def initialLearning(self):
print "initialLearning"
#raw_input()
self.learning = True
self.detectionResult = self.detectorCascade.detectionResult
self.detectorCascade.detect(self.currImg)
patch = NormalizedPatch()
patch.values = tldExtractNormalizedPatchRect(self.currImg, self.currBB)
patch.positive = 1
initVar = tldCalcVariance(patch.values)
self.detectorCascade.varianceFilter.minVar = initVar/2
overlap = tldOverlapRect(self.detectorCascade.windows, self.detectorCascade.numWindows, self.currBB)
#print overlap
#raw_input()
positiveIndices = []
negativeIndices = []
for i in xrange(len(overlap)):
if overlap[i] > 0.6:
positiveIndices.append((i,overlap[i]))
if overlap[i] < 0.2:
variance = self.detectionResult.variances[i]
if not self.detectorCascade.varianceFilter.enabled or variance > self.detectorCascade.varianceFilter.minVar:
negativeIndices.append(i)
positiveIndices.sort(key=lambda p:p[1])
patches = []
patches.append(patch)
numIterations = min(len(positiveIndices), 10)
for i in xrange(numIterations):
print "self.detectorCascade.ensembleClassifier.learn"
raw_input()
idx = positiveIndices[i][0]
self.detectorCascade.ensembleClassifier.learn(self.currImg,
self.detectorCascade.windows[TLD_WINDOW_SIZE*idx:],
True,
self.detectionResult.featureVectors[self.detectorCascade.numTrees*idx:])
shuffle(negativeIndices)
for i in xrange(min(100,len(negativeIndices))):
idx = negativeIndices[i]
patch = NormalizedPatch()
patch.values = tldExtractNormalizedPatchBB(self.currImg, self.detectorCascade.windows[TLD_WINDOW_SIZE*idx:])
patches.append(patch)
self.detectorCascade.nnClassifier.learn(patches)
def learn(self):
print self.learningEnabled, self.valid, self.detectorEnabled
if not self.learningEnabled or not self.valid or not self.detectorEnabled:
self.learning = False
print "not learning"
return
self.learning = True
detectionResult = self.detectorCascade.detectionResult
if not detectionResult.containsValidData:
self.detectorCascade.detect(self.currImg)
patch = NormalizedPatch()
patch.values = tldExtractNormalizedPatchRect(self.currImg, self.currBB)
#print patch.values
print "self.detectorCascade.numWindows",
print self.detectorCascade.numWindows
overlap = tldOverlapRect(self.detectorCascade.windows, self.detectorCascade.numWindows, self.currBB)
#print overlap,
#print "overlap"
positiveIndices = []
negativeIndices = []
negativeIndicesForNN = []
for i in xrange(len(overlap)):
if overlap[i] > 0.6:
positiveIndices.append([i,overlap[i]])
if overlap[i] < 0.2:
if not self.detectorCascade.ensembleClassifier.enabled or self.detectionResult.posteriors[i] > 0.1:
negativeIndices.append(i)
if not self.detectorCascade.ensembleClassifier.enabled or self.detectionResult.posteriors[i] > 0.5:
negativeIndicesForNN.append(i)
positiveIndices.sort(key = lambda p:p[1])
patches = []
patch.positive = 1
patches.append(patch)
numIterations = min(len(positiveIndices), 10)
for i in xrange(len(negativeIndices)):
idx = negativeIndices[i]
self.detectorCascade.ensembleClassifier.learn(self.currImg,
self.detectorCascade.windows[TLD_WINDOW_SIZE*idx:],
False,
self.detectionResult.featureVectors[self.detectorCascade.numTrees*idx:])
for i in xrange(numIterations):
idx = positiveIndices[i][0]
self.detectorCascade.ensembleClassifier.learn(self.currImg,
self.detectorCascade.windows[TLD_WINDOW_SIZE*idx:],
True,
self.detectionResult.featureVectors[self.detectorCascade.numTrees*idx:])
for i in xrange(len(negativeIndicesForNN)):
idx = negativeIndiceForNN[i]
patch = NormalizedPatch()
patch.values = tldExtractNormalizedPatchBB(self.currImg, self.detectorCascade.windows[TLD_WINDOW_SIZE*idx:])
patch.positive = 0
patches.append(patch)
self.detectorCascade.nnClassifier.learn(patches)