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)
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)
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 DispImage(im, title=None,
sliceIdx=None, dim='z',
cmap='gray', newFig=True,
rng=None, t=False, log=False):
# if this volume is not a slice already, extract a slice
sz = im.size().tolist()
if sz[common.DIMMAP[dim]] > 1:
im = common.ExtractSliceIm(im, sliceIdx, dim)
im.toType(core.MEM_HOST)
# transfer to host memory if necessary
if im.memType() == core.MEM_DEVICE:
tmp = core.Image3D(im.grid(), core.MEM_HOST)
core.Copy(tmp, im)
im = tmp
# create the figure if requested
if newFig:
if title is None:
plt.figure()
else:
plt.figure(title)
plt.clf()
# set the range
if rng is None:
vmin = None
vmax = None
else:
vmin = rng[0]
vmax = rng[1]
if log:
norm = matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax)
else:
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
# convert to numpy array
arr = np.squeeze(im.asnp().copy())
# transpose if requested
if t:
arr = arr.T
# display
plt.imshow(arr, cmap=cmap, vmin=vmin, vmax=vmax, norm=norm, interpolation='nearest')
plt.axis('tight')
plt.axis('image')
if title is not None:
plt.title(title)
plt.xticks([])
plt.yticks([])
plt.draw()
def Reduce(A, hA, op=None):
"""Reduce PyCA Image3D or Field3D over MPI
A can live anywhere but hA needs to be of mType MEM_HOST
"""
if not hasMPI:
raise Exception("mpi4py required for Reduce operations: not found")
if op is None:
op = MPI.SUM
if A.memType() == ca.MEM_HOST:
# can do this in place without using hA
if isinstance(A, ca.Image3D):
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.asnp(), op=op)
elif isinstance(A, ca.Field3D):
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.x_asnp(), op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.y_asnp(), op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.z_asnp(), op=op)
else:
raise Exception('Can only reduce Image3D and Field3D')
else:
# will need some uploading and downloading
assert(hA.memType() == ca.MEM_HOST) # make sure we can actually use hA
ca.Copy(hA, A) # download
if isinstance(A, ca.Image3D):
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.asnp(), op=op)
elif isinstance(A, ca.Field3D):
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.x_asnp(), op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.y_asnp(), op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.z_asnp(), op=op)
else:
raise Exception('Can only reduce Image3D and Field3D')
ca.Copy(A, hA) # upload
def Reduce(A, hA, op=MPI.SUM):
"""Reduce PyCA Image3D or Field3D over MPI
A can live anywhere but hA needs to be of mType MEM_HOST
"""
if A.memType() == ca.MEM_HOST:
# can do this in place without using hA
if isinstance(A, ca.Field3D):
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.asnp()[0], op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.asnp()[1], op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.asnp()[2], op=op)
else:
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, A.asnp(), op=op)
else:
# will need some uploading and downloading
assert (hA.memType() == ca.MEM_HOST
) # make sure we can actually use hA
ca.Copy(hA, A) # download
if isinstance(A, ca.Field3D):
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.asnp()[0], op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.asnp()[1], op=op)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.asnp()[2], op=op)
else:
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, hA.asnp(), op=op)
ca.Copy(A, hA) # upload
def MatchingImageMomentaComputeEnergy(geodesicState, m0, J1, n1):
vecEnergy = 0.0
imageMatchEnergy = 0.0
momentaMatchEnergy = 0.0
grid = geodesicState.J0.grid()
mType = geodesicState.J0.memType()
imdiff = ca.ManagedImage3D(grid, mType)
vecdiff = ca.ManagedField3D(grid, mType)
# image match energy
ca.ApplyH(imdiff, geodesicState.J0, geodesicState.rhoinv)
ca.Sub_I(imdiff, J1)
imageMatchEnergy = 0.5 * ca.Sum2(imdiff) / (
float(geodesicState.p0.nVox()) * geodesicState.Sigma *
geodesicState.Sigma * geodesicState.SigmaIntercept *
geodesicState.SigmaIntercept) # save for use in intercept energy term
# momenta match energy
ca.CoAd(geodesicState.p, geodesicState.rhoinv, m0)
ca.Sub_I(geodesicState.p, n1)
ca.Copy(vecdiff, geodesicState.p) # save for use in slope energy term
geodesicState.diffOp.applyInverseOperator(geodesicState.p)
momentaMatchEnergy = ca.Dot(vecdiff, geodesicState.p) / (
float(geodesicState.p0.nVox()) * geodesicState.SigmaSlope *
geodesicState.SigmaSlope)
# vector energy. p is used as scratch variable
ca.Copy(geodesicState.p, geodesicState.p0)
geodesicState.diffOp.applyInverseOperator(geodesicState.p)
vecEnergy = 0.5 * ca.Dot(geodesicState.p0, geodesicState.p) / (
float(geodesicState.p0.nVox()) * geodesicState.SigmaIntercept *
geodesicState.SigmaIntercept)
return (vecEnergy, imageMatchEnergy, momentaMatchEnergy)
def MatchingGradient(p):
# shoot the geodesic forward
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, RK4=p.scratchV4,scratchG=p.scratchV5)
endidx = p.checkpointinds.index(len(p.t)-1)
# compute residual image
ca.ApplyH(p.residualIm,p.I0,p.ginv)
ca.Sub_I(p.residualIm, p.I1)
# while we have residual, save the image energy
IEnergy = ca.Sum2(p.residualIm)/(2*p.sigma*p.sigma*float(p.I0.nVox()))
ca.DivC_I(p.residualIm, p.sigma*p.sigma) # gradient at measurement
# integrate backward
CAvmCommon.IntegrateAdjoints(p.Iadj,p.madj,\
p.I,p.m,p.Iadjtmp, p.madjtmp,p.scratchV1,\
p.scratchV2,p.scratchV3,\
p.I0,p.m0,\
p.t, p.checkpointstates, p.checkpointinds,\
[p.residualIm], [endidx],\
p.diffOp,
p.integMethod, p.nInv, \
scratchV3=p.scratchV7, scratchV4=p.g,scratchV5=p.ginv,scratchV6=p.scratchV8, scratchV7=p.scratchV9, \
scratchV8=p.scratchV10,scratchV9=p.scratchV11,\
RK4=p.scratchV4, scratchG=p.scratchV5, scratchGinv=p.scratchV6)
# compute gradient
ca.Copy(p.scratchV1, p.m0)
p.diffOp.applyInverseOperator(p.scratchV1)
# while we have velocity, save the vector energy
VEnergy = 0.5*ca.Dot(p.m0,p.scratchV1)/float(p.I0.nVox())
ca.Sub_I(p.scratchV1, p.madj)
#p.diffOp.applyOperator(p.scratchV1)
return (p.scratchV1, VEnergy, IEnergy)
def JacDetPlot(vf, title='Jac. Det.',
jd_max=10.0, cmap='PRGn',
nonpos_clr=(1.0, 0.0, 0.0, 1.0),
sliceIdx=None, dim='z',
isVF=True,
newFig=False):
"""
Plot the jacobian determinant using logmapped colors, and setting
zero or negative values to 'nonpos_clr'. jd_max is the maximum
value to display without clamping, and also defines the min value
as 1.0/jd_max to assure 1.0 is centered in the colormap. If vf is
a vector field, compute the jacobian determinant. If it is an
Image3D, assume it is the jacobian determinant to be plotted.
"""
if common.IsField3D(vf):
grid = vf.grid()
mType = vf.memType()
h = core.ManagedField3D(grid, mType)
jacdet = core.ManagedImage3D(grid, mType)
core.Copy(h, vf)
if isVF:
core.VtoH_I(h)
core.JacDetH(jacdet, h)
elif common.IsImage3D(vf):
jacdet = vf
else:
raise Exception('unknown input type to JacDetPlot, %s'%\
str(type(vf)))
jd_cmap = JacDetCMap(jd_max=jd_max, cmap=cmap,
nonpos_clr=nonpos_clr)
DispImage(jacdet, title=title,
sliceIdx=sliceIdx, dim=dim,
rng=[1.0/jd_max, jd_max],
cmap=jd_cmap,
log=True,
newFig=newFig)
def MatchingImageMomentaWriteOuput(cf, geodesicState, EnergyHistory, m0, n1):
grid = geodesicState.J0.grid()
mType = geodesicState.J0.memType()
# save momenta for the gedoesic
common.SaveITKField(geodesicState.p0, cf.io.outputPrefix + "p0.mhd")
# save matched momenta for the geodesic
if cf.vectormomentum.matchImOnly:
m0 = common.LoadITKField(cf.study.m, mType)
ca.CoAd(geodesicState.p, geodesicState.rhoinv, m0)
common.SaveITKField(geodesicState.p, cf.io.outputPrefix + "m1.mhd")
# momenta match energy
if cf.vectormomentum.matchImOnly:
vecdiff = ca.ManagedField3D(grid, mType)
ca.Sub_I(geodesicState.p, n1)
ca.Copy(vecdiff, geodesicState.p)
geodesicState.diffOp.applyInverseOperator(geodesicState.p)
momentaMatchEnergy = ca.Dot(vecdiff, geodesicState.p) / (
float(geodesicState.p0.nVox()) * geodesicState.SigmaSlope *
geodesicState.SigmaSlope)
# save energy
energyFilename = cf.io.outputPrefix + "testMomentaMatchEnergy.csv"
with open(energyFilename, 'w') as f:
print >> f, momentaMatchEnergy
# save matched image for the geodesic
tempim = ca.ManagedImage3D(grid, mType)
ca.ApplyH(tempim, geodesicState.J0, geodesicState.rhoinv)
common.SaveITKImage(tempim, cf.io.outputPrefix + "I1.mhd")
# save energy
energyFilename = cf.io.outputPrefix + "energy.csv"
MatchingImageMomentaWriteEnergyHistoryToFile(EnergyHistory, energyFilename)
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
def Fragmenter():
tmpOb = Config.Load(
frgSpec,
pth.expanduser(
'~/korenbergNAS/3D_database/Working/configuration_files/SidescapeRelateBlockface/M{0}/section_{1}/section_{1}_frag0.yaml'
.format(secOb.mkyNum, secOb.secNum)))
dictBuild = {}
#Load in the whole image so that the fragment can cropped out
ssiSrc, bfiSrc, ssiMsk, bfiMsk = Loader(tmpOb, ca.MEM_HOST)
#Because some of the functions only woth with gray images
bfiGry = ca.Image3D(bfiSrc.grid(), bfiSrc.memType())
ca.Copy(bfiGry, bfiSrc, 1)
lblSsi, _ = ndimage.label(np.squeeze(ssiMsk.asnp()) > 0)
lblBfi, _ = ndimage.label(np.squeeze(bfiMsk.asnp()) > 0)
seedPt = np.squeeze(pp.LandmarkPicker([lblBfi, lblSsi]))
subMskBfi = common.ImFromNPArr(lblBfi == lblBfi[seedPt[0, 0],
seedPt[0,
1]].astype('int8'),
sp=bfiSrc.spacing(),
orig=bfiSrc.origin())
subMskSsi = common.ImFromNPArr(lblSsi == lblSsi[seedPt[1, 0],
seedPt[1,
1]].astype('int8'),
sp=ssiSrc.spacing(),
orig=ssiSrc.origin())
bfiGry *= subMskBfi
bfiSrc *= subMskBfi
ssiSrc *= subMskSsi
#Pick points that are the bounding box of the desired subvolume
corners = np.array(
pp.LandmarkPicker(
[np.squeeze(bfiGry.asnp()),
np.squeeze(ssiSrc.asnp())]))
bfiCds = corners[:, 0]
ssiCds = corners[:, 1]
#Extract the region from the source images
bfiRgn = cc.SubVol(bfiSrc,
xrng=[bfiCds[0, 0], bfiCds[1, 0]],
yrng=[bfiCds[0, 1], bfiCds[1, 1]])
ssiRgn = cc.SubVol(ssiSrc,
xrng=[ssiCds[0, 0], ssiCds[1, 0]],
yrng=[ssiCds[0, 1], ssiCds[1, 1]])
#Extract the region from the mask images
rgnMskSsi = cc.SubVol(subMskSsi,
xrng=[ssiCds[0, 0], ssiCds[1, 0]],
yrng=[ssiCds[0, 1], ssiCds[1, 1]])
rgnMskBfi = cc.SubVol(subMskBfi,
xrng=[bfiCds[0, 0], bfiCds[1, 0]],
yrng=[bfiCds[0, 1], bfiCds[1, 1]])
dictBuild['rgnBfi'] = np.divide(
bfiCds, np.array(bfiSrc.size().tolist()[0:2], 'float')).tolist()
dictBuild['rgnSsi'] = np.divide(
ssiCds, np.array(ssiSrc.size().tolist()[0:2], 'float')).tolist()
#Check the output directory for the source files of the fragment
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)))
#Check the output directory for the mask files of the fragment
if not pth.exists(
pth.expanduser(secOb.ssiMskPath + 'frag{0}'.format(frgNum))):
os.mkdir(pth.expanduser(secOb.ssiMskPath + 'frag{0}'.format(frgNum)))
if not pth.exists(
pth.expanduser(secOb.bfiMskPath + 'frag{0}'.format(frgNum))):
os.mkdir(pth.expanduser(secOb.bfiMskPath + 'frag{0}'.format(frgNum)))
dictBuild[
'ssiSrcName'] = 'frag{0}/M{1}_01_ssi_section_{2}_frag1.tif'.format(
frgNum, secOb.mkyNum, secOb.secNum)
dictBuild[
'bfiSrcName'] = 'frag{0}/M{1}_01_bfi_section_{2}_frag1.mha'.format(
frgNum, secOb.mkyNum, secOb.secNum)
dictBuild[
'ssiMskName'] = 'frag{0}/M{1}_01_ssi_section_{2}_frag1_mask.tif'.format(
frgNum, secOb.mkyNum, secOb.secNum)
dictBuild[
'bfiMskName'] = 'frag{0}/M{1}_01_bfi_section_{2}_frag1_mask.tif'.format(
frgNum, secOb.mkyNum, secOb.secNum)
#Write out the masked and cropped images so that they can be loaded from the YAML file
#The BFI region needs to be saved as color and mha format so that the grid information is carried over.
common.SaveITKImage(
ssiRgn, pth.expanduser(secOb.ssiSrcPath + dictBuild['ssiSrcName']))
cc.WriteColorMHA(
bfiRgn, pth.expanduser(secOb.bfiSrcPath + dictBuild['bfiSrcName']))
common.SaveITKImage(
rgnMskSsi, pth.expanduser(secOb.ssiMskPath + dictBuild['ssiMskName']))
common.SaveITKImage(
rgnMskBfi, pth.expanduser(secOb.bfiMskPath + dictBuild['bfiMskName']))
frgOb = Config.MkConfig(dictBuild, frgSpec)
updateFragOb(frgOb)
return None
if SaveBW:
cc.InsertSlice(BFIDef3D_BW, BFI_def_BW, sliceIdx)
continue
# Run IDiff on VE B/W Images
MRI = common.ExtractSliceIm(MRI3D, sliceIdx)
# Make Memory all fast
MRI.toType(ca.MEM_DEVICE)
BFI.toType(ca.MEM_DEVICE)
# Standardize Grids
MRI.setGrid(grid2D)
BFI.setGrid(grid2D)
MRI /= ca.Max(MRI)
BFI_VE = ca.Image3D(grid2D, BFI.memType())
MRI_VE = ca.Image3D(grid2D, MRI.memType())
ca.Copy(MRI_VE, MRI)
ca.Copy(BFI_VE, BFI)
cc.SetRegionLTE(MRI_VE, MRI, 0.13, 1)
MRI_VE *= -1
square = ca.Image3D(grid2D, BFI.memType())
cc.CreateRect(square, [0, 0], [440, 440])
BFI_VE *= square
cc.VarianceEqualize_I(BFI_VE, sigma=5.0)
cc.VarianceEqualize_I(MRI_VE, sigma=5.0)
grid_orig = BFI_VE.grid().copy()
grid_new = cc.MakeGrid(grid_orig.size(), [1, 1, 1], 'center')
BFI_VE.setGrid(grid_new)
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
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()
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 +
'block{0}_as_MRI_weight_{1}.mha'.format(i, sz))
except IOError:
print 'Warning, weight block does not exist'
weight = ca.Image3D(blk.grid(), blk.memType())
ca.Copy(weight, blk, 0) # take red
cc.SetRegionGTE(weight, weight, .1, 1)
for i in xrange(3):
ca.Copy(weight3, weight, i)
weights += weight3
print ca.MinMax(weights)
for i in xrange(3):
ca.Copy(weight, weights, i)
cc.SetRegionLT(weight, weight, 1, 1)
ca.Copy(weights, weight, i)
print ca.MinMax(weights)
ca.Div_I(blocks, weights)
else: # best
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 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 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 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 HGMPlots(cf,
groupState,
tDiscGroup,
residualState,
tDiscResidual,
index_individual,
writeOutput=True):
"""
Do some summary plots for HGM
"""
#ENERGY
fig = plt.figure(1)
plt.clf()
fig.patch.set_facecolor('white')
TE = [sum(x) for x in groupState.EnergyHistory]
VE = [row[0] for row in groupState.EnergyHistory]
IE = [row[1] for row in groupState.EnergyHistory]
SE = [row[2] for row in groupState.EnergyHistory]
TE = TE[1:]
VE = VE[1:]
IE = IE[1:]
SE = SE[1:]
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('Intercept Energy')
plt.hold(False)
plt.subplot(2, 2, 4)
plt.plot(SE)
plt.title('Slope Energy')
plt.hold(False)
plt.draw()
plt.show()
if cf.io.outputPrefix != None and writeOutput:
plt.savefig(cf.io.outputPrefix + 'energy.pdf')
# GROUP INITIAL CONDITIONS and PSI and PSI inv
# shoot group geodesic forward
CAvmHGMCommon.HGMIntegrateGeodesic(groupState.m0, groupState.t,
groupState.diffOp, groupState.m,
groupState.g, groupState.ginv,
tDiscGroup, groupState.Ninv,
groupState.integMethod)
fig = plt.figure(2)
plt.clf()
fig.patch.set_facecolor('white')
plt.subplot(2, 2, 1)
display.DispImage(groupState.I0,
'I0',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.subplot(2, 2, 2)
ca.ApplyH(groupState.I, groupState.I0, groupState.ginv)
display.DispImage(groupState.I,
'I1',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.subplot(2, 2, 3)
display.GridPlot(groupState.ginv,
every=cf.io.quiverEvery,
color='k',
sliceIdx=cf.io.plotSlice,
isVF=False)
plt.axis('equal')
plt.axis('off')
plt.title('psi^{-1}')
plt.subplot(2, 2, 4)
display.GridPlot(groupState.g,
every=cf.io.quiverEvery,
color='k',
sliceIdx=cf.io.plotSlice,
isVF=False)
plt.axis('equal')
plt.axis('off')
plt.title('psi')
if cf.io.outputPrefix != None and writeOutput:
plt.savefig(cf.io.outputPrefix + 'groupdef.pdf')
# RESIDUAL INITIAL CONDITIONS and RHO and RHO inv
ca.ApplyH(groupState.I, groupState.I0, groupState.ginv)
residualState.J0 = groupState.I
residualState.p0 = tDiscGroup[index_individual].p0
CAvmHGMCommon.HGMIntegrateGeodesic(residualState.p0, residualState.s,
residualState.diffOp, residualState.p,
residualState.rho, residualState.rhoinv,
tDiscResidual, residualState.Ninv,
residualState.integMethod)
fig = plt.figure(3)
plt.clf()
fig.patch.set_facecolor('white')
plt.subplot(2, 2, 1)
display.DispImage(residualState.J0,
'J0',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.subplot(2, 2, 2)
ca.ApplyH(residualState.J, residualState.J0, residualState.rhoinv)
display.DispImage(residualState.J,
'J1',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.subplot(2, 2, 3)
display.GridPlot(residualState.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(residualState.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 + 'resdef.pdf')
# MATCHING DIFFERENCE IMAGES
grid = groupState.I0.grid()
mType = groupState.I0.memType()
imdiff = ca.ManagedImage3D(grid, mType)
vecdiff = ca.ManagedField3D(grid, mType)
# Intercept matching
ca.Copy(imdiff, residualState.J)
ca.Sub_I(imdiff, tDiscGroup[index_individual].J)
fig = plt.figure(4)
plt.clf()
fig.patch.set_facecolor('white')
plt.subplot(1, 3, 1)
display.DispImage(residualState.J0,
'Source J0',
newFig=False,
sliceIdx=cf.io.plotSlice)
plt.colorbar()
plt.subplot(1, 3, 2)
display.DispImage(tDiscGroup[index_individual].J,
'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 + 'diffintercept.pdf')
# Slope matching
'''
ca.CoAd(groupState.m,groupState.ginv,groupState.m0)
ca.CoAd(vecdiff,residualState.rhoinv,groupState.m)
n0 = ca.Field3D(grid, ca.MEM_HOST)
n1 = ca.Field3D(grid, ca.MEM_HOST)
ca.Copy(n0,groupState.m)
ca.Copy(n1,tDiscGroup[index_individual].n)
ca.Sub_I(vecdiff, tDiscGroup[index_individual].n)
vecdiff.toType(ca.MEM_HOST)
n0_x, n0_y, n0_z = n0.asnp()
n1_x, n1_y, n1_z = n1.asnp()
diff_x, diff_y, diff_z = vecdiff.asnp()
fig = plt.figure(5)
plt.clf()
fig.patch.set_facecolor('white')
plt.subplot(2,3,1)
plt.imshow(np.squeeze(n0_x)); plt.colorbar(); plt.title('X: Source n0 ')
plt.subplot(2,3,2)
plt.imshow(np.squeeze(n1_x)); plt.colorbar(); plt.title('X: Target n1')
plt.subplot(2,3,3)
plt.imshow(np.squeeze(diff_x)); plt.colorbar(); plt.title('X: rho.n0-n1')
plt.subplot(2,3,4)
plt.imshow(np.squeeze(n0_y)); plt.colorbar(); plt.title('Y: Source n0')
plt.subplot(2,3,5)
plt.imshow(np.squeeze(n1_y)); plt.colorbar(); plt.title('Y: Target n1')
plt.subplot(2,3,6)
plt.imshow(np.squeeze(diff_y)); plt.colorbar(); plt.title('Y: rho.n0-n1')
if cf.io.outputPrefix != None and writeOutput: plt.savefig(cf.io.outputPrefix+'diffslope.pdf')
'''
del imdiff
del vecdiff
Imprev = cc.LoadTIFF(filelist[0], mType, ds)
origin = [(Imprev.grid().size().x+1)/2.0, # origin for Affine matrix
(Imprev.grid().size().y+1)/2.0,
(Imprev.grid().size().z+1)/2.0]
scratchI = ca.Image3D(Imprev.grid(), Imprev.memType())
scratchI2 = ca.Image3D(Imprev.grid(), Imprev.memType())
# initialize dictionary
Adict = {'origin': origin}
Adict[files.get_file_dist(filelist[0])] = np.identity(3)
# if 'block1' in filelist[0]:
# move first image in block 1
if block == 1:
tcentx, tcenty = cc.CenterImage(Imprev)
ca.Copy(scratchI, Imprev)
# first moves image up, second moves image left
t = ca.Vec3Df(-75, 55, 0) # double check this!
ca.ComposeTranslation(Imprev, scratchI, t)
ttot = [tcentx + t.x, tcenty + t.y]
Adict[files.get_file_dist(filelist[0])] = np.array([[1, 0, tcentx + t.x],
[0, 1, tcenty + t.y],
[0, 0, 1]])
dist_prev = -30 # assure correct numbering
for filename in filelist[1:]:
dist = files.get_file_dist(filename)
num_blanks = (dist - dist_prev)/30 - 1
for _i in xrange(num_blanks):
print 'blank'
# # Convert the landmarks to real coordinates and exchange the ordering of the so live is going to T2
# realLM[:,1] = realLM[:,1] - 127.5
# flipLM = np.fliplr(realLM)
# # Solve for the TPS based off of the landmakrs
# spline = SolveSpline(flipLM)
# h = SplineToHField(spline, T2Grid, memT)
# print ca.MinMax(h)
# liveDef = T2.copy()
# cc.ApplyHReal(liveDef,live,h)
# Variance equalize the volumes and blur the live
T2_VE = ca.Image3D(T2.grid(), memT)
live_VE = ca.Image3D(liveDef.grid(), memT)
ca.Copy(T2_VE, T2)
cc.VarianceEqualize_I(T2_VE, sigma=5)
ca.Copy(live_VE, liveDef)
cc.VarianceEqualize_I(live_VE, sigma=5)
gausfilt = ca.GaussianFilterGPU()
gausfilt.updateParams(live_VE.size(), ca.Vec3Df(3, 3, 3), ca.Vec3Di(3, 3, 3))
live_VEfilt = ca.Image3D(live_VE.grid(), memT)
temp = ca.Image3D(live_VE.grid(), memT)
gausfilt.filter(live_VEfilt, live_VE, temp)
dispslice = [128, 120, 128]
# Display some initial images
cd.Disp3Pane(live_VEfilt,
rng=[-3, 3],
sliceIdx=dispslice,
title='Live VE Filtered')
def WarpGradient(p, t, Imsmts, cpinds, cpstates, msmtinds, gradAtMsmts):
# shoot the geodesic forward
CAvmCommon.IntegrateGeodesic(p.m0,t,p.diffOp, \
p.m, p.g, p.ginv,\
p.scratchV1, p.scratchV2,p. scratchV3,\
cpstates, cpinds,\
Ninv=p.nInv, integMethod = p.integMethod, RK4=p.scratchV4,scratchG=p.scratchV5)
IEnergy = 0.0
# compute residuals for each measurement timepoint along with computing energy
for i in range(len(Imsmts)):
if msmtinds[i] != -1:
(g, ginv) = cpstates[msmtinds[i]]
ca.ApplyH(gradAtMsmts[i], p.I0, ginv)
ca.Sub_I(gradAtMsmts[i], Imsmts[i])
# while we have residual, save the image energy
IEnergy += ca.Sum2(
gradAtMsmts[i]) / (2 * p.sigma * p.sigma * float(p.I0.nVox()))
ca.DivC_I(gradAtMsmts[i],
p.sigma * p.sigma) # gradient at measurement
elif msmtinds[i] == -1:
ca.Copy(gradAtMsmts[i], p.I0)
ca.Sub_I(gradAtMsmts[i], Imsmts[i])
# while we have residual, save the image energy
IEnergy += ca.Sum2(
gradAtMsmts[i]) / (2 * p.sigma * p.sigma * float(p.I0.nVox()))
ca.DivC_I(gradAtMsmts[i],
p.sigma * p.sigma) # gradient at measurement
# integrate backward
CAvmCommon.IntegrateAdjoints(p.Iadj,p.madj,\
p.I,p.m,p.Iadjtmp, p.madjtmp,p.scratchV1,\
p.scratchV2,p.scratchV3,\
p.I0,p.m0,\
t, cpstates, cpinds,\
gradAtMsmts,msmtinds,\
p.diffOp,\
p.integMethod, p.nInv, \
scratchV3=p.scratchV7, scratchV4=p.g,scratchV5=p.ginv,scratchV6=p.scratchV8, scratchV7=p.scratchV9, \
scratchV8=p.scratchV10,scratchV9=p.scratchV11,\
RK4=p.scratchV4, scratchG=p.scratchV5, scratchGinv=p.scratchV6,\
scratchI = p.scratchI1)
# compute gradient
ca.Copy(p.scratchV1, p.m0)
p.diffOp.applyInverseOperator(p.scratchV1)
# while we have velocity, save the vector energy
VEnergy = 0.5 * ca.Dot(p.m0, p.scratchV1) / float(p.I0.nVox())
ca.Sub_I(p.scratchV1, p.madj)
#p.diffOp.applyOperator(p.scratchV1)
# compute closed from terms for image update
# p.Iadjtmp and p.I will be used as scratch images
scratchI = p.scratchI1 #reference assigned
imOnes = p.I #reference assigned
ca.SetMem(imOnes, 1.0)
ca.SetMem(p.sumSplatI, 0.0)
ca.SetMem(p.sumJac, 0.0)
#common.DebugHere()
for i in range(len(Imsmts)):
# TODO: check these indexings for cases when timepoint 0
# is not checkpointed
if msmtinds[i] != -1:
(g, ginv) = cpstates[msmtinds[i]]
CAvmCommon.SplatSafe(scratchI, ginv, Imsmts[i])
ca.Add_I(p.sumSplatI, scratchI)
CAvmCommon.SplatSafe(scratchI, ginv, imOnes)
ca.Add_I(p.sumJac, scratchI)
elif msmtinds[i] == -1:
ca.Add_I(p.sumSplatI, Imsmts[i])
ca.Add_I(p.sumJac, imOnes)
return (p.scratchV1, p.sumJac, p.sumSplatI, VEnergy, IEnergy)
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)
