def SubRegion(src_grid, sub_grid, def_grid):
# The sapcing of the src image was changed, so we have to find the new spacing of the sub region
reg_grid = ConvertGrid(src_grid, def_grid)
spacing = (np.array(reg_grid.spacing().tolist()) / np.array(
src_grid.spacing().tolist())) * np.array(sub_grid.spacing().tolist())
# Because we updated the spacing, the origin of the sub region will change and we have to find the new origin
# The new origin is the upper left real coordinate of the subregion to extract
pixel_origin = np.array(sub_grid.origin().tolist()) / np.array(
src_grid.spacing().tolist())
upL_real = pixel_origin * np.array(reg_grid.spacing().tolist()) + np.array(
reg_grid.origin().tolist())
# Now need to find the bottom right in real coordinates
btR_real = (spacing * np.array(sub_grid.size().tolist())) + upL_real
# Now that we have the top left and bottom right, we need to convert them to index corrdiantes of the deformation grid
upL = np.floor((upL_real - np.array(def_grid.origin().tolist())) /
np.array(def_grid.spacing().tolist()))
btR = np.floor((btR_real - np.array(def_grid.origin().tolist())) /
np.array(def_grid.spacing().tolist()))
xrng = [int(upL[0]), int(btR[0])]
yrng = [int(upL[1]), int(btR[1])]
new_sub_grid = cc.MakeGrid(
ca.Vec3Di(sub_grid.size()[0],
sub_grid.size()[1], 1), ca.Vec3Df(spacing[0], spacing[1], 1),
ca.Vec3Df(upL_real[0], upL_real[1], 0))
return xrng, yrng, new_sub_grid
def bb_grid_solver(source_image, affine):
in_sz = source_image.size().tolist()
in_sp = source_image.spacing().tolist()
in_or = source_image.origin().tolist()
C1temp = in_or[0:2]
C1temp.append(1)
C1 = C1temp
C2 = np.array([in_sz[0] * in_sp[0] + in_or[0], in_or[1], 1])
C3 = np.array([in_or[0], in_sz[1] * in_sp[1] + in_or[1], 1])
C4 = np.array(
[in_sz[0] * in_sp[0] + in_or[0], in_sz[1] * in_sp[1] + in_or[1], 1])
corners = np.matrix([C1, C2, C3, C4])
tCorners = affine * corners.transpose()
bbMax = np.max(tCorners[:, 0:4], 1)
bbMin = np.min(tCorners[:, 0:4], 1)
dim = np.ceil(bbMax - bbMin)
out_sp = (np.squeeze(np.array(dim)) / source_image.size()[0:3]
) # * (1/np.sqrt(2))
if out_sp[2] == 0.0:
out_sp[2] = 1.0
out_sz = np.squeeze(np.array(dim)) / out_sp.transpose()
out_or = np.squeeze(
np.array(bbMin)) # Maybe needs to be the center of the image??
grid = cc.MakeGrid(
[np.int(np.ceil(out_sz[0])),
np.int(np.ceil(out_sz[1])), 1], [out_sp[0], out_sp[1], 1],
[out_or[0], out_or[1], 0])
return grid
def ConvertGrid(I_src_grid, I_tar_grid):
'''Given a source grid and target grid, returns a grid for the source that is in the world coordinates of the target grid'''
nS = np.multiply(
np.divide(I_tar_grid.size().tolist(),
[float(i) for i in I_src_grid.size().tolist()]),
I_tar_grid.spacing().tolist())
return cc.MakeGrid(I_src_grid.size(), ca.Vec3Df(nS[0], nS[1], nS[2]),
'center')
# tmp = cc.LoadMHA(BFIdir + fname)
# cc.WriteGrid(tmp.grid(), BFIdir + 'block{0}_grid.txt'.format(block))
# tmp = cc.LoadMHA(MRI_fname)
# cc.WriteGrid(tmp.grid(), MRIdir + 'T2_grid.txt'.format(block))
# sys.exit()
#BFIgrid = cc.LoadGrid(BFIdir + 'block{0}_grid.txt'.format(block))
#MRIgrid = cc.LoadGrid(MRIdir + 'T2_grid.txt'.format(block))
MRI = cc.LoadMHA(MRI_fname,ca.MEM_HOST)
MRIgrid = MRI.grid()
#MRIgrid = cc.MakeGrid(MRI.size(),MRI.spacing(),'center')
#MRI.setGrid(MRIgrid)
#BFI = cc.LoadMHA(BFIdir + 'M15_01_seg_crop_hd8.mha',ca.MEM_HOST)
BFI_full = cc.LoadMHA(BFIdir + 'M15_01_hd8_VE.mha',ca.MEM_HOST)
BFI = cc.SubVol(BFI_full, yrng=[146,626])
BFIgrid = cc.MakeGrid(ca.Vec3Di(480,480,2239),ca.Vec3Df(0.1185,0.1185,0.030),'center')
BFI.setGrid(BFIgrid)
#BFIgrid = BFI.grid()
print MRIgrid
print BFIgrid
# print 'b4 memory', 480*480*874*4/1024./1024 *7 # 7 blocks, mem size
# print 'mri memory', 256*256*256*4/1024./1024 *7 # 7 blocks, mem size
# print 'mri big memory', 512*512*512*4/1024./1024 *7 # 7 blocks, mem size
# print 'single mri ', 256*256*256*4/1024./1024
#landmarks = get_new_landmarks(block)
# Use these landmarks for an affine
### landmarks = [[[1893.0,123.0,401.0], [187.0,135.0,202.0]],
### [[1798.0,416.0,333.0], [183.0,131.0,48.0]],
### [[892.0,222.0,251.0], [84.0,109.0,132.0]],
### [[2142.0,260.0,332.0], [217.0,114.0,125.0]],
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))
# for a in folderList:
# print a
#Load in the T2 image and downsample it to the resolution of the DW images
####
T2_list = sorted(glob.glob(indir + 'T2DICOM_scan26/*'))
refIm = dicom.read_file(T2_list[0])
PixelDims = (int(refIm.Rows), int(refIm.Columns), len(T2_list))
# PixelSpacing = (0.5,0.5,0.5)
T2Array = np.zeros(PixelDims, dtype=refIm.pixel_array.dtype)
for filename in T2_list:
ds = dicom.read_file(filename)
T2Array[:, :, T2_list.index(filename)] = ds.pixel_array
T2MRI = common.ImFromNPArr(T2Array)
T2MRI.setGrid(cc.MakeGrid(T2MRI.grid().size(), 0.5))
T2MRI.toType(ca.MEM_DEVICE)
#Swap the axis of the images so they align with the gradient directions
T2MRI = cc.SwapAxes(T2MRI, 0, 1)
T2MRI = cc.SwapAxes(T2MRI, 0, 2)
T2MRI = cc.FlipDim(T2MRI, 2)
# T2MRI = cc.FlipDim(T2MRI,2)
DWIgrid = cc.MakeGrid([120, 144, 120], 0.5, [0, 0, 0])
down_T2 = ca.Image3D(DWIgrid, ca.MEM_DEVICE)
ca.Resample(down_T2, T2MRI)
####
#Display the list
# cc.WriteMHA(down_T2,outdir + 'Images/T2_downSample_to_DWImha')
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)
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()
#############################
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:
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()