How to classify a folder of images after training network

I am trying to classify a whole folder of images into 2 classes (eg. cats and dogs) and then I want to save the images classified in one class (eg. dogs).

So, in the last bit of the code I was able to classify 1 image at a time but would like to do it for multiple images and then save the images of one class.

outputFolder = fullfile('DataToClean');
rootFolder = fullfile(outputFolder, 'categories');

categories = {'Cars', 'NotCars'};

imds = imageDatastore(fullfile(rootFolder, categories),'LabelSource','foldernames');

tbl = countEachLabel(imds);

cars = find(imds.Labels == 'Cars',1);
notcars = find(imds.Labels == 'NotCars',1);

% figure
% subplot(2,2,1);
% imshow(readimage(imds,cars));
% subplot(2,2,2);
% imshow(readimage(imds,notcars));

net = resnet50();

% figure
% plot(net)
% title('Architecture Of ResNet-50')
% set(gca,'YLim',[150 170]);

net.Layers(1);
net.Layers(end);

numel(net.Layers(end).ClassNames);

[trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize');

imageSize = net.Layers(1).InputSize;

augmentedTrainingSet = augmentedImageDatastore(imageSize,...
    trainingSet, 'colorPreprocessing', 'gray2rgb');
augmentedTestSet = augmentedImageDatastore(imageSize,...
    testSet, 'colorPreprocessing', 'gray2rgb');

w1 = net.Layers(2).Weights;
w1 = mat2gray(w1);

featureLayer = 'fc1000';
trainingFeatures = activations(net, augmentedTrainingSet,...
    featureLayer, 'MiniBatchSize', 32, 'Outputas', 'columns');

trainingLabels = trainingSet.Labels;
classifier = fitcecoc(trainingFeatures, trainingLabels,...
    'Learner', 'Linear', 'ObservationsIn', 'columns');

testFeatures = activations(net, augmentedTestSet,...
    featureLayer, 'MiniBatchSize', 32, 'Outputas', 'columns');

predictLabels = predict(classifier, testFeatures, 'ObservationsIn', 'columns');

testLabels = testSet.Labels;
confMat = confusionmat(testLabels, predictLabels);
confMat = bsxfun(@rdivide, confMat, sum(confMat,2));

mean(diag(confMat));

newImage = imread(fullfile('test101.jpg'));
ds = augmentedImageDatastore(imageSize,...
    newImage, 'colorPreprocessing', 'gray2rgb');
imageFeatures = activations(net, ds,...
    featureLayer, 'MiniBatchSize', 32, 'Outputas', 'columns');
imLabels = predict(classifier, imageFeatures, 'ObservationsIn', 'columns');

Answer 1

[Walter Roberson]

newImage = imread(fullfile('test101.jpg'));

replace that with an image data store. Something like

newImage = imageDatastore(FolderToClassify);

where FolderToClassify has been set to the name of the folder to run predictions on.

You would then take the imLabels and use them to decide where to move (or copy) each of the images in the data store.