The for loop on classification gives every time different value?

I have one code which gives me 3*3 matrix value of classification in image .but when I run this code 2nd time it will change the output value every time. I got something new 3*3 matrix value can any one help me in this.I already submitted my report on this previously generated results now the problem is I have submission on that this values gina differ so please anyone help me in this

[Image Analyst]

How can we do that unless we can run your code to reproduce what you observed? We can't even begin to fix it. Read this link then fix your post.

[riddhi desai]

for p = 1:10

Selection of Original Image %%%%%

    [a,b]=uigetfile('*.jpg','Select the Input Image');
    str=strcat(b,a);
    f=imread(str);
    % f=imread('1.jpg');
    %%%%%%  Color Space Transformation %%%%%%
    %%%% Separation of Hue, Saturation and Intensity Components   %%%%%
    fhsv=rgb2hsv(f);

      fh=fhsv(:,:,1);
      %figure,imshow(fh);title('The Hue Component of the Original Image');
      fs=fhsv(:,:,2);
      %figure,imshow(fs);title('The Saturation Component of the Original Image');
      fv=fhsv(:,:,3);
      %figure,imshow(fv);title('The Intensity Component of the Original Image');
      % figure,imshow(fv);title('Intensity Component');
      fgr=rgb2gray(f);   %%Conversion of original Image to grayscale Image
      figure,imshow(fgr);title('Original Grayscale Image');

Diffusion for Effective Filtering %%%%%%%

      %%%%%%%%%   Diffusion Parameters    %%%%%%%%%%
      iter =10;
      delta_t = 1/7;
      kappa = 15;
      adi=double(fs);
      diff_im = adi;
      dx = 1;dy = 1;dd=sqrt(2);
      hN = [0 1 0; 0 -1 0; 0 0 0];
      hS = [0 0 0; 0 -1 0; 0 1 0];
      hE = [0 0 0; 0 -1 1; 0 0 0];
      hW = [0 0 0; 1 -1 0; 0 0 0];
      hNE = [0 0 1; 0 -1 0; 0 0 0];
      hSE = [0 0 0; 0 -1 0; 0 0 1];
      hSW = [0 0 0; 0 -1 0; 1 0 0];
      hNW = [1 0 0; 0 -1 0; 0 0 0];
      for t = 1:iter
          nablaN = imfilter(diff_im,hN,'conv');
          nablaS = imfilter(diff_im,hS,'conv');
          nablaW = imfilter(diff_im,hW,'conv');
          nablaE = imfilter(diff_im,hE,'conv');
          nablaNE = imfilter(diff_im,hNE,'conv');
          nablaSE = imfilter(diff_im,hSE,'conv');
          nablaSW = imfilter(diff_im,hSW,'conv');
          nablaNW = imfilter(diff_im,hNW,'conv');
          cN = 1./(1 + (nablaN/kappa).^2);
          cS = 1./(1 + (nablaS/kappa).^2);
          cW = 1./(1 + (nablaW/kappa).^2);
          cE = 1./(1 + (nablaE/kappa).^2);
          cNE = 1./(1 + (nablaNE/kappa).^2);
          cSE = 1./(1 + (nablaSE/kappa).^2);
          cSW = 1./(1 + (nablaSW/kappa).^2);
          cNW = 1./(1 + (nablaNW/kappa).^2);
      end
      diff_im = diff_im+delta_t*((1/(dy^2))*cN.*nablaN + (1/(dy^2))*cS.*nablaS + ...
          (1/(dx^2))*cW.*nablaW + (1/(dx^2))*cE.*nablaE + ...
          (1/(dd^2))*cNE.*nablaNE + (1/(dd^2))*cSE.*nablaSE + ...
          (1/(dd^2))*cSW.*nablaSW + (1/(dd^2))*cNW.*nablaNW );
      % end
      f3=diff_im;

Gabor Filtering %%%

      %%%%%%%%%%%%   Defining Parameters of Gabor Filter     %%%%%%%%%%%%%

      u=5;
      v=8;
      m=39;
      n=39;d1=39;d2=39;
      gaborArray = cell(u,v);
      fmax = 10;
      gama = sqrt(2);
      eta = sqrt(2);
      %%%%%%%      Application of Gabor Filter      %%%%%%%%%
      for i = 1:u
          fu = fmax/((sqrt(2))^(i-1));
          alpha = fu/gama;
          beta = fu/eta;
          for j = 1:v
              tetav = ((j-1)/v)*pi;
              gFilter = zeros(m,n);
              for x = 1:m
                  for y = 1:n
                      xprime = (x-((m+1)/2))*cos(tetav)+(y-((n+1)/2))*sin(tetav);
                      yprime = -(x-((m+1)/2))*sin(tetav)+(y-((n+1)/2))*cos(tetav);
                      gFilter(x,y) = (fu^2/(pi*gama*eta))*exp(-((alpha^2)*(xprime^2)+(beta^2)*(yprime^2)))*exp(1i*2*pi*fu*xprime);
                  end
              end
              gaborArray{i,j} = gFilter;
          end
      end
      img=double(f3);
      [u,v] = size(gaborArray);
      gaborResult = cell(u,v);
      for i = 1:u
          for j = 1:v
              gaborResult{i,j} = imfilter(img, gaborArray{i,j});
          end
      end
      featureVector = [];
      for i = 1:u
          for j = 1:v
              gaborAbs = abs(gaborResult{i,j});
              gaborAbs = downsample(gaborAbs,d1);
              gaborAbs = downsample(gaborAbs.',d2);
              gaborAbs = gaborAbs(:);
              gaborAbs = (gaborAbs-mean(gaborAbs))/std(gaborAbs,1);
              featureVector =  [featureVector; gaborAbs];
          end
      end

      figure;imshow(abs(gaborResult{i,j}),[]);title('Result-1');
      rimg1=abs(gaborResult{2,5});
      rimg2=abs(gaborResult{3,5});
      rimg3=abs(gaborResult{4,5});
      rimg4=abs(gaborResult{5,5});
      figure,subplot(121);imshow(fgr);title('Original Grayscale Image');
      subplot(122);imshow(abs(gaborResult{2,5}),[]);title('Result at [Scale Orientation]=[2 5]');
      figure,subplot(121);imshow(fgr);title('Original Grayscale Image');
      subplot(122);imshow(abs(gaborResult{3,5}),[]);title('Result at [Scale Orientation]=[3 5]');
      figure,subplot(121);imshow(fgr);title('Original Grayscale Image');
      subplot(122);imshow(abs(gaborResult{4,5}),[]);title('Result at [Scale Orientation]=[4 5]');
      figure,subplot(121);imshow(fgr);title('Original Grayscale Image');
      subplot(122);imshow(abs(gaborResult{5,5}),[]);title('Result at [Scale Orientation]=[5 5]');

      fs1=im2bw(fs);
      figure,imshow(fs1);title('Binarized Image');
      se2=strel('disk',3);
      fs2=imclose(fs1,se2);title('Filling of Small Holes');
      figure,imshow(fs2);
      se1=strel('disk',5);title('Removal of small regions');
      fs3=imopen(fs2,se1);
      %figure,imshow(fs3);title('Final Processed Image');
      for i=1:size(fs,1)
          for j=1:size(fs,2)
              if(fs3(i,j)==1)
                  fs4(i,j)=fs(i,j);
                  fk1(i,j)=fh(i,j);
                  fk2(i,j)=fv(i,j);
              else
                  fs4(i,j)=0;
                  fk1(i,j)=0;
                  fk2(i,j)=0;

              end
          end
      end
      figure,imshow(fs4);title('Extracted Region');

Labelling and Connected Component Analysis %%%%%

      [B,L]=bwboundaries(fs3,'noholes');
      hold on
      for k = 1:length(B)
          boundary = B{k};
          plot(boundary(:,2), boundary(:,1), 'y', 'LineWidth', 4)
      end
      fk3=cat(3,fk1,fs4,fk2);
      fk4=hsv2rgb(fk3);

      figure,imshow(fk4);title('Extracted Region as in Original Color Image');
      %%%%%  Computation of color Features    %%%%
      seg_mean=mean2(fs4);
      disp('The mean of the segmented Region is');
      disp(seg_mean);
      seg_sd=std2(fs4);
      disp('The standard deviation for the segmented Region is');
      disp(seg_sd);
      I = im2double(fs4);
      kurt=kurtosis(I(:));
      seg_kurt=kurt;
      disp('The kurtosis value for the segmented region is');
      disp(seg_kurt);
      I2 = im2double(fs4);
      seg_skew=skewness(I2(:));
      disp('The skewness value for the segmented region is');
      disp(seg_skew);
      v = var(fs4);
      seg_v=mean(v);
      disp('The variance  value for the segmented region is');
      disp(seg_v);

      %%%%%  Computation of Textural Features    %%%%
      M=calc_entropy(fs4);
      disp('The entropy value for the segmented region is');
      disp(M);
      %%LBP%%
      imwrite(fk4,'ni.jpg');
      imgk=imread('ni.jpg');
      img=rgb2gray(imgk);
      nFiltSize=7;
      nFiltRadius=1;
      filtR=generateRadialFilterLBP(nFiltSize, nFiltRadius);

      effLBP= efficientLBP(img, 'filtR', filtR, 'isRotInv', false, 'isChanWiseRot', false);
      binsRange=(1:2^nFiltSize)-1;

      figure;
      subplot(1, 2, 1)
      imshow(imgk);
      title('Original segmented image', 'fontSize', 18);

      subplot(1, 2, 2)
      imshow( effLBP );
      title('Efficeint LBP image', 'fontSize', 18);
      imwrite(fk3,'nimg.jpg');
      imgkk=imread('nimg.jpg');
      %figure, imshow(img), title('X axis rock cut'); %figure is for creating a figure window.
      % text(size(img,2),size(img,1)+15,...
      %      'Unconventional shale x axis cut', ...
      %      'FontSize',7,'HorizontalAlignment','right');
      cform = makecform('srgb2lab'); %the form of the conversion is defined as from rgb to l a b
      lab_img = applycform(imgkk,cform); %converting the rgb image to l a b image using the conversion form defined above.
      ab = double(lab_img(:,:,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,'distance','sqEuclidean', ...
          'Replicates',3);
      pixel_labels = reshape(cluster_idx,nrows,ncols);
      %figure, imshow(pixel_labels,[]), title('image labeled by cluster index');

      segmented_images = cell(1,3);
      rgb_label = repmat(pixel_labels,[1 1 3]);

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

      imwrite(segmented_images{k},'nii.jpg');
      imgkl=imread('nii.jpg');
      cl1=imgkl(:,:,1);
      cl2=imgkl(:,:,2);
      cl3=imgkl(:,:,3);

      [mk1 mk2]=size(cl1);
      G=0;
      S=0;
      N=0;

      for i=1:mk2

              if(max(cl1(:,i))>100);

                  G=G+1;
              end
              if(max(cl1(:,i))>135 && max(cl1(:,i)<196));
                  S=S+1;
              end
              if(max(cl1(:,i))<196);
                  N=N+1;
              end
     end

      count_red=[G S N];

      g1(p)=G;
      s1(p)=S;
      n1(p)=N;

      [mk11 mk21]=size(cl2);
      G11=0;
      S11=0;
      N11=0;
      for i=1:mk21

              if(max(cl2(:,i))>100);

                  G11=G11+1;
              end
              if(max(cl2(:,i))>135 && max(cl2(:,i)<196));
                  S11=S11+1;
              end
              if(max(cl2(:,i))<196);
                  N11=N11+1;
              end
         end

      count_black=[G11 S11 N11];

      g11(p)=G11;
      s11(p)=S11;
      n11(p)=N11;

      [mk111 mk211]=size(cl3);
      G111=0;
      S111=0;
      N111=0;
      for i=1:mk211

              if(max(cl3(:,i))>100);

                  G111=G111+1;
              end
              if(max(cl3(:,i))>135 && max(cl3(:,i)<196));
                  S111=S111+1;
              end
              if(max(cl3(:,i))<196);
                  N111=N111+1;
              end
          end

      count_yellow=[G111 S111 N111];

      g111(p)=G111;
      s111(p)=S111;
      n111(p)=N111;

      %%Count is
      count_red1 = [g1(1) s1(1) n1(1)];
      count_black1 = [g11(1) s11(1) n11(1)];
      count_yellow1 = [g111(1) s111(1) n111(1)];
      disp('Count for red pixels is (In ordr of Granulation Slought and Nacrotics)')
      count_red1

      disp('Count for black pixels is (In ordr of Granulation Slought and Nacrotics)')
      count_black1

      disp('Count for Yellow pixels is (In ordr of Granulation Slought and Nacrotics)')
      count_yellow1

      ch = input('Enter your choice either if you want to continew with this image or else go for new image :');

      if (ch == 1)
          pause(0.1);
      end
      if (ch==2)
          break;
      end
   end

clc;clear all;close all;

sir this is my code pls help evryyime i get diffrent output

Answer 1

[Steven Lord]

Most likely you're calling some function that uses the random number generator. Reset the generator to a fixed seed at the start of each classification using the rng function and see if that provides reproducibility.

[riddhi desai]

sir which function are u talking about??