def regAndResample(self, inputFH, i, filesToAvg, nlinFH):
"""For last generation, override default output names.
Note that the defaultDir specified in the minctracc call
is ignored in this instance. """
if i == (self.generations - 1):
if not self.useGradient[i]:
firstRegOutput, resampleOutput = finalGenerationFileNames(
inputFH)
else:
firstRegOutput = None
registerOutput, resampleOutput = finalGenerationFileNames(
inputFH)
else:
firstRegOutput = None
registerOutput = None
resampleOutput = None
"""If self.useGradient is True, then we call minctracc twice: once
with a gradient and once without. Otherwise, we call only once
without a gradient. """
mta = minctracc(inputFH,
self.target,
defaultDir="tmp",
output=firstRegOutput,
blur=self.blurs[i],
gradient=False,
iterations=self.iterations[i],
step=self.stepSize[i],
weight=0.8,
stiffness=0.98,
similarity=0.8,
w_translations=self.w_translations[i],
simplex=self.simplex[i])
self.p.addStage(mta)
if self.useGradient[i]:
mtb = minctracc(inputFH,
self.target,
output=registerOutput,
defaultDir="tmp",
blur=self.blurs[i],
gradient=True,
iterations=self.iterations[i],
step=self.stepSize[i],
weight=0.8,
stiffness=0.98,
similarity=0.8,
simplex=self.simplex[i])
self.p.addStage(mtb)
"""Need to set last xfm so that next generation will use it as the input transform"""
inputFH.setLastXfm(nlinFH, mtb.outputFiles[0])
rs = mincresample(inputFH,
self.target,
likeFile=self.target,
output=resampleOutput,
defaultDir="tmp",
argArray=["-sinc"])
#Do we need to resample any masks?
filesToAvg.append(rs.outputFiles[0])
self.p.addStage(rs)
if i == (self.generations - 1):
inputFH.setLastBasevol(newBaseVol=rs.outputFiles[0])
def regAndResample(self, inputFH, i, filesToAvg, nlinFH):
"""For last generation, override default output names.
Note that the defaultDir specified in the minctracc call
is ignored in this instance. """
if i == (self.generations -1):
if not self.useGradient[i]:
firstRegOutput, resampleOutput = finalGenerationFileNames(inputFH)
else:
firstRegOutput = None
registerOutput, resampleOutput = finalGenerationFileNames(inputFH)
else:
firstRegOutput = None
registerOutput = None
resampleOutput = None
"""If self.useGradient is True, then we call minctracc twice: once
with a gradient and once without. Otherwise, we call only once
without a gradient. """
mta = minctracc(inputFH,
self.target,
defaultDir="tmp",
output=firstRegOutput,
blur=self.blurs[i],
gradient=False,
iterations=self.iterations[i],
step=self.stepSize[i],
weight=self.weight[i],
stiffness=self.stiffness[i],
similarity=self.similarity[i],
w_translations = self.w_translations[i],
simplex=self.simplex[i])
self.p.addStage(mta)
if self.useGradient[i]:
mtb = minctracc(inputFH,
self.target,
output=registerOutput,
defaultDir="tmp",
blur=self.blurs[i],
gradient=True,
iterations=self.iterations[i],
step=self.stepSize[i],
weight=self.weight[i],
stiffness=self.stiffness[i],
similarity=self.similarity[i],
simplex=self.simplex[i])
self.p.addStage(mtb)
"""Need to set last xfm so that next generation will use it as the input transform"""
inputFH.setLastXfm(nlinFH, mtb.outputFiles[0])
rs = mincresample(inputFH,
self.target,
likeFile=self.target,
output=resampleOutput,
defaultDir="tmp",
argArray=["-sinc"])
#Do we need to resample any masks?
filesToAvg.append(rs.outputFiles[0])
self.p.addStage(rs)
if i == (self.generations -1):
inputFH.setLastBasevol(newBaseVol=rs.outputFiles[0])
def buildPipeline(self):
for i in range(len(self.blurs)):
linearStage = ma.minctracc(self.inputFH,
self.targetFH,
blur=self.blurs[i],
defaultDir=self.defaultDir,
gradient=self.gradient[i],
linearparam="lsq12",
step=self.step[i],
simplex=self.simplex[i])
self.p.addStage(linearStage)
def buildPipeline(self):
for i in range(len(self.blurs)):
linearStage = ma.minctracc(self.inputFH,
self.targetFH,
blur=self.blurs[i],
defaultDir=self.defaultDir,
gradient=self.gradient[i],
linearparam="lsq12",
step=self.step[i],
w_translations=self.w_translations[i],
simplex=self.simplex[i])
self.p.addStage(linearStage)
def __init__(self,
inputPipeFH,
templatePipeFH,
steps=[1,0.5,0.5,0.2,0.2,0.1],
blurs=[0.25,0.25,0.25,0.25,0.25, -1],
gradients=[False, False, True, False, True, False],
iterations=[60,60,60,10,10,4],
simplexes=[3,3,3,1.5,1.5,1],
w_translations=0.2,
linearparams = {'type' : "lsq12", 'simplex' : 1, 'step' : 1},
defaultDir="tmp"):
self.p = Pipeline()
for b in blurs:
#MF TODO: -1 case is also handled in blur. Need here for addStage.
#Fix this redundancy and/or better design?
if b != -1:
tblur = ma.blur(templatePipeFH, b, gradient=True)
iblur = ma.blur(inputPipeFH, b, gradient=True)
self.p.addStage(tblur)
self.p.addStage(iblur)
# Do standard LSQ12 alignment prior to non-linear stages
lsq12reg = lsq12.LSQ12(inputPipeFH,
templatePipeFH,
defaultDir=defaultDir)
self.p.addPipeline(lsq12reg.p)
# create the nonlinear registrations
for i in range(len(steps)):
"""For the final stage, make sure the output directory is transforms."""
if i == (len(steps) - 1):
defaultDir = "transforms"
nlinStage = ma.minctracc(inputPipeFH,
templatePipeFH,
defaultDir=defaultDir,
blur=blurs[i],
gradient=gradients[i],
iterations=iterations[i],
step=steps[i],
similarity=0.8,
w_translations=w_translations,
simplex=simplexes[i])
self.p.addStage(nlinStage)
def buildPipeline(self):
# Do LSQ12 alignment prior to non-linear stages if desired
if self.includeLinear:
self.lsq12Params = mp.setLSQ12MinctraccParams(
self.fileRes,
subject_matter=self.subject_matter,
reg_protocol=self.lsq12_protocol)
lsq12reg = lsq12.LSQ12(
self.inputFH,
self.targetFH,
blurs=self.lsq12Params.blurs,
step=self.lsq12Params.stepSize,
gradient=self.lsq12Params.useGradient,
simplex=self.lsq12Params.simplex,
w_translations=self.lsq12Params.w_translations,
defaultDir=self.defaultDir)
self.p.addPipeline(lsq12reg.p)
# create the nonlinear registrations
self.nlinParams = mp.setNlinMinctraccParams(
self.fileRes, reg_protocol=self.nlin_protocol)
for b in self.nlinParams.blurs:
if b != -1:
self.p.addStage(ma.blur(self.inputFH, b, gradient=True))
self.p.addStage(ma.blur(self.targetFH, b, gradient=True))
for i in range(len(self.nlinParams.stepSize)):
#For the final stage, make sure the output directory is transforms.
if i == (len(self.nlinParams.stepSize) - 1):
self.defaultDir = "transforms"
nlinStage = ma.minctracc(
self.inputFH,
self.targetFH,
defaultDir=self.defaultDir,
blur=self.nlinParams.blurs[i],
gradient=self.nlinParams.useGradient[i],
iterations=self.nlinParams.iterations[i],
step=self.nlinParams.stepSize[i],
w_translations=self.nlinParams.w_translations[i],
simplex=self.nlinParams.simplex[i],
optimization=self.nlinParams.optimization[i])
self.p.addStage(nlinStage)
def buildPipeline(self):
# Do LSQ12 alignment prior to non-linear stages if desired
if self.includeLinear:
self.lsq12Params = mp.setLSQ12MinctraccParams(self.fileRes,
subject_matter=self.subject_matter,
reg_protocol=self.lsq12_protocol)
lsq12reg = lsq12.LSQ12(self.inputFH,
self.targetFH,
blurs=self.lsq12Params.blurs,
step=self.lsq12Params.stepSize,
gradient=self.lsq12Params.useGradient,
simplex=self.lsq12Params.simplex,
w_translations=self.lsq12Params.w_translations,
defaultDir=self.defaultDir)
self.p.addPipeline(lsq12reg.p)
# create the nonlinear registrations
self.nlinParams = mp.setNlinMinctraccParams(self.fileRes, reg_protocol=self.nlin_protocol)
for b in self.nlinParams.blurs:
if b != -1:
self.p.addStage(ma.blur(self.inputFH, b, gradient=True))
self.p.addStage(ma.blur(self.targetFH, b, gradient=True))
for i in range(len(self.nlinParams.stepSize)):
#For the final stage, make sure the output directory is transforms.
if i == (len(self.nlinParams.stepSize) - 1):
self.defaultDir = "transforms"
nlinStage = ma.minctracc(self.inputFH,
self.targetFH,
defaultDir=self.defaultDir,
blur=self.nlinParams.blurs[i],
gradient=self.nlinParams.useGradient[i],
iterations=self.nlinParams.iterations[i],
step=self.nlinParams.stepSize[i],
w_translations=self.nlinParams.w_translations[i],
simplex=self.nlinParams.simplex[i],
memory=self.nlinParams.memory[i] if self.nlinParams.memory else None,
optimization=self.nlinParams.optimization[i])
self.p.addStage(nlinStage)
def regAndResample(self, inputFH, i, filesToAvg, nlinFH):
"""For last generation, override default output names.
Note that the defaultDir specified in the minctracc call
is ignored in this instance. """
if i == (self.generations -1):
if not self.useGradient[i]:
firstRegOutput, resampleOutput = finalGenerationFileNames(inputFH)
else:
firstRegOutput = None
registerOutput, resampleOutput = finalGenerationFileNames(inputFH)
else:
firstRegOutput = None
#registerOutput = None
resampleOutput = None
""" In order to make the behavior of different modules / registration
methods work in the same manner, the self.useGradient variable
will determine what the blurring kernel needs to be applied on:
either the image intensities (if self.useGradient = False),
or the image gradients (if self.useGradient = True).
"""
useGradients = self.useGradient[i]
mta = minctracc(inputFH,
self.target,
defaultDir="tmp",
output=firstRegOutput,
blur=self.blurs[i],
gradient=useGradients,
iterations=self.iterations[i],
step=self.stepSize[i],
weight=self.weight[i],
stiffness=self.stiffness[i],
similarity=self.similarity[i],
w_translations = self.w_translations[i],
simplex=self.simplex[i],
memory=self.memory[i])
self.p.addStage(mta)
# after each registration stage, we have to update
# the last transformation between the nlinFH and the input file
# TODO: we should definitely do something about this. This
# assignment below is very cryptic (what is this nlinFH??), but
# I assume that with the new file handler classes coming up, this
# will all become more clear.
inputFH.setLastXfm(nlinFH, mta.outputFiles[0])
#if self.useGradient[i]:
# mtb = minctracc(inputFH,
# self.target,
# output=registerOutput,
# defaultDir="tmp",
# blur=self.blurs[i],
# gradient=True,
# iterations=self.iterations[i],
# step=self.stepSize[i],
# weight=self.weight[i],
# stiffness=self.stiffness[i],
# similarity=self.similarity[i],
# simplex=self.simplex[i])
# self.p.addStage(mtb)
# """Need to set last xfm so that next generation will use it as the input transform"""
# inputFH.setLastXfm(nlinFH, mtb.outputFiles[0])
rs = mincresample(inputFH,
self.target,
likeFile=self.target,
output=resampleOutput,
defaultDir="tmp",
argArray=["-sinc"])
#Do we need to resample any masks?
filesToAvg.append(rs.outputFiles[0])
self.p.addStage(rs)
if i == (self.generations -1):
inputFH.setLastBasevol(newBaseVol=rs.outputFiles[0])