def circleDistance(self):
"""
**SUMMARY**
Compare the hull mask to an ideal circle and count the number of pixels
that deviate as a fraction of total area of the ideal circle.
**RETURNS**
The difference, as a percentage, between the hull of our blob and an idealized
circle of our blob.
"""
w = self.mHullMask.width
h = self.mHullMask.height
idealcircle = Image((w, h))
radius = min(w, h) / 2
idealcircle.dl().circle((w / 2, h / 2), radius, filled=True, color=Color.WHITE)
idealcircle = idealcircle.applyLayers()
print self.mHullMask
print idealcircle
print self.mHullMask.width
print self.mHullMask.height
print idealcircle.width
print idealcircle.height
netdiff = (idealcircle - self.mHullMask) + (self.mHullMask - idealcircle)
numblack, numwhite = netdiff.histogram(2)
return float(numwhite) / (radius * radius * np.pi)
def circleDistance(self):
"""
**SUMMARY**
Compare the hull mask to an ideal circle and count the number of pixels
that deviate as a fraction of total area of the ideal circle.
**RETURNS**
The difference, as a percentage, between the hull of our blob and an idealized
circle of our blob.
"""
w = self.mHullMask.width
h = self.mHullMask.height
idealcircle = Image((w,h))
radius = min(w,h) / 2
idealcircle.dl().circle((w/2, h/2), radius, filled= True, color=Color.WHITE)
idealcircle = idealcircle.applyLayers()
print self.mHullMask
print idealcircle
print self.mHullMask.width
print self.mHullMask.height
print idealcircle.width
print idealcircle.height
netdiff = (idealcircle - self.mHullMask) + (self.mHullMask - idealcircle)
numblack, numwhite = netdiff.histogram(2)
return float(numwhite) / (radius * radius * np.pi)
def circleDistance(self):
"""
Compare the hull mask to an ideal circle and count the number of pixels
that deviate as a fraction of total area of the ideal circle
"""
idealcircle = Image((self.width(), self.height()))
radius = min(self.width(), self.height()) / 2
idealcircle.dl().circle((self.width()/2, self.height()/2), radius, filled= True, color=Color.WHITE)
idealcircle = idealcircle.applyLayers()
netdiff = (idealcircle - self.mHullMask) + (self.mHullMask - idealcircle)
numblack, numwhite = netdiff.histogram(2)
return float(numwhite) / (radius * radius * np.pi)
def circleDistance(self):
"""
Compare the hull mask to an ideal circle and count the number of pixels
that deviate as a fraction of total area of the ideal circle
"""
idealcircle = Image((self.width(), self.height()))
radius = min(self.width(), self.height()) / 2
idealcircle.dl().circle((self.width() / 2, self.height() / 2),
radius,
filled=True,
color=Color.WHITE)
idealcircle = idealcircle.applyLayers()
hullmask = Image(self.mHullMask)
netdiff = (idealcircle - hullmask) + (hullmask - idealcircle)
numblack, numwhite = netdiff.histogram(2)
return float(numwhite) / (radius * radius * np.pi)
