def getBestDecisionStump(feature, posSampleList, negSampleList, sumOfPosWeight, sumOfNegWeight):
sampleImageList = posSampleList + negSampleList
length = len(sampleImageList)
sumOfLeftPosWeight = 0.0 # just represent the sum of positive weights on the left of threshold
sumOfLeftNegWeight = 0.0 # just represent the sum of negative weights on the left of threshold
threshold = (sampleImageList[0].getFeatureValue() + sampleImageList[1].getFeatureValue()) / 2
polarity = POS_POLARITY
error = sumOfPosWeight + sumOfNegWeight
# calculate feature values for all training samples based on given feature
for sampleImage in sampleImageList:
image = sampleImage.getImage()
integralImage = Util.getIntegralImage(image)
featureValue = Util.featureExtracion(integralImage, feature)
sampleImage.setFeatureValue(featureValue)
# sort feature bundle list (quick sort in util)
Util.quickSort(sampleImageList, 0, length - 1)
# (for loop) get the threshold and polarity with lowest error of weight sum
for i in xrange(length - 1):
sampleImage = sampleImageList[i]
sampleType = sampleImage.getSampleType()
featureValue = sampleImage.getFeatureValue()
nextFeatureValue = sampleImageList[i + 1].getFeatureValue()
weight = sampleImage.getWeight()
newThreshold = (featureValue + nextFeatureValue) / 2.0
newPolarity = POS_POLARITY
newError = error
if sampleType == Util.FACE:
sumOfLeftPosWeight += weight
else:
sumOfLeftNegWeight += weight
error1 = sumOfLeftPosWeight + (sumOfNegWeight - sumOfLeftNegWeight)
error2 = (sumOfPosWeight - sumOfLeftPosWeight) + sumOfLeftNegWeight
if error1 > error2:
newError = error2
else:
newPolarity = NEG_POLARITY
newError = error1
if newError < error:
threshold = newThreshold
polarity = newPolarity
error = newError
return DecisionStump(feature, threshold, polarity, error)
def isFace(self, integralWindow):
featureValue = Util.featureExtracion(integralWindow, self.feature)
if (self.polarity * featureValue) < (self.polarity * self.threshold):
return self.alpha
else:
return 0.0
