How to deploy my code on raspberry pi as a standalone?

Question

Ahmed abbasi 2020-3-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/513628-how-to-deploy-my-code-on-raspberry-pi-as-a-standalone

评论： amgad 2020-6-2

vid = videoinput('winvideo', 1);
set(vid, 'ReturnedColorSpace', 'RGB');
img = getsnapshot(vid);
 imshow(img)
h =  findobj('type','figure');
n = length(h);
for k=1:n
    
    baseFileName = sprintf('Img #%d.png', k);
fullFileName = fullfile('C:\Users\Cv\Desktop\image classification2 - copy',['img' '.bmp']);
imwrite(img, fullFileName);
end
outputFolder = fullfile('caltech102');
rootFolder = fullfile(outputFolder, '101_ObjectCategories');
 
categories = {'Bottles', 'NotBottles'};
imds = imageDatastore(fullfile(rootFolder,categories),'LabelSource', 'foldernames');
 
tb1 = countEachLabel(imds)
minSetCount = min(tb1{:,2})
 
imds = splitEachLabel(imds, minSetCount, 'randomize');
countEachLabel(imds);
 
Bottles = find(imds.Labels == 'Bottles', 1);
NotBottles = find(imds.Labels == 'NotBottles', 1);
 
% figure
% subplot(2,2,1);
% imshow(readimage(imds,airplanes));
% subplot(2,2,2);
% imshow(readimage(imds,ferry));
% subplot(2,2,3);
% imshow(readimage(imds,laptop));
net = resnet50();
 figure
 plot(net)
 title('Architecture of ResNet-50');
set(gca, 'YLim', [150 170]);
net.Layers(1);
net.Layers(end);
numel(net.Layers(end).ClassNames);
[trainingSet, testSet] = splitEachLabel(imds, 0.3, 'randomize');
 
imageSize = net.Layers(1).InputSize;
 
augmentedTrainingSet = augmentedImageDatastore(imageSize, ...
    trainingSet, 'ColorPreprocessing', 'gray2rgb');
 
augmentedTestSet = augmentedImageDatastore(imageSize, ...
    testSet, 'ColorPreprocessing', 'gray2rgb');
 
w1 = net.Layers(2).Weights;
w1 = mat2gray(w1);
 
 figure
 montage(w1)
title('First Convolutional Layer Weight')
 
featureLayer = 'fc1000';
trainingFeatures = activations(net, ...
    augmentedTrainingSet, featureLayer, 'MiniBatchSize', 32, 'OutputAs', 'columns');
 
trainingLables = trainingSet.Labels;
 
classifier  = fitcecoc(trainingFeatures,trainingLables, ...
    'Learner', 'Linear', 'Coding', 'onevsall','ObservationsIn', 'columns');
 
testFeatures = activations(net, ...
    augmentedTestSet, featureLayer, 'MiniBatchSize', 32, 'OutputAs', 'columns');
 
predictLabels = predict(classifier, testFeatures, 'ObservationsIn','columns');
 
testLables = testSet.Labels;
confMat = confusionmat(testLables,predictLabels);
confMat = bsxfun(@rdivide, confMat, sum(confMat,2));
 
mean(diag(confMat));
 
newImage = imread(fullfile('img.bmp'));
ds = augmentedImageDatastore(imageSize, ...
    newImage, 'ColorPreprocessing', 'gray2rgb');
imageFeatures = activations(net, ...
    ds, featureLayer, 'MiniBatchSize', 32, 'OutputAs', 'columns');
Label = predict(classifier, imageFeatures, 'ObservationsIn','columns');
 
sprintf('The Loaded image belongs to %s class', Label)

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Ahmed abbasi 2020-3-28

This code basically uses CNN and detects whether the image is bottle or not using image processing.

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Answer 1

Walter Roberson 2020-3-28

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/513628-how-to-deploy-my-code-on-raspberry-pi-as-a-standalone#answer_422585

It is not possible to deploy CNN training to hardware.

It is not possible to deploy augmentedImageDatastore to hardware.

Your strategy would have to be to train on the host, and save the net and activations, and load() it in the code that was deployed to hardware, where you would use it only to predict()