def getSitkGradient(im):
gradient = sitk.Gradient(im)
gradientn = sitk.gafi(gradient)
gradients = [sitk.Cast(sitk.gifa(gradientn[:,:,:,i]),6) for i in range(gradient.GetNumberOfComponentsPerPixel())]
#gradients = imaging.sortAxes(gradient,[3,0,1,2])
return gradients
def getSmoothGradient(im,sigma=2):
shape = im.shape
im = sitk.gifa(im)
gradient = sitk.GradientRecursiveGaussian(im,sigma)
gradient = sitk.gafi(gradient)
gradients = imaging.sortAxes(gradient,[len(shape)]+range(len(shape)))
return gradients
def step(self):
"""Perform a single step of the morphological snake evolution."""
# Assign attributes to local variables for convenience.
u = self._u
gI = self._data
dgI = self._ddata
theta = self._theta
v = self._v
if u is None:
raise ValueError, "the levelset is not set (use set_levelset)"
# res = np.copy(u)
res = u*1
# Balloon.
if v > 0:
aux = sitk.Cast(sitk.BinaryDilate(sitk.Cast(u,3)),self.dtype)
# aux = binary_dilation(u, self.structure)
elif v < 0:
aux = sitk.Cast(sitk.BinaryErode(sitk.Cast(u,3)),self.dtype)
# aux = binary_erosion(u, self.structure)
if v!= 0:
res = sitk.Cast(self._threshold_mask_v==0,self.dtype)*res+\
sitk.Cast(self._threshold_mask_v>00,self.dtype)*aux
# res[self._threshold_mask_v] = aux[self._threshold_mask_v]
# Image attachment.
# aux = np.zeros_like(res)
aux = sitk.gifa(np.zeros(res.GetSize()[::-1],dtype=self.dtype))
dres = getSitkGradient(res)
for el1, el2 in zip(dgI, dres):
aux += el1*el2
res[aux > 0] = 1
res[aux < 0] = 0
res = sitk.Cast(aux>0,self.dtype)
# Smoothing.
# Smoothing.
res = sitk.gafi(res)
for i in xrange(self.smoothing):
res = curvop(res)
res = sitk.gifa(res)
self._u = res
def step(self):
"""Perform a single step of the morphological Chan-Vese evolution."""
# Assign attributes to local variables for convenience.
u = self._u
if u is None:
raise ValueError, "the levelset function is not set (use set_levelset)"
data = self.data
# Determine c0 and c1.
inside = sitk.Cast(u>0,self.dtype)
outside = sitk.Cast(u<=0,self.dtype)
c0 = sitkSum(data*outside)/float(sitkSum(outside))
c1 = sitkSum(data*inside)/float(sitkSum(inside))
# c0 = data[outside].sum() / float(outside.sum())
# c1 = data[inside].sum() / float(inside.sum())
# Image attachment.
dres = getSitkGradient(u)
# abs_dres = sitk.GradientMagnitude(u)
# dres = np.array(np.gradient(u))
# abs_dres = np.abs(dres).sum(0)
abs_dres = sitk.Cast(sitk.Add(sitk.Add(dres[0],dres[1]),dres[2]),self.dtype)
#aux = abs_dres * (c0 - c1) * (c0 + c1 - 2*data)
aux = abs_dres * (self.lambda1*(data - c1)**2 - self.lambda2*(data - c0)**2)
# res = np.copy(u)
res = u*1.
res = sitk.Cast(aux<0,self.dtype)+res*sitk.Cast(aux==0,self.dtype)
# res[aux < 0] = 1
# res[aux > 0] = 0
# Smoothing.
res = sitk.gafi(res)
for i in xrange(self.smoothing):
res = curvop(res)
res = sitk.Cast(sitk.gifa(res),self.dtype)
self._u = res
def getSmoothGradientMagnitude(im,sigma=2):
im = sitk.gifa(im)
gm = sitk.GradientMagnitudeRecursiveGaussian(im,sigma)
gm = sitk.gafi(gm)
return gm
