My pictures have a format of [227 227 1] so I had the idea to triplicate my processed pictures and put them back into one to get a format of [227 227 3], how can I do this?

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Hi!
I'm doing a project where I am writing a hand gesture detection program but i've run into some problems.
I'm getting my picture through a webcam and processing them to be black and white and have a format of [227 227 1]. Unfortunally its just not working with my self written network.
Now I want to try a pretraind network but this requires a format of [227 227 3] but my pictures are still black and withe so they have a format of [227 227 1] so I had the idea to triplicate my processed picture and put them back into one to get a format of [227 227 3]
Does someone have and idea how I could do that?
Or could help me to figure out why my selfwritten network isn't working?
Attached you can find the Network, many thanks in advance!!
allImages = imageDatastore('Handgesten_BW','IncludeSubfolders',true, 'LabelSource','foldernames');
[imdsTrain,imdsVal] = splitEachLabel(allImages,0.7,'randomized');
imageSize = [227 227 1];
OutputSize = 7;
%Layers
layers = [
imageInputLayer(imageSize,"Name","imageinput")
convolution2dLayer([11 11],96,"Name","conv1","BiasLearnRateFactor",2,"Stride",[4 4])
reluLayer("Name","relu1")
crossChannelNormalizationLayer(5,"Name","norm1","K",1)
maxPooling2dLayer([3 3],"Name","pool1","Stride",[2 2])
groupedConvolution2dLayer([5 5],128,2,"Name","conv2","BiasLearnRateFactor",2,"Padding",[2 2 2 2])
reluLayer("Name","relu2")
crossChannelNormalizationLayer(5,"Name","norm2","K",1)
maxPooling2dLayer([3 3],"Name","pool2","Stride",[2 2])
convolution2dLayer([3 3],384,"Name","conv3","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu3")
groupedConvolution2dLayer([3 3],192,2,"Name","conv4","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu4")
groupedConvolution2dLayer([3 3],128,2,"Name","conv5","BiasLearnRateFactor",2,"Padding",[1 1 1 1])
reluLayer("Name","relu5")
maxPooling2dLayer([3 3],"Name","pool5","Stride",[2 2])
fullyConnectedLayer(4096,"Name","fc6","BiasLearnRateFactor",2)
reluLayer("Name","relu6")
dropoutLayer(0.5,"Name","drop6")
fullyConnectedLayer(4096,"Name","fc7","BiasLearnRateFactor",2)
reluLayer("Name","relu7")
dropoutLayer(0.5,"Name","drop7")
fullyConnectedLayer(OutputSize,"Name","fc8","BiasLearnRateFactor",2)
softmaxLayer("Name","prob")
classificationLayer("Name","classoutput")];
%Train/Val
augmenter = imageDataAugmenter( ...
'RandRotation',[-45 45], ...
'RandYReflection', true, ...
'RandScale',[0.8,1.2]);
augimdsTrain = augmentedImageDatastore(imageSize,imdsTrain,'DataAugmentation',augmenter);
augimdsVal = augmentedImageDatastore(imageSize,imdsVal,'DataAugmentation',augmenter);
%Options
options = trainingOptions('sgdm',...
'MiniBatchSize',32, ...
'MaxEpochs',15,...
'InitialLearnRate',0.001,...
'ValidationData',augimdsVal, ...
'ValidationFrequency',20,...
'Verbose',false, ...
'executionenvironment','gpu',...
'Shuffle','every-epoch', ...
'Plots','training-progress');
%'LearnRateSchedule','piecewise',...
%'LearnRateDropFactor',0.5,...
%'LearnRateDropPeriod',25,...
% crossChannelNormalizationLayer(5,'K',1), ...
Netzwerk = trainNetwork(augimdsTrain,layers,options);
save Netzwerk
  1 Comment
Jan
Jan on 12 Jun 2021
"format of [227 227 1]"
This is not possible in Matlab. Trailing dimensions of length 1 (except for the 2nd dimension) vanish automatically:
x = rand(277, 277, 1);
size(x)
What exactly does this mean: "not working with my self written network"? This detail is important, so please share it.

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Answers (2)

Image Analyst
Image Analyst on 12 Jun 2021
Here is the way I usually use
rgbImage = cat(3, grayImage, grayImage, grayImage);

DGM
DGM on 12 Jun 2021
To rearrange a single-channel intensity image into a grayscale RGB image, you can just do
Argb = repmat(Aint,[1 1 3]);
I don't know if it would be possible/practical or more efficient to find a way to process the single-channel images. I don't know anything about the rest of your code or the training topic.

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