107 lines
3.6 KiB
Matlab
107 lines
3.6 KiB
Matlab
clc;
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close all;
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clear all;
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%% Read the data set
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imageFolder = fullfile(toolboxdir('machine'),'machine data','imagesets');
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imds = imageDatastore(imageFolder,'IncludeSubfolders',true,'LabelSource',...
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'foldernames');% Find the first instance of an image for each category
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positive = find(imds.Labels == 'positive', 1);
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figure(1);
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imshow(readimage(imds,positive));
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%%
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tbl = countEachLabel(imds);
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% Determine the smallest amount of images in a category
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minSetCount = min(tbl{:,2});
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maxNumImages = 100;
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minSetCount = min(maxNumImages,minSetCount);
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% Use splitEachLabel method to trim the set.
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imds = splitEachLabel(imds, minSetCount, 'randomize');
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% Notice that each set now has exactly the same number of images.
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countEachLabel(imds)
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% Load pretrained network
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% Visualize the first section of the network.
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% figure(2);
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% net = resnet50();
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net = vgg16();
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% net = vgg16('Weights','imagenet')
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% layers = vgg16('Weights','none')
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% plot(net);
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% title('First section of resnet-50')
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% set(gca,'YLim',[150 170]);
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%%
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% Inspect the first layer
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net.Layers(1)
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% Inspect the last layer
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net.Layers(end)
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% Number of class names for ImageNet classification task
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numel(net.Layers(end).ClassNames)
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[trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize');
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% Create augmentedImageDatastore from training and test sets to resize
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% images in imds to the size required by the network.
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imageSize = net.Layers(1).InputSize;
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augmentedTrainingSet = augmentedImageDatastore(imageSize, trainingSet, 'ColorPreprocessing', 'gray2rgb');
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augmentedTestSet = augmentedImageDatastore(imageSize, testSet, 'ColorPreprocessing', 'gray2rgb');
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% Get the network weights for the second convolutional layer
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w1 = net.Layers(2).Weights;
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% Scale and resize the weights for visualization
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w1 = mat2gray(w1);
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w1 = imresize(w1,5);
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% Display a montage of network weights. There are 96 individual sets of
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% weights in the first layer.
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figure(3);
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montage(w1);
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title('First convolutional layer weights')
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%(resnet-50)
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% featureLayer = 'fc1000';
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%vgg-16
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featureLayer = 'fc8';
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trainingFeatures = activations(net, augmentedTrainingSet, featureLayer, ...
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'MiniBatchSize', 32, 'OutputAs', 'columns');
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%%
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% Get training labels from the trainingSet
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trainingLabels = trainingSet.Labels;
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% Train multiclass SVM classifier using a fast linear solver, and set
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% 'ObservationsIn' to 'columns' to match the arrangement used for training
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% features.
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classifier = fitcecoc(trainingFeatures, trainingLabels, ...
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'Learners', 'Linear', 'Coding', 'onevsall', 'ObservationsIn', 'columns');
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%%
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% Extract test features using the CNN
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testFeatures = activations(net, augmentedTestSet, featureLayer, ...
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'MiniBatchSize', 32, 'OutputAs', 'columns');
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% Pass CNN image features to trained classifier
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predictedLabels = predict(classifier, testFeatures, 'ObservationsIn', 'columns');
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% Get the known labels
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testLabels = testSet.Labels;
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% Tabulate the results using a confusion matrix.
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confMat = confusionmat(testLabels, predictedLabels);
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% Convert confusion matrix into percentage form
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confMat = bsxfun(@rdivide,confMat,sum(confMat,2));
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disp(confMat);
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% Display the mean accuracy
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mean(diag(confMat))
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testImage = readimage(testSet,1);
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testLabel = testSet.Labels(1)
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%%
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% Create augmentedImageDatastore to automatically resize the image when
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% image features are extracted using activations.
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ds = augmentedImageDatastore(imageSize, testImage, 'ColorPreprocessing', 'gray2rgb');
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% Extract image features using the CNN
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imageFeatures = activations(net, ds, featureLayer, 'OutputAs', 'columns');
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% Make a prediction using the classifier
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predictedLabel = predict(classifier, imageFeatures, 'ObservationsIn', 'columns') |