/
function_compute_bbox.m
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function_compute_bbox.m
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function mod_bb= function_compute_bbox(img)
%=================================
% input --> read an image
% output --> compute bounding box
%=================================
mod_bb=[];
mod_bb_new=[];
r_channel=img(:,:,1);
g_channel=img(:,:,2);
b_channel=img(:,:,3);
r_channel=medfilt2(r_channel);
r_enhanced=imadjust(r_channel,stretchlim(r_channel),[]);
g_channel=medfilt2(g_channel);
g_enhanced=imadjust(g_channel,stretchlim(g_channel),[]);
b_channel=medfilt2(b_channel);
b_enhanced=imadjust(b_channel,stretchlim(b_channel),[]);
% this is where the difference from the research paper!!!
blue_sign_1=max(0, max((b_enhanced-g_enhanced-r_enhanced),(r_enhanced-b_enhanced-g_enhanced)));
% Detect MSER regions.
[mserRegions, mserConnComp] = detectMSERFeatures(blue_sign_1,...
'RegionAreaRange',[30 14000],...
'ThresholdDelta',5.5);
% Use regionprops to measure MSER properties
mserStats = regionprops(mserConnComp, 'BoundingBox', 'Eccentricity', ...
'Solidity', 'Extent', 'Euler', 'Image');
% Get bounding boxes for all the regions
bboxes = vertcat(mserStats.BoundingBox);
if ~ isempty(bboxes)
%display(bboxes);
w = bboxes(:,3);
h = bboxes(:,4);
% aspectRatio = w./h;
filterIdx=(w./h)'>1.2;
% Threshold the data to determine which regions to remove. These thresholds
% may need to be tuned for other images.
% filterIdx = aspectRatio' > 1.5;
filterIdx = filterIdx | [mserStats.Eccentricity] > .85 ;
filterIdx = filterIdx | [mserStats.Solidity] < .3;
filterIdx = filterIdx | [mserStats.Extent] < 0.2 | [mserStats.Extent] > 0.9;
filterIdx = filterIdx | [mserStats.EulerNumber] < -4;
% Remove regions
mserStats(filterIdx) = [];
mserRegions(filterIdx) = [];
% Convert from the [x y width height] bounding box format to the [xmin
% ymin xmax ymax] format for convenience.
%from left to right limit
xmin = bboxes(:,1);
ymin = bboxes(:,2);
xmax = xmin + bboxes(:,3) - 1;
ymax = ymin + bboxes(:,4) - 1;
% Expand the bounding boxes by a small amount.
expansionAmount = 0.02;
xmin = (1-expansionAmount) * xmin;
ymin = (1-expansionAmount) * ymin;
xmax = (1+expansionAmount) * xmax;
ymax = (1+expansionAmount) * ymax;
% Clip the bounding boxes to be within the image bounds
xmin = max(xmin, 1);
ymin = max(ymin, 2);
xmax = min(xmax, size(blue_sign_1,2));
ymax = min(ymax, size(blue_sign_1,1));
% Show the expanded bounding boxes
expandedBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
%IExpandedBBoxes = insertShape(img,'Rectangle',expandedBBoxes,'LineWidth',3);
%figure
%imshow(IExpandedBBoxes);
%title('Show Detected Sign')
% Compute the overlap ratio
overlapRatio = bboxOverlapRatio(expandedBBoxes, expandedBBoxes);
% Set the overlap ratio between a bounding box and itself to zero to
% simplify the graph representation.
n = size(overlapRatio,1);
overlapRatio(1:n+1:n^2) = 0;
% Create the graph
g = graph(overlapRatio);
% Find the connected text regions within the graph
componentIndices = conncomp(g);
% Merge the boxes based on the minimum and maximum dimensions.
xmin = accumarray(componentIndices', xmin, [], @min);
ymin = accumarray(componentIndices', ymin, [], @min);
xmax = accumarray(componentIndices', xmax, [], @max);
ymax = accumarray(componentIndices', ymax, [], @max);
% Compose the merged bounding boxes using the [x y width height] format.
textBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
% Remove bounding boxes that only contain one text region
numRegionsInGroup = histcounts(componentIndices);
textBBoxes(numRegionsInGroup == 1, :) = [];
% aspect ratio
mod_bb=[];
scale=1.5;
for image_index = 1:size(textBBoxes,1)
in_box=textBBoxes(image_index,:);
height=in_box(1,4)%-in_box(1,2);
width=in_box(1,3)%-in_box(1,1);
if ~ (height/width >scale | width/height > scale)% | (height<=20 && width <= 20) )
mod_bb=[mod_bb;in_box];
end
end
% Show the final text detection result.
%ITextRegion = insertShape(img, 'Rectangle', mod_bb,'LineWidth',3);
%figure
%imshow(ITextRegion)
%title('Detected Text')
end
end