def Affine(I_src, I_tar, cfOb):
'''Function for finding and applyiing the affine between two image fragments, I_src and I_tar. Returns both images with the affine applied and the affine trasform'''
if cfOb.affine == 'None':
landmarks = pp.LandmarkPicker(
[np.squeeze(I_src.asnp()),
np.squeeze(I_tar.asnp())]) #will be forward from BFI to SSI
for lm in landmarks:
lm[0] = np.ndarray.tolist(
np.multiply(lm[0],
I_src.spacing().tolist()[0:2]) +
I_src.origin().tolist()[0:2])
lm[1] = np.ndarray.tolist(
np.multiply(lm[1],
I_tar.spacing().tolist()[0:2]) +
I_tar.origin().tolist()[0:2])
aff = apps.SolveAffine(landmarks)
cfOb.affine = aff.tolist()
I_src_aff = I_tar.copy()
I_tar_aff = I_src.copy()
cc.ApplyAffineReal(I_src_aff, I_src, cfOb.affine)
cc.ApplyAffineReal(I_tar_aff, I_tar, np.linalg.inv(np.array(cfOb.affine)))
return I_src_aff, I_tar_aff, cfOb.affine
def main():
secNum = sys.argv[1]
mkyNum = sys.argv[2]
channel = sys.argv[3]
region = str(sys.argv[4])
conf_dir = '/home/sci/blakez/korenbergNAS/3D_database/Working/Microscopic/confocal/src_registration/'
side_dir = '/home/sci/blakez/korenbergNAS/3D_database/Working/Microscopic/side_light_microscope/src_registration/'
save_dir = '/home/sci/blakez/korenbergNAS/3D_database/Working/Microscopic/confocal/sidelight_registered/'
# DIC = '/home/sci/blakez/Reflect Affine/DIC_to_Reflect.txt'
src_pt = conf_dir + 'M{0}/section_{1}/{2}/section_{1}_confocal_relation_with_sidelight.txt'.format(mkyNum, secNum, region)
tar_pt = side_dir + 'M{0}/section_{1}/section_{1}_sidelight_relation_with_confocal.txt'.format(mkyNum, secNum)
# SID = '/home/sci/blakez/Reflect Affine/sidelight_to_DIC.txt'
src_im = common.LoadITKImage(conf_dir + 'M{0}/section_{1}/{3}/Ch{2}/M{0}_{1}_LGN_RHS_Ch{2}_z00.tif'.format(mkyNum, secNum, channel, region))
# tar_im = common.LoadITKImage('M{0}/{1}/Crop_ThirdNerve_EGFP_z16.tiff'.format(mkyNum, secNum))
# The points need to be chosen in the origin corrected sidescape for downstream purposes
affine = load_and_solve(tar_pt, src_pt)
out_grid = bb_grid_solver(src_im, affine)
z_stack = []
num_slices = len(glob.glob(conf_dir + 'M{0}/section_{1}/{3}/Ch{2}/*'.format(mkyNum, secNum, channel, region)))
for z in range(0, num_slices):
src_im = common.LoadITKImage(conf_dir + 'M{0}/section_{1}/{4}/Ch{2}/M{0}_{1}_LGN_RHS_Ch{2}_z{3}.tif'.format(mkyNum, secNum, channel, str(z).zfill(2), region))
aff_im = ca.Image3D(out_grid, ca.MEM_HOST)
cc.ApplyAffineReal(aff_im, src_im, affine)
common.SaveITKImage(aff_im, save_dir + 'M{0}/section_{1}/{4}/Ch{2}/M{0}_01_section_{1}_LGN_RHS_Ch{2}_conf_aff_sidelight_z{3}.tiff'.format(mkyNum, secNum, channel, str(z).zfill(2), region))
z_stack.append(aff_im)
print('==> Done with {0}/{1}'.format(z, num_slices - 1))
stacked = cc.Imlist_to_Im(z_stack)
stacked.setSpacing(ca.Vec3Df(out_grid.spacing()[0], out_grid.spacing()[1], 0.03/num_slices))
common.SaveITKImage(stacked, save_dir + 'M{0}/section_{1}/{3}/Ch{2}/M{0}_01_section_{1}_Ch{2}_conf_aff_sidelight_stack.nrrd'.format(mkyNum, secNum, channel, region))
common.DebugHere()
if channel==0:
cc.WriteGrid(stacked.grid(), save_dir + 'M{0}/section_{1}/{2}/affine_registration_grid.txt'.format(mkyNum, secNum, region))
bfAff=[[float(v) for v in line.split()] for line in bfp]
with open(outdir + 'mri_AFF_landmarks_index.txt', 'r') as mrp:
mrAff=[[float(v) for v in line.split()] for line in mrp]
AFF_landmarks = [[bfAff[x],mrAff[x]] for x in range(0,np.shape(mrAff)[0])]
for lm in AFF_landmarks:
lm[0] = np.ndarray.tolist(np.multiply(lm[0],BFIgrid.spacing().tolist()) + BFIgrid.origin().tolist())
lm[1] = np.ndarray.tolist(np.multiply(lm[1],MRIgrid.spacing().tolist()) + MRIgrid.origin().tolist())
print np.array(AFF_landmarks)
Afw = apps.SolveAffine(AFF_landmarks)
BFI_aff = MRI.copy()
cc.ApplyAffineReal(BFI_aff, BFI, Afw)
cd.Disp3Pane(BFI_aff)
sys.exit()
# Use the resulting affine transformed block to define landmarks for TPS
with open(outdir + 'blockface_TPS_points_index.txt', 'r') as bfp:
bfPoints=[[float(v) for v in line.split()] for line in bfp]
with open(outdir + 'mri_TPS_points_index.txt', 'r') as mrp:
mrPoints=[[float(v) for v in line.split()] for line in mrp]
### NOT THE RIGHT WAY TO COMBINE###
TPS_landmarks = [[bfPoints[x],mrPoints[x]] for x in range(0,np.shape(mrPoints)[0])]
for lm in TPS_landmarks:
lm[0] = np.ndarray.tolist(np.multiply(lm[0],MRIgrid.spacing().tolist()) + MRIgrid.origin().tolist())
lm[1] = np.ndarray.tolist(np.multiply(lm[1],MRIgrid.spacing().tolist()) + MRIgrid.origin().tolist())
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
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)
MRI_VE.setGrid(grid_new)
BFI_VE_def = ca.Image3D(grid_new, BFI_VE.memType())
# do rigid reg first
# A = AffineReg(BFI_VE, MRI_VE, constraint='rigid', plot=debug)
A = AffineReg(BFI_VE, MRI_VE, plot=debug, maxIter=400, verbose=0)[1]
cc.ApplyAffineReal(BFI_VE_def, BFI_VE, A)
# cd.DispImage(BFI_VE_def)
# cd.DispImage(MRI_VE)
# hA = ca.Field3D(BFI_VE.grid(), BFI_VE.memType())
# cc.AtoH(hA, A)
# cd.DispHGrid(hA, splat=False)
# sys.exit()
if block == 1:
nIters = 100
sigma = .03
step = .00002
elif block == 2:
nIters = 100
sigma = .003
step = .00002
# [[ 73.0, 92.0, 97.0] , [60.0, 82.0, 77.0]], # TEST # [[ 124.0, 66.0, 66.0] , [147.0,34.0,127.0]], # [[ 100.0, 66.0, 66.0] , [119.0,32.0,128.0]], # [[ 110.0, 35.0, 121.0] , [125.0,135.0,168.0]], # [[ 111.0, 60.0, 97.0] , [128.0,87.0,135.0]]] #good # with open(SaveDir + 'T2_registered/M13_01_Live-T2_Landmarks.json', 'w') as f: # json.dump(landmarks, f) realLM = np.array(landmarks) realLM[:, 0] = realLM[:, 0] * live.spacing() + live.origin() realLM[:, 1] = realLM[:, 1] * T2.spacing() + T2.origin() A = apps.SolveAffine(realLM) liveDef = T2.copy() cc.ApplyAffineReal(liveDef, live, A) # # 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)
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()
M15.spacing().tolist()) + M15.origin().tolist())
#Solve for the affine, inverse is M15 to M13, forward is M13 to M15
aff = apps.SolveAffine(landmarks)
with open(M13dir + 'TPS/M13_01_TPSLandmarks_5.txt', 'r') as m13:
TPS13 = [[float(v) for v in line.split()] for line in m13]
with open(M15dir + 'TPS/M15_01_TPSLandmarks_5.txt', 'r') as m15:
TPS15 = [[float(v) for v in line.split()] for line in m15]
if M15_to_M13:
write = True
def_aff = ca.Image3D(M13_aff.grid(), memT)
cc.ApplyAffineReal(def_aff, M15, np.linalg.inv(aff))
if write:
cc.WriteMHA(def_aff, M15dir + 'Affine/M15_01_MRI_affine_to_M13.mha')
np.save(M15dir + 'Affine/M15_01_MRI_affMat_to_M13.npy',
np.linalg.inv(aff))
landmarks = [[TPS13[x], TPS15[x]] for x in range(0, np.shape(TPS15)[0])]
# Convert to real coordinates
for lm in landmarks:
lm[0] = np.ndarray.tolist(
np.multiply(lm[0],
M13_aff.spacing().tolist()) +
M13_aff.origin().tolist())
lm[1] = np.ndarray.tolist(