清空筛选条件
清空筛选条件

try to implement the image to image regression using CAE but except digit dataset its not working for any other dataset. thank you.

1 次查看（过去 30 天）
Dibyalekha Nayak
Dibyalekha Nayak 2023-1-13
this is the code code :https://in.mathworks.com/help/deeplearning/ug/image-to-image-regression-using-deep-learning.html
only the dataset has changed but output is not shown .
clc;
clear all;
close all;
imagefile=fullfile('C:\Users\dibya\Documents\images\kodak');
imds=imageDatastore(imagefile,...
'IncludeSubfolders',true,...
'LabelSource','Foldernames')
imds.ReadSize = 70;
rng(0);
imds = shuffle(imds);
[imdsTrain,imdsVal,imdsTest] = splitEachLabel(imds,0.8,0.1);
dsTrainNoisy = transform(imdsTrain,@addNoise);
dsValNoisy = transform(imdsVal,@addNoise);
dsTestNoisy = transform(imdsTest,@addNoise);
dsTrain = combine(dsTrainNoisy,imdsTrain);
dsVal = combine(dsValNoisy,imdsVal);
dsTest = combine(dsTestNoisy,imdsTest);
dsTrain = transform(dsTrain,@commonPreprocessing);
dsVal = transform(dsVal,@commonPreprocessing);
dsTest = transform(dsTest,@commonPreprocessing);
dsTrain = transform(dsTrain,@augmentImages);
exampleData = preview(dsTrain);
inputs = exampleData(:,1);
responses = exampleData(:,2);
minibatch = cat(2,inputs,responses);
montage(minibatch','Size',[8 2])
title('Inputs (Left) and Responses (Right)')
imageLayer = imageInputLayer([32,32,1]);
encodingLayers = [ ...
convolution2dLayer(3,16,'Padding','same'), ...
reluLayer, ...
maxPooling2dLayer(2,'Padding','same','Stride',2), ...
convolution2dLayer(3,8,'Padding','same'), ...
reluLayer, ...
maxPooling2dLayer(2,'Padding','same','Stride',2), ...
convolution2dLayer(3,8,'Padding','same'), ...
reluLayer, ...
maxPooling2dLayer(2,'Padding','same','Stride',2)];
% % % % % % % % % % Decoder
decodingLayers = [ ...
createUpsampleTransponseConvLayer(2,8), ...
reluLayer, ...
createUpsampleTransponseConvLayer(2,8), ...
reluLayer, ...
createUpsampleTransponseConvLayer(2,16), ...
reluLayer, ...
convolution2dLayer(3,1,'Padding','same'), ...
clippedReluLayer(1.0), ...
regressionLayer];
layers = [imageLayer,encodingLayers,decodingLayers];
% % % % training
options = trainingOptions('adam', ...
'MaxEpochs',100, ...
'MiniBatchSize',imds.ReadSize, ...
'ValidationData',dsVal, ...
'Shuffle','never', ...
'Plots','training-progress', ...
'Verbose',false);
net = trainNetwork(dsTrain,layers,options);
ypred = predict(net,dsTest);
inputImageExamples = preview(dsTest);
montage({inputImageExamples{1},ypred(:,:,:,1)});
ref = inputImageExamples{1,2};
originalNoisyImage = inputImageExamples{1,1};
psnrNoisy = psnr(originalNoisyImage,ref)
psnrDenoised = psnr(ypred(:,:,:,1),ref)
%
%
% % % % % % % % % % helping function
function dataOut = addNoise(data)
dataOut = data;
for idx = 1:size(data,1)
dataOut{idx} = imnoise(data{idx},'salt & pepper');
end
end
function dataOut = commonPreprocessing(data)
dataOut = cell(size(data));
for col = 1:size(data,2)
for idx = 1:size(data,1)
temp = single(data{idx,col});
temp = imresize(temp,[32,32]);
temp = rescale(temp);
dataOut{idx,col} = temp;
end
end
end
function dataOut = augmentImages(data)
dataOut = cell(size(data));
for idx = 1:size(data,1)
rot90Val = randi(4,1,1)-1;
dataOut(idx,:) = {rot90(data{idx,1},rot90Val),rot90(data{idx,2},rot90Val)};
end
end
function out = createUpsampleTransponseConvLayer(factor,numFilters)
filterSize = 2*factor - mod(factor,2);
cropping = (factor-mod(factor,2))/2;
numChannels = 1;
out = transposedConv2dLayer(filterSize,numFilters, ...
'NumChannels',numChannels,'Stride',factor,'Cropping',cropping);
end

请先登录，再回答此问题。

回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Image Processing and Computer Vision 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by