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image processing and models/arrest5ml.m

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