% Testscript um ein  Bild aus den Daten durch das RCCN_NET mit direkter
% klassifizierung laufen zu lassen

close all;
clear;

%die netze mit besserer Erkennung _2
RCCN_NET = 'netDetectorResNet50_stepthree.mat';
inputSize = [224 224 3];

% first we need the data...
dataDir = 'Picturedata'; % Destination-Folder for provided (img) Data
zippedDataFile = 'PicturesResizedLabelsResizedSignsCutted.zip'; %Data provided by TA
grDataFile = 'signDatasetGroundTruth.mat';
func_setupData(dataDir, zippedDataFile, grDataFile);

%load data
grdata = load(grDataFile);
traficSignDataset = grdata.DataSet;

%Random Index
%shuffledIndices = randperm(height(traficSignDataset));
%testindx = shuffledIndices(1)
%for testindx = 50:200
testindx = 125;

% Bild einlesen
imgname = traficSignDataset.imageFilename{testindx}
I = imresize(imread(imgname),inputSize(1:2));

%RCCN-Detector laden
pretrained = load(RCCN_NET);
detector = pretrained.detector;

[bbox, score, label] = detect(detector, I, 'MiniBatchSize', 32);


sfigTitle = ""
bdetected = height(bbox) > 0;
if bdetected
    I = insertObjectAnnotation(I,'rectangle',bbox,score);
    sfigTitle = "Detected" + string(label);
    
else
    sfigTitle = "Not Detected"
end

%end %end forschleife testindex

figure;
imshow(I);
annotation('textbox', [0.5, 0.2, 0.1, 0.1], 'String', sfigTitle)

%ggf. bild zuschneiden 
if bdetected
   icrop = imcrop(I , bbox);
   figure;
   imshow(icrop);
   
end