how to make the same image size ?

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Mohamed Nasr
Mohamed Nasr 2020-4-28
回答： Sanyam 2022-7-13
clear all;
clc;
close all;
imds = imageDatastore('D:\matlab aml\dataset1','FileExtensions',{'.jpg'},'IncludeSubfolders',true,'LabelSource','foldernames');
imgs = readall(imds);
figure;
perm = randperm(200,20);%Display some of the images in the datastore.
for i = 1:20
subplot(4,5,i);
imshow(imds.Files{perm(i)});
end
img = readimage(imds,1); %Check the size of the first image in digitData. Each image is 201-by-173-by-3 pixels.
size(img)
%Specify Training and Validation Sets
labelCount = countEachLabel(imds) %Calculate the number of images in each category
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);%Divide the data into training and validation data sets. Use 70% of the images for training and 30% for validation.
%Define the convolutional neural network architecture.
%%%%%%%code for resizing
inputSize=[227 227 1];
imdsTrain=augmentedImageDatastore(inputSize, imdsTrain,'ColorPreprocessing','RGB');
imdsValidation=augmentedImageDatastore(inputSize, imdsValidation,'ColorPreprocessing','RGB');
layers = [
imageInputLayer([227 227 1])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
%Specify Training Options
options = trainingOptions('sgdm', ...
'InitialLearnRate',0.01, ...
'MaxEpochs',4, ...
'Shuffle','every-epoch', ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');
%Train Network Using Training Data
net = trainNetwork(imdsTrain,layers,options);
%Classify Validation Images and Compute Accuracy
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)
this is my code and error is
ans =
201 173 3
labelCount =
2×2 table
Label Count
_____ _____
no 91
yes 154
Unrecognized method, property, or field 'Labels' for class 'augmentedImageDatastore'.
Error in ccn (line 59)
YValidation = imdsValidation.Labels;

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回答（1 个）

Sanyam
Sanyam 2022-7-13
Hey @Mohamed Nasr,
The imdsValidation in your code is an augmentedImageDatastore, it is used to apply transformations to imageDatastore and does not have a 'labels' field.
Instead, you can proceed in this manner.
//refer to code
Here we have seperated the imageDatastore and augmentedImageDatastore and we can access the labels by
YValidation = imdsValidation.Labels; i.e referring the labels field of imageDataStore.
Also, when training the model and making predictions, please use the newly created augmentedImageDatastores (augdsTrain, augdsValidation).
Hope that helps! Thanks!!
[imdsTrain imdsValidation] = splitEachLabel(imds,0.7);
augdsTrain=augmentedImageDatastore(inputSize, imdsTrain,'ColorPreprocessing','RGB');
augdsValidation=augmentedImageDatastore(inputSize, imdsValidation,'ColorPreprocessing','RGB');

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