function [ outputImg ] = isolatePlate( inputImg )
% ISOLATEPLATE Draw a box around a license plate in the input image
%  More info.



% Params
%SIZE_THRESHOLD = 60;
TRUE = 1;
FALSE = 0;


% Show the original
inputImg = im2double( inputImg );
figure, imshow( inputImg ), title( 'Original Image' );



%%%%% Image Prep

% Convert To Grayscale Using the NTSC standard

red = immultiply( inputImg(:,:,1), 299 );
green = immultiply(  inputImg(:,:,2), 587 );
blue = immultiply( inputImg(:,:,3), 114 );

newImg = red + green + blue;
newImg = imdivide( newImg, 1000 );

figure, imshow( newImg ), title( 'Grayscale Image' );

% Find the center of the red data, to find the column that the license
% plate should cross

scaledRed = mat2gray( red );
scaledBlue = mat2gray( blue );
scaledGreen = mat2gray( green );

redMap = scaledRed > 0.30 & scaledGreen < 0.40 & scaledBlue < 0.40;

se = strel( 'disk', 5 );
redMap = imclose( redMap, se );

[row,column] = find( redMap );
redColumn = sum( column ) / size( column, 1 );
redRow = sum( row ) / size( row, 1 );

figure, imshow( imcomplement( redMap ) ), title( 'Red Map' );
rectangle( 'Position', [ redColumn 1 1 size( red, 1 ) ] );
rectangle( 'Position', [ 0 redRow size( red, 2 ) 1 ] );

%%%%% Vertical Edge Detection

% Create our mask - a Sobel for vertical edge detection
mask = fspecial( 'sobel' );
mask = rot90( mask, 1 );

% Do the actual detection
edges = imfilter( newImg, mask );

% Show what we have so far
figure, imshow( edges ), title( 'Vertical Edges' );



%%%%% Edge Density Map Generation

% Create our mask of ones to add up the edge values
densityMask = ones( 3, 15 );

% Sum it up
edgeMap = imfilter( edges, densityMask );
edgeMap = imdivide( edgeMap, 45 );

% Show what we have so far
figure, imshow( edgeMap ), title( 'Edge Map' );



%%%%% Binarization / Clean Up / Dilation

% Binarize!!!!  Use Otsu's Method.  It's fast.
level = graythresh( edgeMap );
binary = im2bw( edgeMap, level );

% Show what we have so far
figure, imshow( binary ), title( 'Binarized Edge Map' );

% Clean Up by getting rid of small horizontal lines

%se = strel( 'disk', 1 );
%cleanImage = imopen( binary, se );

% Show what we have so far
%figure, imshow( cleanImage ), title( 'Cleaned Image' );
cleanImage = binary; % skipping the clean up step...don't seem to need it

% Dilate the Image
se = ones( 1, 9 );
dilated = imdilate( cleanImage, se );

% Show what we have so far
figure, imshow( dilated ), title( 'Dilated Image' );



%%%%% License Plate Location

% find all the 8-connected regions in the dilated binary image
[labeled, num] = bwlabel( dilated );
figure, imshow( imcomplement( inputImg ) ), title( 'Original Image - All Connected Regions' );

% Feature Extraction
info = [];
for k = 1:1:num
    [row,column] = find( labeled == k );
    
    topLeftRow = min( row );
    topLeftColumn = min( column );
    bottomRightRow = max( row );
    bottomRightColumn = max( column );
    numPixels = size( row, 1 );
    width = bottomRightColumn - topLeftColumn;
    height = bottomRightRow - topLeftRow;
    
    newRow = [ topLeftRow topLeftColumn bottomRightRow bottomRightColumn numPixels width height ];
    rectangle( 'Position', [ min( column ) min( row ) width + 1 height + 1 ] );

    info = cat( 1, info, newRow );
end

info

% Combination of Candidate Regions
figure, imshow( imcomplement( inputImg ) ), title( 'Original Image - Regions To Connect' );
densityV = [];
for oneIndex = 1:1:size( info, 1 )
    for twoIndex = (oneIndex + 1):1:size( info, 1 )
        oneInfo = info( oneIndex, : );
        twoInfo = info( twoIndex, : );
        
        % Check whether they're overlapping
        overlap = TRUE;
        
        case1 = oneInfo( 1 ) + oneInfo( 7 ) < twoInfo( 1 );
        case2 = oneInfo( 1 ) > twoInfo( 1 ) + twoInfo( 7 );
        case3 = oneInfo( 2 ) + oneInfo( 6 ) < twoInfo( 2 );
        case4 = oneInfo( 2 ) > twoInfo( 2 ) + twoInfo( 6 );
        
        if case1
            overlap = FALSE;
        elseif case2
            overlap = FALSE;
        elseif case3
            overlap = FALSE;
        elseif case4
            overlap = FALSE;
        else   
            overlap = TRUE;
        end
        
        % If they overlap, check their connected density
        if overlap
            topRow = min( oneInfo( 1 ), twoInfo( 1 ) );
            insideTop = max( oneInfo( 1 ), twoInfo( 1 ) );
            insideBottom = min( oneInfo( 3 ), twoInfo( 3 ) );
            bottomRow = max( oneInfo( 3 ), twoInfo( 3 ) );
            
            overlap = ( bottomRow - topRow + 1 ) - ( bottomRow - insideBottom ) - ( insideTop - topRow );
            
            leftColumn = min( oneInfo( 2 ), twoInfo( 2 ) );
            insideLeft = max( oneInfo( 2 ), twoInfo( 2 ) );
            insideRight = min( oneInfo( 4 ), twoInfo( 4 ) );
            rightColumn = max( oneInfo( 4 ), twoInfo( 4 ) );
            
            dis = ( rightColumn - leftColumn + 1 ) - ( insideLeft - leftColumn ) - ( rightColumn - insideRight );
            
            minH = min( oneInfo( 7 ), twoInfo( 7 ) );
            sqr = sqrt( oneInfo( 6 ) * twoInfo( 6 ) );
            
            density = ( overlap / minH ) - ( dis / sqr );
            
            if ( density < 1.0 )
                rectangle( 'Position', [ oneInfo( 2 ) oneInfo( 1 ) oneInfo( 6 ) oneInfo( 7 ) ] );
                rectangle( 'Position', [ twoInfo( 2 ) twoInfo( 1 ) twoInfo( 6 ) twoInfo( 7 ) ] );
            end
            densityV = cat( 1, densityV, density );
        end
    end
end

densityV
max( densityV )
min( densityV )



% Show what we have so far
figure, imshow( labeled ), title( 'Regions of Binary Image' );

% create final image
% final = zeros( size( inv, 1 ), size( inv, 2 ) );
% for k = 1:1:size( iregions )
%    final = final | ( labeled == iregions( k ) );
% end

% now return the processed image...
outputImg = inputImg;