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66 lines
1.4 KiB
66 lines
1.4 KiB
% Testscript um ein Bild aus den Daten durch das RCCN_NET mit direkter
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% klassifizierung laufen zu lassen
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close all;
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clear;
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%die netze mit besserer Erkennung _2
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RCCN_NET = 'netDetectorResNet50_stepthree.mat';
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inputSize = [224 224 3];
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% first we need the data...
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dataDir = 'Picturedata'; % Destination-Folder for provided (img) Data
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zippedDataFile = 'PicturesResizedLabelsResizedSignsCutted.zip'; %Data provided by TA
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grDataFile = 'signDatasetGroundTruth.mat';
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func_setupData(dataDir, zippedDataFile, grDataFile);
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%load data
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grdata = load(grDataFile);
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traficSignDataset = grdata.DataSet;
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%Random Index
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%shuffledIndices = randperm(height(traficSignDataset));
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%testindx = shuffledIndices(1)
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%for testindx = 50:200
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testindx = 125;
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% Bild einlesen
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imgname = traficSignDataset.imageFilename{testindx}
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I = imresize(imread(imgname),inputSize(1:2));
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%RCCN-Detector laden
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pretrained = load(RCCN_NET);
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detector = pretrained.detector;
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[bbox, score, label] = detect(detector, I, 'MiniBatchSize', 32);
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sfigTitle = ""
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bdetected = height(bbox) > 0;
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if bdetected
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I = insertObjectAnnotation(I,'rectangle',bbox,score);
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sfigTitle = "Detected" + string(label);
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else
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sfigTitle = "Not Detected"
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end
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%end %end forschleife testindex
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figure;
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imshow(I);
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annotation('textbox', [0.5, 0.2, 0.1, 0.1], 'String', sfigTitle)
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%ggf. bild zuschneiden
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if bdetected
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icrop = imcrop(I , bbox);
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figure;
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imshow(icrop);
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end
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