#Apply the deformation to all of the DW images
####[ca.ApplyH(def_list[x],scanList[x],Ephi) for x in scanVec]
# Check for the sub directories and if they don't exist, make them
if not os.path.exists(outdir + 'DWI_scan_{}'.format(num)):
os.makedirs(outdir + 'DWI_scan_{}'.format(num))
if not os.path.exists(outdir +
'DWI_scan_{}/individual_volumes'.format(num)):
os.makedirs(outdir + 'DWI_scan_{}/individual_volumes'.format(num))
if not os.path.exists(outdir +
'DWI_scan_{}/individual_volumes/MHA'.format(num)):
os.makedirs(outdir + 'DWI_scan_{}/individual_volumes/MHA'.format(num))
if not os.path.exists(outdir +
'DWI_scan_{}/individual_volumes/NIFTI'.format(num)):
os.makedirs(outdir +
'DWI_scan_{}/individual_volumes/NIFTI'.format(num))
# [cc.WriteMHA(def_list[x],outdir + 'T2_registered_scan{0}_direction{1}.mha'.format(num,str(x).zfill(2))) for x in scanVec]
####
[
cc.WriteMHA(
scanList[x], outdir +
'DWI_scan_{0}/individual_volumes/MHA/non-registered_scan{0}_direction{1}.mha'
.format(num,
str(x).zfill(2))) for x in scanVec
]
total_dirs += scanCount
if MRI_to_BFI:
ca.ThreadMemoryManager.init(BFIgrid, mType, 6)
spline = SolveSpline(TPS_landmarks)
h = SplineToHField(spline, BFIgrid, mType)
print ca.MinMax(h)
MRIdef = ca.ManagedImage3D(BFIgrid, mType)
#MRI = cc.LoadMHA(MRI_fname, mType)
cc.ApplyHReal(MRIdef, MRI, h)
# write data
fname = 'MRI_TPS_to_block{0}.mha'.format(block)
cd.Disp3Pane(MRIdef)
print "Writing", outdir+fname
if Write:
cc.WriteMHA(MRIdef, outdir + fname)
del MRIdef, MRI, h
cc.MemInfo()
if BFI_to_MRI:
TPS_landmarks = [[lmpair[1], lmpair[0]] for lmpair in TPS_landmarks] # reverse
spline = apps.SolveSpline(TPS_landmarks) # only do once, b/c real coords#
print spline
#np.save(spline, outdir + 'block{0}_TPS_Spline_{1}.mha'.format(block,MRIsizes))
for sz in MRIsizes:
print "for size", sz
# ca.ThreadMemoryManager.destroy()
# MRIgrid = cc.MakeGrid([sz, sz, sz], [256.0/sz, 256.0/sz, 256.0/sz], 'center')
# ca.ThreadMemoryManager.init(MRIgrid, mType, 6)
ca.BACKGROUND_STRATEGY_PARTIAL_ZERO)
cc.InsertSlice(BFIDef3D_RGB, BFI_def_RGB, sliceIdx)
if SaveVE:
BFI_def_VE = ca.Image3D(grid2D, BFI.memType())
ca.ApplyH(BFI_def_VE, BFI_VE, phi, ca.BACKGROUND_STRATEGY_PARTIAL_ZERO)
cc.InsertSlice(BFIDef3D_VE, BFI_def_VE, sliceIdx)
if SaveBW:
BFI_def_BW = ca.Image3D(grid2D, BFI.memType())
ca.ApplyH(BFI_def_BW, BFI, phi, ca.BACKGROUND_STRATEGY_PARTIAL_ZERO)
cc.InsertSlice(BFIDef3D_BW, BFI_def_BW, sliceIdx)
# Save BFIDef3D
if SaveInBFICoords:
print 'saving blockface in blockface coords...'
if SaveVE:
cc.WriteMHA(BFIDef3D_VE,
dir_save + 'BFI_block_' + str(block) + '_ve.mha')
if SaveRGB:
print 'Writing RGB'
cc.WriteMHA(BFIDef3D_RGB,
dir_save + 'BFI_block_' + str(block) + '_rgb.mha')
if SaveBW:
cc.WriteMHA(BFIDef3D_BW,
dir_save + 'BFI_block_' + str(block) + '_bw.mha')
if not SaveInMRICoords:
sys.exit()
# Save HD version of the blockface in MRI Coordinates
print 'saving blockface in blockface coords...'
mType = ca.MEM_HOST
import Affine_transforms
print ca.MinMax(weights)
ca.Div_I(blocks, weights)
else: # best
imagedir = '/home/sci/crottman/korenberg/results/'
blocks = cc.LoadMHA(
imagedir + 'BFI_2D_Reg/block1_as_MRI_' + col + '_256.mha', ca.MEM_HOST)
blk = cc.LoadMHA(imagedir + 'BFI_2D_Reg/block2_as_MRI_' + col + '_256.mha',
ca.MEM_HOST)
blocks += blk
blk = cc.LoadMHA(imagedir + 'landmark/block3_as_MRI_' + col + '.mha',
ca.MEM_HOST)
blocks += blk
blk = cc.LoadMHA(imagedir + 'landmark/block4_as_MRI_' + col + '.mha',
ca.MEM_HOST)
blocks += blk
imagedir = '/home/sci/crottman/korenberg/results/best/'
# Save Blocks
maxval = ca.MinMax(blocks)[1]
blocks /= maxval
fname_out = imagedir + 'blocks' + fname_end
cc.WriteMHA(blocks, fname_out)
if col == 'rgb':
fname_out = imagedir + 'blocks_as_MRI_rgba_' + str(sz) + '.mha'
cc.WriteColorMHA(blocks, fname_out)
# 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')
cc.WriteMHA(live_T2reg, SaveDir + 'M13_01_live_as_MRI_full_bw_256.mha')
cc.WriteMHA(totDef,
SaveDir + 'M13_01_live_to_MRI_roty-119_flipy_full_256.mha')
paramDict = {}
paramDict['Number of Itterations ER: '] = EnIter
paramDict['Sigma ER: '] = Esigma
paramDict['Step Size ER: '] = Eeps
paramDict['Fluid Parameters ER: '] = Efp
paramDict['Number of Itterations ID: '] = InIter
paramDict['Sigma ID: '] = Isigma
paramDict['Step Size ID: '] = Ieps
with open(SaveDir + 'M13_01_live_to_MRI_reg_param.txt', 'w') as f:
w = csv.writer(f)
w.writerows(paramDict.items())
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()
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(
np.multiply(lm[1],
M13_aff.spacing().tolist()) +
M13_aff.origin().tolist())