def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
list_warp = self.list_warp # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# list_warp = list_warp.replace(' ', '') # remove spaces
# list_warp = list_warp.split(",") # parse with comma
for idx_warp, path_warp in enumerate(self.list_warp):
# Check if this transformation should be inverted
if path_warp in self.list_warpinv:
use_inverse.append('-i')
# list_warp[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[
use_inverse[idx_warp], list_warp[idx_warp]
]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = list_warp[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and Image(list_warp[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(
fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x, y: x + y,
fname_warp_list_invert)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' +
str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
sct.run([
'isct_antsApplyTransforms', '-d', dim, '-i', fname_src, '-o',
fname_out, '-t'
] + fname_warp_list_invert + ['-r', fname_dest] + interp,
verbose=verbose,
is_sct_binary=True)
# if 4d, loop across the T dimension
else:
dim = '4'
path_tmp = sct.tmp_create(basename="apply_transfo",
verbose=verbose)
# convert to nifti into temp folder
sct.printv(
'\nCopying input data to tmp folder and convert to nii...',
verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in list_warp:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp,
os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = sct.run([
'isct_antsApplyTransforms',
'-d',
'3',
'-i',
file_data_split,
'-o',
file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r',
file_dest + ext_dest,
] + interp,
verbose,
is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(
os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# Copy affine matrix from destination space to make sure qform/sform are the same
sct.printv(
"Copy affine matrix from destination space to make sure qform/sform are the same.",
verbose)
im_src_reg = Image(fname_out)
im_src_reg.copy_qform_from_ref(Image(fname_dest))
im_src_reg.save(
verbose=0
) # set verbose=0 to avoid warning message about rewriting file
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv(
'WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...',
verbose, 'warning')
else:
if crop_reference in [1, 2]:
# Extract only the first ndim of the warping field
img_warp = Image(warping_field)
if dim == '2':
img_warp_ndim = Image(img_src.data[:, :], hdr=img_warp.hdr)
elif dim in ['3', '4']:
img_warp_ndim = Image(img_src.data[:, :, :],
hdr=img_warp.hdr)
# Set zero to everything outside the warping field
cropper = ImageCropper(Image(fname_out))
cropper.get_bbox_from_ref(img_warp_ndim)
if crop_reference == 1:
img_out = cropper.crop(background=0)
elif crop_reference == 2:
img_out = cropper.crop()
img_out.save(fname_out)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
