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121 lines
3.6 KiB
Matlab
121 lines
3.6 KiB
Matlab
function [ ] = func_groundTruthFromLabelPic( dataStorePicturePath, dataStoreLabelPath, outFile )
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%
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% erstellt us einem Picture-Datastore und einem Label-Datastore
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% eine Groundtruth-Tabelle, wie sie z.B. in FasterRCNN.m
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% benoetigt wird
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%
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% file von tas
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% adaptiert als func 2022/12/28 vh
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%
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labelDS = imageDatastore(dataStoreLabelPath, 'IncludeSubfolders', true);
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pictureDS = imageDatastore(dataStorePicturePath, 'IncludeSubfolders', true);
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labelCount = numel(labelDS.Files)
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pictureCount = numel(pictureDS.Files)
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if labelCount ~= pictureCount
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fprintf("!!!! Error: Die Anzahl der Bilder und Anzahl der LabelPicture sind ungleich -> Abbruch");
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return
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end
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fprintf("-----------------------------------------------------------\n");
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fprintf("Picture-Verzeichnis: %s\n", dataStorePicturePath)
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fprintf("Anzahl Images: %d\n", pictureCount)
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fprintf("Label-Verzeichnis: %s\n", dataStoreLabelPath)
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fprintf("Anzahl Images: %d\n", labelCount)
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fprintf("BE PATIENT.... \n")
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fprintf("-----------------------------------------------------------\n");
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% table anlegen
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sz = [pictureCount 3];
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varTypes = ["cellstr","cell","logical"];
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varNames = ["imageFilename","sign","valid"];
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DataSet = table('Size',sz,'VariableTypes',varTypes,'VariableNames',varNames);
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rng(0)
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shuffledIndices = randperm(pictureCount);
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% los gehts
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for i = 1:pictureCount
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shuffeldIndex = shuffledIndices(i);
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[imPic imPic_INFO]= readimage(pictureDS, shuffeldIndex);
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[im_path imPic_name im_ext]=fileparts(imPic_INFO.Filename);
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[imLabel imLabel_INFO]= readimage(labelDS, shuffeldIndex);
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[im_path imLabel_name im_ext]=fileparts(imLabel_INFO.Filename);
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% fprintf("picture: %s label: %s\n", imPic_name, imLabel_name);
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box = [0,0,0,0]; %default if theres no labelimg
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v = true;
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if ~strcmp(imPic_name, imLabel_name)
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fprintf("!!!! Error: zum Picture gibt es kein entsprechendes LabelPicture -> Abbruch");
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imPic_name
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imLabel_name
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else
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% LabelRegion aus Image ausschneiden
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bw = imLabel;
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s = regionprops(bw, 'BoundingBox');
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box = cat(1, s.BoundingBox); % structure to matrix
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box = round(box);
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% falls mehrere Marker vorhanden sind, ignorieren wir den Datensatz
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% das dürfte für das Training Region detection besser sein.
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if (height(box) > 1)
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v = false;
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end
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box = box(1,:);
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if (numel(box) ~= 4)
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fprintf("Boxkoordinaten nicht ok: %s %s\n", imPic_name, imLabel_name)
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box
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v = false
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end
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end
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a = num2cell(box, 2);
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%check for boxes which are somewhat wrong
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if v
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if (box(3) < 8.) ...
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|| (box(4) < 8.) ...
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|| (abs(box(3) - box(4)) > 2) ...
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|| (box(1) + box(3) > 1024) ...
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|| (box(2) + box(4) > 768)
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fprintf("boxkoordinaten nicht i.o. %s (%d %d %d %d) \n", imLabel_name, box(1),box(2),box(3),box(4));
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v = false;
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end
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end
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DataSet(shuffeldIndex,:) = {imPic_INFO.Filename,a, v};
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% display one of the training images and box labels.
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if (shuffeldIndex == 4)
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annotatedImage = insertShape(imPic,'Rectangle',box);
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figure
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imshow(annotatedImage)
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end
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end
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%Die Daten sind teilweise nicht in Ordnung, am einfachsten ist natürlich
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%die Einträge zu löschen, die nicht gut sind.
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fprintf("Groundtruth hat %d eintraege vor der Bereinigung \n ", height(DataSet) )
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toDelete = DataSet.valid == false;
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DataSet(toDelete,:) = [];
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DataSet.valid=[];
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fprintf("Groundtruth hat %d eintraege nach der Bereinigung \n", height(DataSet) )
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save(outFile, 'DataSet' );
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