clear ;

clear ;

org=imread('4Perfect.jpg');

org=imresize(org,[256 256]);

noisy= imnoise(org,'gaussian',0,0.001);%  % add noise of mean 0, variance 0.005

subplot(3,3,1), imshow(org,[]), title('Original Image');

subplot(3,3,2), imshow(noisy,[]), title('Noisy Image');

% start of calling normal shrink denoising algorithm

ns_r = normal_shrink(noisy(:,:,1)); 

ns_g = normal_shrink(noisy(:,:,2));

ns_b = normal_shrink(noisy(:,:,3));

ns_r= uint8(ns_r);

ns_g= uint8(ns_g);

ns_b= uint8(ns_b);

ns = cat(3, ns_r, ns_g, ns_b);

subplot(3,3,4), imshow(ns,[]), title('normal shrink');

% end of calling normal shrink denoising algorithm

% start of calling bilateral filter

ns2gray = rgb2gray(ns);

ns2gray = double(ns2gray);

B = bilateral(ns2gray);

subplot(3,3,5), imshow(B,[]), title('bilateral');

% end of calling bilateral filter

%enhanced image

enha_imadj = imadjust(uint8(B));

enha_histeq = histeq(uint8(B));

enha_adapthisteq = adapthisteq(uint8(B));

% start of edge based segmentation

%kirsch operator

kris = krisch(B);

subplot(3,3,7), imshow(kris,[]), title('krisch');

%extended kirsch operator

kris55 = krisch55(B);

subplot(3,3,8), imshow(kris55,[]), title('krisch55');

%extended sobel operator

sob55 = sobel55(B);

subplot(3,3,9), imshow(sob55,[]), title('sobel 5*5');

% end of  edge based segmentation

% start of threshold based segmentation

%ostu threshold

ostu_img = ostu(B);

figure;imshow(ostu_img,[]),title('ostu');

% end of threshold based segmentation

% kmeans clustering

figure;

 [k, class, img_vect]= kmeans(B, 5);

 for clust = 1:k

    cluster = reshape(class(1:length(img_vect),clust:clust), [256,256] );

    subplot(1,k,clust), imshow(cluster,[]), title('k-means cluster ');

 end

 %adaptive clustering

 [k, class, img_vect, noOfIter]= adaptive_kmeans(B);

 %title(['adaptive kmeans- total iteration' num2str(noOfIter)]);

 figure;

 for clust = 1:k

    cluster = reshape(class(1:length(img_vect),clust:clust), [256,256] );

    subplot(1,k,clust), imshow(cluster,[]), title({'adaptivekmeans:';['iteration ' num2str(noOfIter)]});

 end

 %fuzzy c means

 img = double(B);

 k = 5;

 [ Unew, centroid, obj_func_new ] = fuzzyCMeans( img, k );

 figure;

 for i=1:k

        subplot(1,k,i);

        imshow(Unew(:,:,i),[]), title('fuzzy Cmeans');

 end

 % watershed algorithm

 figure;

 watershedSeg(B, sob55);