii)+.2 1 0.8],'facecolor',[1 1 1],'edgecolor',[1 1 1])
end
axis([0 max(X)+1 0 max(Y)+1])
axis('square')
set(gca,'color',[0 0 0])
set(gca,'XTickLabel',[],'YTickLabel',[],'XTick',[],'YTick',[])
This example uses rectangle instead of patch to generate sharp corners.
The image: