def gradient_affine(self):
"""Gradient of L2 norm for affine matrices only"""
numTransforms = len(self.centers)
gradA_list = []
Phi = DeformationCL(self.fixedCL)
Phi.set_identity()
CoordCL = [Phi.hx, Phi.hy, Phi.hz]
for q in range(numTransforms):
C = self.centers[q]
r = self.radii[q]
A = self.affines[q]
T = self.translations[q]
F = self.fixedCL.getROI(C, r)
M = self.movingCL.getROI(C, r)
XList = []
for d in range(3):
XList.append(CoordCL[d].getROI(C, r))
DiffFM = F.subtract(M)
GList = M.gradient()
CF = numpy.array(F.shape, dtype=numpy.single) / 2.0
if self.normalizeWeights:
W = self.weights[q].divide(self.sum_weights.getROI(C, r))
else:
W = self._get_weights(F.shape, CF, r)
#W = self.weights[q]
#W = self._get_weights(F.shape, C, r)
WD = W.multiply(DiffFM)
gradA = numpy.zeros((3,3), dtype=numpy.single)
for i in range(3):
for j in range(3):
GX = GList[i].multiply(XList[j])
gradA[i,j] = -2.0 * WD.multiply(GX).sum()
gradA_list.append(gradA)
return gradA_list
def gradient_anchor(self):
"""Gradient of L2 norm for anchor positions only"""
numTransforms = len(self.centers)
gradC_list = []
Phi = DeformationCL(self.fixedCL)
Phi.set_identity()
CoordCL = [Phi.hx, Phi.hy, Phi.hz]
for q in range(numTransforms):
C = self.centers[q]
r = self.radii[q]
A = self.affines[q]
T = self.translations[q]
F = self.fixedCL.getROI(C, r)
M = self.movingCL.getROI(C, r)
XList = []
for d in range(3):
XList.append(CoordCL[d].getROI(C, r))
DiffFM = F.subtract(M)
GList = M.gradient()
CF = numpy.array(F.shape, dtype=numpy.single) / 2.0
if self.normalizeWeights:
W = self.weights[q].divide(self.sum_weights.getROI(C, r))
else:
W = self._get_weights(F.shape, CF, r)
#W = self.weights[q]
#W = self._get_weights(F.shape, C, r)
WD = W.multiply(DiffFM)
gradC = numpy.zeros((3,), dtype=numpy.single)
dot_G_XC = F.clone()
dot_G_XC.fill(0.0)
ATList = []
for d in range(3):
AT = F.clone()
AT.fill(0.0)
for j in range(3):
Y = XList[d].clone()
Y.scale(A[d,j])
AT.add_inplace(Y)
AT.shift(T[d])
ATList.append(AT)
XC = XList[d].clone()
XC.shift(-C[d])
XC.scale(2.0 / r[d]**2)
dot_G_XC.add_inplace(GList[d].multiply(XC))
for d in range(3):
gradC[d] = -WD.multiply(ATList[d].multiply(dot_G_XC)).sum()
gradC_list.append(gradC)
return gradC_list
def gradient(self):
"""Gradient of L2 norm"""
numTransforms = len(self.centers)
gradA_list = []
gradT_list = []
gradC_list = []
gradR_list = []
Phi = DeformationCL(self.fixedCL)
Phi.set_identity()
CoordCL = [Phi.hx, Phi.hy, Phi.hz]
for q in range(numTransforms):
C = self.centers[q]
r = self.radii[q]
A = self.affines[q]
T = self.translations[q]
F = self.fixedCL.getROI(C, r)
M = self.movingCL.getROI(C, r)
XList = []
for d in range(3):
XList.append(CoordCL[d].getROI(C, r))
DiffFM = F.subtract(M)
GList = M.gradient()
CF = numpy.array(F.shape, dtype=numpy.single) / 2.0
if self.normalizeWeights:
W = self.weights[q].divide(self.sum_weights.getROI(C, r))
else:
W = self._get_weights(F.shape, CF, r)
#W = self.weights[q]
#W = self._get_weights(F.shape, C, r)
WD = W.multiply(DiffFM)
gradA = numpy.zeros((3,3), dtype=numpy.single)
for i in range(3):
for j in range(3):
GX = GList[i].multiply(XList[j])
gradA[i,j] = -2.0 * WD.multiply(GX).sum()
gradT = numpy.zeros((3,), dtype=numpy.single)
for d in range(3):
gradT[d] = -2.0 * WD.multiply(GList[d]).sum()
gradC = numpy.zeros((3,), dtype=numpy.single)
gradR = numpy.zeros((3,), dtype=numpy.single)
dot_AT_XC = F.clone()
dot_AT_XC.fill(0.0)
dot_AT_XR = F.clone()
dot_AT_XR.fill(0.0)
for d in range(3):
AT = F.clone()
AT.fill(0.0)
for j in range(3):
Y = XList[d].clone()
Y.scale(A[d,j])
AT.add_inplace(Y)
AT.shift(T[d])
XC = XList[d].clone()
XC.shift(-C[d])
XC.scale(2.0 / r[d]**2)
dot_AT_XC.add_inplace(AT.multiply(XC))
XR = XList[d].clone()
XR.shift(-C[d])
XR.scale(4.0 / r[d]**3)
dot_AT_XR.add_inplace(AT.multiply(XR))
for d in range(3):
gradC[d] = -WD.multiply(GList[d].multiply(dot_AT_XC)).sum()
gradR[d] = WD.multiply(GList[d].multiply(dot_AT_XR)).sum()
gradA_list.append(gradA)
gradT_list.append(gradT)
gradC_list.append(gradC)
gradR_list.append(gradR)
return gradA_list, gradT_list, gradC_list, gradR_list
