def get_im_from_list(self, data):
im = Image(data)
# set pix dimension
im.hdr.structarr['pixdim'][1] = self.param_data.axial_res
im.hdr.structarr['pixdim'][2] = self.param_data.axial_res
# set the correct orientation
im.save('im_to_orient.nii.gz')
# TODO explain this quirk
im = msct_image.change_orientation(im, 'IRP')
im = msct_image.change_orientation(im, 'PIL', inverse=True)
return im
def get_im_from_list(self, data):
im = Image(data)
# set pix dimension
im.hdr.structarr['pixdim'][1] = self.param_data.axial_res
im.hdr.structarr['pixdim'][2] = self.param_data.axial_res
# set the correct orientation
im.save('im_to_orient.nii.gz')
# TODO explain this quirk
im = msct_image.change_orientation(im, 'IRP')
im = msct_image.change_orientation(im, 'PIL', inverse=True)
return im
def _call_viewer_centerline(fname_in, interslice_gap=20.0):
from spinalcordtoolbox.gui.base import AnatomicalParams
from spinalcordtoolbox.gui.centerline import launch_centerline_dialog
im_data = Image(fname_in)
# Get the number of slice along the (IS) axis
im_tmp = msct_image.change_orientation(im_data, 'RPI')
_, _, nz, _, _, _, pz, _ = im_tmp.dim
del im_tmp
params = AnatomicalParams()
# setting maximum number of points to a reasonable value
params.num_points = np.ceil(nz * pz / interslice_gap) + 2
params.interval_in_mm = interslice_gap
params.starting_slice = 'top'
im_mask_viewer = msct_image.zeros_like(im_data)
controller = launch_centerline_dialog(im_data, im_mask_viewer, params)
fname_labels_viewer = sct.add_suffix(fname_in, '_viewer')
if not controller.saved:
sct.log.error(
'The viewer has been closed before entering all manual points. Please try again.'
)
sys.exit(1)
# save labels
controller.as_niftii(fname_labels_viewer)
return fname_labels_viewer
def test_change_nd_orientation(fake_4dimage_sct):
import sct_image
im_src = fake_4dimage_sct.copy()
path_tmp = sct.tmp_create(basename="test_reorient")
im_src.save(os.path.join(path_tmp, "src.nii"), mutable=True)
print(im_src.orientation, im_src.data.shape)
def orient2shape(orient):
# test-data-specific thing
letter2dim = dict(
L=2,
R=2,
A=3,
P=3,
I=4,
S=4,
)
return tuple([letter2dim[x] for x in orient] + [5])
orientation = im_src.orientation
assert orientation == "LPI"
assert im_src.header.get_best_affine()[:3,3].tolist() == [0,0,0]
im_dst = msct_image.change_orientation(im_src, "RPI")
assert im_dst.orientation == "RPI"
assert im_dst.data.shape == orient2shape("RPI")
assert im_dst.header.get_best_affine()[:3,3].tolist() == [2-1,0,0]
def test_change_nd_orientation(fake_4dimage_sct):
im_src = fake_4dimage_sct.copy()
path_tmp = tmp_create(basename="test_reorient")
im_src.save(os.path.join(path_tmp, "src.nii"), mutable=True)
print(im_src.orientation, im_src.data.shape)
def orient2shape(orient):
# test-data-specific thing
letter2dim = dict(
L=2,
R=2,
A=3,
P=3,
I=4,
S=4,
)
return tuple([letter2dim[x] for x in orient] + [5])
orientation = im_src.orientation
assert orientation == "LPI"
assert im_src.header.get_best_affine()[:3, 3].tolist() == [0, 0, 0]
im_dst = msct_image.change_orientation(im_src, "RPI")
assert im_dst.orientation == "RPI"
assert im_dst.data.shape == orient2shape("RPI")
assert im_dst.header.get_best_affine()[:3, 3].tolist() == [2 - 1, 0, 0]
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = im_in.orientation
if orientation != 'RPI':
im_in.change_orientation('RPI')
fname = add_suffix(im_in.absolutepath, "_rpi")
im_in.save(path=fname, mutable=True)
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2):
name_resample = extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1:
# when data is 2d: we convert it to a 3d image in order to avoid conversion problem with 2d data
# TODO: check if this above problem is still present (now that we are using nibabel instead of nipy)
run_proc(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
run_proc(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
run_proc(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.save(name_resample)
if binary:
img = Image(name_resample)
img.data = binarize(img.data, thr)
img.save()
if orientation != 'RPI':
img = Image(name_resample)
img.change_orientation(orientation)
name_resample = add_suffix(img.absolutepath, "_{}".format(orientation.lower()))
img.save(path=name_resample, mutable=True)
return name_resample
else:
if orientation != 'RPI':
fname = add_suffix(fname, "_RPI")
im_in = change_orientation(im_in, orientation).save(fname)
printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = im_in.orientation
if orientation != 'RPI':
fname = im_in.change_orientation(im_in, 'RPI', generate_path=True).save().absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1: # when data is 2d: we convert it to a 3d image in order to avoid nipy problem of conversion nifti-->nipy with 2d data
sct.run(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
sct.run(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
sct.run(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.save(name_resample)
if binary:
sct.run(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample])
if orientation != 'RPI':
name_resample = Image(name_resample) \
.change_orientation(orientation, generate_path=True) \
.save() \
.absolutepath
return name_resample
else:
if orientation != 'RPI':
fname = sct.add_suffix(fname, "_RPI")
im_in = msct_image.change_orientation(im_in, orientation).save(fname)
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = im_in.orientation
if orientation != 'RPI':
fname = im_in.change_orientation(im_in, 'RPI', generate_path=True).save().absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1: # when data is 2d: we convert it to a 3d image in order to avoid nipy problem of conversion nifti-->nipy with 2d data
sct.run(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
sct.run(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
sct.run(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.save(name_resample)
if binary:
sct.run(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample])
if orientation != 'RPI':
name_resample = Image(name_resample) \
.change_orientation(orientation, generate_path=True) \
.save() \
.absolutepath
return name_resample
else:
if orientation != 'RPI':
fname = sct.add_suffix(fname, "_RPI")
im_in = msct_image.change_orientation(im_in, orientation).save(fname)
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def main(args=None):
# initializations
output_type = None
param = Param()
dim_list = ['x', 'y', 'z', 't']
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = arguments["-i"]
n_in = len(fname_in)
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
if "-o" in arguments:
fname_out = arguments["-o"]
else:
fname_out = None
# Open file(s)
# im_in_list = [Image(fn) for fn in fname_in]
# run command
if "-concat" in arguments:
dim = arguments["-concat"]
assert dim in dim_list
dim = dim_list.index(dim)
im_out = [concat_data(fname_in, dim)] # TODO: adapt to fname_in
elif "-copy-header" in arguments:
im_in = Image(fname_in[0])
im_dest = Image(arguments["-copy-header"])
im_dest_new = im_in.copy()
im_dest_new.data = im_dest.data.copy()
# im_dest.header = im_in.header
im_dest_new.absolutepath = im_dest.absolutepath
im_out = [im_dest_new]
fname_out = arguments["-copy-header"]
elif '-display-warp' in arguments:
im_in = fname_in[0]
visualize_warp(im_in, fname_grid=None, step=3, rm_tmp=True)
im_out = None
elif "-getorient" in arguments:
im_in = Image(fname_in[0])
orient = im_in.orientation
im_out = None
elif '-keep-vol' in arguments:
index_vol = arguments['-keep-vol']
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='keep')]
elif '-mcs' in arguments:
im_in = Image(fname_in[0])
if n_in != 1:
sct.printv(parser.usage.generate(error='ERROR: -mcs need only one input'))
if len(im_in.data.shape) != 5:
sct.printv(parser.usage.generate(error='ERROR: -mcs input need to be a multi-component image'))
im_out = multicomponent_split(im_in)
elif '-omc' in arguments:
im_ref = Image(fname_in[0])
for fname in fname_in:
im = Image(fname)
if im.data.shape != im_ref.data.shape:
sct.printv(parser.usage.generate(error='ERROR: -omc inputs need to have all the same shapes'))
del im
im_out = [multicomponent_merge(fname_in)] # TODO: adapt to fname_in
elif "-pad" in arguments:
im_in = Image(fname_in[0])
ndims = len(im_in.data.shape)
if ndims != 3:
sct.printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments["-pad"].split(',')
if len(pad_arguments) != 3:
sct.printv('ERROR: you need to specify 3 padding values.', 1, 'error')
padx, pady, padz = pad_arguments
padx, pady, padz = int(padx), int(pady), int(padz)
im_out = [pad_image(im_in, pad_x_i=padx, pad_x_f=padx, pad_y_i=pady,
pad_y_f=pady, pad_z_i=padz, pad_z_f=padz)]
elif "-pad-asym" in arguments:
im_in = Image(fname_in[0])
ndims = len(im_in.data.shape)
if ndims != 3:
sct.printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments["-pad-asym"].split(',')
if len(pad_arguments) != 6:
sct.printv('ERROR: you need to specify 6 padding values.', 1, 'error')
padxi, padxf, padyi, padyf, padzi, padzf = pad_arguments
padxi, padxf, padyi, padyf, padzi, padzf = int(padxi), int(padxf), int(padyi), int(padyf), int(padzi), int(padzf)
im_out = [pad_image(im_in, pad_x_i=padxi, pad_x_f=padxf, pad_y_i=padyi, pad_y_f=padyf, pad_z_i=padzi, pad_z_f=padzf)]
elif '-remove-vol' in arguments:
index_vol = arguments['-remove-vol']
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='remove')]
elif "-setorient" in arguments:
sct.printv(fname_in[0])
im_in = Image(fname_in[0])
im_out = [msct_image.change_orientation(im_in, arguments["-setorient"]).save(fname_out)]
elif "-setorient-data" in arguments:
im_in = Image(fname_in[0])
im_out = [msct_image.change_orientation(im_in, arguments["-setorient-data"], inverse=True).save(fname_out)]
elif "-split" in arguments:
dim = arguments["-split"]
assert dim in dim_list
im_in = Image(fname_in[0])
dim = dim_list.index(dim)
im_out = split_data(im_in, dim)
elif '-type' in arguments:
output_type = arguments['-type']
im_in = Image(fname_in[0])
im_out = [im_in] # TODO: adapt to fname_in
else:
im_out = None
sct.printv(parser.usage.generate(error='ERROR: you need to specify an operation to do on the input image'))
# in case fname_out is not defined, use first element of input file name list
if fname_out == None:
fname_out = fname_in[0]
# Write output
if im_out is not None:
sct.printv('Generate output files...', verbose)
# if only one output
if len(im_out) == 1 and not '-split' in arguments:
im_out[0].save(fname_out, dtype=output_type, verbose=verbose)
sct.display_viewer_syntax([fname_out], verbose=verbose)
if '-mcs' in arguments:
# use input file name and add _X, _Y _Z. Keep the same extension
l_fname_out = []
for i_dim in range(3):
l_fname_out.append(sct.add_suffix(fname_out or fname_in[0], '_' + dim_list[i_dim].upper()))
im_out[i_dim].save(l_fname_out[i_dim], verbose=verbose)
sct.display_viewer_syntax(fname_out)
if '-split' in arguments:
# use input file name and add _"DIM+NUMBER". Keep the same extension
l_fname_out = []
for i, im in enumerate(im_out):
l_fname_out.append(sct.add_suffix(fname_out or fname_in[0], '_' + dim_list[dim].upper() + str(i).zfill(4)))
im.save(l_fname_out[i])
sct.display_viewer_syntax(l_fname_out)
elif "-getorient" in arguments:
sct.printv(orient)
elif '-display-warp' in arguments:
sct.printv('Warping grid generated.', verbose, 'info')
def test_more_change_orientation(fake_3dimage_sct, fake_3dimage_sct_vis):
path_tmp = tmp_create(basename="test_reorient")
path_tmp = "."
im_src = fake_3dimage_sct.copy()
im_src.save(os.path.join(path_tmp, "src.nii"), mutable=True)
print(im_src.orientation, im_src.data.shape)
def orient2shape(orient):
# test-data-specific thing
letter2dim = dict(
L=7,
R=7,
A=8,
P=8,
I=9,
S=9,
)
return tuple([letter2dim[x] for x in orient])
orientation = im_src.orientation # LPI
assert im_src.header.get_best_affine()[:3, 3].tolist() == [0, 0, 0]
im_dst = msct_image.change_orientation(im_src, "RPI")
print(im_dst.orientation, im_dst.data.shape)
assert im_dst.data.shape == orient2shape("RPI")
assert im_dst.header.get_best_affine()[:3, 3].tolist() == [7 - 1, 0, 0]
# spot check
orientation = im_src.orientation # LPI
im_dst = msct_image.change_orientation(im_src, "IRP")
print(im_dst.orientation, im_dst.data.shape)
assert im_dst.data.shape == orient2shape("IRP")
# to & fro
im_dst2 = msct_image.change_orientation(im_dst, orientation)
print(im_dst2.orientation, im_dst2.data.shape)
assert im_dst2.orientation == im_src.orientation
assert im_dst2.data.shape == orient2shape(orientation)
assert (im_dst2.data == im_src.data).all()
assert np.allclose(im_src.header.get_best_affine(),
im_dst2.header.get_best_affine())
#fn = os.path.join(path_tmp, "pouet.nii")
im_ref = fake_3dimage_sct.copy()
im_src = fake_3dimage_sct.copy()
orientation = im_src.orientation
im_src.change_orientation("ASR").change_orientation(orientation)
assert im_src.orientation == im_ref.orientation
assert (im_dst2.data == im_src.data).all()
assert np.allclose(im_src.header.get_best_affine(),
im_ref.header.get_best_affine())
im_dst2 = msct_image.change_orientation(im_dst, orientation)
print(im_dst2.orientation, im_dst2.data.shape)
assert im_dst2.orientation == im_src.orientation
assert im_dst2.data.shape == orient2shape(orientation)
assert (im_dst2.data == im_src.data).all()
assert np.allclose(im_src.header.get_best_affine(),
im_dst2.header.get_best_affine())
# copy
im_dst = im_src.copy().change_orientation("IRP")
assert im_dst.data.shape == orient2shape("IRP")
print(im_dst.orientation, im_dst.data.shape)
print("Testing orientation persistence")
img = im_src.copy()
orientation = img.orientation
fn = os.path.join(path_tmp, "pouet.nii")
img.change_orientation("PIR").save(fn)
assert img.data.shape == orient2shape("PIR")
img = msct_image.Image(fn)
assert img.orientation == "PIR"
assert img.data.shape == orient2shape("PIR")
print(img.orientation, img.data.shape)
# typical pattern
img = fake_3dimage_sct_vis.copy()
print(img.header.get_best_affine())
orientation = img.orientation
path_tmp = "."
fn = os.path.join(path_tmp, "vis.nii")
fn2 = img.save(fn, mutable=True).change_orientation(
"ALS", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "ALS"
assert img.data.shape == orient2shape("ALS")
print(img.header.get_best_affine())
fn2 = img.save(fn, mutable=True).change_orientation(
"RAS", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "RAS"
assert img.data.shape == orient2shape("RAS")
print(img.header.get_best_affine())
fn2 = img.save(fn, mutable=True).change_orientation(
"RPI", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "RPI"
assert img.data.shape == orient2shape("RPI")
print(img.header.get_best_affine())
fn2 = img.save(fn, mutable=True).change_orientation(
"PLI", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "PLI"
assert img.data.shape == orient2shape("PLI")
print(img.header.get_best_affine())
# print(src.header)
possibilities = [
"ASR",
"SRA",
"RAS",
]
possibilities = msct_image.all_refspace_strings()
for orientation in possibilities:
dst = msct_image.change_orientation(im_src, orientation)
# dst.save("pouet-{}.nii".format(dst.orientation))
print(orientation, dst.orientation, dst.data.shape, dst.dim)
assert orientation == dst.orientation
def test_change_orientation(fake_3dimage_sct, fake_3dimage_sct_vis):
path_tmp = tmp_create(basename="test_reorient")
path_tmp = "."
print("Spot-checking that physical coordinates don't change")
for shape_is in (1, 2, 3):
shape = (1, 1, shape_is)
print("Simple image with shape {}".format(shape))
data = np.ones(shape, order="F")
data[:, :, shape_is - 1] += 1
im_src = fake_3dimage_sct_custom(data)
im_dst = msct_image.change_orientation(im_src, "ASR")
# Basic check
assert im_dst.orientation == "ASR"
# Basic data check
assert im_dst.data.mean() == im_src.data.mean()
# Basic header check: check that the same voxel
# remains at the same physical position
aff_src = im_src.header.get_best_affine()
aff_dst = im_dst.header.get_best_affine()
# Take the extremities "a" & "z"...
# consider the original LPI position
pta_src = np.array([[0, 0, 0, 1]]).T
ptz_src = np.array([[0, 0, shape_is - 1, 1]]).T
# and the position in ASR
pta_dst = np.array([[0, shape_is - 1, 0, 1]]).T
ptz_dst = np.array([[0, 0, 0, 1]]).T
# The physical positions should be:
posa_src = np.matmul(aff_src, pta_src)
posa_dst = np.matmul(aff_dst, pta_dst)
print("A at src {}".format(posa_src.T))
print("A at dst {}".format(posa_dst.T))
posz_src = np.matmul(aff_src, ptz_src)
posz_dst = np.matmul(aff_dst, ptz_dst)
# and they should be equal
assert (posa_src == posa_dst).all()
assert (posz_src == posz_dst).all()
fn = "".join(str(x) for x in im_src.data.shape)
im_src.save("{}-src.nii".format(fn))
im_dst.save("{}-dst.nii".format(fn))
np.random.seed(0)
print("More checking that physical coordinates don't change")
if 1:
shape = (7, 8, 9)
print("Simple image with shape {}".format(shape))
data = np.ones(shape, order="F") * 10
data[4, 4, 4] = 4
data[3, 3, 3] = 3
data[0, 0, 0] = 0
values = (0, 3, 4)
im_ref = fake_3dimage_sct_custom(data)
im_ref.header.set_xyzt_units("mm", "msec")
import scipy.linalg
def rand_rot():
q, _ = scipy.linalg.qr(np.random.randn(3, 3))
if scipy.linalg.det(q) < 0:
q[:, 0] = -q[:, 0]
return q
affine = im_ref.header.get_best_affine()
affine[:3, :3] = rand_rot()
affine[3, :3] = 0.0
affine[:3, 3] = np.random.random((3))
affine[3, 3] = 1.0
affine[0, 0] *= 2
im_ref.header.set_sform(affine, code='scanner')
orientations = msct_image.all_refspace_strings()
for ori_src in orientations:
for ori_dst in orientations:
print("{} -> {}".format(ori_src, ori_dst))
im_src = msct_image.change_orientation(im_ref, ori_src)
im_dst = msct_image.change_orientation(im_src, ori_dst)
assert im_src.orientation == ori_src
assert im_dst.orientation == ori_dst
assert im_dst.data.mean() == im_src.data.mean()
# Basic header check: check that the same voxel
# remains at the same physical position
aff_src = im_src.header.get_best_affine()
aff_dst = im_dst.header.get_best_affine()
data_src = np.array(im_src.data)
data_dst = np.array(im_dst.data)
for value in values:
pt_src = np.argwhere(data_src == value)[0]
pt_dst = np.argwhere(data_dst == value)[0]
pos_src = np.matmul(
aff_src,
np.hstack((pt_src, [1])).reshape((4, 1)))
pos_dst = np.matmul(
aff_dst,
np.hstack((pt_dst, [1])).reshape((4, 1)))
if 0:
print("P at src {}".format(pos_src.T))
print("P at dst {}".format(pos_dst.T))
assert np.allclose(pos_src, pos_dst, atol=1e-3)
def main(args=None):
# initializations
output_type = None
param = Param()
dim_list = ['x', 'y', 'z', 't']
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = arguments["-i"]
n_in = len(fname_in)
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
if "-o" in arguments:
fname_out = arguments["-o"]
else:
fname_out = None
# Open file(s)
# im_in_list = [Image(fn) for fn in fname_in]
# run command
if "-concat" in arguments:
dim = arguments["-concat"]
assert dim in dim_list
dim = dim_list.index(dim)
im_out = [concat_data(fname_in, dim)] # TODO: adapt to fname_in
elif "-copy-header" in arguments:
im_in = Image(fname_in[0])
im_dest = Image(arguments["-copy-header"])
im_dest_new = im_in.copy()
im_dest_new.data = im_dest.data.copy()
# im_dest.header = im_in.header
im_dest_new.absolutepath = im_dest.absolutepath
im_out = [im_dest_new]
fname_out = arguments["-copy-header"]
elif '-display-warp' in arguments:
im_in = fname_in[0]
visualize_warp(im_in, fname_grid=None, step=3, rm_tmp=True)
im_out = None
elif "-getorient" in arguments:
im_in = Image(fname_in[0])
orient = im_in.orientation
im_out = None
elif '-keep-vol' in arguments:
index_vol = arguments['-keep-vol']
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='keep')]
elif '-mcs' in arguments:
im_in = Image(fname_in[0])
if n_in != 1:
sct.printv(
parser.usage.generate(error='ERROR: -mcs need only one input'))
if len(im_in.data.shape) != 5:
sct.printv(
parser.usage.generate(
error='ERROR: -mcs input need to be a multi-component image'
))
im_out = multicomponent_split(im_in)
elif '-omc' in arguments:
im_ref = Image(fname_in[0])
for fname in fname_in:
im = Image(fname)
if im.data.shape != im_ref.data.shape:
sct.printv(
parser.usage.generate(
error=
'ERROR: -omc inputs need to have all the same shapes'))
del im
im_out = [multicomponent_merge(fname_in)] # TODO: adapt to fname_in
elif "-pad" in arguments:
im_in = Image(fname_in[0])
ndims = len(im_in.data.shape)
if ndims != 3:
sct.printv('ERROR: you need to specify a 3D input file.', 1,
'error')
return
pad_arguments = arguments["-pad"].split(',')
if len(pad_arguments) != 3:
sct.printv('ERROR: you need to specify 3 padding values.', 1,
'error')
padx, pady, padz = pad_arguments
padx, pady, padz = int(padx), int(pady), int(padz)
im_out = [
pad_image(im_in,
pad_x_i=padx,
pad_x_f=padx,
pad_y_i=pady,
pad_y_f=pady,
pad_z_i=padz,
pad_z_f=padz)
]
elif "-pad-asym" in arguments:
im_in = Image(fname_in[0])
ndims = len(im_in.data.shape)
if ndims != 3:
sct.printv('ERROR: you need to specify a 3D input file.', 1,
'error')
return
pad_arguments = arguments["-pad-asym"].split(',')
if len(pad_arguments) != 6:
sct.printv('ERROR: you need to specify 6 padding values.', 1,
'error')
padxi, padxf, padyi, padyf, padzi, padzf = pad_arguments
padxi, padxf, padyi, padyf, padzi, padzf = int(padxi), int(padxf), int(
padyi), int(padyf), int(padzi), int(padzf)
im_out = [
pad_image(im_in,
pad_x_i=padxi,
pad_x_f=padxf,
pad_y_i=padyi,
pad_y_f=padyf,
pad_z_i=padzi,
pad_z_f=padzf)
]
elif '-remove-vol' in arguments:
index_vol = arguments['-remove-vol']
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='remove')]
elif "-setorient" in arguments:
sct.printv(fname_in[0])
im_in = Image(fname_in[0])
im_out = [
msct_image.change_orientation(
im_in, arguments["-setorient"]).save(fname_out)
]
elif "-setorient-data" in arguments:
im_in = Image(fname_in[0])
im_out = [
msct_image.change_orientation(im_in,
arguments["-setorient-data"],
inverse=True).save(fname_out)
]
elif "-split" in arguments:
dim = arguments["-split"]
assert dim in dim_list
im_in = Image(fname_in[0])
dim = dim_list.index(dim)
im_out = split_data(im_in, dim)
elif '-type' in arguments:
output_type = arguments['-type']
im_in = Image(fname_in[0])
im_out = [im_in] # TODO: adapt to fname_in
else:
im_out = None
sct.printv(
parser.usage.generate(
error=
'ERROR: you need to specify an operation to do on the input image'
))
# in case fname_out is not defined, use first element of input file name list
if fname_out == None:
fname_out = fname_in[0]
# Write output
if im_out is not None:
sct.printv('Generate output files...', verbose)
# if only one output
if len(im_out) == 1 and not '-split' in arguments:
im_out[0].save(fname_out, dtype=output_type, verbose=verbose)
sct.display_viewer_syntax([fname_out], verbose=verbose)
if '-mcs' in arguments:
# use input file name and add _X, _Y _Z. Keep the same extension
l_fname_out = []
for i_dim in range(3):
l_fname_out.append(
sct.add_suffix(fname_out or fname_in[0],
'_' + dim_list[i_dim].upper()))
im_out[i_dim].save(l_fname_out[i_dim], verbose=verbose)
sct.display_viewer_syntax(fname_out)
if '-split' in arguments:
# use input file name and add _"DIM+NUMBER". Keep the same extension
l_fname_out = []
for i, im in enumerate(im_out):
l_fname_out.append(
sct.add_suffix(
fname_out or fname_in[0],
'_' + dim_list[dim].upper() + str(i).zfill(4)))
im.save(l_fname_out[i])
sct.display_viewer_syntax(l_fname_out)
elif "-getorient" in arguments:
sct.printv(orient)
elif '-display-warp' in arguments:
sct.printv('Warping grid generated.', verbose, 'info')
def deep_segmentation_MSlesion(fname_image, contrast_type, output_folder, ctr_algo='svm', ctr_file=None, brain_bool=True, remove_temp_files=1, verbose=1):
"""Pipeline."""
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
# create temporary folder with intermediate results
sct.log.info("\nCreating temporary folder...")
file_fname = os.path.basename(fname_image)
tmp_folder = sct.TempFolder()
tmp_folder_path = tmp_folder.get_path()
fname_image_tmp = tmp_folder.copy_from(fname_image)
if ctr_algo == 'manual': # if the ctr_file is provided
tmp_folder.copy_from(ctr_file)
file_ctr = os.path.basename(ctr_file)
else:
file_ctr = None
tmp_folder.chdir()
# orientation of the image, should be RPI
sct.log.info("\nReorient the image to RPI, if necessary...")
fname_orient = sct.add_suffix(file_fname, '_RPI')
im_2orient = Image(file_fname)
original_orientation = im_2orient.orientation
if original_orientation != 'RPI':
im_orient = msct_image.change_orientation(im_2orient, 'RPI').save(fname_orient)
else:
im_orient = im_2orient
sct.copy(fname_image_tmp, fname_orient)
input_resolution = im_orient.dim[4:7]
del im_2orient, im_orient
# find the spinal cord centerline - execute OptiC binary
sct.log.info("\nFinding the spinal cord centerline...")
contrast_type_ctr = contrast_type.split('_')[0]
fname_res, centerline_filename = find_centerline(algo=ctr_algo,
image_fname=fname_orient,
path_sct=path_sct,
contrast_type=contrast_type_ctr,
brain_bool=brain_bool,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
centerline_fname=file_ctr)
im_nii, ctr_nii = Image(fname_res), Image(centerline_filename)
# crop image around the spinal cord centerline
sct.log.info("\nCropping the image around the spinal cord...")
fname_crop = sct.add_suffix(fname_res, '_crop')
crop_size = 48
X_CROP_LST, Y_CROP_LST, im_crop_nii = crop_image_around_centerline(im_in=im_nii,
ctr_in=ctr_nii,
crop_size=crop_size)
del ctr_nii
# normalize the intensity of the images
sct.log.info("Normalizing the intensity...")
im_norm_in = apply_intensity_normalization(img=im_crop_nii, contrast=contrast_type)
del im_crop_nii
# resample to 0.5mm isotropic
fname_norm = sct.add_suffix(fname_orient, '_norm')
im_norm_in.save(fname_norm)
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_norm, fname_res3d, '0.5x0.5x0.5',
'mm', 'linear', verbose=0)
# segment data using 3D convolutions
sct.log.info("\nSegmenting the MS lesions using deep learning on 3D patches...")
segmentation_model_fname = os.path.join(path_sct, 'data', 'deepseg_lesion_models', '{}_lesion.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_lesionseg')
segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
fname_in=fname_res3d,
fname_out=fname_seg_crop_res)
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_seg_crop_res, fname_seg_res2d, initial_2d_resolution,
'mm', 'linear', verbose=0)
seg_crop_data = Image(fname_seg_res2d).data
# reconstruct the segmentation from the crop data
sct.log.info("\nReassembling the image...")
seg_uncrop_nii = uncrop_image(ref_in=im_nii,
data_crop=seg_crop_data,
x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST)
fname_seg_res_RPI = sct.add_suffix(file_fname, '_res_RPI_seg')
seg_uncrop_nii.save(fname_seg_res_RPI)
del seg_uncrop_nii, im_nii, seg_crop_data
# resample to initial resolution
sct.log.info("Resampling the segmentation to the original image resolution...")
initial_resolution = 'x'.join([str(input_resolution[0]), str(input_resolution[1]), str(input_resolution[2])])
fname_seg_RPI = sct.add_suffix(file_fname, '_RPI_seg')
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_seg_res_RPI, fname_seg_RPI, initial_resolution,
'mm', 'linear', verbose=0)
seg_initres_nii = Image(fname_seg_RPI)
# binarize the resampled image to remove interpolation effects
sct.log.info("\nBinarizing the segmentation to avoid interpolation effects...")
thr = 0.1
seg_initres_nii.data[np.where(seg_initres_nii.data >= thr)] = 1
seg_initres_nii.data[np.where(seg_initres_nii.data < thr)] = 0
# reorient to initial orientation
sct.log.info("\nReorienting the segmentation to the original image orientation...")
fname_seg = sct.add_suffix(file_fname, '_seg')
if original_orientation != 'RPI':
out_nii = msct_image.change_orientation(seg_initres_nii, original_orientation)
seg_initres_nii.save(fname_seg)
del seg_initres_nii
tmp_folder.chdir_undo()
# copy image from temporary folder into output folder
sct.copy(os.path.join(tmp_folder_path, fname_seg), output_folder)
# remove temporary files
if remove_temp_files:
sct.log.info("\nRemove temporary files...")
tmp_folder.cleanup()
return os.path.join(output_folder, fname_seg)
def main(args=None):
"""
Main function
:param args:
:return:
"""
# initializations
output_type = None
dim_list = ['x', 'y', 'z', 't']
# Get parser args
if args is None:
args = None if sys.argv[1:] else ['--help']
parser = get_parser()
arguments = parser.parse_args(args=args)
fname_in = arguments.i
n_in = len(fname_in)
verbose = arguments.v
sct.init_sct(log_level=verbose, update=True) # Update log level
if arguments.o is not None:
fname_out = arguments.o
else:
fname_out = None
# Run command
# Arguments are sorted alphabetically (not according to the usage order)
if arguments.concat is not None:
dim = arguments.concat
assert dim in dim_list
dim = dim_list.index(dim)
im_out = [concat_data(fname_in, dim)] # TODO: adapt to fname_in
elif arguments.copy_header is not None:
im_in = Image(fname_in[0])
im_dest = Image(arguments.copy_header)
im_dest_new = im_in.copy()
im_dest_new.data = im_dest.data.copy()
# im_dest.header = im_in.header
im_dest_new.absolutepath = im_dest.absolutepath
im_out = [im_dest_new]
fname_out = arguments.copy_header
elif arguments.display_warp:
im_in = fname_in[0]
visualize_warp(im_in, fname_grid=None, step=3, rm_tmp=True)
im_out = None
elif arguments.getorient:
im_in = Image(fname_in[0])
orient = im_in.orientation
im_out = None
elif arguments.keep_vol is not None:
index_vol = (arguments.keep_vol).split(',')
for iindex_vol, vol in enumerate(index_vol):
index_vol[iindex_vol] = int(vol)
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='keep')]
elif arguments.mcs:
im_in = Image(fname_in[0])
if n_in != 1:
sct.printv(parser.error('ERROR: -mcs need only one input'))
if len(im_in.data.shape) != 5:
sct.printv(
parser.error(
'ERROR: -mcs input need to be a multi-component image'))
im_out = multicomponent_split(im_in)
elif arguments.omc:
im_ref = Image(fname_in[0])
for fname in fname_in:
im = Image(fname)
if im.data.shape != im_ref.data.shape:
sct.printv(
parser.error(
'ERROR: -omc inputs need to have all the same shapes'))
del im
im_out = [multicomponent_merge(fname_in)] # TODO: adapt to fname_in
elif arguments.pad is not None:
im_in = Image(fname_in[0])
ndims = len(im_in.data.shape)
if ndims != 3:
sct.printv('ERROR: you need to specify a 3D input file.', 1,
'error')
return
pad_arguments = arguments.pad.split(',')
if len(pad_arguments) != 3:
sct.printv('ERROR: you need to specify 3 padding values.', 1,
'error')
padx, pady, padz = pad_arguments
padx, pady, padz = int(padx), int(pady), int(padz)
im_out = [
pad_image(im_in,
pad_x_i=padx,
pad_x_f=padx,
pad_y_i=pady,
pad_y_f=pady,
pad_z_i=padz,
pad_z_f=padz)
]
elif arguments.pad_asym is not None:
im_in = Image(fname_in[0])
ndims = len(im_in.data.shape)
if ndims != 3:
sct.printv('ERROR: you need to specify a 3D input file.', 1,
'error')
return
pad_arguments = arguments.pad_asym.split(',')
if len(pad_arguments) != 6:
sct.printv('ERROR: you need to specify 6 padding values.', 1,
'error')
padxi, padxf, padyi, padyf, padzi, padzf = pad_arguments
padxi, padxf, padyi, padyf, padzi, padzf = int(padxi), int(padxf), int(
padyi), int(padyf), int(padzi), int(padzf)
im_out = [
pad_image(im_in,
pad_x_i=padxi,
pad_x_f=padxf,
pad_y_i=padyi,
pad_y_f=padyf,
pad_z_i=padzi,
pad_z_f=padzf)
]
elif arguments.remove_vol is not None:
index_vol = (arguments.remove_vol).split(',')
for iindex_vol, vol in enumerate(index_vol):
index_vol[iindex_vol] = int(vol)
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='remove')]
elif arguments.setorient is not None:
sct.printv(fname_in[0])
im_in = Image(fname_in[0])
im_out = [msct_image.change_orientation(im_in, arguments.setorient)]
elif arguments.setorient_data is not None:
im_in = Image(fname_in[0])
im_out = [
msct_image.change_orientation(im_in,
arguments.setorient_data,
data_only=True)
]
elif arguments.split is not None:
dim = arguments.split
assert dim in dim_list
im_in = Image(fname_in[0])
dim = dim_list.index(dim)
im_out = split_data(im_in, dim)
elif arguments.type is not None:
output_type = arguments.type
im_in = Image(fname_in[0])
im_out = [im_in] # TODO: adapt to fname_in
elif arguments.to_fsl is not None:
space_files = arguments.to_fsl
if len(space_files) > 2 or len(space_files) < 1:
sct.printv(parser.error('ERROR: -to-fsl expects 1 or 2 arguments'))
return
spaces = [Image(s) for s in space_files]
if len(spaces) < 2:
spaces.append(None)
im_out = [
displacement_to_abs_fsl(Image(fname_in[0]), spaces[0], spaces[1])
]
else:
im_out = None
sct.printv(
parser.error(
'ERROR: you need to specify an operation to do on the input image'
))
# in case fname_out is not defined, use first element of input file name list
if fname_out is None:
fname_out = fname_in[0]
# Write output
if im_out is not None:
sct.printv('Generate output files...', verbose)
# if only one output
if len(im_out) == 1 and not '-split' in arguments:
im_out[0].save(fname_out, dtype=output_type, verbose=verbose)
sct.display_viewer_syntax([fname_out], verbose=verbose)
if arguments.mcs:
# use input file name and add _X, _Y _Z. Keep the same extension
l_fname_out = []
for i_dim in range(3):
l_fname_out.append(
sct.add_suffix(fname_out or fname_in[0],
'_' + dim_list[i_dim].upper()))
im_out[i_dim].save(l_fname_out[i_dim], verbose=verbose)
sct.display_viewer_syntax(fname_out)
if arguments.split is not None:
# use input file name and add _"DIM+NUMBER". Keep the same extension
l_fname_out = []
for i, im in enumerate(im_out):
l_fname_out.append(
sct.add_suffix(
fname_out or fname_in[0],
'_' + dim_list[dim].upper() + str(i).zfill(4)))
im.save(l_fname_out[i])
sct.display_viewer_syntax(l_fname_out)
elif arguments.getorient:
sct.printv(orient)
elif arguments.display_warp:
sct.printv('Warping grid generated.', verbose, 'info')
def test_more_change_orientation(fake_3dimage_sct, fake_3dimage_sct_vis):
path_tmp = sct.tmp_create(basename="test_reorient")
path_tmp = "."
im_src = fake_3dimage_sct.copy()
im_src.save(os.path.join(path_tmp, "src.nii"), mutable=True)
print(im_src.orientation, im_src.data.shape)
def orient2shape(orient):
# test-data-specific thing
letter2dim = dict(
L=7,
R=7,
A=8,
P=8,
I=9,
S=9,
)
return tuple([letter2dim[x] for x in orient])
orientation = im_src.orientation # LPI
assert im_src.header.get_best_affine()[:3,3].tolist() == [0,0,0]
im_dst = msct_image.change_orientation(im_src, "RPI")
print(im_dst.orientation, im_dst.data.shape)
assert im_dst.data.shape == orient2shape("RPI")
assert im_dst.header.get_best_affine()[:3,3].tolist() == [7-1,0,0]
# spot check
orientation = im_src.orientation # LPI
im_dst = msct_image.change_orientation(im_src, "IRP")
print(im_dst.orientation, im_dst.data.shape)
assert im_dst.data.shape == orient2shape("IRP")
# to & fro
im_dst2 = msct_image.change_orientation(im_dst, orientation)
print(im_dst2.orientation, im_dst2.data.shape)
assert im_dst2.orientation == im_src.orientation
assert im_dst2.data.shape == orient2shape(orientation)
assert (im_dst2.data == im_src.data).all()
assert np.allclose(im_src.header.get_best_affine(), im_dst2.header.get_best_affine())
#fn = os.path.join(path_tmp, "pouet.nii")
im_ref = fake_3dimage_sct.copy()
im_src = fake_3dimage_sct.copy()
orientation = im_src.orientation
im_src.change_orientation("ASR").change_orientation(orientation)
assert im_src.orientation == im_ref.orientation
assert (im_dst2.data == im_src.data).all()
assert np.allclose(im_src.header.get_best_affine(), im_ref.header.get_best_affine())
im_dst2 = msct_image.change_orientation(im_dst, orientation)
print(im_dst2.orientation, im_dst2.data.shape)
assert im_dst2.orientation == im_src.orientation
assert im_dst2.data.shape == orient2shape(orientation)
assert (im_dst2.data == im_src.data).all()
assert np.allclose(im_src.header.get_best_affine(), im_dst2.header.get_best_affine())
# copy
im_dst = im_src.copy().change_orientation("IRP")
assert im_dst.data.shape == orient2shape("IRP")
print(im_dst.orientation, im_dst.data.shape)
print("Testing orientation persistence")
img = im_src.copy()
orientation = img.orientation
fn = os.path.join(path_tmp, "pouet.nii")
img.change_orientation("PIR").save(fn)
assert img.data.shape == orient2shape("PIR")
img = msct_image.Image(fn)
assert img.orientation == "PIR"
assert img.data.shape == orient2shape("PIR")
print(img.orientation, img.data.shape)
# typical pattern
img = fake_3dimage_sct_vis.copy()
print(img.header.get_best_affine())
orientation = img.orientation
path_tmp = "."
fn = os.path.join(path_tmp, "vis.nii")
fn2 = img.save(fn, mutable=True).change_orientation("ALS", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "ALS"
assert img.data.shape == orient2shape("ALS")
print(img.header.get_best_affine())
fn2 = img.save(fn, mutable=True).change_orientation("RAS", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "RAS"
assert img.data.shape == orient2shape("RAS")
print(img.header.get_best_affine())
fn2 = img.save(fn, mutable=True).change_orientation("RPI", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "RPI"
assert img.data.shape == orient2shape("RPI")
print(img.header.get_best_affine())
fn2 = img.save(fn, mutable=True).change_orientation("PLI", generate_path=True).save().absolutepath
img = msct_image.Image(fn2)
assert img.orientation == "PLI"
assert img.data.shape == orient2shape("PLI")
print(img.header.get_best_affine())
#print(src.header)
possibilities = [ "ASR", "SRA", "RAS", ]
possibilities = msct_image.all_refspace_strings()
for orientation in possibilities:
dst = msct_image.change_orientation(im_src, orientation)
#dst.save("pouet-{}.nii".format(dst.orientation))
print(orientation, dst.orientation, dst.data.shape, dst.dim)
assert orientation == dst.orientation
def main(argv=None):
"""
Main function
:param argv:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_loglevel(verbose=verbose)
# initializations
output_type = None
dim_list = ['x', 'y', 'z', 't']
fname_in = arguments.i
im_in_list = [Image(fname) for fname in fname_in]
if len(im_in_list
) > 1 and arguments.concat is None and arguments.omc is None:
parser.error(
"Multi-image input is only supported for the '-concat' and '-omc' arguments."
)
# Apply initialization steps to all input images first
if arguments.set_sform_to_qform:
[im.set_sform_to_qform() for im in im_in_list]
elif arguments.set_qform_to_sform:
[im.set_qform_to_sform() for im in im_in_list]
# Most sct_image options don't accept multi-image input, so here we simply separate out the first image
# TODO: Extend the options so that they iterate through the list of images (to support multi-image input)
im_in = im_in_list[0]
if arguments.o is not None:
fname_out = arguments.o
else:
fname_out = None
# Run command
# Arguments are sorted alphabetically (not according to the usage order)
if arguments.concat is not None:
dim = arguments.concat
assert dim in dim_list
dim = dim_list.index(dim)
im_out = [concat_data(im_in_list, dim)]
elif arguments.copy_header is not None:
if fname_out is None:
raise ValueError("Need to specify output image with -o!")
im_dest = Image(arguments.copy_header)
im_dest_new = im_in.copy()
im_dest_new.data = im_dest.data.copy()
# im_dest.header = im_in.header
im_dest_new.absolutepath = im_dest.absolutepath
im_out = [im_dest_new]
elif arguments.display_warp:
visualize_warp(im_warp=im_in, im_grid=None, step=3, rm_tmp=True)
im_out = None
elif arguments.getorient:
orient = im_in.orientation
im_out = None
elif arguments.keep_vol is not None:
index_vol = (arguments.keep_vol).split(',')
for iindex_vol, vol in enumerate(index_vol):
index_vol[iindex_vol] = int(vol)
im_out = [remove_vol(im_in, index_vol, todo='keep')]
elif arguments.mcs:
if len(im_in.data.shape) != 5:
printv(
parser.error(
'ERROR: -mcs input need to be a multi-component image'))
im_out = multicomponent_split(im_in)
elif arguments.omc:
im_ref = im_in_list[0]
for im in im_in_list:
if im.data.shape != im_ref.data.shape:
printv(
parser.error(
'ERROR: -omc inputs need to have all the same shapes'))
del im
im_out = [multicomponent_merge(im_in_list=im_in_list)]
elif arguments.pad is not None:
ndims = len(im_in.data.shape)
if ndims != 3:
printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments.pad.split(',')
if len(pad_arguments) != 3:
printv('ERROR: you need to specify 3 padding values.', 1, 'error')
padx, pady, padz = pad_arguments
padx, pady, padz = int(padx), int(pady), int(padz)
im_out = [
pad_image(im_in,
pad_x_i=padx,
pad_x_f=padx,
pad_y_i=pady,
pad_y_f=pady,
pad_z_i=padz,
pad_z_f=padz)
]
elif arguments.pad_asym is not None:
ndims = len(im_in.data.shape)
if ndims != 3:
printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments.pad_asym.split(',')
if len(pad_arguments) != 6:
printv('ERROR: you need to specify 6 padding values.', 1, 'error')
padxi, padxf, padyi, padyf, padzi, padzf = pad_arguments
padxi, padxf, padyi, padyf, padzi, padzf = int(padxi), int(padxf), int(
padyi), int(padyf), int(padzi), int(padzf)
im_out = [
pad_image(im_in,
pad_x_i=padxi,
pad_x_f=padxf,
pad_y_i=padyi,
pad_y_f=padyf,
pad_z_i=padzi,
pad_z_f=padzf)
]
elif arguments.remove_vol is not None:
index_vol = (arguments.remove_vol).split(',')
for iindex_vol, vol in enumerate(index_vol):
index_vol[iindex_vol] = int(vol)
im_out = [remove_vol(im_in, index_vol, todo='remove')]
elif arguments.setorient is not None:
printv(im_in.absolutepath)
im_out = [change_orientation(im_in, arguments.setorient)]
elif arguments.setorient_data is not None:
im_out = [
change_orientation(im_in, arguments.setorient_data, data_only=True)
]
elif arguments.header is not None:
header = im_in.header
# Necessary because of https://github.com/nipy/nibabel/issues/480#issuecomment-239227821
im_file = nib.load(im_in.absolutepath)
header.structarr['scl_slope'] = im_file.dataobj.slope
header.structarr['scl_inter'] = im_file.dataobj.inter
printv(create_formatted_header_string(header=header,
output_format=arguments.header),
verbose=verbose)
im_out = None
elif arguments.split is not None:
dim = arguments.split
assert dim in dim_list
dim = dim_list.index(dim)
im_out = split_data(im_in, dim)
elif arguments.type is not None:
output_type = arguments.type
im_out = [im_in]
elif arguments.to_fsl is not None:
space_files = arguments.to_fsl
if len(space_files) > 2 or len(space_files) < 1:
printv(parser.error('ERROR: -to-fsl expects 1 or 2 arguments'))
return
spaces = [Image(s) for s in space_files]
if len(spaces) < 2:
spaces.append(None)
im_out = [displacement_to_abs_fsl(im_in, spaces[0], spaces[1])]
# If these arguments are used standalone, simply pass the input image to the output (the affines were set earlier)
elif arguments.set_sform_to_qform or arguments.set_qform_to_sform:
im_out = [im_in]
else:
im_out = None
printv(
parser.error(
'ERROR: you need to specify an operation to do on the input image'
))
# in case fname_out is not defined, use first element of input file name list
if fname_out is None:
fname_out = fname_in[0]
# Write output
if im_out is not None:
printv('Generate output files...', verbose)
# if only one output
if len(im_out) == 1 and arguments.split is None:
im_out[0].save(fname_out, dtype=output_type, verbose=verbose)
display_viewer_syntax([fname_out], verbose=verbose)
if arguments.mcs:
# use input file name and add _X, _Y _Z. Keep the same extension
l_fname_out = []
for i_dim in range(3):
l_fname_out.append(
add_suffix(fname_out or fname_in[0],
'_' + dim_list[i_dim].upper()))
im_out[i_dim].save(l_fname_out[i_dim], verbose=verbose)
display_viewer_syntax(fname_out)
if arguments.split is not None:
# use input file name and add _"DIM+NUMBER". Keep the same extension
l_fname_out = []
for i, im in enumerate(im_out):
l_fname_out.append(
add_suffix(fname_out or fname_in[0],
'_' + dim_list[dim].upper() + str(i).zfill(4)))
im.save(l_fname_out[i])
display_viewer_syntax(l_fname_out)
elif arguments.getorient:
printv(orient)
elif arguments.display_warp:
printv('Warping grid generated.', verbose, 'info')
def deep_segmentation_spinalcord(fname_image,
contrast_type,
output_folder,
ctr_algo='cnn',
ctr_file=None,
brain_bool=True,
kernel_size='2d',
remove_temp_files=1,
verbose=1):
"""Pipeline."""
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
# create temporary folder with intermediate results
sct.log.info("Creating temporary folder...")
file_fname = os.path.basename(fname_image)
tmp_folder = sct.TempFolder()
tmp_folder_path = tmp_folder.get_path()
fname_image_tmp = tmp_folder.copy_from(fname_image)
if ctr_algo == 'manual': # if the ctr_file is provided
tmp_folder.copy_from(ctr_file)
file_ctr = os.path.basename(ctr_file)
else:
file_ctr = None
tmp_folder.chdir()
# orientation of the image, should be RPI
sct.log.info("Reorient the image to RPI, if necessary...")
fname_orient = sct.add_suffix(file_fname, '_RPI')
im_2orient = Image(file_fname)
original_orientation = im_2orient.orientation
if original_orientation != 'RPI':
im_orient = msct_image.change_orientation(im_2orient,
'RPI').save(fname_orient)
else:
im_orient = im_2orient
sct.copy(fname_image_tmp, fname_orient)
# resampling RPI image
sct.log.info("Resample the image to 0.5 mm isotropic resolution...")
fname_res = sct.add_suffix(fname_orient, '_resampled')
im_2res = im_orient
input_resolution = im_2res.dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_orient,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
# find the spinal cord centerline - execute OptiC binary
sct.log.info("Finding the spinal cord centerline...")
centerline_filename = find_centerline(algo=ctr_algo,
image_fname=fname_res,
path_sct=path_sct,
contrast_type=contrast_type,
brain_bool=brain_bool,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
centerline_fname=file_ctr)
# crop image around the spinal cord centerline
sct.log.info("Cropping the image around the spinal cord...")
fname_crop = sct.add_suffix(fname_res, '_crop')
crop_size = 96 if (kernel_size == '3d' and contrast_type == 't2s') else 64
X_CROP_LST, Y_CROP_LST = crop_image_around_centerline(
filename_in=fname_res,
filename_ctr=centerline_filename,
filename_out=fname_crop,
crop_size=crop_size)
# normalize the intensity of the images
sct.log.info("Normalizing the intensity...")
fname_norm = sct.add_suffix(fname_crop, '_norm')
apply_intensity_normalization(img_path=fname_crop, fname_out=fname_norm)
if kernel_size == '2d':
# segment data using 2D convolutions
sct.log.info(
"Segmenting the spinal cord using deep learning on 2D patches...")
segmentation_model_fname = os.path.join(
path_sct, 'data', 'deepseg_sc_models',
'{}_sc.h5'.format(contrast_type))
fname_seg_crop = sct.add_suffix(fname_norm, '_seg')
seg_crop_data = segment_2d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
input_size=(crop_size, crop_size),
fname_in=fname_norm,
fname_out=fname_seg_crop)
elif kernel_size == '3d':
# resample to 0.5mm isotropic
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_norm,
fname_res3d,
'0.5x0.5x0.5',
'mm',
'linear',
verbose=0)
# segment data using 3D convolutions
sct.log.info(
"Segmenting the spinal cord using deep learning on 3D patches...")
segmentation_model_fname = os.path.join(
path_sct, 'data', 'deepseg_sc_models',
'{}_sc_3D.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_seg')
segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
fname_in=fname_res3d,
fname_out=fname_seg_crop_res)
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(
['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(
fname_seg_crop_res,
fname_seg_res2d,
initial_2d_resolution,
'mm',
'linear',
verbose=0)
seg_crop_data = Image(fname_seg_res2d).data
# reconstruct the segmentation from the crop data
sct.log.info("Reassembling the image...")
fname_seg_res_RPI = sct.add_suffix(file_fname, '_res_RPI_seg')
uncrop_image(fname_ref=fname_res,
fname_out=fname_seg_res_RPI,
data_crop=seg_crop_data,
x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST)
# resample to initial resolution
sct.log.info(
"Resampling the segmentation to the original image resolution...")
fname_seg_RPI = sct.add_suffix(file_fname, '_RPI_seg')
initial_resolution = 'x'.join([
str(input_resolution[0]),
str(input_resolution[1]),
str(input_resolution[2])
])
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_seg_res_RPI,
fname_seg_RPI,
initial_resolution,
'mm',
'linear',
verbose=0)
# binarize the resampled image to remove interpolation effects
sct.log.info(
"Binarizing the segmentation to avoid interpolation effects...")
thr = '0.0001' if contrast_type in ['t1', 'dwi'] else '0.5'
sct.run(
['sct_maths', '-i', fname_seg_RPI, '-bin', thr, '-o', fname_seg_RPI],
verbose=0)
# post processing step to z_regularized
post_processing_volume_wise(fname_in=fname_seg_RPI)
# reorient to initial orientation
sct.log.info(
"Reorienting the segmentation to the original image orientation...")
fname_seg = sct.add_suffix(file_fname, '_seg')
if original_orientation != 'RPI':
im_seg_orient = msct_image.change_orientation(
Image(fname_seg_RPI), original_orientation).save(fname_seg)
else:
sct.copy(fname_seg_RPI, fname_seg)
tmp_folder.chdir_undo()
# copy image from temporary folder into output folder
sct.copy(os.path.join(tmp_folder_path, fname_seg), output_folder)
# remove temporary files
if remove_temp_files:
sct.log.info("Remove temporary files...")
tmp_folder.cleanup()
return os.path.join(output_folder, fname_seg)
def test_change_orientation(fake_3dimage_sct, fake_3dimage_sct_vis):
path_tmp = sct.tmp_create(basename="test_reorient")
path_tmp = "."
print("Spot-checking that physical coordinates don't change")
for shape_is in (1,2,3):
shape = (1,1,shape_is)
print("Simple image with shape {}".format(shape))
data = np.ones(shape, order="F")
data[:,:,shape_is-1] += 1
im_src = fake_3dimage_sct_custom(data)
im_dst = msct_image.change_orientation(im_src, "ASR")
# Basic check
assert im_dst.orientation == "ASR"
# Basic data check
assert im_dst.data.mean() == im_src.data.mean()
# Basic header check: check that the same voxel
# remains at the same physical position
aff_src = im_src.header.get_best_affine()
aff_dst = im_dst.header.get_best_affine()
# Take the extremities "a" & "z"...
# consider the original LPI position
pta_src = np.array([[0,0,0,1]]).T
ptz_src = np.array([[0,0,shape_is-1,1]]).T
# and the position in ASR
pta_dst = np.array([[0,shape_is-1,0,1]]).T
ptz_dst = np.array([[0,0,0,1]]).T
# The physical positions should be:
posa_src = np.matmul(aff_src, pta_src)
posa_dst = np.matmul(aff_dst, pta_dst)
print("A at src {}".format(posa_src.T))
print("A at dst {}".format(posa_dst.T))
posz_src = np.matmul(aff_src, ptz_src)
posz_dst = np.matmul(aff_dst, ptz_dst)
# and they should be equal
assert (posa_src == posa_dst).all()
assert (posz_src == posz_dst).all()
fn = "".join(str(x) for x in im_src.data.shape)
im_src.save("{}-src.nii".format(fn))
im_dst.save("{}-dst.nii".format(fn))
np.random.seed(0)
print("More checking that physical coordinates don't change")
if 1:
shape = (7,8,9)
print("Simple image with shape {}".format(shape))
data = np.ones(shape, order="F") * 10
data[4,4,4] = 4
data[3,3,3] = 3
data[0,0,0] = 0
values = (0,3,4)
im_ref = fake_3dimage_sct_custom(data)
im_ref.header.set_xyzt_units("mm", "msec")
import scipy.linalg
def rand_rot():
q, _ = scipy.linalg.qr(np.random.randn(3, 3))
if scipy.linalg.det(q) < 0:
q[:, 0] = -q[:, 0]
return q
affine = im_ref.header.get_best_affine()
affine[:3,:3] = rand_rot()
affine[3,:3] = 0.0
affine[:3,3] = np.random.random((3))
affine[3,3] = 1.0
affine[0,0] *= 2
im_ref.header.set_sform(affine, code='scanner')
orientations = msct_image.all_refspace_strings()
for ori_src in orientations:
for ori_dst in orientations:
print("{} -> {}".format(ori_src, ori_dst))
im_src = msct_image.change_orientation(im_ref, ori_src)
im_dst = msct_image.change_orientation(im_src, ori_dst)
assert im_src.orientation == ori_src
assert im_dst.orientation == ori_dst
assert im_dst.data.mean() == im_src.data.mean()
# Basic header check: check that the same voxel
# remains at the same physical position
aff_src = im_src.header.get_best_affine()
aff_dst = im_dst.header.get_best_affine()
data_src = np.array(im_src.data)
data_dst = np.array(im_dst.data)
for value in values:
pt_src = np.argwhere(data_src == value)[0]
pt_dst = np.argwhere(data_dst == value)[0]
pos_src = np.matmul(aff_src, np.hstack((pt_src, [1])).reshape((4,1)))
pos_dst = np.matmul(aff_dst, np.hstack((pt_dst, [1])).reshape((4,1)))
if 0:
print("P at src {}".format(pos_src.T))
print("P at dst {}".format(pos_dst.T))
assert np.allclose(pos_src, pos_dst, atol=1e-3)
def main(argv=None):
"""
Main function
:param argv:
:return:
"""
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_global_loglevel(verbose=verbose)
# initializations
output_type = None
dim_list = ['x', 'y', 'z', 't']
fname_in = arguments.i
n_in = len(fname_in)
# TODO: The functions for '-concat', '-omc', and '-display-warp' take in filenames, so they ignore 'im_in'.
# Instead, we should harmonize this functionality so that 'im_in' is used everywhere.
im_in = Image(fname_in[0])
if arguments.set_sform_to_qform is not None:
im_in.set_sform_to_qform()
if arguments.o is not None:
fname_out = arguments.o
else:
fname_out = None
# Run command
# Arguments are sorted alphabetically (not according to the usage order)
if arguments.concat is not None:
dim = arguments.concat
assert dim in dim_list
dim = dim_list.index(dim)
# TODO: Modify concat_data to take in a list of Image() objects so that 'im_in' can be passed instead
im_out = [concat_data(fname_in, dim)]
elif arguments.copy_header is not None:
if fname_out is None:
raise ValueError("Need to specify output image with -o!")
im_dest = Image(arguments.copy_header)
im_dest_new = im_in.copy()
im_dest_new.data = im_dest.data.copy()
# im_dest.header = im_in.header
im_dest_new.absolutepath = im_dest.absolutepath
im_out = [im_dest_new]
elif arguments.display_warp:
# TODO: Modify visualize_warp to take in an Image() object so that 'im_in' can be passed instead
visualize_warp(fname_in[0], fname_grid=None, step=3, rm_tmp=True)
im_out = None
elif arguments.getorient:
orient = im_in.orientation
im_out = None
elif arguments.keep_vol is not None:
index_vol = (arguments.keep_vol).split(',')
for iindex_vol, vol in enumerate(index_vol):
index_vol[iindex_vol] = int(vol)
im_out = [remove_vol(im_in, index_vol, todo='keep')]
elif arguments.mcs:
if n_in != 1:
printv(parser.error('ERROR: -mcs need only one input'))
if len(im_in.data.shape) != 5:
printv(
parser.error(
'ERROR: -mcs input need to be a multi-component image'))
im_out = multicomponent_split(im_in)
elif arguments.omc:
im_ref = im_in
for fname in fname_in:
im = Image(fname)
if im.data.shape != im_ref.data.shape:
printv(
parser.error(
'ERROR: -omc inputs need to have all the same shapes'))
del im
# TODO: Modify multicomponent_merge to take in a list of Image() objects so that 'im_in' can be passed instead
im_out = [multicomponent_merge(fname_in)] # TODO: adapt to fname_in
elif arguments.pad is not None:
ndims = len(im_in.data.shape)
if ndims != 3:
printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments.pad.split(',')
if len(pad_arguments) != 3:
printv('ERROR: you need to specify 3 padding values.', 1, 'error')
padx, pady, padz = pad_arguments
padx, pady, padz = int(padx), int(pady), int(padz)
im_out = [
pad_image(im_in,
pad_x_i=padx,
pad_x_f=padx,
pad_y_i=pady,
pad_y_f=pady,
pad_z_i=padz,
pad_z_f=padz)
]
elif arguments.pad_asym is not None:
ndims = len(im_in.data.shape)
if ndims != 3:
printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments.pad_asym.split(',')
if len(pad_arguments) != 6:
printv('ERROR: you need to specify 6 padding values.', 1, 'error')
padxi, padxf, padyi, padyf, padzi, padzf = pad_arguments
padxi, padxf, padyi, padyf, padzi, padzf = int(padxi), int(padxf), int(
padyi), int(padyf), int(padzi), int(padzf)
im_out = [
pad_image(im_in,
pad_x_i=padxi,
pad_x_f=padxf,
pad_y_i=padyi,
pad_y_f=padyf,
pad_z_i=padzi,
pad_z_f=padzf)
]
elif arguments.remove_vol is not None:
index_vol = (arguments.remove_vol).split(',')
for iindex_vol, vol in enumerate(index_vol):
index_vol[iindex_vol] = int(vol)
im_out = [remove_vol(im_in, index_vol, todo='remove')]
elif arguments.setorient is not None:
printv(fname_in[0])
im_out = [change_orientation(im_in, arguments.setorient)]
elif arguments.setorient_data is not None:
im_out = [
change_orientation(im_in, arguments.setorient_data, data_only=True)
]
elif arguments.split is not None:
dim = arguments.split
assert dim in dim_list
dim = dim_list.index(dim)
im_out = split_data(im_in, dim)
elif arguments.type is not None:
output_type = arguments.type
im_out = [im_in] # TODO: adapt to fname_in
elif arguments.to_fsl is not None:
space_files = arguments.to_fsl
if len(space_files) > 2 or len(space_files) < 1:
printv(parser.error('ERROR: -to-fsl expects 1 or 2 arguments'))
return
spaces = [Image(s) for s in space_files]
if len(spaces) < 2:
spaces.append(None)
im_out = [displacement_to_abs_fsl(im_in, spaces[0], spaces[1])]
# If this argument is used standalone, simply pass the input image to the output (sform was set for im_in earlier)
elif arguments.set_sform_to_qform is not None:
im_out = [im_in]
else:
im_out = None
printv(
parser.error(
'ERROR: you need to specify an operation to do on the input image'
))
# in case fname_out is not defined, use first element of input file name list
if fname_out is None:
fname_out = fname_in[0]
# Write output
if im_out is not None:
printv('Generate output files...', verbose)
# if only one output
if len(im_out) == 1 and arguments.split is None:
im_out[0].save(fname_out, dtype=output_type, verbose=verbose)
display_viewer_syntax([fname_out], verbose=verbose)
if arguments.mcs:
# use input file name and add _X, _Y _Z. Keep the same extension
l_fname_out = []
for i_dim in range(3):
l_fname_out.append(
add_suffix(fname_out or fname_in[0],
'_' + dim_list[i_dim].upper()))
im_out[i_dim].save(l_fname_out[i_dim], verbose=verbose)
display_viewer_syntax(fname_out)
if arguments.split is not None:
# use input file name and add _"DIM+NUMBER". Keep the same extension
l_fname_out = []
for i, im in enumerate(im_out):
l_fname_out.append(
add_suffix(fname_out or fname_in[0],
'_' + dim_list[dim].upper() + str(i).zfill(4)))
im.save(l_fname_out[i])
display_viewer_syntax(l_fname_out)
elif arguments.getorient:
printv(orient)
elif arguments.display_warp:
printv('Warping grid generated.', verbose, 'info')
def find_centerline(algo, image_fname, path_sct, contrast_type, brain_bool,
folder_output, remove_temp_files, centerline_fname):
if Image(image_fname).dim[2] == 1: # isct_spine_detect requires nz > 1
from sct_image import concat_data
im_concat = concat_data([image_fname, image_fname], dim=2)
im_concat.save(sct.add_suffix(image_fname, '_concat'))
image_fname = sct.add_suffix(image_fname, '_concat')
bool_2d = True
else:
bool_2d = False
if algo == 'svm':
# run optic on a heatmap computed by a trained SVM+HoG algorithm
optic_models_fname = os.path.join(path_sct, 'data', 'optic_models',
'{}_model'.format(contrast_type))
_, centerline_filename = optic.detect_centerline(
image_fname=image_fname,
contrast_type=contrast_type,
optic_models_path=optic_models_fname,
folder_output=folder_output,
remove_temp_files=remove_temp_files,
output_roi=False,
verbose=0)
elif algo == 'cnn':
# CNN parameters
dct_patch_ctr = {
't2': {
'size': (80, 80),
'mean': 51.1417,
'std': 57.4408
},
't2s': {
'size': (80, 80),
'mean': 68.8591,
'std': 71.4659
},
't1': {
'size': (80, 80),
'mean': 55.7359,
'std': 64.3149
},
'dwi': {
'size': (80, 80),
'mean': 55.744,
'std': 45.003
}
}
dct_params_ctr = {
't2': {
'features': 16,
'dilation_layers': 2
},
't2s': {
'features': 8,
'dilation_layers': 3
},
't1': {
'features': 24,
'dilation_layers': 3
},
'dwi': {
'features': 8,
'dilation_layers': 2
}
}
# load model
ctr_model_fname = os.path.join(path_sct, 'data', 'deepseg_sc_models',
'{}_ctr.h5'.format(contrast_type))
ctr_model = nn_architecture_ctr(
height=dct_patch_ctr[contrast_type]['size'][0],
width=dct_patch_ctr[contrast_type]['size'][1],
channels=1,
classes=1,
features=dct_params_ctr[contrast_type]['features'],
depth=2,
temperature=1.0,
padding='same',
batchnorm=True,
dropout=0.0,
dilation_layers=dct_params_ctr[contrast_type]['dilation_layers'])
ctr_model.load_weights(ctr_model_fname)
sct.log.info("Resample the image to 0.5 mm isotropic resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(image_fname,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
# compute the heatmap
fname_heatmap = sct.add_suffix(image_fname, "_heatmap")
img_filename = ''.join(sct.extract_fname(fname_heatmap)[:2])
fname_heatmap_nii = img_filename + '.nii'
z_max = heatmap(filename_in=fname_res,
filename_out=fname_heatmap_nii,
model=ctr_model,
patch_shape=dct_patch_ctr[contrast_type]['size'],
mean_train=dct_patch_ctr[contrast_type]['mean'],
std_train=dct_patch_ctr[contrast_type]['std'],
brain_bool=brain_bool)
# run optic on the heatmap
centerline_filename = sct.add_suffix(fname_heatmap, "_ctr")
heatmap2optic(fname_heatmap=fname_heatmap_nii,
lambda_value=7 if contrast_type == 't2s' else 1,
fname_out=centerline_filename,
z_max=z_max if brain_bool else None)
elif algo == 'viewer':
centerline_filename = sct.add_suffix(image_fname, "_ctr")
fname_labels_viewer = _call_viewer_centerline(fname_in=image_fname)
centerline_filename = extract_centerline(fname_labels_viewer,
remove_temp_files=True,
algo_fitting='nurbs',
nurbs_pts_number=8000)
elif algo == 'manual':
centerline_filename = sct.add_suffix(image_fname, "_ctr")
image_manual_centerline = Image(centerline_fname)
# Re-orient and Re-sample the manual centerline
msct_image.change_orientation(image_manual_centerline,
'RPI').save(centerline_filename)
else:
sct.log.error(
'The parameter "-centerline" is incorrect. Please try again.')
sys.exit(1)
if algo != 'cnn':
sct.log.info("Resample the image to 0.5 mm isotropic resolution...")
fname_res = sct.add_suffix(image_fname, '_resampled')
input_resolution = Image(image_fname).dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(image_fname,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
spinalcordtoolbox.resample.nipy_resample.resample_file(
centerline_filename,
centerline_filename,
new_resolution,
'mm',
'linear',
verbose=0)
if bool_2d:
from sct_image import split_data
im_split_lst = split_data(Image(centerline_filename), dim=2)
im_split_lst[0].save(centerline_filename)
return fname_res, centerline_filename