Writing TIFFs with 9 components is not supported with IMWRITE. Use Tiff instead. Type "help Tiff" for more information.

Question

sun rise 2022-1-19

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1632255-writing-tiffs-with-9-components-is-not-supported-with-imwrite-use-tiff-instead-type-help-tiff

评论： sun rise 2022-1-29

clear;clc;close all
% Load the Image Dataset of Normal and Malignant WBC
imdsTrain = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
imdsTest = imageDatastore('D:\Project\DB1\test','IncludeSubfolders',true,'LabelSource','foldernames');
%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%% 
%%
newext = '.tif';
while hasdata(imdsTrain)
    [img, info] = read(imdsTrain);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename = fullfile(filedir, [basename, newext]);
    img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    imwrite(img3, newfilename);
end
while hasdata(imdsTest)
    [img, info] = read(imdsTest);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename1 = fullfile(filedir, [basename, newext]);
    img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    imwrite(img3, newfilename1);
end
load('HW');
%%
%Select the Test images and save in Y_test
Y_test = newfilename1.UnderlyingDatastore.Labels;
%%
% optimzation techniques selection and hyperparamter selection
options = trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',newfilename1, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
%%
%CNN model training
netTransfer = trainNetwork(newfilename,HW,options);
%%
% for i=1:numel(imdsValidation.Files)
%      a=[imdsValidation.Files(i)];
%      a = imread(char(a));
% %     featuresTest22 = activations(net,a,layer,'OutputAs','rows');
%     YPred(i) = classify(netTransfer,a);
%     imshow(a),title(char(YPred));
% i
% end
%%
% CNN Model validation
YPred = classify(netTransfer,newfilename1);
%Performance evaluation of Deep Learning Trained Model
plotconfusion(Y_test,YPred)

Error using writetif (line 40)

Writing TIFFs with 9 components is not supported with IMWRITE. Use Tiff instead. Type "help Tiff" for

more information.

Error in imwrite (line 546)

feval(fmt_s.write, data, map, filename, paramPairs{:});

Error in CNN1 (line 19)

imwrite(img3, newfilename);

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Walter Roberson 2022-1-19

https://www.mathworks.com/matlabcentral/answers/1627970-1-bit-tiff-images-having-more-than-1-channel-are-not-supported#comment_1944450

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Answer 1

yanqi liu 2022-1-20

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1632255-writing-tiffs-with-9-components-is-not-supported-with-imwrite-use-tiff-instead-type-help-tiff#answer_877895

在 MATLAB Online 中打开

clear;clc;close all
% Load the Image Dataset of Normal and Malignant WBC
imdsTrain = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
imdsTest = imageDatastore('D:\Project\DB1\test','IncludeSubfolders',true,'LabelSource','foldernames');
%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%%
%%
newext = '.tif';
while hasdata(imdsTrain)
    [img, info] = read(imdsTrain);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename = fullfile(filedir, [basename, newext]);
    if ndims(img) == 2
        img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    else
        img3 =  imresize(im2uint8(img), [299 299]);
    end
    imwrite(img3, newfilename);
end
while hasdata(imdsTest)
    [img, info] = read(imdsTest);
    [filedir, basename, ext] = fileparts(info.Filename);
    newfilename1 = fullfile(filedir, [basename, newext]);
    if ndims(img) == 2
        img3 = repmat( imresize(im2uint8(img), [299 299]), [1 1 3] );
    else
        img3 =  imresize(im2uint8(img), [299 299]);
    end
    imwrite(img3, newfilename1);
end
load('HW');
%%
%Select the Test images and save in Y_test
Y_test = newfilename1.UnderlyingDatastore.Labels;
%%
% optimzation techniques selection and hyperparamter selection
options = trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',newfilename1, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
%%
%CNN model training
netTransfer = trainNetwork(newfilename,HW,options);
%%
% for i=1:numel(imdsValidation.Files)
%      a=[imdsValidation.Files(i)];
%      a = imread(char(a));
% %     featuresTest22 = activations(net,a,layer,'OutputAs','rows');
%     YPred(i) = classify(netTransfer,a);
%     imshow(a),title(char(YPred));
% i
% end
%%
% CNN Model validation
YPred = classify(netTransfer,newfilename1);
%Performance evaluation of Deep Learning Trained Model
plotconfusion(Y_test,YPred)