remove mirror from augmentation for step three

RCNN_for_traficsigns.m

@@ -207,31 +207,4 @@ grid on
 title(sprintf('Average Precision = %.2f', ap))
 
 
-% ----- Helper functions
 
-% function data = augmentData(data)
-% % Randomly flip images and bounding boxes horizontally.
-% tform = randomAffine2d('XReflection',true);
-% sz = size(data{1});
-% rout = affineOutputView(sz,tform);
-% data{1} = imwarp(data{1},tform,'OutputView',rout);
-% 
-% % Sanitize box data, if needed.
-% data{2} = helperSanitizeBoxes(data{2}, sz);
-% 
-% % Warp boxes.
-% data{2} = bboxwarp(data{2},tform,rout);
-% end
-% 
-% function data = preprocessData(data,targetSize)
-% % Resize image and bounding boxes to targetSize.
-% sz = size(data{1},[1 2]);
-% scale = targetSize(1:2)./sz;
-% data{1} = imresize(data{1},targetSize(1:2));
-% 
-% % Sanitize box data, if needed.
-% data{2} = helperSanitizeBoxes(data{2}, sz);
-% 
-% % Resize boxes.
-% data{2} = bboxresize(data{2},scale);
-% end

RCNN_for_traficsigns_stepthree.m

@@ -33,13 +33,12 @@
 % adjustable parameters
 % - if there is no trained net, it can be trained with this script:
 %   set doTraining to true
-% - the training can use augmentation or not:
+
-%   set doAugmentation accordingly
 
 close all;
 clear;
 
-doTraining = false;
+doTraining = true;
 
 % first we need the data...
 dataDir = 'Picturedata'; % Destination-Folder for provided (img) Data
@@ -103,17 +102,6 @@ inputSize = [224 224 3];
 preprocessedTrainingData = transform(trainingData, @(data)preprocessData(data,inputSize));
 % Achtung: dieser DS wird nur zur Ermittlung der BoundingBoxes verwendet
 
-% display one of the training images and box labels.
-while 1==0  %hasdata(preprocessedTrainingData)
-    data = read(preprocessedTrainingData);
-    I = data{1};
-    bbox = data{2};
-    annotatedImage = insertShape(I,'Rectangle',bbox);
-    annotatedImage = imresize(annotatedImage,4);  % nur fuer Darstellung
-    figure(1)
-    imshow(annotatedImage)
-    pause(0.100)
-end
 
 % Auswahl der anchor boxes
 %   Infos dazu: https://de.mathworks.com/help/vision/ug/estimate-anchor-boxes-from-training-data.html
@@ -164,7 +152,7 @@ I = imresize(I,inputSize(1:2));
 [bboxes,scores] = detect(detector,I);
 % Display the results.
  
-sfigTitle = ""
+sfigTitle = "";
 if height(bboxes) > 0
     I = insertObjectAnnotation(I,'rectangle',bboxes,scores);
     sfigTitle = "Detected";
@@ -198,31 +186,4 @@ grid on
 title(sprintf('Average Precision = %.2f', ap))
 
 
-% ----- Helper functions
 
-% function data = augmentData(data)
-% % Randomly flip images and bounding boxes horizontally.
-% tform = randomAffine2d('XReflection',true);
-% sz = size(data{1});
-% rout = affineOutputView(sz,tform);
-% data{1} = imwarp(data{1},tform,'OutputView',rout);
-% 
-% % Sanitize box data, if needed.
-% data{2} = helperSanitizeBoxes(data{2}, sz);
-% 
-% % Warp boxes.
-% data{2} = bboxwarp(data{2},tform,rout);
-% end
-% 
-% function data = preprocessData(data,targetSize)
-% % Resize image and bounding boxes to targetSize.
-% sz = size(data{1},[1 2]);
-% scale = targetSize(1:2)./sz;
-% data{1} = imresize(data{1},targetSize(1:2));
-% 
-% % Sanitize box data, if needed.
-% data{2} = helperSanitizeBoxes(data{2}, sz);
-% 
-% % Resize boxes.
-% data{2} = bboxresize(data{2},scale);
-% end

augmentData_stepthree.m

@@ -1,8 +1,4 @@
 function data = augmentData_stepthree(data)
-% Randomly flip images and bounding boxes horizontally.
-tform = randomAffine2d('XReflection',true);
-sz = size(data{1});
-rout = affineOutputView(sz,tform);
 
 % jitter
 data{1} = jitterColorHSV(data{1},...
@@ -11,11 +7,4 @@ data{1} = jitterColorHSV(data{1},...
         Saturation=0.1,...
         Brightness=0.2);
 
-data{1} = imwarp(data{1},tform,'OutputView',rout);
-
-% Sanitize box data, if needed.
-data{2} = helperSanitizeBoxes(data{2}, sz);
-
-% Warp boxes.
-data{2} = bboxwarp(data{2},tform,rout);
 end

test_stepthree_for.m

@@ -0,0 +1,66 @@
+% 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 = 126:200
+%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);
+
+
+sfigTitle = "";
+bdetected = height(bbox) > 0;
+if bdetected
+    I = insertObjectAnnotation(I,'rectangle',bbox,score);
+    sfigTitle = "Detected" + string(label);
+    return;
+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
+
+
+
+
+    
+