590 bread/classifier/cascade.xml
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19 bread/classifier/params.xml
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32 bread/classifier/stage0.xml
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32 bread/classifier/stage1.xml
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37 bread/classifier/stage2.xml
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32 bread/classifier/stage3.xml
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42 bread/classifier/stage4.xml
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37 bread/classifier/stage5.xml
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<?xml version="1.0"?>
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37 bread/classifier/stage6.xml
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<?xml version="1.0"?>
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32 bread/classifier/stage7.xml
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<?xml version="1.0"?>
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0 train_classifier/bread/images/bread0000.jpg → bread/images/bread0000.jpg
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19 oranges/classifier/params.xml
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@@ -0,0 +1,19 @@
<?xml version="1.0"?>
<opencv_storage>
<params>
<stageType>BOOST</stageType>
<featureType>HAAR</featureType>
<height>24</height>
<width>24</width>
<stageParams>
<boostType>GAB</boostType>
<minHitRate>9.9900001287460327e-01</minHitRate>
<maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
<weightTrimRate>9.4999999999999996e-01</weightTrimRate>
<maxDepth>1</maxDepth>
<maxWeakCount>100</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount>
<featSize>1</featSize>
<mode>BASIC</mode></featureParams></params>
</opencv_storage>
27 oranges/classifier/stage0.xml
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@@ -0,0 +1,27 @@
<?xml version="1.0"?>
<opencv_storage>
<stage0>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-2.7402453422546387e+00</stageThreshold>
<weakClassifiers>
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</opencv_storage>
32 oranges/classifier/stage1.xml
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<?xml version="1.0"?>
<opencv_storage>
<stage1>
<maxWeakCount>5</maxWeakCount>
<stageThreshold>-3.0233626365661621e+00</stageThreshold>
<weakClassifiers>
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</opencv_storage>
27 oranges/classifier/stage2.xml
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@@ -0,0 +1,27 @@
<?xml version="1.0"?>
<opencv_storage>
<stage2>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-2.5235764980316162e+00</stageThreshold>
<weakClassifiers>
<_>
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</opencv_storage>
32 oranges/classifier/stage3.xml
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<?xml version="1.0"?>
<opencv_storage>
<stage3>
<maxWeakCount>5</maxWeakCount>
<stageThreshold>-2.8285069465637207e+00</stageThreshold>
<weakClassifiers>
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</opencv_storage>
32 oranges/classifier/stage4.xml
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<?xml version="1.0"?>
<opencv_storage>
<stage4>
<maxWeakCount>5</maxWeakCount>
<stageThreshold>-2.2519843578338623e+00</stageThreshold>
<weakClassifiers>
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</opencv_storage>
32 oranges/classifier/stage5.xml
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<?xml version="1.0"?>
<opencv_storage>
<stage5>
<maxWeakCount>5</maxWeakCount>
<stageThreshold>-2.5218288898468018e+00</stageThreshold>
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</opencv_storage>
27 oranges/classifier/stage6.xml
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<?xml version="1.0"?>
<opencv_storage>
<stage6>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-2.5810649394989014e+00</stageThreshold>
<weakClassifiers>
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</opencv_storage>
32 oranges/classifier/stage7.xml
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<?xml version="1.0"?>
<opencv_storage>
<stage7>
<maxWeakCount>5</maxWeakCount>
<stageThreshold>-3.0298569202423096e+00</stageThreshold>
<weakClassifiers>
<_>
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</opencv_storage>
32 oranges/classifier/stage8.xml
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@@ -0,0 +1,32 @@
<?xml version="1.0"?>
<opencv_storage>
<stage8>
<maxWeakCount>5</maxWeakCount>
<stageThreshold>-2.4592263698577881e+00</stageThreshold>
<weakClassifiers>
<_>
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</opencv_storage>
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0 train_classifier/oranges/info.txt → oranges/info.txt
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3 train_classifier/src/opencvSupport.py → src/opencvSupport.py
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@@ -48,8 +48,7 @@ def detect_object(image_directory, classifier):
image = cv2.imread(full_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detector = cv2.CascadeClassifier(classifier)
print(classifier)
rects = detector.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(100, 100))
rects = detector.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(75, 75))
for (i, (x, y, w, h)) in enumerate(rects):
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.putText(image, "#{}".format(i + 1), (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 0, 255), 2)
14 train_classifier/src/run.py → src/run.py
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@@ -17,6 +17,7 @@
vec_file = directory + "vec_files/vec.vec"
info = directory + "info.txt"
non_images = directory + "non_images/"
classifier = directory + "classifier/"

rename = input("Do you need to rename image files (Y/N): ").lower()
validInput = False
@@ -80,24 +81,24 @@

train_classifier = input("Do you want to train the classifier (Y/N): ").lower()
validInput = False
classifer_trained = False
classifier_trained = False
if not use_annotations:
num_images = str(int(image_multiplier) * num_images)
else:
num_images = str(num_images)
while not validInput:
if train_classifier == "y":
os.system("opencv_traincascade -data classifier/ -vec " + vec_file + " -bg " + directory + "bg.txt -numStages 12 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numNeg 3380 -numPos " + num_images)
os.system("opencv_traincascade -data " + classifier + " -vec " + vec_file + " -bg " + directory + "bg.txt -numStages 12 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numNeg 3380 -numPos " + num_images)
validInput = True
classifer_trained = True
classifier_trained = True
elif train_classifier == "n":
validInput = True
have_classifier_trained = input("Do you have a trained classifier (Y/N): ")
nested_validInput = False
while not nested_validInput:
if have_classifier_trained == "y":
nested_validInput = True
classifer_trained = True
classifier_trained = True
elif have_classifier_trained == "n":
nested_validInput = True
else:
@@ -106,7 +107,6 @@
else:
train_classifier = input("ERROR: Please use Y/N to indicate whether to train the classifier: ")

if classifer_trained:
if classifier_trained:
image_folder = input("Location of images to detect (e.g. images/): ")
classifier = "classifier/cascade.xml"
opencvSupport.detect_object(images, classifier)
opencvSupport.detect_object(images, classifier + "cascade.xml")
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