How to properly combine heat map over an image using coordinates from an excel file

Hello!

I am trying to create a heatmap over an image using x,y coordinates. I took a square-sized screenshot of the image (729 x 728) and assigned it to the variable 'img' in matlab. I also have x,y coordinates in an excel file representing where the person was looking at in each every frame. However, when I run the script, the heatmap do not represent the same points (from my data) in the image (see below image for reference). Can anyone tell me how I can properly add a heatmap over an image that represent correct points?

The codes I used are shown below:

function hAxes = dscatter_JS(X,Y, varargin)

% DSCATTER creates a scatter plot coloured by density.

%

% DSCATTER(X,Y) creates a scatterplot of X and Y at the locations

% specified by the vectors X and Y (which must be the same size), colored

% by the density of the points.

%

% DSCATTER(...,'MARKER',M) allows you to set the marker for the

% scatter plot. Default is 's', square.

%

% DSCATTER(...,'MSIZE',MS) allows you to set the marker size for the

% scatter plot. Default is 10.

%

% DSCATTER(...,'FILLED',false) sets the markers in the scatter plot to be

% outline. The default is to use filled markers.

%

% DSCATTER(...,'PLOTTYPE',TYPE) allows you to create other ways of

% plotting the scatter data. Options are "surf','mesh' and 'contour'.

% These create surf, mesh and contour plots colored by density of the

% scatter data.

%

% DSCATTER(...,'BINS',[NX,NY]) allows you to set the number of bins used

% for the 2D histogram used to estimate the density. The default is to

% use the number of unique values in X and Y up to a maximum of 200.

%

% DSCATTER(...,'SMOOTHING',LAMBDA) allows you to set the smoothing factor

% used by the density estimator. The default value is 20 which roughly

% means that the smoothing is over 20 bins around a given point.

%

% DSCATTER(...,'LOGY',true) uses a log scale for the yaxis.

%

% Examples:

%

% [data, params] = fcsread('SampleFACS');

% dscatter(data(:,1),10.^(data(:,2)/256),'log',1)

% % Add contours

% hold on

% dscatter(data(:,1),10.^(data(:,2)/256),'log',1,'plottype','contour')

% hold off

% xlabel(params(1).LongName); ylabel(params(2).LongName);

%

% See also FCSREAD, SCATTER.

% Copyright 2003-2004 The MathWorks, Inc.

% $Revision: $ $Date: $

% Reference:

% Paul H. C. Eilers and Jelle J. Goeman

% Enhancing scatterplots with smoothed densities

% Bioinformatics, Mar 2004; 20: 623 - 628.

lambda = [];

nbins = [];

plottype = 'scatter';

contourFlag = false;

msize = 100; % increased marker from 10 to 100

marker = 'o'; % changed from s (square) to o (circle)

logy = false;

filled = true;

if nargin > 2

if rem(nargin,2) == 1

error('Bioinfo:IncorrectNumberOfArguments',...

'Incorrect number of arguments to %s.',mfilename);

end

okargs = {'smoothing','bins','plottype','logy','marker','msize','filled'};

for j=1:2:nargin-2

pname = varargin{j};

pval = varargin{j+1};

k = strmatch(lower(pname), okargs); %#ok

if isempty(k)

error('Bioinfo:UnknownParameterName',...

'Unknown parameter name: %s.',pname);

elseif length(k)>1

error('Bioinfo:AmbiguousParameterName',...

'Ambiguous parameter name: %s.',pname);

else

switch(k)

case 1 % smoothing factor

if isnumeric(pval)

lambda = pval;

else

error('Bioinfo:InvalidScoringMatrix','Invalid smoothing parameter.');

end

case 2

if isscalar(pval)

nbins = [ pval pval];

else

nbins = pval;

end

case 3

plottype = pval;

case 4

logy = pval;

Y = log10(Y);

case 5

contourFlag = pval;

case 6

marker = pval;

case 7

msize = pval;

case 8

filled = pval;

end

end

end

end

minx = min(X,[],1);

maxx = max(X,[],1);

miny = min(Y,[],1);

maxy = max(Y,[],1);

if isempty(nbins)

nbins = [min(numel(unique(X)),200) ,min(numel(unique(Y)),200) ];

end

if isempty(lambda)

lambda = 20;

end

edges1 = linspace(minx, maxx, nbins(1)+1);

ctrs1 = edges1(1:end-1) + .5*diff(edges1);

edges1 = [-Inf edges1(2:end-1) Inf];

edges2 = linspace(miny, maxy, nbins(2)+1);

ctrs2 = edges2(1:end-1) + .5*diff(edges2);

edges2 = [-Inf edges2(2:end-1) Inf];

[n,p] = size(X);

bin = zeros(n,2);

% Reverse the columns to put the first column of X along the horizontal

% axis, the second along the vertical.

[dum,bin(:,2)] = histc(X,edges1);

[dum,bin(:,1)] = histc(Y,edges2);

H = accumarray(bin,1,nbins([2 1])) ./ n;

G = smooth1D(H,nbins(2)/lambda);

F = smooth1D(G',nbins(1)/lambda)';

% = filter2D(H,lambda);

if logy

ctrs2 = 10.^ctrs2;

Y = 10.^Y;

end

okTypes = {'surf','mesh','contour','image','scatter'};

k = strmatch(lower(plottype), okTypes); %#ok

if isempty(k)

error('dscatter:UnknownPlotType',...

'Unknown plot type: %s.',plottype);

elseif length(k)>1

error('dscatter:AmbiguousPlotType',...

'Ambiguous plot type: %s.',plottype);

else

switch(k)

case 1 %'surf'

h = surf(ctrs1,ctrs2,F,'edgealpha',0);

case 2 % 'mesh'

h = mesh(ctrs1,ctrs2,F);

case 3 %'contour'

[dummy, h] =contour(ctrs1,ctrs2,F);

case 4 %'image'

nc = 256;

F = F./max(F(:));

colormap(repmat(linspace(1,0,nc)',1,3));

h =image(ctrs1,ctrs2,floor(nc.*F) + 1);

case 5 %'scatter'

F = F./max(F(:));

ind = sub2ind(size(F),bin(:,1),bin(:,2));

col = F(ind);

if filled

h = scatter(X,Y,msize,col,marker,'filled');

else

h = scatter(X,Y,msize,col,marker);

end

end

end

if logy

set(gca,'yscale','log');

end

if nargout > 0

hAxes = get(h,'parent');

end

%%%% This method is quicker for symmetric data.

% function Z = filter2D(Y,bw)

% z = -1:(1/bw):1;

% k = .75 * (1 - z.^2);

% k = k ./ sum(k);

% Z = filter2(k'*k,Y);

function Z = smooth1D(Y,lambda)

[m,n] = size(Y);

E = eye(m);

D1 = diff(E,1);

D2 = diff(D1,1);

P = lambda.^2 .* D2'*D2 + 2.*lambda .* D1'*D1;

Z = (E + P) \ Y;

And..

GazeMatrix = readmatrix('eyedata_P1_S1_CLEANED.xlsx');

img = imread('1x1.PNG');

img = flip(img,2);

img = imrotate(img,180);

x_data = GazeMatrix(:,33);

y_data = GazeMatrix(:,34);

image('CData',img,'XData',[-1000 1000],'YData',[-1000 1000])

hold on

dscatter_JS(x_data,y_data);

hold off

colorbar();

axis off