def computeLayer(self, layer):
"""
@param layer: the output HmaxLayer to store results in.
"""
LevelFilter.computeLayer(layer)
#need to enable learning mode from UI (after S2 trained)
#during training, need to pass in class labels
vec = layer.array[:,0,0].tolist()
if self.isLearning:
if len(self.learned)>=100 and self.svmModel==None:
#model = svm_train(y, x [, 'training_options'])
self.svmModel = svmutil.svm_train(self.classes, self.learned)
return
self.classes.append(0)
self.learned.append(vec)
elif self.svmModel!=None:
#p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])
pLabs, pAcc, pVals = svmutil.svm_predict([0], vec, self.svmModel)
print "SVM Result: ", pLabs, pAcc, pVals
def computeLayer(self, layer):
"""
Override the computeLayer from LevelFilter in order to continue
with processing the learning or inferring using our SVM model.
If the SVM is trained and we are inferring, then the SVM inference
results are stored in the layer (which is assumed to be a LayerC2).
The layers are able to render themselves onto a wx canvas for
inspection.
@param layer: the output HmaxLayer to store results in.
"""
LevelFilter.computeLayer(self, layer)
#need to enable learning mode from UI (after S2 trained)
#during training, need to pass in class labels
vec = layer.array[:, 0, 0].tolist() #contains vector of C2 maxes
if self.isLearning and self.__svmModel == None:
#add to count for how many of this class have been learned
count = self.__classCounts.get(self.learningClass, 0)
self.__classCounts[self.learningClass] = count + 1
#copy base input image to use as example when showing SVM result
if count == 0:
layer.saveExampleImage(self.learningClass)
self.classes.append(self.learningClass)
self.learned.append(vec)
#print "learned svm ",len(self.learned),self.learningClass
elif self.__svmModel != None:
#p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])
pLabs, pAcc, pVals = svmutil.svm_predict([0], [vec],
self.__svmModel, "-b 1")
pVals = pVals[0]
#sort ids in case SVM classIDs not consecutive
ids = sorted(self.__classCounts.keys())
layer.setAccuracyResult(sorted(zip(pVals, ids), reverse=True))
if HMAX.DEBUG:
print "SVM Result: ", pLabs, pAcc, pVals
def computeLayer(self, layer):
"""
Override the computeLayer from LevelFilter in order to continue
with processing the learning or inferring using our SVM model.
If the SVM is trained and we are inferring, then the SVM inference
results are stored in the layer (which is assumed to be a LayerC2).
The layers are able to render themselves onto a wx canvas for
inspection.
@param layer: the output HmaxLayer to store results in.
"""
LevelFilter.computeLayer(self, layer)
#need to enable learning mode from UI (after S2 trained)
#during training, need to pass in class labels
vec = layer.array[:,0,0].tolist() #contains vector of C2 maxes
if self.isLearning and self.__svmModel==None:
#add to count for how many of this class have been learned
count = self.__classCounts.get(self.learningClass, 0)
self.__classCounts[self.learningClass] = count+1
#copy base input image to use as example when showing SVM result
if count==0:
layer.saveExampleImage(self.learningClass)
self.classes.append(self.learningClass)
self.learned.append(vec)
#print "learned svm ",len(self.learned),self.learningClass
elif self.__svmModel!=None:
#p_labs, p_acc, p_vals = svm_predict(y, x, model [,'predicting_options'])
pLabs, pAcc, pVals = svmutil.svm_predict([0], [vec], self.__svmModel, "-b 1")
pVals = pVals[0]
#sort ids in case SVM classIDs not consecutive
ids = sorted(self.__classCounts.keys())
layer.setAccuracyResult(sorted(zip(pVals, ids),reverse=True))
if HMAX.DEBUG:
print "SVM Result: ", pLabs, pAcc, pVals
