def mask(self,index,margin=0):
"""Return the mask for component index."""
b = self.objects[index]
#print "@@@mask",index,b
m = self.labels[b]
m[m!=index] = 0
if margin>0: m = improc.pad_by(m,margin)
return array(m!=0,'B')
def mask(self,index,margin=0):
"""Return the mask for component index."""
b = self.objects[index]
#print "@@@mask",index,b
m = self.labels[b]
m[m!=index] = 0
if margin>0: m = improc.pad_by(m,margin)
return array(m!=0,'B')
def best_correlation(u,l,pad=1):
"""Given an image `u` and a list of images `l`, finds the image
with the best normalized cross correlation in `l`. Correlation
is carried out with `mode="valid"` after padding with size pad,
and only for images whose dimensions are within `pad` of each other."""
def normalize(u):
u = u-mean(u)
return u/norm(u)
u = normalize(u)
padded = improc.pad_by(u,pad)
normalized = []
for v in l:
if amax(abs(array(u.shape)-array(v.shape)))<=pad:
normalized.append(normalize(v))
convs = []
for v in normalized:
convs.append(signal.correlate(padded,v,mode='valid'))
global _convs
_convs += sum([c.size for c in convs])
return amax([amax(c) for c in convs]) if len(convs)>0 else 0.0
