function [] = step_C_varpcanet(dirTest, dirUtilities, ext, numCoresFeatExtr, numCoresKnn, fidLogs, logS, savefile, plotFigures)

%--------------------------------------

%General parameters

stepPrint = 100;

%PCA Params

run('./params/paramsPCATuning.m');

%--------------------------------------

%Dir DBs

dbname_All = { ...

    'ALL_IDB'

    };

dbname_part_All = { ...

    'ALL_IDB2'

    };

dbname_ROI_All = { ...

    'ROI_256'

    };

%nets

net_name{1} = 'AlexNet';

net_name{2} = 'VGG16';

net_name{3} = 'VGG19';

net_name{4} = 'ResNet18';

net_name{5} = 'ResNet50';

net_name{6} = 'ResNet101';

net_name{7} = 'DenseNet201';

%--------------------------------------

colorS_init = 1;

colorS_tune = 1;

colorS_test = 1;

%--------------------------------------

% processDummyDirs();

%--------------------------------------

%Loop on dbs

for db = 1 : numel(dbname_All)

    % for db = 2

    %Close

    close all

    pause(0.2);

    %DB selection

    dbname = dbname_All{db};

    dbnamePart = dbname_part_All{db};

    ROI = dbname_ROI_All{db};

    dirDB_wROI = [dirTest dbname '/' dbnamePart '/' ROI '/'];

    %dirDB_noROI = [dirWorkspace dbname '/' dbnamePart '/' ];

    %--------------------------------------

    %loop on nets

    for n = 1 : numel(net_name)

        %switch net

        switch n

            case 1

                net = alexnet;

                layer = 'fc6';

                conv_layer = 'conv5';

            case 2

                net = vgg16;

                layer = 'fc6';

                conv_layer = 'conv5_3';

            case 3

                net = vgg19;

                layer = 'fc6';

                conv_layer = 'conv5_4';

            case 4

                net = resnet18;

                layer = 'fc1000';

                conv_layer = 'res5b_relu';

            case 5

                net = resnet50;

                layer = 'fc1000';

                conv_layer = 'res5c_branch2c';

            case 6

                net = resnet101;

                layer = 'fc1000';

                conv_layer = 'res5c_branch2c';

            case 7

                net = densenet201;

                layer = 'fc1000';

                conv_layer = 'conv5_block32_2_conv';

        end %switch

        %--------------------------------------

        %Folder creation

        %RESULTS: dirs net

        dirResults = ['./Results/' dbname '/' dbnamePart '/' net_name{n} '/'];

        mkdir_pers(dirResults, savefile);

        %RESULTS: log file

        timeStampRaw = datestr(datetime);

        timeStamp = strrep(timeStampRaw, ':', '-');

        if savefile && logS

            logFile = [dirResults dbname '_log_' timeStamp '.txt'];

            fidLog = fopen(logFile, 'w');

            fidLogs{2} = fidLog;

        end %if savefile && log

        %--------------------------------------

        %Display

        fprintf_pers(fidLogs, '\n');

        fprintf_pers(fidLogs, '---------------\n');

        fprintf_pers(fidLogs, 'ALL-Unsharpen\n');

        fprintf_pers(fidLogs, [dbname '\n']);

        fprintf_pers(fidLogs, [dbnamePart '\n']);

        fprintf_pers(fidLogs, '---------------\n');

        fprintf_pers(fidLogs, '\n');

        %--------------------------------------

        %display

        fprintf_pers(fidLogs, '---------------\n');

        fprintf_pers(fidLogs, ['Net: ' net_name{n} '\n']);

        fprintf_pers(fidLogs, '---------------\n');

        fprintf_pers(fidLogs, '\n')

        %--------------------------------------

        %DB processing

        %Extract samples

        files = dir([dirDB_wROI '*.' ext]);

        %Compute labels

        [problem, labels, numImagesAll] = computeLabels(dirDB_wROI, files);

        %--------------------------------------

        %Display

        fprintf_pers(fidLogs, 'Extracting samples...\n');

        fprintf_pers(fidLogs, ['\t' num2str(numImagesAll) ' images in total\n']);

        fprintf_pers(fidLogs, '\n');

        %--------------------------------------

        %LOOP ON ITERATIONS

        %Init

        accuracy_knnAll = zeros(param.numIterations, 1);

        cmc = cell(param.numIterations, 1);

        %--------------------------------------

        %Compute random fold indexes

        %if outside iteration loop, random fold once (es. 10-fold)

        %[allIndexes, cvIndices] = computeAllIndexesFold(numImagesAll, labels, param);

        %Loop

        for r = 1 : param.numIterations

            %--------------------------------------

            %Display

            fprintf_pers(fidLogs, ['Iteration N. ' num2str(r) '\n']);

            %--------------------------------------

            %File save info

            fileSaveTest_iter = [dirResults '/results_iter_' num2str(r) '.mat'];

            %--------------------------------------

            %Compute random fold indexes

            %--10-fold

            %[indImagesTrain, indImagesTest, numImagesTrain, numImagesTest] = computeIndexesFold(cvIndices, r);

            %--2-fold if inside iteration loop, random fold each iteration (repeated 2-fold)

            [allIndexes, cvIndices] = computeAllIndexesFold(numImagesAll, labels, param);

            [indImagesTrain, indImagesTest, numImagesTrain, numImagesTest] = computeIndexesFold(cvIndices, randi(2, 1));

            %Corresponding labels

            TrnLabels = labels(indImagesTrain);

            TestLabels = labels(indImagesTest);

            %--------------------------------------

            %Display output number of images

            fprintf_pers(fidLogs, ['\t' num2str(numImagesTrain) ' images are chosen for training\n']);

            fprintf_pers(fidLogs, ['\t' num2str(numImagesTest) ' images are chosen for testing\n']);

            %%%%%%%%%%%%%%  TRAINING  %%%%%%%%%%%%%

            start_pool(numCoresFeatExtr);

            %--------------------------------------

            fprintf_pers(fidLogs, '\tTraining... \n')

            %--------------------------------------

            %Load images for training

            fprintf_pers(fidLogs, '\t\tLoading images for training... \n')

            [imagesCellTrain, filenameTrn, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_init, 100, dirUtilities, 0);

            imagesCellTrain = adjustFormat(imagesCellTrain);

            %norm

            %[imagesCellTrain, dd1, dd2] = computeNorm(imagesCellTrain);

            %find th_focus

            fprintf_pers(fidLogs, '\tComputing th_focus init... \n')

            %%%%%% QUI %%%%%%

            th_focus_init = find_th_focus(imagesCellTrain, TrnLabels, [128 128], dirUtilities, fidLogs);

            %th_focus_init = 7.3;

            %puliamo

            clear imagesCellTrain

            %tune

            %th_focus_start = round((th_focus_init - th_focus_init*percC/100)*10)/10;

            %th_focus_end = round((th_focus_init + th_focus_init*percC/100)*10)/10;

            th_focus_start = round((th_focus_init - 0.5)*10)/10;

            th_focus_end = round((th_focus_init + 0.5)*10)/10;

            %th_focus_start = th_focus_init;

            %th_focus_end = th_focus_init;

            %init

            accuracy_knnALLFOCUS = [];

            %--------------------------------------

            %tuning th_focus

            fprintf_pers(fidLogs, '\tTuning th_focus... \n')

            %loop on th_focus

            allfocuses = th_focus_start : 0.1 : th_focus_end;

            %%%%%% QUI %%%%%%

            for th_focus = allfocuses

            %for th_focus = th_focus_start

                %Display

                fprintf_pers(fidLogs, '\n')

                fprintf_pers(fidLogs, ['\t\tth_focus: ' num2str(th_focus) '\n']);

                fprintf_pers(fidLogs, '\t\tLoading images... \n')

                [imagesCellTrain, filenameTrn, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_tune, th_focus, dirUtilities, 0);

                imagesCellTrain = adjustFormatForPCANet(imagesCellTrain, [128, 128]);

                %--------------------------------------

                %PCANet Training

                %1 layer: PCA filters

                [V, PCANet] = trainPCANet(imagesCellTrain, PCANet, fidLogs, param, numCoresFeatExtr);

                %Feature extraction

                fprintf_pers(fidLogs, '\t\tFeature extraction... \n')

                [ftrain_all, numFeaturesTrain] = featExtrGaborAdapt(imagesCellTrain, V, PCANet, [], param, numImagesTrain, stepPrint);

                %size

                sizeTrain = size(ftrain_all, 2);

                %--------------------------------------

                %performance

                fprintf_pers(fidLogs, '\t\tClassification - original... \n')

                errorStruct_original_temp = computeClassificationPerformance(numFeaturesTrain, sizeTrain, ftrain_all, TrnLabels, stepPrint, numCoresKnn, fidLogs, param);

                %compute cmc

                [cmc_original_temp, cmc_sum_original_temp] = computeCMC(errorStruct_original_temp.distMatrixTest, TrnLabels, ['cmc original temp iteration ' num2str(r)], 0);

                %Error metrics

                fprintf_pers(fidLogs, ['\t\tTraining accuracy (perc. of correctly classified samples, at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_original_temp.accuracy_knn*100));

                fprintf_pers(fidLogs, ['\t\tAUC of CMC (at iteration n. ' num2str(r) '): %s\n'], num2str(cmc_sum_original_temp));

                %assign

                accuracy_knnALLFOCUS = [accuracy_knnALLFOCUS errorStruct_original_temp.accuracy_knn];

                %accuracy_knnALLFOCUS = [accuracy_knnALLFOCUS cmc_sum_original_temp]; %maximize AUC of CMC

            end %th_focus

            %Puliamo

            clear imagesCellTrain ftrain_all

            %%%%%%%%%%%%%%  APPLY BEST FOCUS  %%%%%%%%%%%%%

            fprintf_pers(fidLogs, '\n')

            fprintf_pers(fidLogs, '\tApply best focus\n')

            %best focusc

            %[maxAcc, i_best_focus] = max(accuracy_knnALLFOCUS);

            %i_best_focus = i_best_focus(1);

            [sortA, isort] = sort(accuracy_knnALLFOCUS);

            i_best_focus = isort(end); %highest th_focus that gives the best result

            best_th_focus = allfocuses(i_best_focus);

            fprintf_pers(fidLogs, ['\t\tBest focus: ' num2str(best_th_focus) ' \n']);

            %training data

            fprintf_pers(fidLogs, '\t\tLoading training images - original... \n')

            [imagesCellTrain_original, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_test, 100, dirUtilities, 0);

            imagesCellTrain_original = adjustFormat(imagesCellTrain_original);

            %norm

            %[imagesCellTrain_original, meanA_original, stdA_original] = computeNorm(imagesCellTrain_original);

            fprintf_pers(fidLogs, '\t\tLoading training images - unsharpened... \n')

            [imagesCellTrain_unsharp, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTrain, numImagesTrain, param, colorS_test, best_th_focus, dirUtilities, 0);

            imagesCellTrain_unsharp = adjustFormat(imagesCellTrain_unsharp);

            %norm

            %[imagesCellTrain_unsharp, meanA_unsharp, stdA_unsharp] = computeNorm(imagesCellTrain_unsharp);

            %testing data

            fprintf_pers(fidLogs, '\t\tLoading testing images - original... \n')

            [imagesCellTest_original, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, 100, dirUtilities, 0);

            [imagesCellTest_original, meanAll_test_original] = adjustFormat(imagesCellTest_original);

            %imagesCellTest_original = applyNorm(imagesCellTest_original, meanA_original, stdA_original);

            fprintf_pers(fidLogs, '\t\tLoading testing images - unsharpened... \n')

            [imagesCellTest_unsharp, filenameTest, indexes_test_imUnsharpened] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, best_th_focus, dirUtilities, 0);

            [imagesCellTest_unsharp, meanAll_test_unsharp] = adjustFormat(imagesCellTest_unsharp);

            %imagesCellTest_unsharpened = applyNorm(imagesCellTest_unsharpened, meanA_unsharp, stdA_unsharp);

            %%%%%%%%%%%%%%  TESTING - PRE-TRAINED CNNs %%%%%%%%%%%%%

            %pre-trained

            fprintf_pers(fidLogs, '\t\tPretrained CNNs... \n')

            %Feature extraction - ORIGINAL

            fprintf_pers(fidLogs, '\t\t\tFeature extraction - original... \n')

            ftrain_all_original = feature_extraction_cnn(imagesCellTrain_original, net, layer, colorS_test);

            ftest_all_original = feature_extraction_cnn(imagesCellTest_original, net, layer, colorS_test);

            %Feature extraction - UNSHARP

            fprintf_pers(fidLogs, '\t\t\tFeature extraction - unsharp... \n')

            ftrain_all_unsharp = feature_extraction_cnn(imagesCellTrain_unsharp, net, layer, colorS_test);

            ftest_all_unsharp = feature_extraction_cnn(imagesCellTest_unsharp, net, layer, colorS_test);

            %size

            numFeatures = size(ftest_all_unsharp, 1);

            sizeTest = size(ftest_all_unsharp, 2);

            %--------------------------------------

            %Classification performance

            %Original

            %fprintf_pers(fidLogs, '\t\t\tClassification - original... \n')

            %errorStruct_pretrained_original(r) = computeClassificationPerformance(numFeatures, sizeTest, ftest_all_original, TestLabels, stepPrint, numCoresKnn, fidLogs, param);

            errorStruct_pretrained_original(r) = computeClassificationPerformanceTrainTest(numFeatures, sizeTest, ftrain_all_original, ftest_all_original, TrnLabels, TestLabels, stepPrint, numCoresKnn, fidLogs, param);

            %Unsharp

            %fprintf_pers(fidLogs, '\t\t\tClassification - unsharp... \n')

            %errorStruct_pretrained_unsharp(r) = computeClassificationPerformance(numFeatures, sizeTest, ftest_all_unsharp, TestLabels, stepPrint, numCoresKnn, fidLogs, param);

            errorStruct_pretrained_unsharp(r) = computeClassificationPerformanceTrainTest(numFeatures, sizeTest, ftrain_all_unsharp, ftest_all_unsharp, TrnLabels, TestLabels, stepPrint, numCoresKnn, fidLogs, param);

            %puliamo

            clear ftest_all_original ftest_all_unsharp

            %compute cmc

            [cmc1, cmc_sum1] = computeCMC_trainTest(errorStruct_pretrained_original(r).distMatrixTest, TestLabels, ['cmc original iteration ' num2str(r)], plotFigures);

            [cmc2, cmc_sum2] = computeCMC_trainTest(errorStruct_pretrained_unsharp(r).distMatrixTest, TestLabels, ['cmc unsharp iteration ' num2str(r)], plotFigures);

            cmc_original{r} = cmc1;

            cmc_sum_original{r} = cmc_sum1;

            cmc_unsharp{r} = cmc2;

            cmc_sum_unsharp{r} = cmc_sum2;

            errorStruct_pretrained_original(r).rank5 = cmc1(5);

            errorStruct_pretrained_unsharp(r).rank5 = cmc2(5);

            %Display

            fprintf_pers(fidLogs, '\n')

            fprintf_pers(fidLogs, ['\tPretrained - Accuracy original (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_pretrained_original(r).accuracy_knn*100));

            fprintf_pers(fidLogs, ['\tPretrained - Accuracy unsharp (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_pretrained_unsharp(r).accuracy_knn*100));

            fprintf_pers(fidLogs, ['\tPretrained - Rank 5 accuracy original (at iteration n. ' num2str(r) '): %s%%\n'], num2str(cmc1(5)*100));

            fprintf_pers(fidLogs, ['\tPretrained - Rank 5 accuracy unsharp (at iteration n. ' num2str(r) '): %s%%\n'], num2str(cmc2(5)*100));

            pause(0.1)

            %%%%%%%%%%%%%%  TESTING - FINE TUNING CNNs %%%%%%%%%%%%%

            %pre-trained

            fprintf_pers(fidLogs, '\n')

            fprintf_pers(fidLogs, '\tFine tuning CNNs... \n')

            %fprintf_pers(fidLogs, '\t\t\tTraining... \n')

            pixelRange = [-30 30];

            rotRange = [-180 180];

            imageAugmenter = imageDataAugmenter( ...

                'RandXReflection', true, ...

                'RandYReflection', true, ...

                'RandRotation', rotRange);

            %'RandXTranslation', pixelRange, ...

            %'RandYTranslation', pixelRange ...

            numClasses = numel(unique(labels));

            inputSize = net.Layers(1).InputSize;

            %             layersTransfer = net.Layers(1:end-3);

            %             layers = [

            %                 layersTransfer

            %                 fullyConnectedLayer(numClasses, 'WeightLearnRateFactor', 20, 'BiasLearnRateFactor', 20)

            %                 softmaxLayer

            %                 classificationLayer];

            %change last layers

            lgraph = replaceLayers(net, numClasses);

            %options

            options = trainingOptions('sgdm', ...

                'MiniBatchSize', 20, ... %128 20

                'MaxEpochs', 100, ...

                'InitialLearnRate', 1e-4, ...

                'Shuffle', 'every-epoch', ...   'never'

                'ValidationFrequency', 3, ...

                'Verbose', false, ...

                'Plots', 'none'); %   'training-progress'

            fprintf_pers(fidLogs, '\t\tTraining original... \n')

            %netTransfer_original = fineTuneCNN(imagesCellTrain_original, TrnLabels, './dummy_train_original/', inputSize, imageAugmenter, layers, options);

            netTransfer_original = fineTuneCNN(imagesCellTrain_original, TrnLabels, './dummy_train_original/', inputSize, imageAugmenter, lgraph, options);

            fprintf_pers(fidLogs, '\t\tTraining unsharp... \n')

            %netTransfer_unsharp = fineTuneCNN(imagesCellTrain_unsharp, TrnLabels, './dummy_train_unsharp/', inputSize, imageAugmenter, layers, options);

            netTransfer_unsharp = fineTuneCNN(imagesCellTrain_unsharp, TrnLabels, './dummy_train_unsharp/', inputSize, imageAugmenter, lgraph, options);

            %

            %fprintf_pers(fidLogs, '\t\t\tTesting... \n');

            %cm

            fprintf_pers(fidLogs, '\t\tTesting original... \n')

            errorStruct_finetune_original(r) = computeClassPerformanceFineTuneCNN(imagesCellTest_original, TestLabels, './dummy_test_original/', inputSize, netTransfer_original, fidLogs);

            fprintf_pers(fidLogs, '\t\tTesting unsharp... \n')

            errorStruct_finetune_unsharp(r) = computeClassPerformanceFineTuneCNN(imagesCellTest_unsharp, TestLabels, './dummy_test_unsharp/', inputSize, netTransfer_unsharp, fidLogs);

            %Display

            fprintf_pers(fidLogs, ['\tFine tuning - Accuracy original (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_finetune_original(r).accuracy_knn*100));

            fprintf_pers(fidLogs, ['\tFine tuning - Accuracy unsharp (at iteration n. ' num2str(r) '): %s%%\n'], num2str(errorStruct_finetune_unsharp(r).accuracy_knn*100));

            %--------------------------------------

            %Save

            if savefile

                save(fileSaveTest_iter, 'errorStruct_pretrained_original', 'errorStruct_pretrained_unsharp', 'errorStruct_finetune_original', 'errorStruct_finetune_unsharp', 'cmc_original', 'cmc_unsharp');

            end %if savefile

            %--------------------------------------

            %Display progress

            fprintf_pers(fidLogs, '\n');

            %GRAD-CAM

            fprintf_pers(fidLogs, 'Grad-CAM\n');

            fprintf_pers(fidLogs, '\n');

            dirGcam = [dirResults 'gcam_iter_' num2str(r) '/'];

            mkdir_pers(dirGcam, savefile);

            [imagesCellTest_original, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, 100, dirUtilities, 0);

            [imagesCellTest_unsharp, ~, ~] = loadImages(files, dirDB_wROI, allIndexes, indImagesTest, numImagesTest, param, colorS_test, best_th_focus, dirUtilities, 0);

            computeGradCam(imagesCellTest_original, imagesCellTest_unsharp, meanAll_test_original, meanAll_test_unsharp, indexes_test_imUnsharpened, ...

               netTransfer_original, netTransfer_unsharp, conv_layer, inputSize, filenameTest, TestLabels, dirGcam);

            %computeGradCam2(imagesCellTest_original, imagesCellTest_unsharp, meanAll_test_original, meanAll_test_unsharp, indexes_test_imUnsharpened, ...

               %netTransfer_original, netTransfer_unsharp, conv_layer, inputSize, filenameTest, TestLabels, dirGcam);

            %Puliamo

            clear imagesCellTrain_original imagesCellTrain_unsharpenend imagesCellTest_original imagesCellTest_unsharpened netTransfer_original netTransfer_unsharp layers

        end %for r = 1 : param.numIterations

        close all

        pause(0.1)

        %display

        fprintf_pers(fidLogs, '\n');

        %--------------------------------------

        %Average classification performance

        %PRETRAINED

        %Error metrics

        fprintf_pers(fidLogs, '\n');

        %original

        fprintf_pers(fidLogs, 'Pretrained - Original\n')

        stampaErrors(errorStruct_pretrained_original, fidLogs);

        fprintf_pers(fidLogs, '\tRank 5 accuracy (mean; std): %s%%; %s%% \n', num2str(mean([errorStruct_pretrained_original.rank5])*100), num2str(std([errorStruct_pretrained_original.rank5])*100));

        %unsharp

        fprintf_pers(fidLogs, 'Pretrained - Unsharp\n')

        stampaErrors(errorStruct_pretrained_unsharp, fidLogs);

        fprintf_pers(fidLogs, '\tRank 5 accuracy (mean; std): %s%%; %s%% \n', num2str(mean([errorStruct_pretrained_unsharp.rank5])*100), num2str(std([errorStruct_pretrained_unsharp.rank5])*100));

        %FINE TUNING

        %Error metrics

        fprintf_pers(fidLogs, '\n');

        %original

        fprintf_pers(fidLogs, 'Fine tuning - Original\n')

        stampaErrors(errorStruct_finetune_original, fidLogs);

        %unsharp

        fprintf_pers(fidLogs, 'Fine tuning - Unsharp\n')

        stampaErrors(errorStruct_finetune_unsharp, fidLogs);

        %--------------------------------------

        %Average CMC

        %original

        stampaAvgCMC(cmc_original, 'original', dirResults, savefile, plotFigures);

        %unsharp

        stampaAvgCMC(cmc_unsharp, 'unsharp', dirResults, savefile, plotFigures);

        %--------------------------------------

        %Display progress

        fprintf_pers(fidLogs, '\n');

        %--------------------------------------

        %Close file log

        if savefile && logS

            fclose(fidLog);

        end %if savefile && log

        %         delete(gcp('nocreate'));

        fclose('all');

        %close

        close all

        pause(0.1)

    end %for n

end %for db