HOW TO CALCULATE RECALL, PRECISION AND IoU test data deep learning

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Dear all,
I want to calculate precision and recall for my test data. But I gor Error. Because my data is 3D. (as attached)
[precision,recall] = bboxPrecisionRecall(volMask1,tempSeg1)
ERROR
Error using bboxPrecisionRecall
Expected boundingBoxes to be two-dimensional.
Error in bboxPrecisionRecall>validateNonTableInput (line 153)
validateattributes(bbox, {'numeric'},...
Error in bboxPrecisionRecall (line 110)
validateNonTableInput(boundingBoxes, 'boundingBoxes');

Accepted Answer

Anusha
Anusha on 25 Aug 2022
Hi,
I understand that you are trying to calculate the precision, recall and IoU metrics on the deep learning predicted output and groundtruth. I also see from the .mat files attached that your volumetric groundtruth (volMask1) and predicted output (tempSeg1) are of the size 128x128x64.
The bboxPrecisionRecall() function currently supports only 2-D inputs for bboxes and groundTruthBboxes. Therefore, convert the 3-D volumes into 2-D images and you can refer to the following code that does this:
% Access 2-D images from 3-D volume and find the metric average
avgPrecision=0; totPrecision=0;
avgRecall=0;totRecall=0;
for i= 1:size(volmask1,3)
[precision,recall] = bboxPrecisionRecall(volMask1(:,:,i),tempSeg1(:,:,i));
totPrecision=totPrecision+precision
totRecall=totRecall+recall
end
avgPrecision = totalPrecision/size(volmask1,3);
avgRecall = totalRecall/size(volmask1,3);
Please refer to the following documentation for more details regarding precision recall computation on the data:
Thanks,
Anusha
  1 Comment
mohd akmal masud
mohd akmal masud on 27 Sep 2022
but I got problems
Error using bboxPrecisionRecall
Expected boundingBoxes to be of size Mx4, but it is of size 128x128.
Error in bboxPrecisionRecall>validateNonTableInput (line 153)
validateattributes(bbox, {'numeric'},...
Error in bboxPrecisionRecall (line 110)
validateNonTableInput(boundingBoxes, 'boundingBoxes');

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