def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = tmp_create()
im_warp = Image(fname_warp)
status, out = sct.run(['fslhd', fname_warp])
curdir = os.getcwd()
os.chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(
out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(
out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(
out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = add_suffix(fname_grid, '_resample')
sct.run([
'sct_resample', '-i', fname_grid, '-f', '3x3x1', '-x', 'nn', '-o',
fname_grid_resample
])
fname_grid = tmp_dir + fname_grid_resample
os.chdir(curdir)
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = path_warp + extract_fname(
fname_grid)[1] + '_' + file_warp + ext_warp
sct.run([
'sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w',
fname_warp, '-o', grid_warped
])
if rm_tmp:
sct.rmtree(tmp_dir)
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = tmp_create()
im_warp = Image(fname_warp)
status, out = run('fslhd ' + fname_warp)
from os import chdir
chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(
out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(
out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(
out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = add_suffix(fname_grid, '_resample')
run('sct_resample -i ' + fname_grid + ' -f 3x3x1 -x nn -o ' +
fname_grid_resample)
fname_grid = tmp_dir + fname_grid_resample
chdir('..')
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = path_warp + extract_fname(
fname_grid)[1] + '_' + file_warp + ext_warp
run('sct_apply_transfo -i ' + fname_grid + ' -d ' + fname_grid + ' -w ' +
fname_warp + ' -o ' + grid_warped)
if rm_tmp:
run('rm -rf ' + tmp_dir, error_exit='warning')
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
os.chdir(tmp_dir)
assert len(im_warp.data.shape
) == 5, 'ERROR: Warping field does bot have 5 dimensions...'
nx, ny, nz, nt, ndimwarp = im_warp.data.shape
# nx, ny, nz, nt, px, py, pz, pt = im_warp.dim
# This does not work because dimensions of a warping field are not correctly read : it would be 1,1,1,1,1,1,1,1
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, '_resample')
sct.run('sct_resample -i ' + fname_grid + ' -f 3x3x1 -x nn -o ' +
fname_grid_resample)
fname_grid = tmp_dir + fname_grid_resample
os.chdir('..')
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = path_warp + 'grid_warped_gm' + ext_warp
sct.run('sct_apply_transfo -i ' + fname_grid + ' -d ' + fname_grid +
' -w ' + fname_warp + ' -o ' + grid_warped)
if rm_tmp:
sct.run('rm -rf ' + tmp_dir, error_exit='warning')
return grid_warped
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
os.chdir(tmp_dir)
assert len(im_warp.data.shape) == 5, "ERROR: Warping field does bot have 5 dimensions..."
nx, ny, nz, nt, ndimwarp = im_warp.data.shape
# nx, ny, nz, nt, px, py, pz, pt = im_warp.dim
# This does not work because dimensions of a warping field are not correctly read : it would be 1,1,1,1,1,1,1,1
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i : i + step, j : j + step, k].shape == (step, step):
dat[i : i + step, j : j + step, k] = sq
fname_grid = "grid_" + str(step) + ".nii.gz"
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, "_resample")
sct.run("sct_resample -i " + fname_grid + " -f 3x3x1 -x nn -o " + fname_grid_resample)
fname_grid = tmp_dir + fname_grid_resample
os.chdir("..")
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = path_warp + "grid_warped_gm" + ext_warp
sct.run("sct_apply_transfo -i " + fname_grid + " -d " + fname_grid + " -w " + fname_warp + " -o " + grid_warped)
if rm_tmp:
sct.run("rm -rf " + tmp_dir, error_exit="warning")
return grid_warped
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
curdir = os.getcwd()
os.chdir(tmp_dir)
assert len(im_warp.data.shape) == 5, 'ERROR: Warping field does bot have 5 dimensions...'
nx, ny, nz, nt, ndimwarp = im_warp.data.shape
# nx, ny, nz, nt, px, py, pz, pt = im_warp.dim
# This does not work because dimensions of a warping field are not correctly read : it would be 1,1,1,1,1,1,1,1
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, '_resample')
sct.run(['sct_resample', '-i', fname_grid, '-f', '3x3x1', '-x', 'nn', '-o', fname_grid_resample])
fname_grid = os.path.join(tmp_dir, fname_grid_resample)
os.chdir(curdir)
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = os.path.join(path_warp, 'grid_warped_gm' + ext_warp)
sct.run(['sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w', fname_warp, '-o', grid_warped])
if rm_tmp:
sct.rmtree(tmp_dir)
return grid_warped
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = tmp_create()
im_warp = Image(fname_warp)
status, out = run('fslhd '+fname_warp)
from os import chdir
chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step-1] = 1
sq[:, step-1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i+step, j:j+step, k].shape == (step, step):
dat[i:i+step, j:j+step, k] = sq
fname_grid = 'grid_'+str(step)+'.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = add_suffix(fname_grid, '_resample')
run('sct_resample -i '+fname_grid+' -f 3x3x1 -x nn -o '+fname_grid_resample)
fname_grid = tmp_dir+fname_grid_resample
chdir('..')
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = path_warp+extract_fname(fname_grid)[1]+'_'+file_warp+ext_warp
run('sct_apply_transfo -i '+fname_grid+' -d '+fname_grid+' -w '+fname_warp+' -o '+grid_warped)
if rm_tmp:
run('rm -rf '+tmp_dir, error_exit='warning')
def resample():
# extract resampling factor
sct.printv('\nParse resampling factor...', param.verbose)
factor_split = param.factor.split('x')
factor = [float(factor_split[i]) for i in range(len(factor_split))]
# check if it has three values
if not len(factor) == 3:
sct.printv('\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1, 'error')
else:
fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))]
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_out, file_out, ext_out = path_data, file_data, ext_data
if param.fname_out != '':
file_out = sct.extract_fname(param.fname_out)[1]
else:
file_out.append(param.file_suffix)
input_im = Image(param.fname_data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = input_im.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
#TODO : adapt for 2D too or change description
sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error')
# Calculate new dimensions
sct.printv('\nCalculate new dimensions...', param.verbose)
nx_new = int(round(nx*fx))
ny_new = int(round(ny*fy))
nz_new = int(round(nz*fz))
px_new = px/fx
py_new = py/fy
pz_new = pz/fz
sct.printv(' ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new)+ ' x ' + str(nt), param.verbose)
zooms = input_im.hdr.get_zooms()[:3]
affine = input_im.hdr.get_base_affine()
new_zooms = (px_new, py_new, pz_new)
if type(param.interpolation) == int:
order = param.interpolation
elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys():
order = param.x_to_order[param.interpolation]
else:
order = 1
sct.printv('WARNING: wrong input for the interpolation. Using default value = trilinear', param.verbose, 'warning')
new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order)
new_im = Image(param=new_data)
new_im.absolutepath = path_out+file_out+ext_out
new_im.path = path_out
new_im.file_name = file_out
new_im.ext = ext_out
zooms_to_set = list(new_zooms)
if dim == 4:
zooms_to_set.append(nt)
new_im.hdr = input_im.hdr
new_im.hdr.set_zooms(zooms_to_set)
new_im.save()
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')
print
def resample():
# extract resampling factor
sct.printv('\nParse resampling factor...', param.verbose)
factor_split = param.factor.split('x')
factor = [float(factor_split[i]) for i in range(len(factor_split))]
# check if it has three values
if not len(factor) == 3:
sct.printv(
'\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1,
'error')
else:
fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))]
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_out, file_out, ext_out = path_data, file_data, ext_data
if param.fname_out != '':
file_out = sct.extract_fname(param.fname_out)[1]
else:
file_out.append(param.file_suffix)
input_im = Image(param.fname_data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = input_im.dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
param.verbose)
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
#TODO : adapt for 2D too or change description
sct.run(
'ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program',
param.verbose, 'error')
# Calculate new dimensions
sct.printv('\nCalculate new dimensions...', param.verbose)
nx_new = int(round(nx * fx))
ny_new = int(round(ny * fy))
nz_new = int(round(nz * fz))
px_new = px / fx
py_new = py / fy
pz_new = pz / fz
sct.printv(
' ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new) +
' x ' + str(nt), param.verbose)
zooms = input_im.hdr.get_zooms()[:3]
affine = input_im.hdr.get_base_affine()
new_zooms = (px_new, py_new, pz_new)
if type(param.interpolation) == int:
order = param.interpolation
elif type(param.interpolation
) == str and param.interpolation in param.x_to_order.keys():
order = param.x_to_order[param.interpolation]
else:
order = 1
sct.printv(
'WARNING: wrong input for the interpolation. Using default value = trilinear',
param.verbose, 'warning')
new_data, new_affine = dp_iso.reslice(input_im.data,
affine,
zooms,
new_zooms,
mode=param.mode,
order=order)
new_im = Image(param=new_data)
new_im.absolutepath = path_out + file_out + ext_out
new_im.path = path_out
new_im.file_name = file_out
new_im.ext = ext_out
zooms_to_set = list(new_zooms)
if dim == 4:
zooms_to_set.append(nt)
new_im.hdr = input_im.hdr
new_im.hdr.set_zooms(zooms_to_set)
new_im.save()
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview ' + param.fname_out + ' &', param.verbose, 'info')
print
def resample():
# extract resampling factor
sct.printv('\nParse resampling factor...', param.verbose)
new_size_split = param.new_size.split('x')
new_size = [float(new_size_split[i]) for i in range(len(new_size_split))]
# check if it has three values
if not len(new_size) == 3:
sct.printv('\nERROR: new size should have three dimensions. E.g., 2x2x1.\n', 1, 'error')
else:
ns_x, ns_y, ns_z = new_size
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_out, file_out, ext_out = '', file_data, ext_data
if param.fname_out != '':
path_out, file_out, ext_out = sct.extract_fname(param.fname_out)
else:
file_out += param.file_suffix
param.fname_out = path_out+file_out+ext_out
input_im = Image(param.fname_data)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = input_im.dim
sct.printv(' ' + str(px) + ' x ' + str(py) + ' x ' + str(pz)+ ' x ' + str(pt)+'mm', param.verbose)
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error')
# Calculate new dimensions
sct.printv('\nCalculate new dimensions...', param.verbose)
if param.new_size_type == 'factor':
px_new = px/ns_x
py_new = py/ns_y
pz_new = pz/ns_z
elif param.new_size_type == 'vox':
px_new = px*nx/ns_x
py_new = py*ny/ns_y
pz_new = pz*nz/ns_z
else:
px_new = ns_x
py_new = ns_y
pz_new = ns_z
sct.printv(' ' + str(px_new) + ' x ' + str(py_new) + ' x ' + str(pz_new)+ ' x ' + str(pt)+'mm', param.verbose)
zooms = (px, py, pz) # input_im.hdr.get_zooms()[:3]
affine = input_im.hdr.get_qform() # get_base_affine()
new_zooms = (px_new, py_new, pz_new)
if type(param.interpolation) == int:
order = param.interpolation
elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys():
order = param.x_to_order[param.interpolation]
else:
order = 1
sct.printv('WARNING: wrong input for the interpolation. Using default value = linear', param.verbose, 'warning')
new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order)
new_im = Image(param=new_data)
new_im.absolutepath = param.fname_out
new_im.path = path_out
new_im.file_name = file_out
new_im.ext = ext_out
zooms_to_set = list(new_zooms)
if dim == 4:
zooms_to_set.append(nt)
new_im.hdr = input_im.hdr
new_im.hdr.set_zooms(zooms_to_set)
# Set the new sform and qform:
new_im.hdr.set_sform(new_affine)
new_im.hdr.set_qform(new_affine)
new_im.save()
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')