I am getting error in function imds=image​Datastore(​fullfile(r​ootFolder, categories), 'LabelSource', 'foldernames'); why?

i am getting an error for above code in matlab 2016a

[Walter Roberson]

Please show the complete error message, everything in red.

[yogita]

Error in readAndPreprocessImage (line 8)
imds = imageDatastore(fullfile(rootFolder, categories), 'LabelSource', 'foldernames');

[yogita]

Error in readAndPreprocessImage (line 8)

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

[Walter Roberson]

That is not the complete error message. The complete error message would contain information such as "not enough input arguments" or "subscript out of range", and the complete error message would also show the complete traceback of the calls, showing where the above line was called from, which line called that, and so on.

[yogita]

I am using trial pack

[yogita]

no other information in error message is found

[Walter Roberson]

The trial license still gives complete error messages.

Have you considered the possibility of posting your actual code so that the people who are trying to help you do not have to make wild guesses about what might be wrong?

Please post the output of

which imageDatastore

[yogita]

ok i am posting the code here

[yogita]

url = 'http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz';
% Store the output in a temporary folder
outputFolder = fullfile(tempdir, 'caltech101'); % define output folder

rootFolder = fullfile(outputFolder, '101_ObjectCategories');
categories = {'airplanes', 'ferry', 'laptop'};
imds = imageDatastore(fullfile(rootFolder, categories), 'LabelSource', 'foldernames');
tbl = countEachLabel(imds);

minSetCount = min(tbl{:,2}); % determine the smallest amount of images in a category

% Use splitEachLabel method to trim the set.
imds = splitEachLabel(imds, minSetCount, 'randomize');

% Notice that each set now has exactly the same number of images.
countEachLabel(imds);

% Find the first instance of an image for each category
airplanes = find(imds.Labels == 'airplanes', 1);
ferry = find(imds.Labels == 'ferry', 1);
laptop = find(imds.Labels == 'laptop', 1);

figure
subplot(1,3,1);
imshow(imds.Files{airplanes})
subplot(1,3,2);
imshow(imds.Files{ferry})
subplot(1,3,3);
imshow(imds.Files{laptop})

%Store CNN model in a temporary folder

cnnMatFile = fullfile(tempdir, 'imagenet-caffe-alex.mat');

% Load MatConvNet network into a SeriesNetwork
convnet = helperImportMatConvNet(cnnMatFile);
% Inspect the last layer
convnet.Layers(end)

% Number of class names for ImageNet classification task
numel(convnet.Layers(end).ClassNames);

% Set the ImageDatastore ReadFcn
imds.ReadFcn = @(filename)readAndPreprocessImage(filename);
     Iout = readAndPreprocessImage(filename);

          I = imread(filename);

          % Some images may be grayscale. Replicate the image 3 times to
          % create an RGB image.
          if ismatrix(I)
              I = cat(3,I,I,I);
          end

          % Resize the image as required for the CNN.
          Iout = imresize(I, [227 227]);

          % Note that the aspect ratio is not preserved. In Caltech 101, the
          % object of interest is centered in the image and occupies a
          % majority of the image scene. Therefore, preserving the aspect
          % ratio is not critical. However, for other data sets, it may prove
          % beneficial to preserve the aspect ratio of the original image
          % when resizing.
  [trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize');
  % Get the network weights for the second convolutional layer
  w1 = convnet.Layers(2).Weights;

% Scale and resize the weights for visualization
w1 = mat2gray(w1);
w1 = imresize(w1,5);

% Display a montage of network weights. There are 96 individual sets of
% weights in the first layer.
figure
montage(w1)
title('First convolutional layer weights')

featureLayer = 'fc7';
trainingFeatures = activations(convnet, trainingSet, featureLayer, ...
    'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns');
% Get training labels from the trainingSet
trainingLabels = trainingSet.Labels;

% Train multiclass SVM classifier using a fast linear solver, and set
% 'ObservationsIn' to 'columns' to match the arrangement used for training
% features.
classifier = fitcecoc(trainingFeatures, trainingLabels, ...
    'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns');
% Extract test features using the CNN
testFeatures = activations(convnet, testSet, featureLayer, 'MiniBatchSize',32);

% Pass CNN image features to trained classifier
predictedLabels = predict(classifier, testFeatures);

% Get the known labels
testLabels = testSet.Labels;

% Tabulate the results using a confusion matrix.
confMat = confusionmat(testLabels, predictedLabels);

% Convert confusion matrix into percentage form
confMat = bsxfun(@rdivide,confMat,sum(confMat,2));

% Display the mean accuracy
mean(diag(confMat))

newImage = fullfile(rootFolder, 'airplanes', 'image_0690.jpg');

% Pre-process the images as required for the CNN
img = readAndPreprocessImage(newImage);

% Extract image features using the CNN
imageFeatures = activations(convnet, img, featureLayer);
% Make a prediction using the classifier
label = predict(classifier, imageFeatures);

Answer 1

[Walter Roberson]

You removed some of the code that is needed to download data. Go back to http://www.mathworks.com/examples/matlab-computer-vision/mw/vision_product-DeepLearningImageClassificationExample-image-category-classification-using-deep-learning and pull out the entire code.

In particular you need the lines

if ~exist(outputFolder, 'dir') % download only once
    disp('Downloading 126MB Caltech101 data set...');
    untar(url, outputFolder);
end

and

% Location of pre-trained "AlexNet"
cnnURL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-caffe-alex.mat';

and

if ~exist(cnnMatFile, 'file') % download only once
    disp('Downloading pre-trained CNN model...');
    websave(cnnMatFile, cnnURL);
end

Answer 2

[Arthur]

Consider to adapt your code to:

    path = char(path) %pass to this variable your complet data set path
    categories = {'airplanes', 'ferry', 'laptop'};
    imds = imageDatastore(fullfile(path, categories), 'LabelSource', 'foldernames');
    tbl = countEachLabel(imds);