My Project CU 2016 [2-May-2016]

clear;
clc;
%nlist           = {'aerial6.jpg';'aerial10.jpg';'roadA.jpg';'aerial8.jpg'};
nlist1 = {'test_demo.png'};   % theshold = 23000
nlist2 = {'test_paper_1.png'}; % theshold =
nlist3 = {'test_per_1.png'};   % theshold =
nlist4 = {'testroad3.png'};   % theshold = 7500
nlist5 = {'test_demo1.png'};  % theshold = 29000
nlist6 = {'test_paper_3.png'};

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 = nlist5{cta};
    rgb = imread(nm);
    normImage = im2double(rgb);
    nm_input = rgb2gray(normImage);

    %figure, imshow(rgb);

%     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')


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

    smallArea = La;
    smallArea2 = La;
    smallArea3 = 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);
    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)];
    cen1 = s_centroid(:,1);
    cen2 = s_centroid(:,2);
    threshold_use = max(s_area)/2;
    s_area > threshold_use & shape_index > 2.5;
    score = shape_index;
    score2 = s_area;
    j = 1;
    k = 1;
    for i=1:size(s)
        if s_area(i) > threshold_use & shape_index(i) > 2.5;
           q1 = s_num(i);
           q2 = s_area(i);
           q3 = shape_index(i);
           num(j) = q1;
           area(j) = q2;
           shape(j) = q3;
           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'];
        end
    end
    for i=1:size(s)
        if shape_index(i) > 2.5;
           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;

        end
    end
    region_state1 = [num' area' shape'];
    region_state2 = [num2' area2' shape2' cent1' cent2'];
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    %%%%%%%%%%%%%%%%%%%%%%%%%% 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_max = max(region_matrix_used(:,2))/2
%shape_index = 2.5;

small_select = ((s_area >= threshold_max) & (shape_index > 2.5));
smallArea( vertcat( s(~small_select).PixelIdxList ) ) = 0; %// set all other regions to zero


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 > 2.5));
smallArea3( vertcat( s(~small_select3).PixelIdxList ) ) = 0; %// set all other regions to zero

figure, imshow( smallArea ); colormap( summer );
figure, imshow( smallArea2 ); colormap( summer );
figure, imshow( smallArea3 ); colormap( summer );


%     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(smallArea3), set(himage, 'AlphaData', 0.6);


%%% Comment %%%

    % Thining the image
    skeletionization_image = bwmorph(smallArea,'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(smallArea, '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(smallArea,'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( smallArea ); 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


% % --------------- Plot R-G-B Histrogram -----------------------------
% % Read in standard MATLAB color demo image.
% rgbImage = imread('test_demo.png');
% workspace; % Make sure the workspace panel is showing.
% fontSize = 20;
% [rows columns numberOfColorBands] = size(rgbImage);
% subplot(2, 2, 1);
% imshow(rgbImage, []);
% title('Original Color Image', 'Fontsize', fontSize);
% set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
%
% redPlane = rgbImage(:, :, 1);
% greenPlane = rgbImage(:, :, 2);
% bluePlane = rgbImage(:, :, 3);
%
% % Let's get its histograms.
% [pixelCountR grayLevelsR] = imhist(redPlane);
% subplot(2, 2, 2);
% plot(pixelCountR, 'r');
% title('Histogram of red plane', 'Fontsize', fontSize);
% xlim([0 grayLevelsR(end)]); % Scale x axis manually.
%
% [pixelCountG grayLevelsG] = imhist(greenPlane);
% subplot(2, 2, 3);
% plot(pixelCountG, 'g');
% title('Histogram of green plane', 'Fontsize', fontSize);
% xlim([0 grayLevelsG(end)]); % Scale x axis manually.
%
% [pixelCountB grayLevelsB] = imhist(bluePlane);
% subplot(2, 2, 4);
% plot(pixelCountB, 'b');
% title('Histogram of blue plane', 'Fontsize', fontSize);
% xlim([0 grayLevelsB(end)]); % Scale x axis manually.
% %------------------ End to plot R-G-B Histrogram ------------------------