How to resize test images for neural network deep learning?

Hi I am currently experiencing a problem with reading picture recognition with deep learning.

I am consistently getting the following error despite trying to resize the image with a few attempts.

>> labels = classify(net,imds_test);
Error using DAGNetwork/predict>predictBatch (line 232)
Incorrect input size. The input images must have a size of [32 32 1].

Error in DAGNetwork/predict (line 118)
    Y = predictBatch( ...

Error in DAGNetwork/classify (line 115)
scores = this.predict( X, varargin{:} );

Error in SeriesNetwork/classify (line 458)
            [labels, scores] = this.UnderlyingDAGNetwork.classify(X, varargin{:});

By following the tutorial closely from matlab, I have been able to get the neural network up and training with my own pictures of line (which i generated from a series of matrix manipulation from matlab) in the form of [32 32 1] however those are just training data.

How can I convert or make the images that are not from the training data set (say I downloaded a line picture off google) for the testing phase be of the same variable at [32 32 1].

I tried the repmat function which caused the image to be enlarged to 1024x1024, thereafter trying the imresize, my line is totally gone.

the pictures i am training looks like this : and its in [32 32 1] format

and those picture downloaded off google will be larger in pixels and I cant resize them using the imresize function because it doesnt shift the properties to a [32 32 1] array. I have included the code for the neural net below too which, is mostly the same as per the matlab tutorial.

Please help, thanks in advance.

categories = {'Horizontal','Slanted','Vertical'};

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

%% Layer Definitions

varSize = 32;
conv1 = convolution2dLayer(5,varSize,'Padding',2,'BiasLearnRateFactor',2);
conv1.Weights = gpuArray(single(randn([5 5 1 varSize])*0.0001));
fc1 = fullyConnectedLayer(64,'BiasLearnRateFactor',2);
fc1.Weights = gpuArray(single(randn([64 576])*0.1));
fc2 = fullyConnectedLayer(3,'BiasLearnRateFactor',2);
fc2.Weights = gpuArray(single(randn([3 64])*0.1));

layers = [
    imageInputLayer([varSize varSize 1]);
    conv1;
    maxPooling2dLayer(3,'Stride',2);
    reluLayer();
    convolution2dLayer(5,32,'Padding',2,'BiasLearnRateFactor',2);
    reluLayer();
    averagePooling2dLayer(3,'Stride',2);
    convolution2dLayer(5,64,'Padding',2,'BiasLearnRateFactor',2);
    reluLayer();
    averagePooling2dLayer(3,'Stride',2);
    fc1;
    reluLayer();
    fc2;
    softmaxLayer()
    classificationLayer()];

%% Training parameters 

options = trainingOptions('sgdm', ...
    'InitialLearnRate', 0.001, ...
    'LearnRateSchedule', 'piecewise', ...
    'LearnRateDropFactor', 0.1, ...
    'LearnRateDropPeriod', 8, ...
    'L2Regularization', 0.004, ...
    'MaxEpochs', 10, ...
    'MiniBatchSize', 100, ...
    'Verbose', true);

%% Training

[net, info] = trainNetwork(imds, layers, options);

rootFolder = 'linetest';
imds_test = imageDatastore(fullfile(rootFolder,categories), ...
    'LabelSource','foldernames');

%% Testing

labels = classify(net,imds_test);

ii = randi(3);
im = imread(imds_test.Files{ii});

imshow(im);

if labels(ii) == imds_test.Labels(ii)
    colorText = 'g';
else
    colorText = 'r';
end
title(char(labels(ii)),'Color',colorText)
    

Answer 1

[Neuropragmatist]

Did you just substitute your own input image for the Matlab tutorial one? Because it looks like the whole code is built around 32x32 images - i.e. in the 'layer definitions' section.

If you change these to match the size of your real images does it still not work?

M.

[Joon Yong Chu]

Yes i substitued it with my own images and i need it to be 32x32. Hence I would like to know how i can trasform any input image of any size to a 32x32x1 image for the program.

Answer 2

[Jefferson Pinzon Morantes]

I also have the same error, could you help me with that?

[niaz ahmad]

i have same error

[kevin harianto]

I also have the same error

Answer 3

[Image Analyst]

Use imresize()