def getFeatures(f1,f2,o1,o2): #what to do with NaNs?
res=[]; res2=[]
for key in f1:
if key == "Global<Maximum >" or key=="Global<Minimum >": #this ones have only one element
res.append(f1[key]-f2[key])
res2.append(f1[key]*f2[key])
elif key == 'RegionCenter':
res.append(np.linalg.norm(f1[key][o1]-f2[key][o2])) #difference of features
res2.append(np.linalg.norm(f1[key][o1]*f2[key][o2])) #product of features
elif key=='Histogram': #contains only zeros, so trying to see what the prediction is without it
continue
elif key == 'Polygon': #vect has always another length for different objects, so center would be relevant
continue
else:
res.append((f1[key][o1]-f2[key][o2]).tolist() ) #prepare for flattening
res2.append((f1[key][o1]*f2[key][o2]).tolist() ) #prepare for flattening
x= np.asarray(flatten(res)) #flatten
x2= np.asarray(flatten(res2)) #flatten
#x= x[~np.isnan(x)]
#x2= x2[~np.isnan(x2)] #not getting the nans out YET
return np.concatenate((x,x2))
def addSample(self, f1, f2, label):
#if self.labels == []:
self.labels.append(label)
#else:
# self.labels = np.concatenate((np.array(self.labels),label)) # for adding batches of features
res=[]
res2=[]
for key in selectedFeatures:
if key == "Global<Maximum >" or key=="Global<Minimum >":
# the global min/max intensity is not interesting
continue
elif key == 'RegionCenter':
res.append(np.linalg.norm(f1[key]-f2[key])) #difference of features
res2.append(np.linalg.norm(f1[key]*f2[key])) #product of features
elif key == 'Histogram': #contains only zeros, so trying to see what the prediction is without it
continue
elif key == 'Polygon': #vect has always another length for different objects, so center would be relevant
continue
else:
if not isinstance(f1[key], np.ndarray):
res.append(float(f1[key]) - float(f2[key]) ) #prepare for flattening
res2.append(float(f1[key]) * float(f2[key]) ) #prepare for flattening
else:
res.append((f1[key]-f2[key]).tolist() ) #prepare for flattening
res2.append((f1[key]*f2[key]).tolist() ) #prepare for flattening
x= np.asarray(flatten(res)) #flatten
x2= np.asarray(flatten(res2)) #flatten
assert(np.any(np.isnan(x)) == False)
assert(np.any(np.isnan(x2)) == False)
assert(np.any(np.isinf(x)) == False)
assert(np.any(np.isinf(x2)) == False)
#x= x[~np.isnan(x)]
#x2= x2[~np.isnan(x2)] #not getting the nans out YET
features = np.concatenate((x,x2))
if self.mydata is None:
self.mydata = features
else:
self.mydata = np.vstack((self.mydata, features))
