clear;clc
numObservations = 4;
filenameImage = 'kobi.png';
trainImages = repelem({filenameImage},numObservations,1);
filenameLabels = 'kobiPixelLabeled.png';
trainLabels = repelem({filenameLabels},numObservations,1);
classes = ["floor","dog"];
ids = [1 2];
imds = imageDatastore(trainImages);
pxds = pixelLabelDatastore(trainLabels,classes,ids);
trainingData = combine(imds,pxds);
augmentedTrainingData = transform(trainingData,@jitterImageColorAndWarp);
data = readall(augmentedTrainingData);
inputSize=size(read(imds));
targetSize = [400 400];
I = imread(filenameImage);
L = imread(filenameLabels);
C = categorical(L,ids,classes);
resizedI = imresize(I,targetSize);
resizedC = imresize(C,targetSize);
B = labeloverlay(resizedI,resizedC);
figure;imshow(B)
imwrite(B,'dog.jpg')
preprocessedTrainingData = transform(augmentedTrainingData,...
@(data)randomCropImageAndLabel(data,targetSize));
data = readall(preprocessedTrainingData);
rgb = cell(numObservations,1);
for k = 1:numObservations
I = data{k,1};
C = data{k,2};
rgb{k} = labeloverlay(I,C);
end
figure;montage(rgb)
Cropped_I=getframe;
imwrite(imresize(Cropped_I.cdata,.5),'cropped.jpg')
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