File size: 1,362 Bytes
20b6b2a 08876b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
unzip vehicleDatasetImages.zip
data = load("vehicleDatasetGroundTruth.mat");
vehicleDataset = data.vehicleDataset;
rng(0)
shuffledIndices = randperm(height(vehicleDataset));
idx = floor(0.6 * height(vehicleDataset));
trainingIdx = 1:idx;
trainingDataTbl = vehicleDataset(shuffledIndices(trainingIdx),:);
validationIdx = idx+1 : idx + 1 + floor(0.1 * length(shuffledIndices) );
validationDataTbl = vehicleDataset(shuffledIndices(validationIdx),:);
testIdx = validationIdx(end)+1 : length(shuffledIndices);
testDataTbl = vehicleDataset(shuffleIndices(testIdx),:);
imdsTrain = imageDatastore(trainingDatatbl{:,"imageFilename"});
bldsTrain = boxLabelDatastore(trainingDataTbl(:,"vehicle"));
imdsValidation = imageDatastore(validationDataTbl{:,"imageFilename"});
bldsValidation = boxLabelDataStore(validationDatatbl(:,"vehicle"));
imdsTest = imageDatastore(testDataTbl{:"imageFilename"});
bldsTest = boxLabelDatastore(testDataTbl(:,"vehicle"));
// Combine image and box label datastores
trainData = combine(imdsTrain,bldsTrain);
validationData = combine(imdsValidation,bldsValidation);
testData = combine(imdsTest,bldsTest);
//Display one of the training images and box labels
data = read(trainingData);
I = data{1};
bbox = data{2};
annotatedImage = insertShape(I"rectangle",bbox);
annotatedImage = imresize(annotatedImage,2);
figure
imshow(annotatedImage) |