Error using semanticSe​gmentation​Metrics The categorical data returned by dsResults and dsTruth must have the same categories.

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I used the documentation to evaluate my results but I get this error, can someone tell me what's wrong please
Evaluating semantic segmentation results
----------------------------------------
* Selected metrics: global accuracy, class accuracy, IoU, weighted IoU, BF score.
* Processed 0 images.Error using semanticSegmentationMetrics>iAssertCategoricalsHaveSameCategories
The categorical data returned by dsResults and dsTruth must have the same
categories.
my code is below
dataSetDir = fullfile('LungTS','preprocessedDataset');
testImagesDir = fullfile(dataSetDir,'imagesTest');
imdReader = @(x) matRead(x);
imds = imageDatastore(testImagesDir, ...
'FileExtensions','.mat','ReadFcn',imdReader);
%imds = imageDatastore(testImagesDir);
classNames = ["nodule" "background"];
labelIDs = [255 0];
labelReader = @(x) matRead(x);
testLabelsDir = fullfile(dataSetDir,'labelsTest');
pxdsTruth = pixelLabelDatastore(testLabelsDir,classNames,labelIDs, ...
'FileExtensions','.mat','ReadFcn',labelReader);
net = load('trained3DUNet-16-Sep-2022-14-34-13-Epoch-250.mat');
net = net.net;
pxdsResults = semanticseg(imds,net,"WriteLocation",tempdir);
metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth);
metrics.ClassMetrics
metrics.ConfusionMatrix
cm = confusionchart(metrics.ConfusionMatrix.Variables, ...
classNames, Normalization ='row-normalized');
cm.Title = 'Normalized Confusion Matrix (%)';
imageIoU = metrics.ImageMetrics.MeanIoU;
figure
histogram(imageIoU)
title('Image Mean IoU')
[minIoU, worstImageIndex] = min(imageIoU);
minIoU = minIoU(1);
worstImageIndex = worstImageIndex(1);
worstTestImage = readimage(imds,worstImageIndex);
worstTrueLabels = readimage(pxdsTruth,worstImageIndex);
worstPredictedLabels = readimage(pxdsResults,worstImageIndex);
worstTrueLabelImage = im2uint8(worstTrueLabels == classNames(1));
worstPredictedLabelImage = im2uint8(worstPredictedLabels == classNames(1));
worstMontage = cat(4,worstTestImage,worstTrueLabelImage,worstPredictedLabelImage);
worstMontage = imresize(worstMontage,4,"nearest");
figure
montage(worstMontage,'Size',[1 3])
title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(minIoU)])
[minIoU, worstImageIndex] = min(imageIoU);
minIoU = minIoU(1);
worstImageIndex = worstImageIndex(1);
worstTestImage = readimage(imds,worstImageIndex);
worstTrueLabels = readimage(pxdsTruth,worstImageIndex);
worstPredictedLabels = readimage(pxdsResults,worstImageIndex);
worstTrueLabelImage = im2uint8(worstTrueLabels == classNames(1));
worstPredictedLabelImage = im2uint8(worstPredictedLabels == classNames(1));
worstMontage = cat(4,worstTestImage,worstTrueLabelImage,worstPredictedLabelImage);
worstMontage = imresize(worstMontage,4,"nearest");
figure
montage(worstMontage,'Size',[1 3])
title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(minIoU)])
[maxIoU, bestImageIndex] = max(imageIoU);
maxIoU = maxIoU(1);
bestImageIndex = bestImageIndex(1);
bestTestImage = readimage(imds,bestImageIndex);
bestTrueLabels = readimage(pxdsTruth,bestImageIndex);
bestPredictedLabels = readimage(pxdsResults,bestImageIndex);
bestTrueLabelImage = im2uint8(bestTrueLabels == classNames(1));
bestPredictedLabelImage = im2uint8(bestPredictedLabels == classNames(1));
bestMontage = cat(4,bestTestImage,bestTrueLabelImage,bestPredictedLabelImage);
bestMontage = imresize(bestMontage,4,"nearest");
figure
montage(bestMontage,'Size',[1 3])
title(['Test Image vs. Truth vs. Prediction. IoU = ' num2str(maxIoU)])
evaluationMetrics = ["accuracy" "iou"];
metrics = evaluateSemanticSegmentation(pxdsResults,pxdsTruth,"Metrics",evaluationMetrics);
metrics.ClassMetrics

回答 (1 件)

Birju Patel
Birju Patel 2022 年 10 月 6 日
Check the categories of the data coming out of pxdsResults and pxdsTruth:
A = read(pxdsResults);
categories(A{1})
B = read(pxdsTruth);
categories(B{1})
The error message suggests that these are not the same.

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