def test_GroupAdjointAction(self, disp=False):
hM = common.RandField(self.sz,
nSig=5.0,
gSig=4.0,
mType=ca.MEM_HOST,
sp=self.imSp)
hV = common.RandField(self.sz,
nSig=5.0,
gSig=4.0,
mType=ca.MEM_HOST,
sp=self.imSp)
hPhi = common.RandField(self.sz,
nSig=5.0,
gSig=4.0,
mType=ca.MEM_HOST,
sp=self.imSp)
tmp = ca.Field3D(self.grid, ca.MEM_HOST)
# compute < m, Ad_\phi v >
ca.Ad(tmp, hPhi, hV)
rhs = ca.Dot(tmp, hM)
# compute < Ad^*_\phi m, v >
ca.CoAd(tmp, hPhi, hM)
lhs = ca.Dot(tmp, hV)
#print "a=%f b=%f" % (rhs, lhs)
self.assertLess(abs(rhs - lhs), 2e-6)
def Matching(cf):
if cf.compute.useCUDA and cf.compute.gpuID is not None:
ca.SetCUDADevice(cf.compute.gpuID)
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(MatchingConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
I0 = common.LoadITKImage(cf.study.I0, mType)
I1 = common.LoadITKImage(cf.study.I1, mType)
#ca.DivC_I(I0,255.0)
#ca.DivC_I(I1,255.0)
grid = I0.grid()
ca.ThreadMemoryManager.init(grid, mType, 1)
#common.DebugHere()
# TODO: need to work on these
t = [x*1./cf.optim.nTimeSteps for x in range(cf.optim.nTimeSteps+1)]
checkpointinds = range(1,len(t))
checkpointstates = [(ca.Field3D(grid,mType),ca.Field3D(grid,mType)) for idx in checkpointinds]
p = MatchingVariables(I0,I1, cf.vectormomentum.sigma, t,checkpointinds, checkpointstates, cf.vectormomentum.diffOpParams[0], cf.vectormomentum.diffOpParams[1], cf.vectormomentum.diffOpParams[2], cf.optim.Niter, cf.optim.stepSize, cf.optim.maxPert, cf.optim.nTimeSteps, integMethod = cf.optim.integMethod, optMethod=cf.optim.method, nInv=cf.optim.NIterForInverse,plotEvery=cf.io.plotEvery, plotSlice = cf.io.plotSlice, quiverEvery = cf.io.quiverEvery, outputPrefix = cf.io.outputPrefix)
RunMatching(p)
# write output
if cf.io.outputPrefix is not None:
# reset all variables by shooting once, may have been overwritten
CAvmCommon.IntegrateGeodesic(p.m0,p.t,p.diffOp,\
p.m, p.g, p.ginv,\
p.scratchV1, p.scratchV2,p. scratchV3,\
p.checkpointstates, p.checkpointinds,\
Ninv=p.nInv, integMethod = p.integMethod)
common.SaveITKField(p.m0, cf.io.outputPrefix+"m0.mhd")
common.SaveITKField(p.ginv, cf.io.outputPrefix+"phiinv.mhd")
common.SaveITKField(p.g, cf.io.outputPrefix+"phi.mhd")
def ComposeDef(V,
t,
asVField=False,
inverse=False,
scratchV1=None,
scratchV2=None):
"""
Takes an array of Field3Ds and returns a Field3Ds
containting the vectors Composed to non-integer time t
"""
vlen = len(V)
grid = V[0].grid()
mType = V[0].memType()
# just clamp to final time
if t > vlen:
t = vlen
t_int = int(math.floor(t))
t_frac = t - t_int
h = core.Field3D(grid, mType)
if scratchV1 is None:
scratchV1 = core.Field3D(grid, mType)
core.SetToIdentity(h)
for s in range(t_int):
if inverse:
core.ComposeHVInv(scratchV1, h, V[s])
else:
core.ComposeVH(scratchV1, V[s], h)
h.swap(scratchV1)
if t_frac != 0.0:
if scratchV2 is None:
scratchV2 = core.Field3D(grid, mType)
core.Copy(scratchV2, V[t_int])
core.MulC_I(scratchV2, core.Vec3Df(t_frac, t_frac, t_frac))
if inverse:
core.ComposeHVInv(scratchV1, h, scratchV2)
else:
core.ComposeVH(scratchV1, scratchV2, h)
core.Copy(h, scratchV1)
if asVField:
core.SetToIdentity(scratchV1)
core.Sub_I(h, scratchV1)
return h
def DefSeriesIter(I, V, t, func, args):
grid = I.grid()
mType = I.memType()
tlen = len(t)
h = core.Field3D(grid, mType)
IDef = core.Image3D(grid, mType)
core.SetToIdentity(h)
scratchV1 = core.Field3D(grid, mType)
scratchV2 = core.Field3D(grid, mType)
rtnarr = []
for tidx in range(tlen):
curt = t[tidx]
h = common.ComposeDef(V, curt, inverse=True,
asVField=False,
scratchV1=scratchV1,
scratchV2=scratchV2)
core.ApplyH(IDef, I, h)
r = func(IDef, curt, *args)
rtnarr.append(r)
return rtnarr
def geodesic_shooting_diffOp(moving, target, m0, steps, mType, config):
grid = moving.grid()
m0.setGrid(grid)
ca.ThreadMemoryManager.init(grid, mType, 1)
if mType == ca.MEM_HOST:
diffOp = ca.FluidKernelFFTCPU()
else:
diffOp = ca.FluidKernelFFTGPU()
diffOp.setAlpha(config['deformation_params']['diffOpParams'][0])
diffOp.setBeta(config['deformation_params']['diffOpParams'][1])
diffOp.setGamma(config['deformation_params']['diffOpParams'][2])
diffOp.setGrid(grid)
g = ca.Field3D(grid, mType)
ginv = ca.Field3D(grid, mType)
mt = ca.Field3D(grid, mType)
It = ca.Image3D(grid, mType)
It_inv = ca.Image3D(grid, mType)
if (steps <= 0):
time_steps = config['deformation_params']['timeSteps'];
else:
time_steps = steps;
t = [x*1./time_steps for x in range(time_steps+1)]
checkpointinds = range(1,len(t))
checkpointstates = [(ca.Field3D(grid,mType),ca.Field3D(grid,mType)) for idx in checkpointinds]
scratchV1 = ca.Field3D(grid,mType)
scratchV2 = ca.Field3D(grid,mType)
scratchV3 = ca.Field3D(grid,mType)
CAvmCommon.IntegrateGeodesic(m0,t,diffOp, mt, g, ginv,\
scratchV1,scratchV2,scratchV3,\
keepstates=checkpointstates,keepinds=checkpointinds,
Ninv=config['deformation_params']['NIterForInverse'], integMethod = config['deformation_params']['integMethod'])
ca.ApplyH(It,moving,ginv)
ca.ApplyH(It_inv,target,g)
output={'I1':It, 'I1_inv': It_inv, "phiinv":ginv}
return output
def FieldFromNPArr(arr,
mType=core.MEM_HOST,
sp=core.Vec3Df(1.0, 1.0, 1.0),
orig=core.Vec3Df(0.0, 0.0, 0.0)):
"""
Expects X-by-Y-by-Z-by-3 or X-by-Y-by-2 array
"""
arrx, arry, arrz = _ComponentArraysFromField(arr)
fSz = core.Vec3Di()
fSz.fromlist(arrx.shape)
grid = core.GridInfo(fSz, sp, orig)
f = core.Field3D(grid, core.MEM_HOST)
(fArrX, fArrY, fArrZ) = f.asnp()
fArrX[:, :, :] = arrx
fArrY[:, :, :] = arry
fArrZ[:, :, :] = arrz
f.toType(mType)
return f
def ExtractSliceVF(vf, sliceIdx=None, dim='z'):
"""
Given a Field3D 'vf', extract the given slice along the given dimension.
If sliceIdx is None, extract the middle slice.
"""
sz = vf.size().tolist()
# take mid slice if none specified
if sliceIdx is None:
sliceIdx = sz[DIMMAP[dim]] // 2
roiStart = core.Vec3Di(0, 0, 0)
roiSize = core.Vec3Di(sz[0], sz[1], sz[2])
roiStart.set(DIMMAP[dim], sliceIdx)
roiSize.set(DIMMAP[dim], 1)
roiGrid = core.GridInfo(vf.grid().size(),
vf.grid().spacing(),
vf.grid().origin())
roiGrid.setSize(roiSize)
sliceVF = core.Field3D(roiGrid, vf.memType())
core.SubVol(sliceVF, vf, roiStart)
return sliceVF
def AsNPCopy(indata):
origType = indata.memType()
indata.toType(core.MEM_HOST)
if IsImage3D(indata):
out = indata.asnp().copy()
elif IsField3D(indata):
core.Field3D(indata.grid(), core.MEM_HOST)
out_x, out_y, out_z = indata.asnp()
sz = indata.grid().size().tolist()
sz.append(3)
out = np.zeros(sz)
out[:, :, :, 0] = out_x
out[:, :, :, 1] = out_y
out[:, :, :, 2] = out_z
else:
raise Exception('Expected `out` to be Image3D or Field3D')
indata.toType(origType)
return out
def ApplyAffine(Iout, Im, A, bg=ca.BACKGROUND_STRATEGY_PARTIAL_ZERO):
'''Applies an Affine matrix A to an image Im using the Image3D
grid (size, spacing, origin) of the two images (Input and Output)
'''
# algorithm outline: Create a temporary large grid, then perform
# real affine transforms here, then crop to be the size of the out grid
ca.SetMem(Iout, 0.0)
A = np.matrix(A)
bigsize = [max(Iout.grid().size().x, Im.grid().size().x),
max(Iout.grid().size().y, Im.grid().size().y),
max(Iout.grid().size().z, Im.grid().size().z)]
idgrid = ca.GridInfo(ca.Vec3Di(bigsize[0], bigsize[1], bigsize[2]),
ca.Vec3Df(1, 1, 1),
ca.Vec3Df(0, 0, 0))
# newgrid = Iout.grid() # not a true copy!!!!!
newgrid = ca.GridInfo(Iout.grid().size(),
Iout.grid().spacing(),
Iout.grid().origin())
mType = Iout.memType()
Imbig = cc.PadImage(Im, bigsize)
h = ca.Field3D(idgrid, mType)
ca.SetToIdentity(h)
if isinstance(Im, ca.Field3D):
Ioutbig = ca.Field3D(idgrid, mType)
else:
Ioutbig = ca.Image3D(idgrid, mType)
# note: x_real' = A*x_real; x_real' given (input grid)
# solution: x_real = A^-1 * x_real
# where x_real = x_index*spacing + origin
# and x_real' = x_index'*spacing' + origin'
# x_index' is really given, as is both spacings/origins
# and we plug in the solution for x_index' into applyH
if A.shape[1] == 3: # 2D affine matrix
x = ca.Image3D(idgrid, mType)
y = ca.Image3D(idgrid, mType)
xnew = ca.Image3D(idgrid, mType)
ynew = ca.Image3D(idgrid, mType)
ca.Copy(x, h, 0)
ca.Copy(y, h, 1)
# convert x,y to world coordinates
x *= Iout.grid().spacing().x
y *= Iout.grid().spacing().y
x += Iout.grid().origin().x
y += Iout.grid().origin().y
# Matrix Multiply (All in real coords)
Ainv = A.I
ca.MulC_Add_MulC(xnew, x, Ainv[0, 0], y, Ainv[0, 1])
ca.MulC_Add_MulC(ynew, x, Ainv[1, 0], y, Ainv[1, 1])
xnew += (Ainv[0, 2])
ynew += (Ainv[1, 2]) # xnew and ynew are now in real coords
# convert back to index coordinates
xnew -= Im.grid().origin().x
ynew -= Im.grid().origin().y
xnew /= Im.grid().spacing().x
ynew /= Im.grid().spacing().y
ca.SetToZero(h)
ca.Copy(h, xnew, 0)
ca.Copy(h, ynew, 1)
elif A.shape[1] == 4: # 3D affine matrix
x = ca.Image3D(idgrid, mType)
y = ca.Image3D(idgrid, mType)
z = ca.Image3D(idgrid, mType)
xnew = ca.Image3D(idgrid, mType)
ynew = ca.Image3D(idgrid, mType)
znew = ca.Image3D(idgrid, mType)
ca.Copy(x, h, 0)
ca.Copy(y, h, 1)
ca.Copy(z, h, 2)
x *= Iout.grid().spacing().x
y *= Iout.grid().spacing().y
z *= Iout.grid().spacing().z
x += Iout.grid().origin().x
y += Iout.grid().origin().y
z += Iout.grid().origin().z
# Matrix Multiply (All in real coords)
Ainv = A.I
ca.MulC_Add_MulC(xnew, x, Ainv[0, 0], y, Ainv[0, 1])
ca.Add_MulC_I(xnew, z, Ainv[0, 2])
xnew += (Ainv[0, 3])
ca.MulC_Add_MulC(ynew, x, Ainv[1, 0], y, Ainv[1, 1])
ca.Add_MulC_I(ynew, z, Ainv[1, 2])
ynew += (Ainv[1, 3])
ca.MulC_Add_MulC(znew, x, Ainv[2, 0], y, Ainv[2, 1])
ca.Add_MulC_I(znew, z, Ainv[2, 2])
znew += (Ainv[2, 3])
# convert to index coordinates
xnew -= Im.grid().origin().x
ynew -= Im.grid().origin().y
znew -= Im.grid().origin().z
xnew /= Im.grid().spacing().x
ynew /= Im.grid().spacing().y
znew /= Im.grid().spacing().z
ca.Copy(h, xnew, 0)
ca.Copy(h, ynew, 1)
ca.Copy(h, znew, 2)
Imbig.setGrid(idgrid)
ca.ApplyH(Ioutbig, Imbig, h, bg)
# crop Ioutbig -> Iout
ca.SubVol(Iout, Ioutbig, ca.Vec3Di(0, 0, 0))
Iout.setGrid(newgrid) # change back
SaveRGB = False
SaveVE = False
SaveBW = True
SaveVF = False
debug = False
# load BFI slice and MRI_as_BFI slices
BFI3D = cc.LoadMHA(dir_bf + 'block' + str(block) + 'as_MRI_bw_256.mha',
ca.MEM_HOST) # B/W
MRI3D = cc.LoadMHA(dir_mri + 'T2Seg.mha', ca.MEM_HOST)
# Initialize Deformed 3D Volumes
if SaveRGB:
BFI_color3D = cc.LoadMHA(dir_bf + 'block' + str(block) + '_reg_rgb.mha',
ca.MEM_HOST)
BFIDef3D_RGB = ca.Field3D(BFI3D.grid(), BFI3D.memType())
ca.SetMem(BFIDef3D_RGB, 0.0)
if SaveVE:
BFIDef3D_VE = ca.Image3D(BFI3D.grid(), BFI3D.memType())
ca.SetMem(BFIDef3D_VE, 0.0)
if SaveBW:
BFIDef3D_BW = ca.Image3D(BFI3D.grid(), BFI3D.memType())
ca.SetMem(BFIDef3D_BW, 0.0)
if SaveVF:
BFIDef3D_VF = ca.Image3D(BFI3D.grid(), BFI3D.memType())
ca.SetMem(BFIDef3D_VF, 0.0)
# Initialize the 2D slices
BFI = common.ExtractSliceIm(BFI3D, 0)
BFI.toType(ca.MEM_DEVICE)
BFI.setOrigin(ca.Vec3Df(0, 0, 0))
def GeoRegIteration(subid, cf, p, t, Imsmts, cpinds, cpstates, msmtinds,
gradAtMsmts, EnergyHistory, it):
# compute gradient for regression
(grad_m, sumJac, sumSplatI, VEnergy,
IEnergy) = GeoRegGradient(p, t, Imsmts, cpinds, cpstates, msmtinds,
gradAtMsmts)
# do energy related stuff for printing and bookkeeping
#if it>0:
EnergyHistory.append([VEnergy + IEnergy, VEnergy, IEnergy])
print VEnergy + IEnergy, '(Total) = ', VEnergy, '(Vector)+', IEnergy, '(Image)'
# plot some stuff
if cf.io.plotEvery > 0 and (((it + 1) % cf.io.plotEvery) == 0
or it == cf.optim.Niter - 1):
GeoRegPlots(subid, cf, p, t, Imsmts, cpinds, cpstates, msmtinds,
gradAtMsmts, EnergyHistory)
# end if
if cf.optim.method == 'FIXEDGD':
# automatic stepsize selection in the first three steps
if it == 1:
# TODO: BEWARE There are hardcoded numbers here for 2D and 3D
#first find max absolute value across voxels in gradient
temp = ca.Field3D(grad_m.grid(), ca.MEM_HOST)
ca.Copy(temp, grad_m)
temp_x, temp_y, temp_z = temp.asnp()
temp1 = np.square(temp_x.flatten()) + np.square(
temp_y.flatten()) + np.square(temp_z.flatten())
medianval = np.median(temp1[temp1 > 0.0000000001])
del temp, temp1, temp_x, temp_y, temp_z
#2D images for 2000 iters
#p.stepSize = float(0.000000002*medianval)
#3D images for 2000 iters
p.stepSize = float(0.000002 * medianval)
print 'rank:', Compute.GetMPIInfo(
)['rank'], ', localRank:', Compute.GetMPIInfo(
)['local_rank'], 'subid: ', subid, ' Selecting initial step size in the beginning to be ', str(
p.stepSize)
if it > 3:
totalEnergyDiff = EnergyHistory[-1][0] - EnergyHistory[-2][0]
if totalEnergyDiff > 0.0:
if cf.optim.maxPert is not None:
print 'rank:', Compute.GetMPIInfo(
)['rank'], ', localRank:', Compute.GetMPIInfo(
)['local_rank'], 'subid: ', subid, ' Reducing stepsize for gradient descent by ', str(
cf.optim.maxPert *
100), '%. The new step size is ', str(
p.stepSize * (1 - cf.optim.maxPert))
p.stepSize = p.stepSize * (1 - cf.optim.maxPert)
# take gradient descent step
ca.Add_MulC_I(p.m0, grad_m, -p.stepSize)
else:
raise Exception("Unknown optimization scheme: " + cf.optim.optMethod)
# end if
# now divide to get new base image
ca.Div(p.I0, sumSplatI, sumJac)
return (EnergyHistory)
imagedir = '/home/sci/crottman/korenberg/results/' + reg_type + '/'
if reg_type in ['landmark', 've_reg']:
fname_end = '_as_MRI_' + col + '.mha'
else:
fname_end = '_as_MRI_' + col + '_' + str(sz) + '.mha'
if sz >= 512:
mType = ca.MEM_HOST
else:
mType = ca.MEM_DEVICE
if reg_type is not 'best':
grid = cc.MakeGrid([sz, sz, sz], [256.0 / sz, 256.0 / sz, 256.0 / sz],
'center')
blocks = ca.Field3D(grid, mType)
ca.SetMem(blocks, 0.0)
weights = blocks.copy()
ca.SetMem(weights, 0.0)
for i in xrange(1, 5):
fname = imagedir + 'block' + str(i) + fname_end
try:
blk = cc.LoadMHA(fname, mType)
except IOError:
print 'Warning... block ' + str(i) + ' does not exist'
continue
blocks += blk
weight3 = blk.copy()
try:
weight = cc.LoadMHA(imagedir +
def GeodesicShooting(cf):
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(GeodesicShootingConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
mType = ca.MEM_DEVICE if cf.useCUDA else ca.MEM_HOST
#common.DebugHere()
I0 = common.LoadITKImage(cf.study.I0, mType)
m0 = common.LoadITKField(cf.study.m0, mType)
grid = I0.grid()
ca.ThreadMemoryManager.init(grid, mType, 1)
# set up diffOp
if mType == ca.MEM_HOST:
diffOp = ca.FluidKernelFFTCPU()
else:
diffOp = ca.FluidKernelFFTGPU()
diffOp.setAlpha(cf.diffOpParams[0])
diffOp.setBeta(cf.diffOpParams[1])
diffOp.setGamma(cf.diffOpParams[2])
diffOp.setGrid(grid)
g = ca.Field3D(grid, mType)
ginv = ca.Field3D(grid, mType)
mt = ca.Field3D(grid, mType)
It = ca.Image3D(grid, mType)
t = [
x * 1. / cf.integration.nTimeSteps
for x in range(cf.integration.nTimeSteps + 1)
]
checkpointinds = range(1, len(t))
checkpointstates = [(ca.Field3D(grid, mType), ca.Field3D(grid, mType))
for idx in checkpointinds]
scratchV1 = ca.Field3D(grid, mType)
scratchV2 = ca.Field3D(grid, mType)
scratchV3 = ca.Field3D(grid, mType)
# scale momenta to shoot
cf.study.scaleMomenta = float(cf.study.scaleMomenta)
if abs(cf.study.scaleMomenta) > 0.000000:
ca.MulC_I(m0, float(cf.study.scaleMomenta))
CAvmCommon.IntegrateGeodesic(m0,t,diffOp, mt, g, ginv,\
scratchV1,scratchV2,scratchV3,\
keepstates=checkpointstates,keepinds=checkpointinds,
Ninv=cf.integration.NIterForInverse, integMethod = cf.integration.integMethod)
else:
ca.Copy(It, I0)
ca.Copy(mt, m0)
ca.SetToIdentity(ginv)
ca.SetToIdentity(g)
# write output
if cf.io.outputPrefix is not None:
# scale back shotmomenta before writing
if abs(cf.study.scaleMomenta) > 0.000000:
ca.ApplyH(It, I0, ginv)
ca.CoAd(mt, ginv, m0)
ca.DivC_I(mt, float(cf.study.scaleMomenta))
common.SaveITKImage(It, cf.io.outputPrefix + "I1.mhd")
common.SaveITKField(mt, cf.io.outputPrefix + "m1.mhd")
common.SaveITKField(ginv, cf.io.outputPrefix + "phiinv.mhd")
common.SaveITKField(g, cf.io.outputPrefix + "phi.mhd")
GeodesicShootingPlots(g, ginv, I0, It, cf)
if cf.io.saveFrames:
SaveFrames(checkpointstates, checkpointinds, I0, It, m0, mt, cf)
def SaveFrames(checkpointstates, checkpointinds, I0, It, m0, mt, cf):
momentathresh = 0.00002
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix) + '/frames/')
image_idx = 0
fig = plt.figure(1, frameon=False)
plt.clf()
display.DispImage(I0,
'',
newFig=False,
cmap='gray',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice)
plt.draw()
outfilename = cf.io.outputPrefix + '/frames/I' + str(image_idx).zfill(
5) + '.png'
fig.set_size_inches(4, 4)
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
fig = plt.figure(2, frameon=False)
plt.clf()
temp = ca.Field3D(I0.grid(), I0.memType())
ca.SetToIdentity(temp)
common.DebugHere()
CAvmCommon.MyGridPlot(temp,
every=cf.io.gridEvery,
color='k',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice,
isVF=False,
plotBase=False)
#fig.patch.set_alpha(0)
#fig.patch.set_visible(False)
a = fig.gca()
#a.set_frame_on(False)
a.set_xticks([])
a.set_yticks([])
plt.axis('tight')
plt.axis('image')
plt.axis('off')
plt.draw()
fig.set_size_inches(4, 4)
outfilename = cf.io.outputPrefix + '/frames/invdef' + str(image_idx).zfill(
5) + '.png'
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
fig = plt.figure(3, frameon=False)
plt.clf()
CAvmCommon.MyGridPlot(temp,
every=cf.io.gridEvery,
color='k',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice,
isVF=False,
plotBase=False)
#fig.patch.set_alpha(0)
#fig.patch.set_visible(False)
a = fig.gca()
#a.set_frame_on(False)
a.set_xticks([])
a.set_yticks([])
plt.axis('tight')
plt.axis('image')
plt.axis('off')
plt.draw()
fig.set_size_inches(4, 4)
outfilename = cf.io.outputPrefix + '/frames/def' + str(image_idx).zfill(
5) + '.png'
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
fig = plt.figure(4, frameon=False)
plt.clf()
display.DispImage(I0,
'',
newFig=False,
cmap='gray',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice)
plt.hold('True')
CAvmCommon.MyQuiver(m0,
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice,
every=cf.io.quiverEvery,
thresh=momentathresh,
scaleArrows=0.25,
arrowCol='r',
lineWidth=0.5,
width=0.005)
plt.draw()
plt.hold('False')
outfilename = cf.io.outputPrefix + '/frames/m' + str(image_idx).zfill(
5) + '.png'
fig.set_size_inches(4, 4)
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
for i in range(len(checkpointinds)):
image_idx = image_idx + 1
ca.ApplyH(It, I0, checkpointstates[i][1])
fig = plt.figure(1, frameon=False)
plt.clf()
display.DispImage(It,
'',
newFig=False,
cmap='gray',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice)
plt.draw()
outfilename = cf.io.outputPrefix + '/frames/I' + str(image_idx).zfill(
5) + '.png'
fig.set_size_inches(4, 4)
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
fig = plt.figure(2, frameon=False)
plt.clf()
CAvmCommon.MyGridPlot(checkpointstates[i][1],
every=cf.io.gridEvery,
color='k',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice,
isVF=False,
plotBase=False)
#fig.patch.set_alpha(0)
#fig.patch.set_visible(False)
a = fig.gca()
#a.set_frame_on(False)
a.set_xticks([])
a.set_yticks([])
plt.axis('tight')
plt.axis('image')
plt.axis('off')
plt.draw()
outfilename = cf.io.outputPrefix + '/frames/invdef' + str(
image_idx).zfill(5) + '.png'
fig.set_size_inches(4, 4)
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
fig = plt.figure(3, frameon=False)
plt.clf()
CAvmCommon.MyGridPlot(checkpointstates[i][0],
every=cf.io.gridEvery,
color='k',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice,
isVF=False,
plotBase=False)
#fig.patch.set_alpha(0)
#fig.patch.set_visible(False)
a = fig.gca()
#a.set_frame_on(False)
a.set_xticks([])
a.set_yticks([])
plt.axis('tight')
plt.axis('image')
plt.axis('off')
plt.draw()
outfilename = cf.io.outputPrefix + '/frames/def' + str(
image_idx).zfill(5) + '.png'
fig.set_size_inches(4, 4)
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
ca.CoAd(mt, checkpointstates[i][1], m0)
fig = plt.figure(4, frameon=False)
plt.clf()
display.DispImage(It,
'',
newFig=False,
cmap='gray',
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice)
plt.hold('True')
CAvmCommon.MyQuiver(mt,
dim=cf.io.plotSliceDim,
sliceIdx=cf.io.plotSlice,
every=cf.io.quiverEvery,
thresh=momentathresh,
scaleArrows=0.40,
arrowCol='r',
lineWidth=0.5,
width=0.005)
plt.draw()
plt.hold('False')
outfilename = cf.io.outputPrefix + '/frames/m' + str(image_idx).zfill(
5) + '.png'
fig.set_size_inches(4, 4)
plt.savefig(outfilename, bbox_inches='tight', pad_inches=0, dpi=100)
def BuildGeoReg(cf):
"""Worker for running geodesic estimation on a subset of individuals
"""
#common.DebugHere()
localRank = Compute.GetMPIInfo()['local_rank']
rank = Compute.GetMPIInfo()['rank']
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# just one reporter process on each node
isReporter = rank == 0
# load filenames and times for all subjects
(subjectsIds, subjectsImagePaths,
subjectsTimes) = GeoRegLoadSubjectsDetails(cf.study.subjectFile)
cf.study.numSubjects = len(subjectsIds)
if isReporter:
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(GeoRegConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
# if MPI check if processes are greater than number of subjects. it is okay if there are more subjects than processes
if cf.compute.useMPI and (len(subjectsIds) < cf.compute.numProcesses):
raise Exception("Please don't use more processes " +
"than total number of individuals")
nodeSubjectsIds = subjectsIds[rank::cf.compute.numProcesses]
nodeSubjectsImagePaths = subjectsImagePaths[rank::cf.compute.numProcesses]
nodeSubjectsTimes = subjectsTimes[rank::cf.compute.numProcesses]
numLocalSubjects = len(nodeSubjectsImagePaths)
if cf.study.initializationsFile is not None:
(subjectsInitialImages,
subjectsInitialMomenta) = GeoRegLoadSubjectsInitializations(
cf.study.initializationsFile)
nodeSubjectsInitialImages = subjectsInitialImages[rank::cf.compute.
numProcesses]
nodeSubjectsInitialMomenta = subjectsInitialMomenta[rank::cf.compute.
numProcesses]
print 'rank:', rank, ', localRank:', localRank, ', numberSubjects/TotalSubjects:', len(
nodeSubjectsImagePaths
), '/', cf.study.numSubjects, ', nodeSubjectsImagePaths:', nodeSubjectsImagePaths, ', nodeSubjectsTimes:', nodeSubjectsTimes
# mem type is determined by whether or not we're using CUDA
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
# setting gpuid should be handled in gpu
# if using GPU set device based on local rank
#if cf.compute.useCUDA:
# ca.SetCUDADevice(localRank)
# get image size information
dummyImToGetGridInfo = common.LoadITKImage(nodeSubjectsImagePaths[0][0],
mType)
imGrid = dummyImToGetGridInfo.grid()
if cf.study.setUnitSpacing:
imGrid.setSpacing(ca.Vec3Df(1.0, 1.0, 1.0))
if cf.study.setZeroOrigin:
imGrid.setOrigin(ca.Vec3Df(0, 0, 0))
#del dummyImToGetGridInfo;
# start up the memory manager for scratch variables
ca.ThreadMemoryManager.init(imGrid, mType, 0)
# allocate memory
p = GeoRegVariables(imGrid,
mType,
cf.vectormomentum.diffOpParams[0],
cf.vectormomentum.diffOpParams[1],
cf.vectormomentum.diffOpParams[2],
cf.optim.NIterForInverse,
cf.vectormomentum.sigma,
cf.optim.stepSize,
integMethod=cf.optim.integMethod)
# for each individual run geodesic regression for each subject
for i in range(numLocalSubjects):
# initializations for this subject
if cf.study.initializationsFile is not None:
# assuming the initializations are already preprocessed, in terms of intensities, origin and voxel scalings.
p.I0 = common.LoadITKImage(nodeSubjectsInitialImages[i], mType)
p.m0 = common.LoadITKField(nodeSubjectsInitialMomenta[i], mType)
else:
ca.SetMem(p.m0, 0.0)
ca.SetMem(p.I0, 0.0)
# allocate memory specific to this subject in steps a, b and c
# a. create time array with checkpointing info for regression geodesic, allocate checkpoint memory
(t, msmtinds, cpinds) = GeoRegSetUpTimeArray(cf.optim.nTimeSteps,
nodeSubjectsTimes[i],
0.001)
cpstates = [(ca.Field3D(imGrid, mType), ca.Field3D(imGrid, mType))
for idx in cpinds]
# b. allocate gradAtMeasurements of the length of msmtindex for storing residuals
gradAtMsmts = [ca.Image3D(imGrid, mType) for idx in msmtinds]
# c. load timepoint images for this subject
Imsmts = [
common.LoadITKImage(f, mType) if isinstance(f, str) else f
for f in nodeSubjectsImagePaths[i]
]
# reset stepsize if adaptive stepsize changed it inside
p.stepSize = cf.optim.stepSize
# preprocessimages
GeoRegPreprocessInput(nodeSubjectsIds[i], cf, p, t, Imsmts, cpinds,
cpstates, msmtinds, gradAtMsmts)
# run regression for this subject
# REMEMBER
# msmtinds index into cpinds
# gradAtMsmts is parallel to msmtinds
# cpinds index into t
EnergyHistory = RunGeoReg(nodeSubjectsIds[i], cf, p, t, Imsmts, cpinds,
cpstates, msmtinds, gradAtMsmts)
# write output images and fields for this subject
# TODO: BEWARE There are hardcoded numbers inside preprocessing code specific for ADNI/OASIS brain data.
GeoRegWriteOuput(nodeSubjectsIds[i], cf, p, t, Imsmts, cpinds,
cpstates, msmtinds, gradAtMsmts, EnergyHistory)
# clean up memory specific to this subject
del t, Imsmts, cpinds, cpstates, msmtinds, gradAtMsmts
def __init__(self,
I0,
I1,
sigma,
t,
cpinds,
cpstates,
alpha,
beta,
gamma,
nIter,
stepSize,
maxPert,
nTimeSteps,
integMethod='RK4',
optMethod='FIXEDGD',
nInv=10,
plotEvery=None,
plotSlice=None,
quiverEvery=None,
outputPrefix='./'):
"""
Initialize everything with the size and type given
"""
print I0
self.I0 = I0
self.I1 = I1
self.grid = I0.grid()
print(self.grid)
self.memtype = I0.memType()
# matching param
self.sigma = sigma
# initial conditions
self.m0 = ca.Field3D(self.grid, self.memtype)
ca.SetMem(self.m0, 0.0)
# state variables
self.g = ca.Field3D(self.grid, self.memtype)
self.ginv = ca.Field3D(self.grid, self.memtype)
self.m = ca.Field3D(self.grid, self.memtype)
self.I = ca.Image3D(self.grid, self.memtype)
self.residualIm = ca.Image3D(self.grid, self.memtype)
# adjoint variables
self.madj = ca.Field3D(self.grid, self.memtype)
self.Iadj = ca.Image3D(self.grid, self.memtype)
self.madjtmp = ca.Field3D(self.grid, self.memtype)
self.Iadjtmp = ca.Image3D(self.grid, self.memtype)
# time array
self.t = t
# checkpointing variables
self.checkpointinds = cpinds
self.checkpointstates = cpstates
# set up diffOp
if self.memtype == ca.MEM_HOST:
self.diffOp = ca.FluidKernelFFTCPU()
else:
self.diffOp = ca.FluidKernelFFTGPU()
self.diffOp.setAlpha(alpha)
self.diffOp.setBeta(beta)
self.diffOp.setGamma(gamma)
self.diffOp.setGrid(self.grid)
# energy
self.Energy = None
# optimization stuff
self.nIter = nIter
self.stepSize = stepSize
self.maxPert = maxPert
self.nTimeSteps = nTimeSteps
self.optMethod = optMethod
self.integMethod = integMethod
self.nInv = nInv # for interative update to inverse deformation
# plotting variables
self.plotEvery = plotEvery
self.plotSlice = plotSlice
self.quiverEvery = quiverEvery
self.outputPrefix = outputPrefix
# scratch variables
self.scratchV1 = ca.Field3D(self.grid, self.memtype)
self.scratchV2 = ca.Field3D(self.grid, self.memtype)
self.scratchV3 = ca.Field3D(self.grid, self.memtype)
self.scratchV4 = ca.Field3D(self.grid, self.memtype)
self.scratchV5 = ca.Field3D(self.grid, self.memtype)
self.scratchV6 = ca.Field3D(self.grid, self.memtype)
self.scratchV7 = ca.Field3D(self.grid, self.memtype)
self.scratchV8 = ca.Field3D(self.grid, self.memtype)
self.scratchV9 = ca.Field3D(self.grid, self.memtype)
self.scratchV10 = ca.Field3D(self.grid, self.memtype)
self.scratchV11 = ca.Field3D(self.grid, self.memtype)
def BuildAtlas(cf):
"""Worker for running Atlas construction on a subset of individuals.
Runs Atlas on this subset sequentially. The variations retuned are
summed up to get update for all individuals
"""
localRank = Compute.GetMPIInfo()['local_rank']
rank = Compute.GetMPIInfo()['rank']
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# just one reporter process on each node
isReporter = rank == 0
cf.study.numSubjects = len(cf.study.subjectImages)
if isReporter:
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(AtlasConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
#common.DebugHere()
# if MPI check if processes are greater than number of subjects. it is okay if there are more subjects than processes
if cf.compute.useMPI and (cf.study.numSubjects < cf.compute.numProcesses):
raise Exception("Please don't use more processes " +
"than total number of individuals")
# subdivide data, create subsets for this thread to work on
nodeSubjectIds = cf.study.subjectIds[rank::cf.compute.numProcesses]
nodeImages = cf.study.subjectImages[rank::cf.compute.numProcesses]
nodeWeights = cf.study.subjectWeights[rank::cf.compute.numProcesses]
numLocalSubjects = len(nodeImages)
print 'rank:', rank, ', localRank:', localRank, ', nodeImages:', nodeImages, ', nodeWeights:', nodeWeights
# mem type is determined by whether or not we're using CUDA
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
# load data in memory
# load intercepts
J_array = [
common.LoadITKImage(f, mType) if isinstance(f, str) else f
for f in nodeImages
]
# get imGrid from data
imGrid = J_array[0].grid()
# atlas image
atlas = ca.Image3D(imGrid, mType)
# allocate memory to store only the initial momenta for each individual in this thread
m_array = [ca.Field3D(imGrid, mType) for i in range(numLocalSubjects)]
# allocate only one copy of scratch memory to be reused for each local individual in this thread in loop
p = WarpVariables(imGrid,
mType,
cf.vectormomentum.diffOpParams[0],
cf.vectormomentum.diffOpParams[1],
cf.vectormomentum.diffOpParams[2],
cf.optim.NIterForInverse,
cf.vectormomentum.sigma,
cf.optim.stepSize,
integMethod=cf.optim.integMethod)
# memory to accumulate numerators and denominators for atlas from
# local individuals which will be summed across MPI threads
sumSplatI = ca.Image3D(imGrid, mType)
sumJac = ca.Image3D(imGrid, mType)
# start up the memory manager for scratch variables
ca.ThreadMemoryManager.init(imGrid, mType, 0)
# need some host memory in np array format for MPI reductions
if cf.compute.useMPI:
mpiImageBuff = None if mType == ca.MEM_HOST else ca.Image3D(
imGrid, ca.MEM_HOST)
t = [
x * 1. / (cf.optim.nTimeSteps) for x in range(cf.optim.nTimeSteps + 1)
]
cpinds = range(1, len(t))
msmtinds = [
len(t) - 2
] # since t=0 is not in cpinds, thats just identity deformation so not checkpointed
cpstates = [(ca.Field3D(imGrid, mType), ca.Field3D(imGrid, mType))
for idx in cpinds]
gradAtMsmts = [ca.Image3D(imGrid, mType) for idx in msmtinds]
EnergyHistory = []
# TODO: better initializations
# initialize atlas image with zeros.
ca.SetMem(atlas, 0.0)
# initialize momenta with zeros
for m0_individual in m_array:
ca.SetMem(m0_individual, 0.0)
'''
# initial template image
ca.SetMem(groupState.I0, 0.0)
tmp = ca.ManagedImage3D(imGrid, mType)
for tdisc in tdiscGroup:
if tdisc.J is not None:
ca.Copy(tmp, tdisc.J)
groupState.I0 += tmp
del tmp
if cf.compute.useMPI:
Compute.Reduce(groupState.I0, mpiImageBuff)
# divide by total num subjects
groupState.I0 /= cf.study.numSubjects
'''
# preprocessinput
# assign atlas reference to p.I0. This reference will not change.
p.I0 = atlas
# run the loop
for it in range(cf.optim.Niter):
# run one iteration of warp for each individual and update
# their own initial momenta and also accumulate SplatI and Jac
ca.SetMem(sumSplatI, 0.0)
ca.SetMem(sumJac, 0.0)
TotalVEnergy = np.array([0.0])
TotalIEnergy = np.array([0.0])
for itsub in range(numLocalSubjects):
# initializations for this subject, this only assigns
# reference to image variables
p.m0 = m_array[itsub]
Imsmts = [J_array[itsub]]
# run warp iteration
VEnergy, IEnergy = RunWarpIteration(nodeSubjectIds[itsub], cf, p,
t, Imsmts, cpinds, cpstates,
msmtinds, gradAtMsmts, it)
# gather relevant results
ca.Add_I(sumSplatI, p.sumSplatI)
ca.Add_I(sumJac, p.sumJac)
TotalVEnergy[0] += VEnergy
TotalIEnergy[0] += IEnergy
# if there are multiple nodes we'll need to sum across processes now
if cf.compute.useMPI:
# do an MPI sum
Compute.Reduce(sumSplatI, mpiImageBuff)
Compute.Reduce(sumJac, mpiImageBuff)
# also sum up energies of other nodes
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
TotalVEnergy,
op=mpi4py.MPI.SUM)
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
TotalIEnergy,
op=mpi4py.MPI.SUM)
EnergyHistory.append([TotalVEnergy[0], TotalIEnergy[0]])
# now divide to get the new atlas image
ca.Div(atlas, sumSplatI, sumJac)
# keep track of energy in this iteration
if isReporter and cf.io.plotEvery > 0 and ((
(it + 1) % cf.io.plotEvery == 0) or (it == cf.optim.Niter - 1)):
# plots
AtlasPlots(cf, p, atlas, m_array, EnergyHistory)
if isReporter:
# print out energy
(VEnergy, IEnergy) = EnergyHistory[-1]
print "Iter", it, "of", cf.optim.Niter, ":", VEnergy + IEnergy, '(Total) = ', VEnergy, '(Vector) + ', IEnergy, '(Image)'
# write output images and fields
AtlasWriteOutput(cf, atlas, m_array, nodeSubjectIds, isReporter)
def MatchingImageMomentaPlots(cf,
geodesicState,
tDiscGeodesic,
EnergyHistory,
m0,
J1,
n1,
writeOutput=True):
"""
Do some summary plots for MatchingImageMomenta
"""
#ENERGY
fig = plt.figure(1)
plt.clf()
fig.patch.set_facecolor('white')
TE = [row[0] for row in EnergyHistory]
VE = [row[1] for row in EnergyHistory]
IE = [row[2] for row in EnergyHistory]
ME = [row[3] for row in EnergyHistory]
plt.subplot(2, 2, 1)
plt.plot(TE)
plt.title('Total Energy')
plt.hold(False)
plt.subplot(2, 2, 2)
plt.plot(VE)
plt.title('Vector Energy')
plt.hold(False)
plt.subplot(2, 2, 3)
plt.plot(IE)
plt.title('Image Match Energy')
plt.hold(False)
plt.subplot(2, 2, 4)
plt.plot(ME)
plt.title('Momenta Match Energy')
plt.hold(False)
plt.draw()
plt.show()
if cf.io.outputPrefix != None and writeOutput:
plt.savefig(cf.io.outputPrefix + 'energy.pdf')
# GEODESIC INITIAL CONDITIONS and RHO and RHO inv
CAvmHGMCommon.HGMIntegrateGeodesic(geodesicState.p0, geodesicState.s,
geodesicState.diffOp, geodesicState.p,
geodesicState.rho, geodesicState.rhoinv,
tDiscGeodesic, geodesicState.Ninv,
geodesicState.integMethod)
fig = plt.figure(2)
plt.clf()
fig.patch.set_facecolor('white')
plt.subplot(2, 2, 1)
display.DispImage(geodesicState.J0,
'J0',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.subplot(2, 2, 2)
ca.ApplyH(geodesicState.J, geodesicState.J0, geodesicState.rhoinv)
display.DispImage(geodesicState.J,
'J1',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.subplot(2, 2, 3)
display.GridPlot(geodesicState.rhoinv,
every=cf.io.quiverEvery,
color='k',
sliceIdx=cf.io.plotSlice,
isVF=False)
plt.axis('equal')
plt.axis('off')
plt.title('rho^{-1}')
plt.subplot(2, 2, 4)
display.GridPlot(geodesicState.rho,
every=cf.io.quiverEvery,
color='k',
sliceIdx=cf.io.plotSlice,
isVF=False)
plt.axis('equal')
plt.axis('off')
plt.title('rho')
if cf.io.outputPrefix != None and writeOutput:
plt.savefig(cf.io.outputPrefix + 'def.pdf')
# MATCHING DIFFERENCE IMAGES
grid = geodesicState.J0.grid()
mType = geodesicState.J0.memType()
imdiff = ca.ManagedImage3D(grid, mType)
# Image matching
ca.Copy(imdiff, geodesicState.J)
ca.Sub_I(imdiff, J1)
fig = plt.figure(3)
plt.clf()
fig.patch.set_facecolor('white')
plt.subplot(1, 3, 1)
display.DispImage(geodesicState.J0,
'Source J0',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.colorbar()
plt.subplot(1, 3, 2)
display.DispImage(J1, 'Target J1', newFig=False, sliceIdx=cf.io.plotSlice)
plt.colorbar()
plt.subplot(1, 3, 3)
display.DispImage(imdiff,
'rho.J0-J1',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.colorbar()
if cf.io.outputPrefix != None and writeOutput:
plt.savefig(cf.io.outputPrefix + 'diffImage.pdf')
# Momenta matching
if mType == ca.MEM_DEVICE:
scratchV1 = ca.Field3D(grid, mType)
scratchV2 = ca.Field3D(grid, mType)
scratchV3 = ca.Field3D(grid, mType)
else:
scratchV1 = ca.ManagedField3D(grid, mType)
scratchV2 = ca.ManagedField3D(grid, mType)
scratchV3 = ca.ManagedField3D(grid, mType)
fig = plt.figure(4)
plt.clf()
fig.patch.set_facecolor('white')
ca.Copy(scratchV1, m0)
scratchV1.toType(ca.MEM_HOST)
m0_x, m0_y, m0_z = scratchV1.asnp()
plt.subplot(2, 3, 1)
plt.imshow(np.squeeze(m0_x))
plt.colorbar()
plt.title('X: Source m0 ')
plt.subplot(2, 3, 4)
plt.imshow(np.squeeze(m0_y))
plt.colorbar()
plt.title('Y: Source m0')
ca.Copy(scratchV2, n1)
scratchV2.toType(ca.MEM_HOST)
n1_x, n1_y, n1_z = scratchV2.asnp()
plt.subplot(2, 3, 2)
plt.imshow(np.squeeze(n1_x))
plt.colorbar()
plt.title('X: Target n1')
plt.subplot(2, 3, 5)
plt.imshow(np.squeeze(n1_y))
plt.colorbar()
plt.title('Y: Target n1')
ca.CoAd(scratchV3, geodesicState.rhoinv, m0)
ca.Sub_I(scratchV3, n1)
scratchV3.toType(ca.MEM_HOST)
diff_x, diff_y, diff_z = scratchV3.asnp()
plt.subplot(2, 3, 3)
plt.imshow(np.squeeze(diff_x))
plt.colorbar()
plt.title('X: rho.m0-n1')
plt.subplot(2, 3, 6)
plt.imshow(np.squeeze(diff_y))
plt.colorbar()
plt.title('Y: rho.m0-n1')
if cf.io.outputPrefix != None and writeOutput:
plt.savefig(cf.io.outputPrefix + 'diffMomenta.pdf')
del scratchV1, scratchV2, scratchV3
del imdiff
def DefReg(I_src, I_tar, config, memT, idConf):
I_src.toType(memT)
I_tar.toType(memT)
# Convert to 2D spacing (because it really matters)
sp2D = I_src.spacing().tolist()
sp2D = ca.Vec3Df(sp2D[0], sp2D[1], 1)
I_tar.setSpacing(sp2D)
I_src.setSpacing(sp2D)
gridReg = I_tar.grid()
# Blur the images
I_tar_blur = I_tar.copy()
I_src_blur = I_src.copy()
temp = ca.Image3D(I_tar.grid(), memT)
gausFilt = ca.GaussianFilterGPU()
scaleList = config.scale
# Initiate the scale manager
scaleManager = ca.MultiscaleManager(gridReg)
for s in scaleList:
scaleManager.addScaleLevel(s)
if memT == ca.MEM_HOST:
resampler = ca.MultiscaleResamplerGaussCPU(gridReg)
else:
resampler = ca.MultiscaleResamplerGaussGPU(gridReg)
# Generate the scratch images
scratchITar = ca.Image3D(gridReg, memT)
scratchISrc = ca.Image3D(gridReg, memT)
scratchI = ca.Image3D(gridReg, memT)
scratchF = ca.Field3D(gridReg, memT)
compF = ca.Field3D(gridReg, memT)
def SetScale(scale):
'''Scale Management for Multiscale'''
scaleManager.set(scale)
resampler.setScaleLevel(scaleManager)
curGrid = scaleManager.getCurGrid()
curGrid.spacing().z = 1 # Because only 2D
print 'Inside setScale(). Current grid is ', curGrid
if scaleManager.isLastScale():
print 'Inside setScale(): **Last Scale**'
if scaleManager.isFirstScale():
print 'Inside setScale(): **First Scale**'
scratchISrc.setGrid(curGrid)
scratchITar.setGrid(curGrid)
scratchI.setGrid(curGrid)
compF.setGrid(curGrid)
idConf.study.I0 = ca.Image3D(curGrid, memT)
idConf.study.I1 = ca.Image3D(curGrid, memT)
if scaleManager.isLastScale():
s = config.sigBlur[scaleList.index(sc)]
r = config.kerBlur[scaleList.index(sc)]
gausFilt.updateParams(I_tar.size(), ca.Vec3Df(r, r, r),
ca.Vec3Di(s, s, s))
gausFilt.filter(scratchITar, I_tar, temp)
gausFilt.filter(scratchI, I_src, temp)
# ca.Copy(scratchI, I_src)
# ca.Copy(scratchITar, I_tar)
else:
s = config.sigBlur[scaleList.index(sc)]
r = config.kerBlur[scaleList.index(sc)]
gausFilt.updateParams(I_tar.size(), ca.Vec3Df(r, r, r),
ca.Vec3Di(s, s, s))
gausFilt.filter(I_tar_blur, I_tar, temp)
gausFilt.filter(I_src_blur, I_src, temp)
resampler.downsampleImage(scratchI, I_src_blur)
resampler.downsampleImage(scratchITar, I_tar_blur)
if scaleManager.isFirstScale():
scratchF.setGrid(curGrid)
scratchITar.setGrid(curGrid)
ca.SetToIdentity(scratchF)
ca.ApplyH(scratchISrc, scratchI, scratchF)
else:
compF.setGrid(scratchF.grid())
ca.ComposeHH(compF, scratchF, h)
resampler.updateHField(scratchF)
resampler.updateHField(compF)
ca.Copy(scratchF, compF)
ca.ApplyH(scratchISrc, scratchI, compF)
for sc in scaleList:
SetScale(scaleList.index(sc))
#Set the optimize parameters in the IDiff configuration object
idConf.optim.Niter = config.iters[scaleList.index(sc)]
idConf.optim.stepSize = config.epsReg[scaleList.index(sc)]
idConf.idiff.regWeight = config.sigReg[scaleList.index(sc)]
ca.Copy(idConf.study.I0, scratchISrc)
ca.Copy(idConf.study.I1, scratchITar)
idConf.io.plotEvery = config.iters[scaleList.index(sc)]
h = IDiff.Matching.Matching(idConf)
tempScr = scratchISrc.copy()
ca.ApplyH(tempScr, scratchISrc, h)
#Plot the images to see the change
cd.DispImage(scratchISrc - scratchITar,
rng=[-2, 2],
title='Orig Diff',
colorbar=True)
cd.DispImage(tempScr - scratchITar,
rng=[-2, 2],
title='Reg Diff',
colorbar=True)
# common.DebugHere()
# I_src_def = idConf.study.I0.copy()
# scratchITar = idConf.study.I1
# eps = config.epsReg[scaleList.index(sc)]
# sigma = config.sigReg[scaleList.index(sc)]
# nIter = config.iters[scaleList.index(sc)]
# # common.DebugHere()
# [I_src_def, h, energy] = apps.IDiff(scratchISrc, scratchITar, eps, sigma, nIter, plot=True, verbose=1)
ca.ComposeHH(scratchF, compF, h)
I_src_def = idConf.study.I0.copy()
return I_src_def, scratchF
def main():
# Extract the Monkey number and section number from the command line
global frgNum
global secOb
mkyNum = sys.argv[1]
secNum = sys.argv[2]
frgNum = int(sys.argv[3])
write = True
# if not os.path.exists(os.path.expanduser('~/korenbergNAS/3D_database/Working/configuration_files/SidescapeRelateBlockface/M{0}/section_{1}/include_configFile.yaml'.format(mkyNum,secNum))):
# cf = initial(secNum, mkyNum)
try:
secOb = Config.Load(
secSpec,
pth.expanduser(
'~/korenbergNAS/3D_database/Working/configuration_files/SidescapeRelateBlockface/M{0}/section_{1}/include_configFile.yaml'
.format(mkyNum, secNum)))
except IOError as e:
try:
temp = Config.LoadYAMLDict(pth.expanduser(
'~/korenbergNAS/3D_database/Working/configuration_files/SidescapeRelateBlockface/M{0}/section_{1}/include_configFile.yaml'
.format(mkyNum, secNum)),
include=False)
secOb = Config.MkConfig(temp, secSpec)
except IOError:
print 'It appears there is no configuration file for this section. Please initialize one and restart.'
sys.exit()
if frgNum == int(secOb.yamlList[frgNum][-6]):
Fragmenter()
try:
secOb = Config.Load(
secSpec,
pth.expanduser(
'~/korenbergNAS/3D_database/Working/configuration_files/SidescapeRelateBlockface/M{0}/section_{1}/include_configFile.yaml'
.format(mkyNum, secNum)))
except IOError:
print 'It appeas that the include yaml file list does not match your fragmentation number. Please check them and restart.'
sys.exit()
if not pth.exists(
pth.expanduser(secOb.ssiOutPath + 'frag{0}'.format(frgNum))):
common.Mkdir_p(
pth.expanduser(secOb.ssiOutPath + 'frag{0}'.format(frgNum)))
if not pth.exists(
pth.expanduser(secOb.bfiOutPath + 'frag{0}'.format(frgNum))):
common.Mkdir_p(
pth.expanduser(secOb.bfiOutPath + 'frag{0}'.format(frgNum)))
if not pth.exists(
pth.expanduser(secOb.ssiSrcPath + 'frag{0}'.format(frgNum))):
os.mkdir(pth.expanduser(secOb.ssiSrcPath + 'frag{0}'.format(frgNum)))
if not pth.exists(
pth.expanduser(secOb.bfiSrcPath + 'frag{0}'.format(frgNum))):
os.mkdir(pth.expanduser(secOb.bfiSrcPath + 'frag{0}'.format(frgNum)))
frgOb = Config.MkConfig(secOb.yamlList[frgNum], frgSpec)
ssiSrc, bfiSrc, ssiMsk, bfiMsk = Loader(frgOb, ca.MEM_HOST)
#Extract the saturation Image from the color iamge
bfiHsv = common.FieldFromNPArr(
matplotlib.colors.rgb_to_hsv(
np.rollaxis(np.array(np.squeeze(bfiSrc.asnp())), 0, 3)),
ca.MEM_HOST)
bfiHsv.setGrid(bfiSrc.grid())
bfiSat = ca.Image3D(bfiSrc.grid(), bfiHsv.memType())
ca.Copy(bfiSat, bfiHsv, 1)
#Histogram equalize, normalize and mask the blockface saturation image
bfiSat = cb.HistogramEqualize(bfiSat, 256)
bfiSat.setGrid(bfiSrc.grid())
bfiSat *= -1
bfiSat -= ca.Min(bfiSat)
bfiSat /= ca.Max(bfiSat)
bfiSat *= bfiMsk
bfiSat.setGrid(bfiSrc.grid())
#Write out the blockface region after adjusting the colors with a format that supports header information
if write:
common.SaveITKImage(
bfiSat,
pth.expanduser(secOb.bfiSrcPath +
'frag{0}/M{1}_01_bfi_section_{2}_frag{0}_sat.nrrd'.
format(frgNum, secOb.mkyNum, secOb.secNum)))
#Set the sidescape grid relative to that of the blockface
ssiSrc.setGrid(ConvertGrid(ssiSrc.grid(), bfiSat.grid()))
ssiMsk.setGrid(ConvertGrid(ssiMsk.grid(), bfiSat.grid()))
ssiSrc *= ssiMsk
#Write out the sidescape masked image in a format that stores the header information
if write:
common.SaveITKImage(
ssiSrc,
pth.expanduser(secOb.ssiSrcPath +
'frag{0}/M{1}_01_ssi_section_{2}_frag{0}.nrrd'.
format(frgNum, secOb.mkyNum, secOb.secNum)))
#Update the image parameters of the sidescape image for future use
frgOb.imSize = ssiSrc.size().tolist()
frgOb.imOrig = ssiSrc.origin().tolist()
frgOb.imSpac = ssiSrc.spacing().tolist()
updateFragOb(frgOb)
#Find the affine transform between the two fragments
bfiAff, ssiAff, aff = Affine(bfiSat, ssiSrc, frgOb)
updateFragOb(frgOb)
#Write out the affine transformed images in a format that stores header information
if write:
common.SaveITKImage(
bfiAff,
pth.expanduser(
secOb.bfiOutPath +
'frag{0}/M{1}_01_bfi_section_{2}_frag{0}_aff_ssi.nrrd'.format(
frgNum, secOb.mkyNum, secOb.secNum)))
common.SaveITKImage(
ssiAff,
pth.expanduser(
secOb.ssiOutPath +
'frag{0}/M{1}_01_ssi_section_{2}_frag{0}_aff_bfi.nrrd'.format(
frgNum, secOb.mkyNum, secOb.secNum)))
bfiVe = bfiAff.copy()
ssiVe = ssiSrc.copy()
cc.VarianceEqualize_I(bfiVe, sigma=frgOb.sigVarBfi, eps=frgOb.epsVar)
cc.VarianceEqualize_I(ssiVe, sigma=frgOb.sigVarSsi, eps=frgOb.epsVar)
#As of right now, the largest pre-computed FFT table is 2048, so resample onto that grid for registration
regGrd = ConvertGrid(
cc.MakeGrid(ca.Vec3Di(2048, 2048, 1), ca.Vec3Df(1, 1, 1),
ca.Vec3Df(0, 0, 0)), ssiSrc.grid())
ssiReg = ca.Image3D(regGrd, ca.MEM_HOST)
bfiReg = ca.Image3D(regGrd, ca.MEM_HOST)
cc.ResampleWorld(ssiReg, ssiVe)
cc.ResampleWorld(bfiReg, bfiVe)
#Create the default configuration object for IDiff Matching and then set some parameters
idCf = Config.SpecToConfig(IDiff.Matching.MatchingConfigSpec)
idCf.compute.useCUDA = True
idCf.io.outputPrefix = '/home/sci/blakez/IDtest/'
#Run the registration
ssiDef, phi = DefReg(ssiReg, bfiReg, frgOb, ca.MEM_DEVICE, idCf)
#Turn the deformation into a displacement field so it can be applied to the large tif with C++ code
affV = phi.copy()
cc.ApplyAffineReal(affV, phi, np.linalg.inv(frgOb.affine))
ca.HtoV_I(affV)
#Apply the found deformation to the input ssi
ssiSrc.toType(ca.MEM_DEVICE)
cc.HtoReal(phi)
affPhi = phi.copy()
ssiBfi = ssiSrc.copy()
upPhi = ca.Field3D(ssiSrc.grid(), phi.memType())
cc.ApplyAffineReal(affPhi, phi, np.linalg.inv(frgOb.affine))
cc.ResampleWorld(upPhi, affPhi, bg=2)
cc.ApplyHReal(ssiBfi, ssiSrc, upPhi)
# ssiPhi = ca.Image3D(ssiSrc.grid(), phi.memType())
# upPhi = ca.Field3D(ssiSrc.grid(), phi.memType())
# cc.ResampleWorld(upPhi, phi, bg=2)
# cc.ApplyHReal(ssiPhi, ssiSrc, upPhi)
# ssiBfi = ssiSrc.copy()
# cc.ApplyAffineReal(ssiBfi, ssiPhi, np.linalg.inv(frgOb.affine))
# #Apply affine to the deformation
# affPhi = phi.copy()
# cc.ApplyAffineReal(affPhi, phi, np.linalg.inv(frgOb.affine))
if write:
common.SaveITKImage(
ssiBfi,
pth.expanduser(
secOb.ssiOutPath +
'frag{0}/M{1}_01_ssi_section_{2}_frag{0}_def_bfi.nrrd'.format(
frgNum, secOb.mkyNum, secOb.secNum)))
cc.WriteMHA(
affPhi,
pth.expanduser(
secOb.ssiOutPath +
'frag{0}/M{1}_01_ssi_section_{2}_frag{0}_to_bfi_real.mha'.
format(frgNum, secOb.mkyNum, secOb.secNum)))
cc.WriteMHA(
affV,
pth.expanduser(
secOb.ssiOutPath +
'frag{0}/M{1}_01_ssi_section_{2}_frag{0}_to_bfi_disp.mha'.
format(frgNum, secOb.mkyNum, secOb.secNum)))
#Create the list of names that the deformation should be applied to
# nameList = ['M15_01_0956_SideLight_DimLED_10x_ORG.tif',
# 'M15_01_0956_TyrosineHydroxylase_Ben_10x_Stitching_c1_ORG.tif',
# 'M15_01_0956_TyrosineHydroxylase_Ben_10x_Stitching_c2_ORG.tif',
# 'M15_01_0956_TyrosineHydroxylase_Ben_10x_Stitching_c3_ORG.tif']
# appLarge(nameList, affPhi)
common.DebugHere()
def LoadITKField(fname, mType=core.MEM_HOST):
f = core.Field3D(mType)
core._ITKFileIO.LoadField(f, fname)
return f
def RigidReg(
Is,
It,
theta_step=.0001,
t_step=.01,
a_step=0,
maxIter=350,
plot=True,
origin=None,
theta=0, # only applies for 2D
t=None, # only applies for 2D
Ain=np.matrix(np.identity(3))):
Idef = ca.Image3D(It.grid(), It.memType())
gradIdef = ca.Field3D(It.grid(), It.memType())
h = ca.Field3D(It.grid(), It.memType())
ca.SetToIdentity(h)
x = ca.Image3D(It.grid(), It.memType())
y = ca.Image3D(It.grid(), It.memType())
DX = ca.Image3D(It.grid(), It.memType())
DY = ca.Image3D(It.grid(), It.memType())
diff = ca.Image3D(It.grid(), It.memType())
scratchI = ca.Image3D(It.grid(), It.memType())
ca.Copy(x, h, 0)
ca.Copy(y, h, 1)
if origin is None:
origin = [(Is.grid().size().x + 1) / 2.0,
(Is.grid().size().y + 1) / 2.0,
(Is.grid().size().z + 1) / 2.0]
x -= origin[0]
y -= origin[1]
numel = It.size().x * It.size().y * It.size().z
immin, immax = ca.MinMax(It)
imrng = max(immax - immin, .01)
t_step /= numel * imrng
theta_step /= numel * imrng
a_step /= numel * imrng
energy = []
a = 1
if cc.Is3D(Is):
if theta:
print "theta is not utilized in 3D registration"
z = ca.Image3D(It.grid(), It.memType())
DZ = ca.Image3D(It.grid(), It.memType())
ca.Copy(z, h, 2)
z -= origin[2]
A = np.matrix(np.identity(4))
cc.ApplyAffineReal(Idef, Is, A)
# cc.ApplyAffine(Idef, Is, A, origin)
t = [0, 0, 0]
for i in xrange(maxIter):
ca.Sub(diff, Idef, It)
ca.Gradient(gradIdef, Idef)
ca.Copy(DX, gradIdef, 0)
ca.Copy(DY, gradIdef, 1)
ca.Copy(DZ, gradIdef, 2)
# take gradient step for the translation
ca.Mul(scratchI, DX, diff)
t[0] += t_step * ca.Sum(scratchI)
ca.Mul(scratchI, DY, diff)
t[1] += t_step * ca.Sum(scratchI)
ca.Mul(scratchI, DZ, diff)
t[2] += t_step * ca.Sum(scratchI)
A[0, 3] = t[0]
A[1, 3] = t[1]
A[2, 3] = t[2]
if a_step > 0:
DX *= x
DY *= y
DZ *= z
DZ += DX
DZ += DY
DZ *= diff
d_a = a_step * ca.Sum(DZ)
a_prev = a
a += d_a
# multiplying by a/a_prev is equivalent to adding (a-aprev)
A = A * np.matrix([[a / a_prev, 0, 0, 0], [
0, a / a_prev, 0, 0
], [0, 0, a / a_prev, 0], [0, 0, 0, 1]])
# Z rotation
ca.Copy(DX, gradIdef, 0)
ca.Copy(DY, gradIdef, 1)
DX *= y
ca.Neg_I(DX)
DY *= x
ca.Add(scratchI, DX, DY)
scratchI *= diff
theta = -theta_step * ca.Sum(scratchI)
# % Recalculate A
A = A * np.matrix(
[[np.cos(theta), np.sin(theta), 0, 0],
[-np.sin(theta), np.cos(theta), 0, 0], [0, 0, 1, 0],
[0, 0, 0, 1]])
# Y rotation
ca.Copy(DX, gradIdef, 0)
ca.Copy(DZ, gradIdef, 2)
DX *= z
ca.Neg_I(DX)
DZ *= x
ca.Add(scratchI, DX, DZ)
scratchI *= diff
theta = -theta_step * ca.Sum(scratchI)
# % Recalculate A
A = A * np.matrix(
[[np.cos(theta), 0, np.sin(theta), 0], [0, 1, 0, 0],
[-np.sin(theta), 0, np.cos(theta), 0], [0, 0, 0, 1]])
# X rotation
ca.Copy(DY, gradIdef, 1)
ca.Copy(DZ, gradIdef, 2)
DY *= z
ca.Neg_I(DY)
DZ *= y
ca.Add(scratchI, DY, DZ)
scratchI *= diff
theta = -theta_step * ca.Sum(scratchI)
# Recalculate A
A = A * np.matrix(
[[1, 0, 0, 0], [0, np.cos(theta),
np.sin(theta), 0],
[0, -np.sin(theta), np.cos(theta), 0], [0, 0, 0, 1]])
cc.ApplyAffineReal(Idef, Is, A)
# cc.ApplyAffine(Idef, Is, A, origin)
# % display Energy (and other figures) at the end
energy.append(ca.Sum2(diff))
if (i == maxIter -
1) or (i > 75 and abs(energy[-1] - energy[-50]) < immax):
cd.DispImage(diff, title='Difference Image', colorbar=True)
plt.figure()
plt.plot(energy)
cd.DispImage(Idef, title='Deformed Image')
break
elif cc.Is2D(Is):
# theta = 0
if t is None:
t = [0, 0]
# A = np.array([[a*np.cos(theta), np.sin(theta), t[0]],
# [-np.sin(theta), a*np.cos(theta), t[1]],
# [0, 0, 1]])
A = np.copy(Ain)
cc.ApplyAffineReal(Idef, Is, A)
# ca.Copy(Idef, Is)
for i in xrange(1, maxIter):
# [FX,FY] = gradient(Idef)
ca.Sub(diff, Idef, It)
ca.Gradient(gradIdef, Idef)
ca.Copy(DX, gradIdef, 0)
ca.Copy(DY, gradIdef, 1)
# take gradient step for the translation
ca.Mul(scratchI, DX, diff)
t[0] += t_step * ca.Sum(scratchI)
ca.Mul(scratchI, DY, diff)
t[1] += t_step * ca.Sum(scratchI)
# take gradient step for the rotation theta
if a_step > 0:
# d/da
DX *= x
DY *= y
DY += DX
DY *= diff
d_a = a_step * ca.Sum(DY)
a += d_a
# d/dtheta
ca.Copy(DX, gradIdef, 0)
ca.Copy(DY, gradIdef, 1)
DX *= y
ca.Neg_I(DX)
DY *= x
ca.Add(scratchI, DX, DY)
scratchI *= diff
d_theta = theta_step * ca.Sum(scratchI)
theta -= d_theta
# Recalculate A, Idef
A = np.matrix([[a * np.cos(theta),
np.sin(theta), t[0]],
[-np.sin(theta), a * np.cos(theta), t[1]],
[0, 0, 1]])
A = Ain * A
cc.ApplyAffineReal(Idef, Is, A)
# cc.ApplyAffine(Idef, Is, A, origin)
# % display Energy (and other figures) at the end
energy.append(ca.Sum2(diff))
if (i == maxIter -
1) or (i > 75 and abs(energy[-1] - energy[-50]) < immax):
if i == maxIter - 1:
print "not converged in ", maxIter, " Iterations"
if plot:
cd.DispImage(diff, title='Difference Image', colorbar=True)
plt.figure()
plt.plot(energy)
cd.DispImage(Idef, title='Deformed Image')
break
return A
T2_VE,
Ieps,
Isigma,
InIter,
plot=False,
verbose=1)
Idiff = T2_VE - live_VEfilt_ID
cd.Disp3Pane(Idiff, rng=[-3, 3], sliceIdx=dispslice)
cd.EnergyPlot(Ienergy, legend=['Reg', 'Data', 'Total'])
cd.DispHGrid(Iphi)
print ca.MinMax(Iphi)
# Compose the deformations and apply the total deformation to the initial live volume
# tempDef = ca.Field3D(phi.grid(), memT)
totDef = ca.Field3D(phi.grid(), memT)
ca.ComposeHH(totDef, phi, Iphi, ca.BACKGROUND_STRATEGY_CLAMP)
# ca.ComposeHH(totDef, h, tempDef, ca.BACKGROUND_STRATEGY_CLAMP)
#Apply the deformation to the TPS live volume and rotate to the original volume
live_T2reg_rot = T2.copy()
cc.HtoReal(totDef)
cc.ApplyHReal(live_T2reg_rot, liveDef, totDef)
cd.Disp3Pane(live_T2reg_rot)
# live_T2reg = T2.copy()
# cc.ApplyAffineReal(live_T2reg,live_T2reg_rot,np.linalg.inv(rotMat))
# cd.Disp3Pane(live_T2reg)
if write:
cc.WriteMHA(live_T2reg_rot,
SaveDir + 'M13_01_live_as_MRI_full_bw_256_roty-119_flipy.mha')
def MatchingImageMomenta(cf):
"""Runs matching for image momenta pair."""
if cf.compute.useCUDA and cf.compute.gpuID is not None:
ca.SetCUDADevice(cf.compute.gpuID)
common.DebugHere()
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(MatchingImageMomentaConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
# mem type is determined by whether or not we're using CUDA
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
# load data in memory
I0 = common.LoadITKImage(cf.study.I, mType)
m0 = common.LoadITKField(cf.study.m, mType)
J1 = common.LoadITKImage(cf.study.J, mType)
n1 = common.LoadITKField(cf.study.n, mType)
# get imGrid from data
imGrid = I0.grid()
# create time array with checkpointing info for this geodesic to be estimated
(s, scratchInd,
rCpinds) = CAvmHGM.HGMSetUpTimeArray(cf.optim.nTimeSteps, [1.0], 0.001)
tDiscGeodesic = CAvmHGMCommon.HGMSetupTimeDiscretizationResidual(
s, rCpinds, imGrid, mType)
# create the state variable for geodesic that is going to hold all info
p0 = ca.Field3D(imGrid, mType)
geodesicState = CAvmHGMCommon.HGMResidualState(
I0,
p0,
imGrid,
mType,
cf.vectormomentum.diffOpParams[0],
cf.vectormomentum.diffOpParams[1],
cf.vectormomentum.diffOpParams[2],
s,
cf.optim.NIterForInverse,
1.0,
cf.vectormomentum.sigmaM,
cf.vectormomentum.sigmaI,
cf.optim.stepSize,
integMethod=cf.optim.integMethod)
# initialize with zero
ca.SetMem(geodesicState.p0, 0.0)
# start up the memory manager for scratch variables
ca.ThreadMemoryManager.init(imGrid, mType, 0)
EnergyHistory = []
# run the loop
for it in range(cf.optim.Niter):
# shoot the geodesic forward
CAvmHGMCommon.HGMIntegrateGeodesic(geodesicState.p0, geodesicState.s,
geodesicState.diffOp,
geodesicState.p, geodesicState.rho,
geodesicState.rhoinv, tDiscGeodesic,
geodesicState.Ninv,
geodesicState.integMethod)
# integrate the geodesic backward
CAvmHGMCommon.HGMIntegrateAdjointsResidual(geodesicState,
tDiscGeodesic, m0, J1, n1)
# TODO: verify it should just be log map/simple image matching when sigmaM=\infty
# gradient descent step for geodesic.p0
CAvmHGMCommon.HGMTakeGradientStepResidual(geodesicState)
# compute and print energy
(VEnergy, IEnergy,
MEnergy) = MatchingImageMomentaComputeEnergy(geodesicState, m0, J1,
n1)
EnergyHistory.append(
[VEnergy + IEnergy + MEnergy, VEnergy, IEnergy, MEnergy])
print "Iter", it, "of", cf.optim.Niter, ":", VEnergy + IEnergy + MEnergy, '(Total) = ', VEnergy, '(Vector) + ', IEnergy, '(Image Match) + ', MEnergy, '(Momenta Match)'
# plots
if cf.io.plotEvery > 0 and (((it + 1) % cf.io.plotEvery == 0) or
(it == cf.optim.Niter - 1)):
MatchingImageMomentaPlots(cf,
geodesicState,
tDiscGeodesic,
EnergyHistory,
m0,
J1,
n1,
writeOutput=True)
# write output
MatchingImageMomentaWriteOuput(cf, geodesicState)
def BuildHGM(cf):
"""Worker for running Hierarchical Geodesic Model (HGM)
n for group geodesic estimation on a subset of individuals.
Runs HGM on this subset sequentially. The variations retuned
are summed up to get update for all individuals"""
size = Compute.GetMPIInfo()['size']
rank = Compute.GetMPIInfo()['rank']
name = Compute.GetMPIInfo()['name']
localRank = Compute.GetMPIInfo()['local_rank']
nodename = socket.gethostname()
# prepare output directory
common.Mkdir_p(os.path.dirname(cf.io.outputPrefix))
# just one reporter process on each node
isReporter = rank == 0
cf.study.numSubjects = len(cf.study.subjectIntercepts)
if isReporter:
# Output loaded config
if cf.io.outputPrefix is not None:
cfstr = Config.ConfigToYAML(HGMConfigSpec, cf)
with open(cf.io.outputPrefix + "parsedconfig.yaml", "w") as f:
f.write(cfstr)
#common.DebugHere()
# if MPI check if processes are greater than number of subjects. it is okay if there are more subjects than processes
if cf.compute.useMPI and (cf.study.numSubjects < cf.compute.numProcesses):
raise Exception("Please don't use more processes " +
"than total number of individuals")
# subdivide data, create subsets for this thread to work on
nodeSubjectIds = cf.study.subjectIds[rank::cf.compute.numProcesses]
nodeIntercepts = cf.study.subjectIntercepts[rank::cf.compute.numProcesses]
nodeSlopes = cf.study.subjectSlopes[rank::cf.compute.numProcesses]
nodeBaselineTimes = cf.study.subjectBaselineTimes[rank::cf.compute.
numProcesses]
sys.stdout.write(
"This is process %d of %d with name: %s on machinename: %s and local rank: %d.\nnodeIntercepts: %s\n nodeSlopes: %s\n nodeBaselineTimes: %s\n"
% (rank, size, name, nodename, localRank, nodeIntercepts, nodeSlopes,
nodeBaselineTimes))
# mem type is determined by whether or not we're using CUDA
mType = ca.MEM_DEVICE if cf.compute.useCUDA else ca.MEM_HOST
# load data in memory
# load intercepts
J = [
common.LoadITKImage(f, mType) if isinstance(f, str) else f
for f in nodeIntercepts
]
# load slopes
n = [
common.LoadITKField(f, mType) if isinstance(f, str) else f
for f in nodeSlopes
]
# get imGrid from data
imGrid = J[0].grid()
# create time array with checkpointing info for group geodesic
(t, Jind, gCpinds) = HGMSetUpTimeArray(cf.optim.nTimeStepsGroup,
nodeBaselineTimes, 0.0000001)
tdiscGroup = CAvmHGMCommon.HGMSetupTimeDiscretizationGroup(
t, J, n, Jind, gCpinds, mType, nodeSubjectIds)
# create time array with checkpointing info for residual geodesic
(s, scratchInd, rCpinds) = HGMSetUpTimeArray(cf.optim.nTimeStepsResidual,
[1.0], 0.0000001)
tdiscResidual = CAvmHGMCommon.HGMSetupTimeDiscretizationResidual(
s, rCpinds, imGrid, mType)
# create group state and residual state
groupState = CAvmHGMCommon.HGMGroupState(
imGrid,
mType,
cf.vectormomentum.diffOpParamsGroup[0],
cf.vectormomentum.diffOpParamsGroup[1],
cf.vectormomentum.diffOpParamsGroup[2],
t,
cf.optim.NIterForInverse,
cf.vectormomentum.varIntercept,
cf.vectormomentum.varSlope,
cf.vectormomentum.varInterceptReg,
cf.optim.stepSizeGroup,
integMethod=cf.optim.integMethodGroup)
#ca.Copy(groupState.I0, common.LoadITKImage('/usr/sci/projects/ADNI/nikhil/software/vectormomentumtest/TestData/FlowerData/Longitudinal/GroupGeodesic/I0.mhd', mType))
# note that residual state is treated a scratch variable in this algorithm and reused for computing residual geodesics of multiple individual
residualState = CAvmHGMCommon.HGMResidualState(
None,
None,
imGrid,
mType,
cf.vectormomentum.diffOpParamsResidual[0],
cf.vectormomentum.diffOpParamsResidual[1],
cf.vectormomentum.diffOpParamsResidual[2],
s,
cf.optim.NIterForInverse,
cf.vectormomentum.varIntercept,
cf.vectormomentum.varSlope,
cf.vectormomentum.varInterceptReg,
cf.optim.stepSizeResidual,
integMethod=cf.optim.integMethodResidual)
# start up the memory manager for scratch variables
ca.ThreadMemoryManager.init(imGrid, mType, 0)
# need some host memory in np array format for MPI reductions
if cf.compute.useMPI:
mpiImageBuff = None if mType == ca.MEM_HOST else ca.Image3D(
imGrid, ca.MEM_HOST)
mpiFieldBuff = None if mType == ca.MEM_HOST else ca.Field3D(
imGrid, ca.MEM_HOST)
for i in range(len(groupState.t) - 1, -1, -1):
if tdiscGroup[i].J is not None:
indx_last_individual = i
break
'''
# initial template image
ca.SetMem(groupState.I0, 0.0)
tmp = ca.ManagedImage3D(imGrid, mType)
for tdisc in tdiscGroup:
if tdisc.J is not None:
ca.Copy(tmp, tdisc.J)
groupState.I0 += tmp
del tmp
if cf.compute.useMPI:
Compute.Reduce(groupState.I0, mpiImageBuff)
# divide by total num subjects
groupState.I0 /= cf.study.numSubjects
'''
# run the loop
for it in range(cf.optim.Niter):
# compute HGM variation for group
HGMGroupVariation(groupState, tdiscGroup, residualState, tdiscResidual,
cf.io.outputPrefix, rank, it)
common.CheckCUDAError("Error after HGM iteration")
# compute gradient for momenta (m is used as scratch)
# if there are multiple nodes we'll need to sum across processes now
if cf.compute.useMPI:
# do an MPI sum
Compute.Reduce(groupState.sumSplatI, mpiImageBuff)
Compute.Reduce(groupState.sumJac, mpiImageBuff)
Compute.Reduce(groupState.madj, mpiFieldBuff)
# also sum up energies of other nodes
# intercept
Eintercept = np.array([groupState.EnergyHistory[-1][1]])
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
Eintercept,
op=mpi4py.MPI.SUM)
groupState.EnergyHistory[-1][1] = Eintercept[0]
Eslope = np.array([groupState.EnergyHistory[-1][2]])
mpi4py.MPI.COMM_WORLD.Allreduce(mpi4py.MPI.IN_PLACE,
Eslope,
op=mpi4py.MPI.SUM)
groupState.EnergyHistory[-1][2] = Eslope[0]
ca.Copy(groupState.m, groupState.m0)
groupState.diffOp.applyInverseOperator(groupState.m)
ca.Sub_I(groupState.m, groupState.madj)
#groupState.diffOp.applyOperator(groupState.m)
# now take gradient step in momenta for group
if cf.optim.method == 'FIXEDGD':
# take fixed stepsize gradient step
ca.Add_MulC_I(groupState.m0, groupState.m, -cf.optim.stepSizeGroup)
else:
raise Exception("Unknown optimization scheme: " + cf.optim.method)
# end if
# now divide to get the new base image for group
ca.Div(groupState.I0, groupState.sumSplatI, groupState.sumJac)
# keep track of energy in this iteration
if isReporter and cf.io.plotEvery > 0 and ((
(it + 1) % cf.io.plotEvery == 0) or (it == cf.optim.Niter - 1)):
HGMPlots(cf,
groupState,
tdiscGroup,
residualState,
tdiscResidual,
indx_last_individual,
writeOutput=True)
if isReporter:
(VEnergy, IEnergy, SEnergy) = groupState.EnergyHistory[-1]
print datetime.datetime.now().time(
), " Iter", it, "of", cf.optim.Niter, ":", VEnergy + IEnergy + SEnergy, '(Total) = ', VEnergy, '(Vector) + ', IEnergy, '(Intercept) + ', SEnergy, '(Slope)'
# write output images and fields
HGMWriteOutput(cf, groupState, tdiscGroup, isReporter)
def __init__(self,
grid,
mType,
alpha,
beta,
gamma,
nInv,
sigma,
StepSize,
integMethod='EULER'):
"""
Initialize everything with the size and type given
"""
self.grid = grid
self.memtype = mType
# initial conditions
self.I0 = None # this is a reference that always points to the atlas image
self.m0 = None # this is a reference that gets assigned to momenta for an individual each time
# state variables
self.g = ca.Field3D(self.grid, self.memtype)
self.ginv = ca.Field3D(self.grid, self.memtype)
self.m = ca.Field3D(self.grid, self.memtype)
self.I = ca.Image3D(self.grid, self.memtype)
# adjoint variables
self.madj = ca.Field3D(self.grid, self.memtype)
self.Iadj = ca.Image3D(self.grid, self.memtype)
self.madjtmp = ca.Field3D(self.grid, self.memtype)
self.Iadjtmp = ca.Image3D(self.grid, self.memtype)
# image variables for closed-form template update
self.sumSplatI = ca.Image3D(self.grid, self.memtype)
self.sumJac = ca.Image3D(self.grid, self.memtype)
# set up diffOp
if self.memtype == ca.MEM_HOST:
self.diffOp = ca.FluidKernelFFTCPU()
else:
self.diffOp = ca.FluidKernelFFTGPU()
self.diffOp.setAlpha(alpha)
self.diffOp.setBeta(beta)
self.diffOp.setGamma(gamma)
self.diffOp.setGrid(self.grid)
# some extras
self.nInv = nInv # for interative update to inverse deformation
self.integMethod = integMethod
self.sigma = sigma
self.stepSize = StepSize
# TODO: scratch variables to be changed to using managed memory
self.scratchV1 = ca.Field3D(self.grid, self.memtype)
self.scratchV2 = ca.Field3D(self.grid, self.memtype)
self.scratchV3 = ca.Field3D(self.grid, self.memtype)
self.scratchV4 = ca.Field3D(self.grid, self.memtype)
self.scratchV5 = ca.Field3D(self.grid, self.memtype)
self.scratchV6 = ca.Field3D(self.grid, self.memtype)
self.scratchV7 = ca.Field3D(self.grid, self.memtype)
self.scratchV8 = ca.Field3D(self.grid, self.memtype)
self.scratchV9 = ca.Field3D(self.grid, self.memtype)
self.scratchV10 = ca.Field3D(self.grid, self.memtype)
self.scratchV11 = ca.Field3D(self.grid, self.memtype)
self.scratchI1 = ca.Image3D(
self.grid,
self.memtype) #only used for geodesic regression with RK4
def ElastReg(I0Orig,
I1Orig,
scales=[1],
nIters=[1000],
maxPert=[0.2],
fluidParams=[0.1, 0.1, 0.001],
VFC=0.2,
Mask=None,
plotEvery=100):
mType = I0Orig.memType()
origGrid = I0Orig.grid()
# allocate vars
I0 = ca.Image3D(origGrid, mType)
I1 = ca.Image3D(origGrid, mType)
u = ca.Field3D(origGrid, mType)
Idef = ca.Image3D(origGrid, mType)
diff = ca.Image3D(origGrid, mType)
gI = ca.Field3D(origGrid, mType)
gU = ca.Field3D(origGrid, mType)
scratchI = ca.Image3D(origGrid, mType)
scratchV = ca.Field3D(origGrid, mType)
# mask
if Mask != None:
MaskOrig = Mask.copy()
# allocate diffOp
if mType == ca.MEM_HOST:
diffOp = ca.FluidKernelFFTCPU()
else:
diffOp = ca.FluidKernelFFTGPU()
# initialize some vars
nScales = len(scales)
scaleManager = ca.MultiscaleManager(origGrid)
for s in scales:
scaleManager.addScaleLevel(s)
# Initalize the thread memory manager (needed for resampler)
# num pools is 2 (images) + 2*3 (fields)
ca.ThreadMemoryManager.init(origGrid, mType, 8)
if mType == ca.MEM_HOST:
resampler = ca.MultiscaleResamplerGaussCPU(origGrid)
else:
resampler = ca.MultiscaleResamplerGaussGPU(origGrid)
def setScale(scale):
global curGrid
scaleManager.set(scale)
curGrid = scaleManager.getCurGrid()
# since this is only 2D:
curGrid.spacing().z = 1.0
resampler.setScaleLevel(scaleManager)
diffOp.setAlpha(fluidParams[0])
diffOp.setBeta(fluidParams[1])
diffOp.setGamma(fluidParams[2])
diffOp.setGrid(curGrid)
# downsample images
I0.setGrid(curGrid)
I1.setGrid(curGrid)
if scaleManager.isLastScale():
ca.Copy(I0, I0Orig)
ca.Copy(I1, I1Orig)
else:
resampler.downsampleImage(I0, I0Orig)
resampler.downsampleImage(I1, I1Orig)
if Mask != None:
if scaleManager.isLastScale():
Mask.setGrid(curGrid)
ca.Copy(Mask, MaskOrig)
else:
resampler.downsampleImage(Mask, MaskOrig)
# initialize / upsample deformation
if scaleManager.isFirstScale():
u.setGrid(curGrid)
ca.SetMem(u, 0.0)
else:
resampler.updateVField(u)
# set grids
gI.setGrid(curGrid)
Idef.setGrid(curGrid)
diff.setGrid(curGrid)
gU.setGrid(curGrid)
scratchI.setGrid(curGrid)
scratchV.setGrid(curGrid)
# end function
energy = [[] for _ in xrange(3)]
for scale in range(len(scales)):
setScale(scale)
ustep = None
# update gradient
ca.Gradient(gI, I0)
for it in range(nIters[scale]):
print 'iter %d' % it
# compute deformed image
ca.ApplyV(Idef, I0, u, 1.0)
# update u
ca.Sub(diff, I1, Idef)
if Mask != None:
ca.Mul_I(diff, Mask)
ca.ApplyV(scratchV, gI, u, ca.BACKGROUND_STRATEGY_CLAMP)
ca.Mul_I(scratchV, diff)
diffOp.applyInverseOperator(gU, scratchV)
vfcEn = VFC * ca.Dot(scratchV, gU)
# why is this negative necessary?
ca.MulC_I(gU, -1.0)
# u = u*(1-VFC*ustep) + (-2.0*ustep)*gU
# MulC_Add_MulC_I(u, (1-VFC*ustep),
# gU, 2.0*ustep)
# u = u - ustep*(VFC*u + 2.0*gU)
ca.MulC_I(gU, 2.0)
# subtract average if gamma is zero (result of nullspace
# of L for K(L(u)))
if fluidParams[2] == 0:
av = ca.SumComp(u)
av /= scratchI.nVox()
ca.SubC(scratchV, u, av)
# continue computing gradient
ca.MulC(scratchV, u, VFC)
ca.Add_I(gU, scratchV)
ca.Magnitude(scratchI, gU)
gradmax = ca.Max(scratchI)
if ustep is None or ustep * gradmax > maxPert:
ustep = maxPert[scale] / gradmax
print 'step is %f' % ustep
ca.MulC_I(gU, ustep)
# apply gradient
ca.Sub_I(u, gU)
# compute energy
energy[0].append(ca.Sum2(diff))
diffOp.applyOperator(scratchV, u)
energy[1].append(0.5 * VFC * ca.Dot(u, scratchV))
energy[2].append(energy[0][-1]+\
energy[1][-1])
if plotEvery > 0 and \
((it+1) % plotEvery == 0 or \
(scale == nScales-1 and it == nIters[scale]-1)):
print 'plotting'
clrlist = ['r', 'g', 'b', 'm', 'c', 'y', 'k']
plt.figure('energy')
for i in range(len(energy)):
plt.plot(energy[i], clrlist[i])
if i == 0:
plt.hold(True)
plt.hold(False)
plt.draw()
plt.figure('results')
plt.clf()
plt.subplot(3, 2, 1)
display.DispImage(I0, 'I0', newFig=False)
plt.subplot(3, 2, 2)
display.DispImage(I1, 'I1', newFig=False)
plt.subplot(3, 2, 3)
display.DispImage(Idef, 'def', newFig=False)
plt.subplot(3, 2, 4)
display.DispImage(diff, 'diff', newFig=False)
plt.colorbar()
plt.subplot(3, 2, 5)
display.GridPlot(u, every=4)
plt.subplot(3, 2, 6)
display.JacDetPlot(u)
plt.colorbar()
plt.draw()
plt.show()
# end plot
# end iteration
# end scale
return (Idef, u, energy)
def main():
secNum = sys.argv[1]
mkyNum = sys.argv[2]
region = str(sys.argv[3])
# channel = sys.argv[3]
ext = 'M{0}/section_{1}/{2}/'.format(mkyNum, secNum, region)
ss_dir = '/home/sci/blakez/korenbergNAS/3D_database/Working/Microscopic/side_light_microscope/'
conf_dir = '/home/sci/blakez/korenbergNAS/3D_database/Working/Microscopic/confocal/'
memT = ca.MEM_DEVICE
try:
with open(
ss_dir +
'src_registration/M{0}/section_{1}/M{0}_01_section_{1}_regions.txt'
.format(mkyNum, secNum), 'r') as f:
region_dict = json.load(f)
f.close()
except IOError:
region_dict = {}
region_dict[region] = {}
region_dict['size'] = map(
int,
raw_input("What is the size of the full resolution image x,y? ").
split(','))
region_dict[region]['bbx'] = map(
int,
raw_input(
"What are the x indicies of the bounding box (Matlab Format x_start,x_stop? "
).split(','))
region_dict[region]['bby'] = map(
int,
raw_input(
"What are the y indicies of the bounding box (Matlab Format y_start,y_stop? "
).split(','))
if region not in region_dict:
region_dict[region] = {}
region_dict[region]['bbx'] = map(
int,
raw_input(
"What are the x indicies of the bounding box (Matlab Format x_start,x_stop? "
).split(','))
region_dict[region]['bby'] = map(
int,
raw_input(
"What are the y indicies of the bounding box (Matlab Format y_start,y_stop? "
).split(','))
img_region = common.LoadITKImage(
ss_dir +
'src_registration/M{0}/section_{1}/M{0}_01_section_{1}_{2}.tiff'.
format(mkyNum, secNum, region), ca.MEM_HOST)
ssiSrc = common.LoadITKImage(
ss_dir +
'src_registration/M{0}/section_{1}/frag0/M{0}_01_ssi_section_{1}_frag0.nrrd'
.format(mkyNum, secNum), ca.MEM_HOST)
bfi_df = common.LoadITKField(
ss_dir +
'Blockface_registered/M{0}/section_{1}/frag0/M{0}_01_ssi_section_{1}_frag0_to_bfi_real.mha'
.format(mkyNum, secNum), ca.MEM_DEVICE)
# Figure out the same region in the low resolution image: There is a transpose from here to matlab so dimensions are flipped
low_sz = ssiSrc.size().tolist()
yrng_raw = [(low_sz[1] * region_dict[region]['bbx'][0]) /
np.float(region_dict['size'][0]),
(low_sz[1] * region_dict[region]['bbx'][1]) /
np.float(region_dict['size'][0])]
xrng_raw = [(low_sz[0] * region_dict[region]['bby'][0]) /
np.float(region_dict['size'][1]),
(low_sz[0] * region_dict[region]['bby'][1]) /
np.float(region_dict['size'][1])]
yrng = [np.int(np.floor(yrng_raw[0])), np.int(np.ceil(yrng_raw[1]))]
xrng = [np.int(np.floor(xrng_raw[0])), np.int(np.ceil(xrng_raw[1]))]
low_sub = cc.SubVol(ssiSrc, xrng, yrng)
# Figure out the grid for the sub region in relation to the sidescape
originout = [
ssiSrc.origin().x + ssiSrc.spacing().x * xrng[0],
ssiSrc.origin().y + ssiSrc.spacing().y * yrng[0], 0
]
spacingout = [
(low_sub.size().x * ssiSrc.spacing().x) / (img_region.size().x),
(low_sub.size().y * ssiSrc.spacing().y) / (img_region.size().y), 1
]
gridout = cc.MakeGrid(img_region.size().tolist(), spacingout, originout)
img_region.setGrid(gridout)
only_sub = np.zeros(ssiSrc.size().tolist()[0:2])
only_sub[xrng[0]:xrng[1], yrng[0]:yrng[1]] = np.squeeze(low_sub.asnp())
only_sub = common.ImFromNPArr(only_sub)
only_sub.setGrid(ssiSrc.grid())
# Deform the only sub region to
only_sub.toType(ca.MEM_DEVICE)
def_sub = ca.Image3D(bfi_df.grid(), bfi_df.memType())
cc.ApplyHReal(def_sub, only_sub, bfi_df)
def_sub.toType(ca.MEM_HOST)
# Now have to find the bounding box in the deformation space (bfi space)
if 'deformation_bbx' not in region_dict[region]:
bb_def = np.squeeze(pp.LandmarkPicker([np.squeeze(def_sub.asnp())]))
bb_def_y = [bb_def[0][0], bb_def[1][0]]
bb_def_x = [bb_def[0][1], bb_def[1][1]]
region_dict[region]['deformation_bbx'] = bb_def_x
region_dict[region]['deformation_bby'] = bb_def_y
with open(
ss_dir +
'src_registration/M{0}/section_{1}/M{0}_01_section_{1}_regions.txt'
.format(mkyNum, secNum), 'w') as f:
json.dump(region_dict, f)
f.close()
# Now need to extract the region and create a deformation and image that have the same resolution as the img_region
deform_sub = cc.SubVol(bfi_df, region_dict[region]['deformation_bbx'],
region_dict[region]['deformation_bby'])
common.DebugHere()
sizeout = [
int(
np.ceil((deform_sub.size().x * deform_sub.spacing().x) /
img_region.spacing().x)),
int(
np.ceil((deform_sub.size().y * deform_sub.spacing().y) /
img_region.spacing().y)), 1
]
region_grid = cc.MakeGrid(sizeout,
img_region.spacing().tolist(),
deform_sub.origin().tolist())
def_im_region = ca.Image3D(region_grid, deform_sub.memType())
up_deformation = ca.Field3D(region_grid, deform_sub.memType())
img_region.toType(ca.MEM_DEVICE)
cc.ResampleWorld(up_deformation, deform_sub,
ca.BACKGROUND_STRATEGY_PARTIAL_ZERO)
cc.ApplyHReal(def_im_region, img_region, up_deformation)
ss_out = ss_dir + 'Blockface_registered/M{0}/section_{1}/{2}/'.format(
mkyNum, secNum, region)
if not pth.exists(pth.expanduser(ss_out)):
os.mkdir(pth.expanduser(ss_out))
common.SaveITKImage(
def_im_region,
pth.expanduser(ss_out) +
'M{0}_01_section_{1}_{2}_def_to_bfi.nrrd'.format(
mkyNum, secNum, region))
common.SaveITKImage(
def_im_region,
pth.expanduser(ss_out) +
'M{0}_01_section_{1}_{2}_def_to_bfi.tiff'.format(
mkyNum, secNum, region))
del img_region, def_im_region, ssiSrc, deform_sub
# Now apply the same deformation to the confocal images
conf_grid = cc.LoadGrid(
conf_dir +
'sidelight_registered/M{0}/section_{1}/{2}/affine_registration_grid.txt'
.format(mkyNum, secNum, region))
cf_out = conf_dir + 'blockface_registered/M{0}/section_{1}/{2}/'.format(
mkyNum, secNum, region)
# confocal.toType(ca.MEM_DEVICE)
# def_conf = ca.Image3D(region_grid, deform_sub.memType())
# cc.ApplyHReal(def_conf, confocal, up_deformation)
for channel in range(0, 4):
z_stack = []
num_slices = len(
glob.glob(conf_dir +
'sidelight_registered/M{0}/section_{1}/{3}/Ch{2}/*.tiff'.
format(mkyNum, secNum, channel, region)))
for z in range(0, num_slices):
src_im = common.LoadITKImage(
conf_dir +
'sidelight_registered/M{0}/section_{1}/{3}/Ch{2}/M{0}_01_section_{1}_LGN_RHS_Ch{2}_conf_aff_sidelight_z{4}.tiff'
.format(mkyNum, secNum, channel, region,
str(z).zfill(2)))
src_im.setGrid(
cc.MakeGrid(
ca.Vec3Di(conf_grid.size().x,
conf_grid.size().y, 1), conf_grid.spacing(),
conf_grid.origin()))
src_im.toType(ca.MEM_DEVICE)
def_im = ca.Image3D(region_grid, ca.MEM_DEVICE)
cc.ApplyHReal(def_im, src_im, up_deformation)
def_im.toType(ca.MEM_HOST)
common.SaveITKImage(
def_im, cf_out +
'Ch{2}/M{0}_01_section_{1}_{3}_Ch{2}_conf_def_blockface_z{4}.tiff'
.format(mkyNum, secNum, channel, region,
str(z).zfill(2)))
if z == 0:
common.SaveITKImage(
def_im, cf_out +
'Ch{2}/M{0}_01_section_{1}_{3}_Ch{2}_conf_def_blockface_z{4}.nrrd'
.format(mkyNum, secNum, channel, region,
str(z).zfill(2)))
z_stack.append(def_im)
print('==> Done with Ch {0}: {1}/{2}'.format(
channel, z, num_slices - 1))
stacked = cc.Imlist_to_Im(z_stack)
stacked.setSpacing(
ca.Vec3Df(region_grid.spacing().x,
region_grid.spacing().y,
conf_grid.spacing().z))
common.SaveITKImage(
stacked, cf_out +
'Ch{2}/M{0}_01_section_{1}_{3}_Ch{2}_conf_def_blockface_stack.nrrd'
.format(mkyNum, secNum, channel, region))
if channel == 0:
cc.WriteGrid(
stacked.grid(),
cf_out + 'deformed_registration_grid.txt'.format(
mkyNum, secNum, region))
sigma_I = 0.09
nIter_I = 200
[def_ID, theta, energy] = apps.IDiff(def_TPS,
M13_aff,
eps,
sigma_I,
nIter_I,
plot=True,
verbose=1)
if write:
cc.WriteMHA(def_ID, M15dir + 'IDiff/M15_01_ID_to_M13.mha')
cc.WriteMHA(theta, M15dir + 'IDiff/M15_01_ID_Field_to_M13.mha')
h = cc.LoadMHA(M15dir + 'TPS/M15_01_TPS_Field_to_M13.mha', memT)
compDef = ca.Field3D(M13_aff.grid(), memT)
ca.ComposeHH(compDef, h, theta, bg=ca.BACKGROUND_STRATEGY_CLAMP)
common.DebugHere()
Final_aff = ca.Image3D(M13_aff.grid(), memT)
cc.ApplyAffineReal(Final_aff, M15, np.linalg.inv(aff))
Final = ca.Image3D(M13_aff.grid(), memT)
cc.ApplyHReal(Final, Final_aff, compDef)
if write:
cc.WriteMHA(Final, M15dir + 'FullDef/M15_01_MRI_as_M13.mha')
cc.WriteMHA(compDef, M15dir + 'FullDef/M15_01_Field_to_M13.mha')
if not M15_to_M13:
