lhiplot.m

This code can parse any image in matlab. Very elaborate code

function [h, class] = LMplot(varargin)
% Visualizes the polygons in an image.
%
% LMplot(annotation, img)
% or
% LMplot(database, ndx, HOMEIMAGES)
%
% Example:
%   [annotation, img] = LMread(filename, HOMEIMAGES)
%   LMplot(annotation, img)
%
%   thumbnail = size of the image
%   The plot uses only 7 colors, therefore, some times, different object.
%   Classes might have the same color assigned.
%
% If an object has the field 'confidence', the thickness of the bounding
% box will be equal to the confidence.
%
% If the object has the filed 'detection', then when the state is 'false'
% it will show the outline in green, and when the state is 'correct' it
% will show the outline in red.

switch length(varargin)
   case 1
       annotation = varargin{1};
       img = -1;
   case 2
       annotation = varargin{1};
       img = varargin{2};
   case 3
       D = varargin{1};
       ndx = varargin{2};
       
       if ndx>length(D)
           return
       end
       
       HOMEIMAGES = varargin{3};

       annotation = D(ndx).annotation;
       %img = imread(fullfile(HOMEIMAGES, annotation.folder, annotation.filename));
       img = LHIimread(D, ndx, HOMEIMAGES); % Load image
   otherwise
       error('Too many input arguments.')
end

% Define colors
colors = 'rgbcmyw';
colors = hsv(15);

% Draw image
%figure;
if size(img,1)>1
    image(uint8(img)); axis('off'); axis('equal'); hold on
    if size(img,3) == 1
        colormap(gray(256))
    end
else
    axis('off'); axis('equal'); hold on
end

% Draw each object (only non deleted ones)
h = []; class = [];
if isfield(annotation, 'object')
   Nobjects = length(annotation.object); n=0;
   for i = 1:Nobjects
       n = n+1;
       class{n} = annotation.object(i).name; % get object name
       col = colors(mod(sum(double(class{n})),15)+1, :);
       if (isfield(annotation.object(i),'regions'))
           Nregions = length(annotation.object(i).regions);
           for j = 1:Nregions
               [X,Y] = getLHIpoints(annotation.object(i).regions(j));

               if isfield(annotation.object(i), 'confidence')
                   p = annotation.object(i).confidence;
                   LineWidth = round(annotation.object(i).confidence);
                   LineWidth = min(max([2 LineWidth]), 8);

                   if isfield(annotation.object(i), 'detection')
                       if strcmp(annotation.object(i).detection, 'false')
                           LineWidth = 1; col = 'g';
                       else
                           LineWidth = 2; col = 'r';
                       end
                   end

                   dim = get(gca,'position');
                   if dim(3)>.5
                       % If the plot occupies the entire figure, then we can plot the
                       % confidence as a string.
                       hold on
                       xx = mean(X);
                       %yy = mean(Y);
                       yy = min(Y);
                       ht=text(xx,yy, ...
                           sprintf('%5.2f', annotation.object(i).confidence), ...
                           'horizontalAlignment', 'center', 'verticalAlignment', 'bottom');
                       %sprintf('%d %5.3f', i, annotation.object(i).confidence), ...
                       %'horizontalAlignment', 'center', 'verticalAlignment', 'bottom');

                       set(ht, 'color', col, 'fontsize', 10);
                   end
               else
                   LineWidth = 2;
               end

               plot([X; X(1)],[Y; Y(1)], 'LineWidth', LineWidth, 'color', [0 0 0]); hold on
               h(n) = plot([X; X(1)],[Y; Y(1)], '-o','MarkerFaceColor','g','MarkerEdgeColor','k',...
                'MarkerSize',1, 'LineWidth', LineWidth/2, 'color', col);
               hold on
           end
       end
   end

   if nargout == 0
       legend(h, class);
   end
   %drawnow
end