plotObjects.m

function plotObjects( dmsDataStruct, type, sampleNum, ridgesPlot )
%plotObjects plots highlighted found peak regions based on
%   detectSplineObjects function.

%% Compare type input and initialize correct histogram matrix
if strncmpi(type, 'pos', 3)
    BWfinal = dmsDataStruct(sampleNum).bwfinal_pos;
    I = dmsDataStruct(sampleNum).dispersion_pos;
    xplot = ridgesPlot.xplotPos;
    yplot = ridgesPlot.yplotPos;
elseif strncmpi(type, 'neg', 4)
    BWfinal = dmsDataStruct(sampleNum).bwfinal_neg;
    I = dmsDataStruct(sampleNum).dispersion_neg;
    xplot = ridgesPlot.xplotNeg;
    yplot = ridgesPlot.yplotNeg;
else
    error('Inputs to plotTrainedHist need to include pos or neg char string.');
end

%% Calculate boundaries of regions and display
[~,L,~,~] = bwboundaries(BWfinal, 'noholes');
% if above line does not work, then use [B,L,N,A] = bwboundaries(BWfinal,'noholes');

% transparency to superimpose pseudo-color label matrix
figure; imshow(I);
hold on;
himage = imshow(label2rgb(L, @jet, [0.5 0.5 0.5]));
alpha = 0.4;
set(himage, 'AlphaData', alpha);
title('Highlighted spline objects detected');

%% plot interpreted data
figure; imshow(I)
hold on;
plot(xplot,yplot, 'r.', 'markerSize', 7);
title('polynomial interpretation');

%% Possible future functionality of different views of the segmentation
% colors = ['b' 'g' 'r' 'c' 'm' 'y'];
% figure; imshow(label2rgb(L, @jet, [0.5 0.5 0.5]));
% title('boundaries');
% hold on;
% for k = 1:length(B)
%     boundary = B{k};
%     cidx = mod(k, length(colors))+1;
%     plot(boundary(:,2), boundary(:,1), colors(cidx), 'LineWidth', 1);
%     
%     % randomize text position for better visibility
%     rndRow = ceil(length(boundary)/(mod(rand*k,7)+1));
%     col = boundary(rndRow,2);
%     row = boundary(rndRow,1);
%     h = text(col+1, row-1, num2str(L(row,col)));
%     set(h, 'Color', colors(cidx), 'FontSize', 10, 'FontWeight', 'bold');
% end


end

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