myCode_July2016[1]

clear;
clc;
%nlist           = {'aerial6.jpg';'aerial10.jpg';'roadA.jpg';'aerial8.jpg'};
nlist1 = {'all_used/1.png'};   % theshold = 23000
nlist2 = {'all_used/2.png'}; % theshold =
nlist3 = {'all_used/3.png'};   % theshold =
nlist4 = {'all_used/4.png'};   % theshold = 7500
nlist5 = {'all_used/5.png'};  % theshold = 29000
nlist6 = {'all_used/6.png'};
nlist7 = {'all_used/7.png'};
nlist8 = {'all_used/8.png'};
nlist9 = {'all_used/9.png'};
nlist10 = {'all_used/10.png'};

nlistDeep = {'datasets/Test_set/Input/18328735_15.tiff'};
nlistDeep2 = {'datasets/Test_set/Target/18328735_15.tif'};


threshold_fix = 23000;

% test kmeans
X = [randn(20,2)+ones(20,2); randn(20,2)-ones(20,2)];
      opts = statset('Display','final');
      [cidx, ctrs] = kmeans(X, 4, 'Distance','city', ...
                            'Replicates',5, 'Options',opts);

for cta = 1:1 % all in my img folder is 7
    nm = nlist10{cta};
    rgb = imread(nm);
    normImage = im2double(rgb);
    nm_input = rgb2gray(normImage);

    target = imread(nlistDeep2{1});
   % figure, imshow(target);

%     a=imread('test1.jpg');
%     b=imresize(a,[500 800]);

% Step 1: Read Image
    img_resize = imresize(rgb, 1);
    he = img_resize;
    he2 = img_resize;
 %   figure, imshow(he), title('1'); % Showing a original image.

    img_graythresh = rgb2gray(he );
    threshold = graythresh(img_graythresh);
    bw_img = im2bw(he,threshold);
 %   figure, imshow(bw_img), title('2');
  %  figure, imhist(img_graythresh), title('3');

% Step 2: Convert Image from RGB Color Space to L*a*b* Color Space
    cform = makecform('srgb2lab');
    cform_rgb = makecform('lab2srgb');
    lab_he = applycform(he,cform);
    rgb_he = applycform(he,cform_rgb);

% Step 3: Classify the Colors in 'a*b*' Space Using K-Means Clustering
    ab = double(rgb_he(:,:,2:3));
    ab_all_band = double(rgb_he(:,:,2:3));
    nrows = size(ab,1);
    ncols = size(ab,2);
    ab = reshape(ab,nrows*ncols,2);

    nColors = 3;
    [cluster_idx, cluster_center] = kmeans(ab,nColors, ...
                                    'Replicates',1, ...
                                    'Options',opts, ...
                                    'start',[118 143; 127 140; 133 127]);
                                % 'start',[118 143; 127 140; 133 127]);

% Step 4: Label Every Pixel in the Image Using the Results from KMEANS
    pixel_labels = reshape(cluster_idx,nrows,ncols);
    %figure(2), imshow(pixel_labels,[]), title('image labeled by cluster index')

% Step 5: Create Images that Segment the H&E Image by Color.
    segmented_images = cell(1,3);
    rgb_label = repmat(pixel_labels,[1 1 3]);

    for k = 1:3
         color = rgb;
         color(rgb_label ~= k) = 0;
         segmented_images{k} = color;
    end

    %figure, imshow(segmented_images{1}), title('objects in cluster 1')
    %figure, imshow(segmented_images{2}), title('objects in cluster 2')
    figure, imshow(segmented_images{3}), title('objects in cluster 3')

    %Normaliztion for clustering road area.
    normImage_c3 = mat2gray(segmented_images{3});
    normImage_c3_gray =rgb2gray(segmented_images{3});
    %figure, imhist(normImage_c3_gray);

    % to Binary image
    gray_convert_image = rgb2gray(normImage_c3);
    binary_convert_image = im2bw(gray_convert_image);
    figure, imshow(binary_convert_image)
    bwarea(binary_convert_image);

    %bw = imread('text.tif');
    %se = strel('line',20,150);
    se = strel('line',10,10);
    bw2 = imdilate(binary_convert_image,se);% graythresh = 'bw_img' % kmeans = 'binary_convert_image'
    %imshow(bw), title('Original')
    figure, imshow(bw2), title('Dilated')
    bw3 = ~bw2;
    bw4 = im2bw(bw3);
    %figure, imshow(bw4);
    CC = bwconncomp(bw2); %Connected Component Finding all area.
    %CC = bwconncomp(my_image);
    L = labelmatrix(CC);
    A = cell( size(CC.PixelIdxList,1) , size(CC.PixelIdxList,2) );
    A = CC.PixelIdxList;
    size(CC.PixelIdxList,2);

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    La=bwlabel(bw2,8); %%% labeledImage is a binary image
    figure,imshow(La,[]);
    coloredLabel = label2rgb(La, 'hsv', 'k', 'shuffle');
    imshow(coloredLabel);

  %  pr = regionprops( La, 'Area', 'PixelIdxList' );

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    stats = regionprops(bw2,'Area')
    stats2 = regionprops(bw2,'Centroid')


    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    smallAreaA = La;
    smallAreaSh = La;
    smallArea = La;
    smallArea2 = La;
    smallArea3 = La;
    smallArea4 = La;
    smallArea5 = La;
    s = regionprops(La, 'Orientation','Area','PixelIdxList','Perimeter', 'BoundingBox',...
        'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Centroid');

    %%%%%%%%%%%%%%%%%%%%%%%  regionprop to matrix %%%%%%%%%%%%%%%%%%%%%%%%%
%     numel(s.Area)
%     for i=1:numel(s)
%         s(i).Centroid(1)
%         s(i).Centroid(2)
%     end
    num_boject = 1:1:size(s);
    s_centroid = vertcat(s.Centroid);
    cen1 = s_centroid(:,1);
    cen2 = s_centroid(:,2);
    region_matrix = [s.Area; s.Perimeter; num_boject;]';

    s_area = region_matrix(:,1);
    s_perimeter = region_matrix(:,2);
    s_num = region_matrix(:,3);
    min_perimeter = (sqrt(region_matrix(:,1)))*4;
    shape_index = s_perimeter./min_perimeter;
   % region_matrix_used = [s_num s_area s_perimeter min_perimeter shape_index s_centroid(:,1) s_centroid(:,2)];
    region_matrix_used = [s_num s_area s_perimeter min_perimeter shape_index];
    threshold_use = max(s_area)/2;

    %    x(x(:,1)>2 & x(:,1)<6 , :)
    %%AA = region_matrix_used(region_matrix_used(:,5)>2.25);

    %s_area > threshold_use & shape_index > 2.25;
    score = shape_index;
    score2 = s_area;
    num2 = 0;
    area2 = 0;
    shape2 = 0;
    cent1 = 0;
    cent2 = 0;
    j = 1;
    k = 1;

    for i=1:size(s)
        if s_area(i) > threshold_use & shape_index(i) > 2.25;
           q1 = s_num(i);
           q2 = s_area(i);
           q3 = shape_index(i);
           q4 = cen1(i);
           q5 = cen2(i);
           num(j) = q1;
           area(j) = q2;
           shape(j) = q3;
           centA(j) = q4;
           centB(j) = q5;
          % figure, imshow(bw4);
          % text(s(i).Centroid(1),s(i).Centroid(2),num2str(score(i)),'FontSize',15,'color','red');
           j = j+1;
           %region_state1 = [num' area' shape'];
%        else
%            q1 = s_num(i);
%            q2 = 0;
%            q3 = 0;
%            q4 = 0;
%            q5 = 0;
%            num(j) = q1;
%            area(j) = q2;
%            shape(j) = q3;
%            centA(j) = q4;
%            centB(j) = q5;
%           % figure, imshow(bw4);
%           % text(s(i).Centroid(1),s(i).Centroid(2),num2str(score(i)),'FontSize',15,'color','red');
%            j = j+1;
        end
    end


    for i=1:size(s)
        %if shape_index(i) > 2.25;
        if s_area(i) < threshold_use & shape_index(i) > 2.25;
           q1 = s_num(i);
           q2 = s_area(i);
           q3 = shape_index(i);
           q4 = cen1(i);
           q5 = cen2(i);
           num2(k) = q1;
           area2(k) = q2;
           shape2(k) = q3;
           cent1(k) = q4;
           cent2(k) = q5;
%            figure, imshow(bw4);
%            text(s(i).Centroid(1),s(i).Centroid(2),num2str(score(i)),'FontSize',15,'color','red');
           k = k+1;
%         else
%            q1 = s_num(i);
%            q2 = 0;
%            q3 = 0;
%            q4 = 0;
%            q5 = 0;
%            num2(k) = q1;
%            area2(k) = q2;
%            shape2(k) = q3;
%            cent1(k) = q4;
%            cent2(k) = q5;
% %            figure, imshow(bw4);
% %            text(s(i).Centroid(1),s(i).Centroid(2),num2str(score(i)),'FontSize',15,'color','red');
%            k = k+1;
        end
    end
    region_state1 = [num' area' shape' centA' centB'];
    region_state2 = [num2' area2' shape2' cent1' cent2'];

    % x = [0,0;1,1];
    % d = pdist(x,'euclidean');
     temp1 = 1;
     all_object = max(size(region_state2(region_state2(:,3)>0)));
   %  for i=1:2
         area = region_state1(:,2);
         cen_road1 = region_state1(:,4);
         cen_road2 = region_state1(:,5);
         cen_check1 = region_state2(:,4);
         cen_check2 = region_state2(:,5);

       for i=1:3  %% i = number of checking for local road.
         dis11 = [cen_road1(1),cen_road2(1);cen_check1(i),cen_check2(i)];
         dis12 = [cen_road1(2),cen_road2(2);cen_check1(i),cen_check2(i)];
         d11 = pdist(dis11,'euclidean');
         d12 = pdist(dis12,'euclidean');

         similarity(i) =  ((area(1)/(d11^2))+(area(2)/(d12^2)))/2

       end

  %   end

   % similarity = similarity;
    region_state3 = [num2' area2' shape2' similarity'];

    iso_num = region_state3(:,1);
    iso_iso = region_state3(:,4);
    indexIso=1;
    for i=1:size(s_num)
       Test(i) = 0;

       for j=1:3  % take isolation following numners of it.
           if i == iso_num(j)
               Test(i) = iso_iso(j);
           end
       end


    end

    region_state4 = [s_num Test'];
    isolate = region_state4(:,2);

%     used_column = region_matrix_used(:,1);
%     test_column = region_state3(:,1);
%     isolate = region_state3(:,7);

%     for i=1:size(region_matrix_used)
%         if used_column == test_column
%              region_matrix_used = [region_matrix_used region_state3(:,7)];
%         end
%     end
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    %%%%%%%%%%%%%%%%%%%%%%%%%% Show value on objects %%%%%%%%%%%%%%%%%%%%%%
    % score = (min(sqrt([s.Area]),[s.Perimeter]/4)./(max(sqrt([s.Area]),[s.Perimeter]/4))).^2;
    % score = shape_index;
%     figure, imshow(bw4);
%     for cnt = 1:length(s)
%         text(s(cnt).Centroid(1),s(cnt).Centroid(2),num2str(score(cnt)),'FontSize',15,'color','red');
%     end
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
threshold_max2 = max(region_matrix_used(:,2))
threshold_max = max(region_matrix_used(:,2))/2
%shape_index = 2.5;

%small_selectSh = ((shape_index > 2.25));
%smallAreaSh( vertcat( s(~small_selectSh).PixelIdxList ) ) = 0; %// set all other regions to zero

%small_selectA = ((s_area >= threshold_max));
%smallAreaA( vertcat( s(~small_selectA).PixelIdxList ) ) = 0; %// set all other regions to zero

small_select = ((s_area >= threshold_max) & (shape_index > 2.25));
smallArea( vertcat( s(~small_select).PixelIdxList ) ) = 0; %// set all other regions to zero

MainRoad = (s_area >= threshold_max) & (shape_index > 2.25);
%small_select2 = ((s_area < threshold_max) & (shape_index > 2.49));
%smallArea2( vertcat( s(~small_select2).PixelIdxList ) ) = 0; %// set all other regions to zero

small_select3 = ((shape_index > 2.25)); %all
smallArea3( vertcat( s(~small_select3).PixelIdxList ) ) = 0; %// set all other regions to zero

small_select4 = (isolate > 0.4);
smallArea4( vertcat( s(~small_select4).PixelIdxList ) ) = 0; %// set all other regions to zero
LocalRoad = (isolate > 0.4);

small_select5 = (MainRoad + LocalRoad);
smallArea5( vertcat( s(~small_select5).PixelIdxList ) ) = 0; %// set all other regions to zero

%figure, imshow( smallAreaSh ); colormap( summer );
%figure, imshow( smallAreaA ); colormap( summer );
figure, imshow( smallArea ); colormap( summer );
%figure, imshow( smallArea2 ); colormap( summer );
%figure, imshow( smallArea3 ); colormap( summer );
figure, imshow( smallArea3 ); %all
figure, imshow( smallArea4 ); colormap( summer );
figure, imshow( smallArea5 );
%figure, imshow( ~smallArea4 );

%     if (s_area >= threshold_max) & (shape_index > 2.5)
%         smallArea( vertcat( s(~small_select).PixelIdxList ) ) = 0;
%         coloredLabelA = label2rgb(smallArea, 'hsv', 'g');
%     elseif (s_area < threshold_max) & (shape_index > 2.5)
%         smallArea( vertcat( s(~small_select).PixelIdxList ) ) = 0;
%         coloredLabelA = label2rgb(smallArea, 'hsv', 'b');
%     end
% figure, imshow( coloredLabelA );


figure, imshow(nm), hold on, himage = imshow(smallArea5), set(himage, 'AlphaData', 0.6);


% Comment %%%
%
%     % Thining the image
%     skeletionization_image = bwmorph(smallArea3,'thin',Inf);
%     se2 = strel('line',2,2); % 'line',5,8  'best',2,2
%     bw2 = imdilate(skeletionization_image,se2);
%     coloredLabel1 = label2rgb(bw2, 'hsv', 'k', 'shuffle');
%     % figure, imshow(coloredLabel1);
%     % ps     = dpsimplify(skeletionization_image,1);
%
%     % SHOW ---------------- Thining the image ---------------------
%
% %     figure, imshow( smallArea ); colormap( summer ), hold on
% %     himage = imshow(coloredLabel1);
% %     set(himage, 'AlphaData', 0.7);
% %
% %     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% %
% %     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% %     % Skeleton the image
% %     skeletionization_image = bwmorph(smallArea,'skel',Inf);
% %     se2 = strel('line',2,2); % 'line',5,8  'best',2,2
% %     bw2 = imdilate(skeletionization_image,se2);
% %     coloredLabel2 = label2rgb(bw2, 'hsv', 'k', 'shuffle');
% %     % figure, imshow(coloredLabel1);
% %     % ps     = dpsimplify(skeletionization_image,1);
% %
% %     % SHOW ---------------- Skeleton the image ---------------------
% %     figure, imshow(nm), hold on
% %     himage = imshow(coloredLabel2);
% %     set(himage, 'AlphaData', 0.7);
% %
%     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%
%     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% %  - - - - - - - - - - - - - Find brach I - - - - - - - - - - - - - - - -
%     skelImg   = bwmorph(smallArea3, 'thin', 'inf');
%     branchImg = bwmorph(skelImg, 'branchpoints');
%     endImg    = bwmorph(skelImg, 'endpoints');
%
%     [row, column] = find(endImg);
%     endPts        = [row column];
%     [row, column] = find(branchImg);
%     branchPts     = [row column];
%
%     figure; imshow(skelImg); hold on;
%     plot(branchPts(:,2),branchPts(:,1),'r*'); hold on;
%     plot(endPts(:,2),endPts(:,1),'*');
%
%   % - - - - - - - - - - - - Find branch II - deleting some node - - - - - -
%     skel2= bwmorph(smallArea3,'skel',Inf);
%
%     B = bwmorph(skel2, 'branchpoints');
%     E = bwmorph(skel2, 'endpoints');
%
%     [y,x] = find(E);
%     B_loc = find(B);
%
%
%     Dmask = false(size(skel2));
%     for k = 1:numel(x)
%         D = bwdistgeodesic(skel2,x(k),y(k));
%         distanceToBranchPt = min(D(B_loc));
%         Dmask(D < distanceToBranchPt) =true;
%     end
%     skelD = skel2 - Dmask;
%     coloredLabel2 = label2rgb(skelD, 'hsv', 'k', 'shuffle');
%     figure, imshow(skelD);
%     hold all;
%     [y,x] = find(B); plot(x,y,'ro')
%
%    % figure, imshow(nm), hold on
%     figure, imshow( smallArea3 ); colormap( summer ), hold on % my output
%     himage = imshow(coloredLabel2);
%     set(himage, 'AlphaData', 0.7);
%
%
% %     figure, imshow( smallArea ), hold on % my output
% %     himage = imshow(coloredLabel2);
% %     set(himage, 'AlphaData', 0.7);
%     % - - - - - - - - - - - - Find HOUGH lines - - - - - - - - - - - - - -
% %    % rotI = imrotate(smallArea,1,'crop');
% %     BW = edge(smallArea,'canny');
% %     [H,T,R] = hough(BW);
% %     figure, imshow(~BW);
% % %     figure, imshow(H,[],'XData',T,'YData',R,...
% % %                 'InitialMagnification','fit');
% %     xlabel('\theta'), ylabel('\rho');
% %     axis on, axis normal, hold on;
% %     P  = houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));
% %     x = T(P(:,2)); y = R(P(:,1));
% %     plot(x,y,'s','color','white');
% %     % Find lines and plot them
% %     lines = houghlines(BW,T,R,P,'FillGap',5,'MinLength',7);
% %     figure, imshow(smallArea), hold on
% %     max_len = 0;
% %     for k = 1:length(lines)
% %        xy = [lines(k).point1; lines(k).point2];
% %        plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
% %
% %        % Plot beginnings and ends of lines
% %        plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
% %        plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
% %
% %        % Determine the endpoints of the longest line segment
% %        len = norm(lines(k).point1 - lines(k).point2);
% %        if ( len > max_len)
% %           max_len = len;
% %           xy_long = xy;
% %        end
% %     end
% %
% %     %  - - - - - - - -  Simplipfy with DP  - - - - - - - - - - - - - - - -
% % %     B2 = num2cell(B);
% % %     tol    = 0.5;
% % %     ps     = dpsimplify(B2,tol);
% % %     hold on
% % %     figure, plot(ps(:,1),ps(:,2),'r','LineWidth',2);
% % %     legend('original polyline','simplified')
%
%
%
%
%
% % Comment %%
 end