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))
def allFeatures(features, labels, neg_labels):
j=0
lab=[]
for i in range(0,len(features)-1):
for k in labels[i]:
if j == 0:
x = getFeatures(features[i],features[i+1],k[0],k[1])
j+=1
else:
x = np.vstack((x,getFeatures(features[i],features[i+1],k[0],k[1])))
lab.append(1)
for k in neg_labels[i]:
if k not in labels[i].tolist():
x = np.vstack((x,getFeatures(features[i],features[i+1],k[0],k[1])))
lab.append(0)
x = x[:,~np.isnan(x).any(axis=0)] #now removing the nans
return x,np.asarray(lab)
def find_features_without_NaNs(features):
"""
Remove all features from the list of selected features which have NaNs
"""
selectedFeatures = features[0].keys()
for featuresPerFrame in features:
for key, value in featuresPerFrame.iteritems():
if not isinstance(value, list) and (np.any(np.isnan(value)) or np.any(np.isinf(value))):
try:
selectedFeatures.remove(key)
except:
pass # has already been deleted
forbidden = ["Global<Maximum >", "Global<Minimum >", 'Histogram', 'Polygon']
for f in forbidden:
if f in selectedFeatures:
selectedFeatures.remove(f)
return selectedFeatures
def find_features_without_NaNs(features):
"""
Remove all features from the list of selected features which have NaNs
"""
selectedFeatures = features[0].keys()
for featuresPerFrame in features:
for key, value in featuresPerFrame.items():
if not isinstance(value, list) and (np.any(np.isnan(value)) or np.any(np.isinf(value))):
try:
selectedFeatures.remove(key)
except:
pass # has already been deleted
forbidden = ["Global<Maximum >", "Global<Minimum >", 'Histogram', 'Polygon', 'Defect Center',
'Center', 'Input Center', 'Weighted<RegionCenter>']
for f in forbidden:
if f in selectedFeatures:
selectedFeatures.remove(f)
selectedFeatures.sort()
return selectedFeatures