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training/recurse_dwt_isic17_final.m

@@ -1,5 +1,3 @@
-% Start with a folder and get a list of all subfolders.
-% Finds all images in that folder and all of its subfolders
 % mix the training, validation and test set together to obtain two classes
 % benign and malignant
 tic;
@@ -61,5 +59,4 @@
 %% save data
 fn = strcat('data/myISICcr80plus256_Wavelet1.mat');    
 save(fn, 'Yw', 'Yw_range', '-v7.3');
-toc;
-% end
+toc;

training/recurse_dwt_isic17_malignant.m

@@ -1,4 +1,4 @@
-% get additional malignant images from ISIC18 and add to the ISIC17
+% get additional malignant images from ISIC Archive
 % database to balance the two classes (benign and malignant)
 tic;
 clear all; close all;
@@ -20,6 +20,7 @@
     imgSubMat = [];%imgSubMat1 = [];
     if (mySubDirs(i).isdir == 1) %myRootDir might have files as well
         currentSubDir = fullfile(myRootDir, mySubDirs(i).name);
+
         %get all filenames that match myFilePattern
         myFiles = dir(fullfile(currentSubDir, myFileExtension));
         numFiles = length(myFiles);
@@ -30,6 +31,7 @@
             numPreviousFiles = length(previousFiles);
             position = position + numPreviousFiles;
         end
+
         %read all files in sub directory and transform to wavelet space
         for k = 1:(numFiles)
             fileName = myFiles(k).name;
@@ -39,29 +41,27 @@
                 Xgray = rgb2gray(X);
             end
             [m, n] = size(Xgray);
+
             % crop the image first
             r = 0.8; % ratio between cropped image and the original one
             p = round(r*m); q = round(r*n);
             xmin = round((m-p)/2);
             ymin = round((n-q)/2);
             win = [xmin ymin p q];
             Xcr = Xgray(xmin:p, ymin:q);
+
             % resize the image
-            Xfin = imresize(Xcr, [256, 192], 'bicubic'); %192x256 or 256x192 or 600x450 or 256x256 or 128x96
+            Xfin = imresize(Xcr, [256, 192], 'bicubic');
             % =========================================================================
             [cA1, cH1, cV1, cD1] = dwt2(Xfin,'db1'); %haar transformation
 
-            cA1_tmp(:,k+position) = double(cA1(:));
-            %             cH1_tmp(:,k+position) = double(cH1(:));
-            %             cV1_tmp(:,k+position) = double(cV1(:));
-            %             cD1_tmp(:,k+position) = double(cD1(:));         
+            cA1_tmp(:,k+position) = double(cA1(:));      
             Y_label(k+position) = 1;
         end
     end
 end
-   
+
 Y = cA1_tmp;
 %% save data
-
 fn = strcat('data/myISIC_test_cr80plus_256x192.mat' );
 save(fn, 'Y', 'Y_label', '-v7.3');

training/recurse_dwt_isic17_mela.m

@@ -5,7 +5,7 @@
 clear all; close all;
 addpath(genpath('data'));
 addpath('utils');
-mode = 3;
+mode = 3; % choose mode to get the data from the training/validation/test set
 
 switch mode
     case 1
@@ -27,7 +27,6 @@
 imgSubMat_mela = []; imgSubMat_benign = [];
 tmp_label_mela = []; tmp_label_benign = [];
 s = 1; t = 1;
-%%% recurse sub-directories
 
 %% get all filenames that match�myFilePattern
 myFiles = dir(fullfile(myRootDir, myFileExtension));
@@ -51,12 +50,12 @@
     Xcr = Xgray(xmin:p, ymin:q);
 
     % resize the image
-    Xfin = imresize(Xcr, [256, 192], 'bicubic'); %192x256 or 256x192 or 600x450 or 256x256 or 128x96 
-    % or 230x172 or 272x204
-    % wavelet decomposition in quaternions
-    [cA1,cH1,cV1,cD1] = dwt2(Xfin, 'db1'); %daubechies transformation    
+    Xfin = imresize(Xcr, [256, 192], 'bicubic');
+
+    % wavelet decomposition 
+    [cA1,cH1,cV1,cD1] = dwt2(Xfin, 'db1'); % haar transformation    
     tmp = double(cA1(:));    
-%     tmp = double(cD1(:));    
+
     if groundTruth(i,1)==1;
         imgSubMat_mela(:,s) = tmp;    
         tmp_label_mela(s) = 1;
@@ -81,5 +80,4 @@
         fn = strcat('data/myISIC17-PhiHatVF_test_256x192.mat');
 end
 save(fn, 'Y', 'Y_label', '-v7.3');
-toc;
-% end
+toc;

training/recurse_dwt_sub.m

@@ -1,7 +1,5 @@
-% function [imgMat, imgMat_range] = recurse_dwt_sub(myRootDir, mySubDirPattern, myFileExtension)
-% Start with a folder and get a list of all subfolders.
-% Finds all images in that folder and all of its subfolders
-% tic;
+
+tic;
 clear all; close all;
 
 addpath(genpath('utils'));
@@ -22,92 +20,47 @@
         mySubDirPattern = 'obj*';
         myFileExtension = '*.png';
     case 4
-        myRootDir = './data/myFlower17';
-        mySubDirPattern = 'flower_*';
-        myFileExtension = '*.jpg';
-    case 5
         myRootDir = './data/Cropped_ARgender';
         mySubDirPattern = '*';
         myFileExtension = '*.bmp';
-    case 6
-        %         myRootDir = './data/coil-100_PhiHatVF_PNG';
-        myRootDir = './data/coil-100_PsiHatVF_PNG';
-        %         myRootDir = './data/coil-100_PGVF_PNG';
-        mySubDirPattern = 'obj*';
-        myFileExtension = '*.png';
-    case 7
-        myRootDir = './data/Cropped_Yale_PhiHat_Large';
-        mySubDirPattern = 'yaleB*';
-        myFileExtension = '*.png';
-    case 8
-        myRootDir = './data/ISIC2018_PhiHatVF';
-        mySubDirPattern = '*';
-        myFileExtension = '*.png';
 end
 
 mySubDirs = dir(fullfile(myRootDir, mySubDirPattern));
 mySubDirs = mySubDirs(~ismember({mySubDirs.name},{'.','..'}));
 numSubDirs = length(mySubDirs);
-imgMat = []; %imgMat1 = [];
+imgMat = []; 
 imgMat_range = 0;
-%recurse sub-directories
+
+%% recurse sub-directories
 for i = 1:numSubDirs
     imgSubMat = [];%imgSubMat1 = [];
-    if (mySubDirs(i).isdir == 1) %myRootDir might have files as well
+    if (mySubDirs(i).isdir == 1) % myRootDir might have files as well
 
         currentSubDir = fullfile(myRootDir, mySubDirs(i).name);
 
-        %get all filenames that match myFilePattern
+        % get all filenames that match myFilePattern
         myFiles = dir(fullfile(currentSubDir, myFileExtension));
         numFiles = length(myFiles);
 
-        %read all files in sub directory and transform to wavelet space
+        % read all files in sub directory and transform to wavelet space
         for k = 1:(numFiles)
             fileName = myFiles(k).name;
             fullFileName = fullfile(currentSubDir, fileName);
             X = imread(fullFileName);
             if size(X,3)==3
                 X = rgb2gray(X);
-                %                 X = rgb2ycbcr(X);
-                %                 X = X(:,:,1);
             end
-            % =========== Resize training images ======================================
-            if database == 6
-                %                 X = imresize(X, [64, 64], 'bicubic');
-                X = imresize(X, [420, 420], 'bicubic');
-            elseif database == 7
-                X = imresize(X, [384, 336], 'bicubic');
-            elseif database == 8
-%                 [m, n] = size(X);
-%                 r = 0.8; % ratio between cropped image and the original one
-%                 p = round(r*m); q = round(r*n);
-%                 xmin = round((m-p)/2) +1;
-%                 ymin = round((n-q)/2) +1;
-%                 win = [xmin ymin p q];
-%                 X = X(xmin:p, ymin:q);
-                X1 = imresize(X, [500, 500], 'bicubic');
-%                 % crop the image to size [pxq]
-%                 [m, n] = size(X);
-%                 p = 500; q = 500;
-%                 xmin = round((m-p)/2)+1;   xmax = xmin + p -1;
-%                 ymin = round((n-q)/2)+1;   ymax = ymin + p -1;
-%                 win = [xmin ymin xmax ymax];
-%                 X2 = X(xmin:xmax, ymin:ymax);
-            end
-            % =========================================================================
+            % ===========================================================
             [cA1,~,~,~] = dwt2(X1,'db1'); %daubechies transformation
-%             [cA2,~,~,~] = dwt2(X2,'db1'); %daubechies transformation
             cA1_tmp = double(cA1(:));
-%             cA2_tmp = double(cA2(:));
-            %             cD_tmp = double(cD(:));
             imgSubMat(:,k) = cA1_tmp;
-%             imgSubMat(:,k + numFiles) = cA2_tmp;
         end
-        imgMat = [imgMat imgSubMat]; %imgMat1 = [imgMat1 imgSubMat1];
+        imgMat = [imgMat imgSubMat];
         imgMat_range(i+1) = imgMat_range(i) + k;
     end
 end
-% PCA dimensionality reduction
+
+%% PCA dimensionality reduction
 C = double(imgMat * imgMat');
 [V, D] = eig(C);
 D = diag(D); % perform PCA on features matrix
@@ -117,6 +70,7 @@
 imgMat_pca = V_pca' * imgMat;
 Yw = imgMat_pca; Yw_range = imgMat_range;
 
+%% save data
 switch database
     case 1
         fn = strcat('data/myYaleB_Wavelet1.mat');
@@ -125,18 +79,7 @@
     case 3
         fn = strcat('data/myCoil-100_Wavelet1.mat' );
     case 4
-        fn = strcat('data/myFlower_Wavelet1');
-    case 5
         fn = strcat('data/myARgender_Wavelet1');
-    case 6
-        fn = strcat('data/myCoil-100_PsiHatVF_resize420x420_Wavelet1.mat');
-        %         fn = strcat('data/myCoil-100_PhiHatVF_resize64x64_Wavelet1.mat');
-        %         fn = strcat('data/myCoil-100_PGVF_Wavelet1.mat');
-    case 7
-        fn = strcat('data/myYaleB_PhiHatVF_Large_Wavelet1.mat');
-    case 8
-        fn = strcat('data/myISIC2018_PhiHatVF_resize500x500_Wavelet1.mat');
 end
 save(fn, 'Yw', 'Yw_range');
-%toc;
-% end
+toc;