def resample_file(fname_data, fname_out, new_size, new_size_type,
interpolation, verbose):
"""This function will resample the specified input
image file to the target size.
Can deal with 2d, 3d or 4d image objects.
:param fname_data: The input image filename.
:param fname_out: The output image filename.
:param new_size: The target size, i.e. 0.25x0.25
:param new_size_type: Unit of resample (mm, vox, factor)
:param interpolation: The interpolation type
:param verbose: verbosity level
"""
# Load data
sct.printv('\nLoad data...', verbose)
nii = nipy.load_image(fname_data)
nii_r = resample_image(nii, new_size, new_size_type, interpolation,
verbose)
# build output file name
if fname_out == '':
fname_out = sct.add_suffix(fname_data, '_r')
else:
fname_out = fname_out
# save data
nipy.save_image(nii_r, fname_out)
# to view results
sct.display_viewer_syntax([fname_out], verbose=verbose)
return nii_r
def save_to_image(data,
template_file=DEFAULT_template,
output_file=DEFAULT_output):
template = load_image(template_file)
newimg = Image(data, vox2mni(template.affine))
save_image(newimg, output_file)
return output_file
def save_niigz(file_path, vol, affine=None, header=None):
"""Saves a volume into a Nifti (.nii.gz) file.
Parameters
----------
vol: Numpy 3D or 4D array
Volume with the data to be saved.
file_path: string
Output file name path
affine: 4x4 Numpy array
Array with the affine transform of the file.
header: nibabel.nifti1.Nifti1Header, optional
Header for the file, optional but recommended.
Note
----
affine and header only work for numpy volumes.
"""
if isinstance(vol, np.ndarray):
log.debug('Saving numpy nifti file: ' + file_path)
ni = nib.Nifti1Image(vol, affine, header)
nib.save(ni, file_path)
elif isinstance(vol, nib.Nifti1Image):
log.debug('Saving nibabel nifti file: ' + file_path)
nib.save(vol, file_path)
elif isinstance(vol, niim.Image):
log.debug('Saving nibabel nifti file: ' + file_path)
save_image(vol, file_path)
def expandFrames(imgFn, saveDir):
"""
Expand a timeseries image into a set of individual frames in the
specified directory
Inputs:
- imgFn: the timeseries image's filename
- saveDir: the directory in which the frames will be stored
Returns:
- frameFns: the list of filenames
"""
# Load the image
img = load_image(imgFn)
coord = img.coordmap
frameFns = []
# Make the save directory
framesDir = saveDir + '/frames/' # need to check for //
# check for duplicate //
framesDir = framesDir.replace("//", '/')
if not os.path.exists(framesDir):
os.mkdir(framesDir)
for i in xrange(img.get_data().shape[3]):
frame = img[:, :, :, i].get_data()[:, :, :, None]
frameImg = Image(frame, coord)
outFn = framesDir + str(i).zfill(3) + ".nii.gz"
save_image(frameImg, outFn)
frameFns.append(outFn)
return frameFns
def resample_file(fname_data, fname_out, new_size, new_size_type,
interpolation, verbose):
"""This function will resample the specified input
image file to the target size.
:param fname_data: The input image filename.
:param fname_out: The output image filename.
:param new_size: The target size, i.e. 0.25x0.25
:param new_size_type: Unit of resample (mm, vox, factor)
:param interpolation: The interpolation type
:param verbose: verbosity level
"""
# Load data
sct.printv('\nLoad data...', verbose)
nii = nipy.load_image(fname_data)
nii_r = resample_image(nii, new_size, new_size_type, interpolation,
verbose)
# build output file name
if fname_out == '':
fname_out = sct.add_suffix(fname_data, '_r')
else:
fname_out = fname_out
# save data
nipy.save_image(nii_r, fname_out)
# to view results
sct.printv('\nDone! To view results, type:', verbose)
sct.printv('fslview ' + fname_out + ' &', verbose, 'info')
return nii_r
def space_time_realign(Images,TR=2,numslices=None,SliceTime='asc_alt_2',RefScan=None):
'''
4D simultaneous slice timing and spatial realignment. Adapted from
Alexis Roche's example script, and extend to be used for multiplex
imaging sequences
Inputs:
Images: list of images, input as a list of strings
numslices: for non-multiplex sequence, default to be the number of
slices in the image. For multiplex sequence, enter as a tuple,
such that the first element is the number of planes acquired in
parallel between each other, and the second element is the number
of slices of each parallel plane/slab
SliceTime:enter as a string to specify how the slices are ordered.
Choices are the following
1).'ascending': sequential ascending acquisition
2).'descending': sequential descending acquisition
3).'asc_alt_2': ascending interleaved, starting at first slice
4).'asc_alt_2_1': ascending interleaved, starting at the second
slice
5).'desc_alt_2': descending interleaved, starting at last slice
6).'asc_alt_siemens': ascending interleaved, starting at the first
slice if odd number of slices, or second slice if even number
of slices
7).'asc_alt_half': ascending interleaved by half the volume
8).'desc_alt_half': descending interleaved by half the volume
RefScan: reference volume for spatial realignment movement estimation
'''
# load images
runs = [load_image(run) for run in Images]
# parse data info
if numslices is None:
numslices = runs[0].shape[2]
numplanes = 1
elif isinstance(numslices,tuple):
numslices = numslices[0]
numplanes = numplanes[1]
# parse slice timing according to the input
slice_timing = getattr(timefuncs,SliceTime)(TR,numslices)
#repeat the slice timing for multiplex seqquence
slice_timing = np.tile(slice_timing,numplanes)
# Spatio-temporal realigner assuming interleaved ascending slice order
R = SpaceTimeRealign(runs, tr=TR, slice_times=slice_timing, slice_info=2,
affine_class='Rigid')
print('Slice times: %s' % slice_timing)
# Estimate motion within- and between-sessions
R.estimate(refscan=RefScan)
# Resample data on a regular space+time lattice using 4d interpolation
print('Saving results ...')
for i in range(len(runs)):
corr_run = R.resample(i)
fname = os.path.join(os.path.split(Images[i])[0],'ra' + os.path.split(Images[i])[1])
save_image(corr_run, fname)
print(fname)
def get_nifti(dataset,
features,
out_file=None,
split_files=False,
base_nifti=None):
"""
Function to get nifti image and save nifti files.
Parameters
----------
dataset: MRI class.
A dataset of the MRI class for processing the nifti from.
Must implement get_nifti.
features: array-like.
Features for nifti processing.
out_file: str, optional.
Output file for nifti image.
Returns
-------
nifti: nipy image.
"""
logger.info("Getting nifti for dataset of type %r and %d features." %
(type(dataset), features.shape[0]))
if not isinstance(dataset, MRI):
raise ValueError("Dataset type is %r and not an instance of %r" %
(type(dataset), MRI))
weights_view = dataset.get_weights_view(features)
image = dataset.get_nifti(weights_view, base_nifti=base_nifti)
if out_file is not None:
nipy.save_image(image, out_file + ".gz")
return image
def sources_to_nifti(CHECKPOINT, MASKMAT, BASENIFTI, ONAME, savepath, voxels, win):
bnifti = load_image(BASENIFTI)
mask = loadmat(MASKMAT)['mask']
model = np.load(CHECKPOINT) # Numpy array of sources from Infomax ICA
for i in range(len(model)): # Goes component by component
W = model[i,:].reshape([voxels,win])
f = zeros(len(mask))
idx = where(mask==1)
data = zeros((bnifti.shape[0],bnifti.shape[1],bnifti.shape[2],W.shape[1]))
f[idx[0].tolist()] = detrend(W)/std(W)
for j in range(0,W.shape[1]):
data[:,:,:,j] = reshape(f,(bnifti.shape[0],bnifti.shape[1],bnifti.shape[2] ), order='F')
img = Image.from_image(bnifti,data=data)
os.chdir(savepath)
fn = ONAME + "%s.nii" % (str(i)) # Where result should be saved and under what name
save_image(img,fn)
def save_nii(data, coord, save_file):
"""
Saves a numpy array (data) as a nifti file
The coordinate space must match the array dimensions
"""
arr_img = Image(data, coord)
save_image(arr_img, save_file)
return 0
def resample_image(source_file, target_file, outdir, w2wmap=None, order=3,
cval=0, verbose=0):
""" Resample the source image to match the target image using Nipy.
Parameters
----------
source_file: str (mandatory)
the image to resample.
target_file: str (mandatory)
the reference image.
outdir: str (mandatory)
the folder where the resampled image will be saved.
w2wmap: array (4, 4) or callable
physical to physical transformation.
verbose: int (optional, default 0)
the verbosity level.
Returns
-------
resampled_file: str
the resampled image.
"""
# Get target image information
target_image = nipy.load_image(target_file)
onto_shape = target_image.shape[:3]
onto_aff = xyz_affine(target_image.affine, xyz=[0, 1, 2], verbose=verbose)
# Define index and physical coordinate systems
arraycoo = "ijklmnopq"[:len(onto_shape)]
spacecoo = "xyztrsuvw"[:len(onto_shape)]
if verbose > 0:
print("\narraycoo: ", arraycoo, "\nspacecoo: ", spacecoo,
"\nonto_aff\n", onto_aff)
dmaker = CoordSysMaker(arraycoo, 'generic-array')
rmaker = CoordSysMaker(spacecoo, 'generic-scanner')
cm_maker = cmap.CoordMapMaker(dmaker, rmaker)
cmap_out = cm_maker.make_affine(onto_aff)
if verbose > 0:
print("cmap_out:\n", cmap_out)
# Define the default physical to physical transformation
if w2wmap is None:
w2wmap = np.eye(onto_aff.shape[0])
if verbose > 0:
print("w2wmap:\n", w2wmap)
# Resample
source_image = nipy.load_image(source_file)
resampled_image = resample(
source_image, cmap_out, w2wmap, onto_shape, order=order, cval=cval)
# Save the resampled image
resampled_file = os.path.join(
outdir, "resampled_{0}".format(os.path.basename(source_file)))
nipy.save_image(resampled_image, resampled_file)
return resampled_file
def sample_map_allen_space(image_path, annot_csv_path, save_path, type='well_id'):
#assign values to samples in MNI space
I=nipy.load_image(image_path)
image_name=os.path.basename(image_path)
df=pd.DataFrame.from_csv(annot_csv_path)
coordinate, well_id=(np.array( df['mri_voxel_x']) , np.array(df['mri_voxel_y']), np.array(df['mri_voxel_z'] )), df[type]
I._data[np.where(I._data!=0)]=0
I._data[coordinate]=well_id
nipy.save_image(I, os.path.join(save_path, image_name))
def sample_map_allen_space(image_path, annot_csv_path, save_path, type='well_id'):
#assign values to samples in MNI space
I=nipy.load_image(image_path)
image_name=os.path.basename(image_path)
df=pd.DataFrame.from_csv(annot_csv_path)
coordinate, well_id=(np.array( df['mri_voxel_x']) , np.array(df['mri_voxel_y']), np.array(df['mri_voxel_z'] )), df[type]
I._data[np.where(I._data!=0)]=0
I._data[coordinate]=well_id
nipy.save_image(I, os.path.join(save_path, image_name))
def tsdiffana(args):
""" Generate tsdiffana plots from command line params `args`
Parameters
----------
args : object
object with attributes
* filename : str - 4D image filename
* out_file : str - graphics file to write to instead of leaving
graphics on screen
* time_axis : str - name or number of time axis in `filename`
* slice_axis : str - name or number of slice axis in `filename`
* write_results : bool - if True, write images and plots to files
* out_path : None or str - path to which to write results
* out_fname_label : None or filename - suffix of output results files
Returns
-------
axes : Matplotlib axes
Axes on which we have done the plots.
"""
if args.out_file is not None and args.write_results:
raise ValueError("Cannot have OUT_FILE and WRITE_RESULTS options "
"together")
img, time_axis, slice_axis = parse_fname_axes(args.filename,
args.time_axis,
args.slice_axis)
results = time_slice_diffs_image(img, time_axis, slice_axis)
axes = plot_tsdiffs(results)
if args.out_file is None and not args.write_results:
# interactive mode
return axes
if args.out_file is not None:
# plot only mode
axes[0].figure.savefig(args.out_file)
return axes
# plot and images mode
froot, ext, addext = splitext_addext(args.filename)
fpath, fbase = psplit(froot)
fpath = fpath if args.out_path is None else args.out_path
fbase = fbase if args.out_fname_label is None else args.out_fname_label
axes[0].figure.savefig(pjoin(fpath, 'tsdiff_' + fbase + '.png'))
# Save image volumes
for key, prefix in (('slice_diff2_max_vol', 'dv2_max_'), ('diff2_mean_vol',
'dv2_mean_')):
fname = pjoin(fpath, prefix + fbase + ext + addext)
nipy.save_image(results[key], fname)
# Save time courses into npz
np.savez(
pjoin(fpath, 'tsdiff_' + fbase + '.npz'),
volume_means=results['volume_means'],
slice_mean_diff2=results['slice_mean_diff2'],
)
return axes
def tissue_classification(img,
mask=None,
niters=25,
beta=0.5,
ngb_size=6,
probc=None,
probg=None,
probw=None):
import numpy as np
from nipy import load_image, save_image
from nipy.core.image.image_spaces import (make_xyz_image, xyz_affine)
from nipy.algorithms.segmentation import BrainT1Segmentation
import os
# Input image
img = load_image(img)
# Input mask image
mask_img = mask
if mask_img == None:
mask_img = img
else:
mask_img = load_image(mask_img)
# Other optional arguments
#niters = int(get_argument('niters', 25))
#beta = float(get_argument('beta', 0.5))
#ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(),
mask=mask,
model='5k',
niters=niters,
beta=beta,
ngb_size=ngb_size)
# Save label image
outfile = os.path.abspath('hard_classif.nii')
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'), outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if not probc == None and\
not probg == None and\
not probw == None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (probc, probg, probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
def tsdiffana(args):
""" Generate tsdiffana plots from command line params `args`
Parameters
----------
args : object
object with attributes
* filename : str - 4D image filename
* out_file : str - graphics file to write to instead of leaving
graphics on screen
* time_axis : str - name or number of time axis in `filename`
* slice_axis : str - name or number of slice axis in `filename`
* write_results : bool - if True, write images and plots to files
* out_path : None or str - path to which to write results
* out_fname_label : None or filename - suffix of output results files
Returns
-------
axes : Matplotlib axes
Axes on which we have done the plots.
"""
if args.out_file is not None and args.write_results:
raise ValueError("Cannot have OUT_FILE and WRITE_RESULTS options "
"together")
img, time_axis, slice_axis = parse_fname_axes(args.filename,
args.time_axis,
args.slice_axis)
results = time_slice_diffs_image(img, time_axis, slice_axis)
axes = plot_tsdiffs(results)
if args.out_file is None and not args.write_results:
# interactive mode
return axes
if args.out_file is not None:
# plot only mode
axes[0].figure.savefig(args.out_file)
return axes
# plot and images mode
froot, ext, addext = splitext_addext(args.filename)
fpath, fbase = psplit(froot)
fpath = fpath if args.out_path is None else args.out_path
fbase = fbase if args.out_fname_label is None else args.out_fname_label
axes[0].figure.savefig(pjoin(fpath, 'tsdiff_' + fbase + '.png'))
# Save image volumes
for key, prefix in (('slice_diff2_max_vol', 'dv2_max_'),
('diff2_mean_vol', 'dv2_mean_')):
fname = pjoin(fpath, prefix + fbase + ext + addext)
nipy.save_image(results[key], fname)
# Save time courses into npz
np.savez(pjoin(fpath, 'tsdiff_' + fbase + '.npz'),
volume_means=results['volume_means'],
slice_mean_diff2=results['slice_mean_diff2'],
)
return axes
def affine_registration_nipy(in_path, ref_path, out_path,
in_ref_mat = '', ref_in_mat = '',
T = None, extra_params={}):
"""
Affine registation and resampling. Use Histogram registration from nipy.
inputs:
in_path: path to the source (input) image.
ref_path: path to the target (reference) image.
out_path: path to use to save the registered image.
in_ref_mat: if bool(in_ref_mat) is True, save the 4x4 transformation
matrix to a text file <in_ref_mat>.
ref_in_mat: if bool(ref_in_mat) is True, save the reverse of the 4x4
transformation matrix to a text file <ref_in_mat>.
T: affine transformation to use. if None, T will be estimated using
HistogramRegistration and optimizers; if type(T) is not Affine,
T = Affine(array=T)
extra_params: extra parameters passing to HistogramRegistration,
HistogramRegistration.optimize, resample
return T
"""
source_image = load_image(in_path)
target_image = load_image(ref_path)
if T is None:
print('assess the affine transformation using histogram registration. ')
# R = HistogramRegistration(source_image, target_image)
R = AllFeatures(HistogramRegistration,extra_params).run(source_image, target_image)
# T = R.optimize('affine', optimizer='powell')
T = AllFeatures(R.optimize,extra_params).run('affine', optimizer='powell')
print('receive affine transformation %s' % T)
else:
if type(T) is not Affine:
print('create Affine from T')
T = Affine(array=T)
print('using a predefined affine:\n%s\nwith a 4x4 matrix:\n%s\n' % (T, T.as_affine()))
# It = resample(source_image, T.inv(), target_image)
It = AllFeatures(resample,extra_params).run(source_image, T.inv(), target_image)
# the second argument of resample takes an transformation from ref to mov
# so that's why we need T.inv() here
save_image(It, out_path)
if in_ref_mat:
np.savetxt(in_ref_mat, T.as_affine())
if ref_in_mat:
np.savetxt(ref_in_mat, T.inv().as_affine())
return T
def segment_file(input_filename, output_filename,
model_name, threshold, verbosity):
"""Segment a volume file.
:param input_filename: the input filename.
:param output_filename: the output filename.
:param model_name: the name of model to use.
:param threshold: threshold to apply in predictions (if None,
no threshold will be applied)
:param verbosity: the verbosity level.
:return: the output filename.
"""
nii_original = nipy.load_image(input_filename)
pixdim = nii_original.header["pixdim"][3]
target_resample = "0.25x0.25x{:.5f}".format(pixdim)
nii_resampled = nipy_resample.resample_image(nii_original,
target_resample,
'mm', 'linear',
verbosity)
if (nii_resampled.shape[0] < 200) \
or (nii_resampled.shape[1] < 200):
raise RuntimeError("Image too small ({}, {})".format(
nii_resampled.shape[0],
nii_resampled.shape[1]))
nii_resampled = nipy2nifti(nii_resampled)
pred_slices = segment_volume(nii_resampled, model_name, threshold)
original_res = "{:.5f}x{:.5f}x{:.5f}".format(
nii_original.header["pixdim"][1],
nii_original.header["pixdim"][2],
nii_original.header["pixdim"][3])
volume_affine = nii_resampled.affine
volume_header = nii_resampled.header
nii_segmentation = nib.Nifti1Image(pred_slices, volume_affine,
volume_header)
nii_segmentation = nifti2nipy(nii_segmentation)
nii_resampled_original = nipy_resample.resample_image(nii_segmentation,
original_res,
'mm', 'linear',
verbosity)
res_data = nii_resampled_original.get_data()
# Threshold after resampling, only if specified
if threshold is not None:
res_data = threshold_predictions(res_data, 0.5)
nipy.save_image(nii_resampled_original, output_filename)
return output_filename
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-1',
'--roi1',
type=str,
help='Filename of the first ROI image')
parser.add_argument('-2',
'--roi2',
type=str,
help='Filename of the second ROI image')
parser.add_argument('-c',
'--coord-image',
type=str,
help='Filename of the coordinates image')
parser.add_argument('-o',
'--output-file',
type=str,
help='Filename to use for combined ROIs')
args = parser.parse_args()
print(args)
# check that both images exist
if not os.path.exists(args.roi1):
print("File '" + args.roi1 + "' does not exist")
if not os.path.exists(args.roi2):
print("File '" + args.roi2 + "' does not exist")
if not os.path.exists(args.coord_image):
print("File '" + args.coord_image)
# load both images for the purpose of checking coordinates
roi1, coords1 = mil.loadBOLD(args.roi1)
roi2, coords2 = mil.loadBOLD(args.roi2)
img, imgCoords = mil.loadBOLD(args.coord_image)
print("roi1", roi1.shape)
print("roi2", roi2.shape)
print("coords", img.shape)
# perform the OR-ing of the 2 rois
addingImgs = BinaryMaths()
addingImgs.inputs.in_file = args.roi1
addingImgs.inputs.operand_file = args.roi2
addingImgs.inputs.operation = "max"
addingImgs.inputs.out_file = args.output_file
addingImgs.run()
# resample the new roi to be in the coordinate frame of the coordinate image
jointRoi = load_image(args.output_file)
coordsImg = load_image(args.coord_image)
print("joint rois", jointRoi.get_data().shape)
newRoiImg = resample_img2img(jointRoi, coordsImg, order=0)
save_image(newRoiImg, args.output_file)
def save_niigz(filepath, vol, header=None, affine=None):
"""Saves a volume into a Nifti (.nii.gz) file.
Parameters
----------
vol: Numpy 3D or 4D array
Volume with the data to be saved.
file_path: string
Output file name path
affine: (optional) 4x4 Numpy array
Array with the affine transform of the file.
This is needed if vol is a np.ndarray.
header: (optional) nibabel.nifti1.Nifti1Header, optional
Header for the file, optional but recommended.
This is needed if vol is a np.ndarray.
Note
----
affine and header only work for numpy volumes.
"""
# delayed import because could not install nipy on Python 3 on OSX
we_have_nipy = False
try:
import nipy.core.image as niim
from nipy import save_image
except:
pass
else:
we_have_nipy = True
if isinstance(vol, np.ndarray):
log.debug('Saving numpy nifti file: {}.'.format(filepath))
ni = nib.Nifti1Image(vol, affine, header)
nib.save(ni, filepath)
elif isinstance(vol, nib.Nifti1Image):
log.debug('Saving nibabel nifti file: {}.'.format(filepath))
nib.save(vol, filepath)
elif we_have_nipy and isinstance(vol, niim.Image):
log.debug('Saving nipy nifti file: {}.'.format(filepath))
save_image(vol, filepath)
#elif isinstance(vol, NeuroImage):
# log.debug('Saving boyle.NeuroImage nifti file: {}.'.format(filepath))
# nib.save(vol.img, filepath)
else:
raise ValueError('Could not recognise input vol filetype. Got: {}.'.format(repr_imgs(vol)))
def save_npy_to_nifti(npy_data, filename, base_nifti_filename):
"""
Saves numpy to nifti.
Arguments:
npy_data: numpy array
filename: filename to save
base_nifti_filename: base nifti filename
"""
bnifti = load_image(base_nifti_filename)
img = Image.from_image(bnifti, data=npy_data.astype('uint8'))
save_image(img, filename)
print('Saved {}..'.format(filename))
def _run_interface(self, runtime):
from nipy import save_image, load_image
all_ims = [load_image(fname) for fname in self.inputs.in_file]
if not isdefined(self.inputs.slice_times):
from nipy.algorithms.registration.groupwise_registration import SpaceRealign
R = SpaceRealign(all_ims)
else:
from nipy.algorithms.registration import SpaceTimeRealign
R = SpaceTimeRealign(
all_ims,
tr=self.inputs.tr,
slice_times=self.inputs.slice_times,
slice_info=self.inputs.slice_info,
)
R.estimate(refscan=None)
corr_run = R.resample()
self._out_file_path = []
self._par_file_path = []
for j, corr in enumerate(corr_run):
self._out_file_path.append(
os.path.abspath(
"corr_%s.nii.gz" % (split_filename(self.inputs.in_file[j])[1])
)
)
save_image(corr, self._out_file_path[j])
self._par_file_path.append(
os.path.abspath("%s.par" % (os.path.split(self.inputs.in_file[j])[1]))
)
mfile = open(self._par_file_path[j], "w")
motion = R._transforms[j]
# nipy does not encode euler angles. return in original form of
# translation followed by rotation vector see:
# http://en.wikipedia.org/wiki/Rodrigues'_rotation_formula
for i, mo in enumerate(motion):
params = [
"%.10f" % item for item in np.hstack((mo.translation, mo.rotation))
]
string = " ".join(params) + "\n"
mfile.write(string)
mfile.close()
return runtime
def motion_correction_nipy(in_file, out_path, mc_alg, extra_params={}):
"""
an attempt at motion correction using NiPy package.
inputs:
in_file: Full path to the resting-state scan.
out_path: Full path to the (to be) output file.
mc_alg: can be either 'nipy_spacerealign' or 'nipy_spacetimerealign'
extra_params: extra parameters to SpaceRealign, SpaceTimeRealign, estimate
return: the motion corrected image
"""
alg_dict = {
'nipy_spacerealign': (SpaceRealign, {}),
'nipy_spacetimerealign': (SpaceTimeRealign, {
'tr': 2,
'slice_times': 'asc_alt_2',
'slice_info': 2
})
}
# format: {'function_name':(function, kwargs), ...}
# processing starts here
if type(in_file) in nib.all_image_classes:
I = nifti2nipy(in_file) # assume Nifti1Image
else:
I = load_image(in_file)
print 'source image loaded. '
# initialize the registration algorithm
reg = AllFeatures(alg_dict[mc_alg][0],
extra_params).run(I, **alg_dict[mc_alg][1])
# reg = alg_dict[mc_alg][0](I, **alg_dict[mc_alg][1]) # SpaceTimeRealign(I, tr=2, ...)
print 'motion correction algorithm established. '
print 'estimating...'
if USE_CACHE:
mem = Memory("func_preproc_cache_2")
mem.cache(AllFeatures(reg.estimate, extra_params).run)(refscan=None)
# mem.cache(reg.estimate)(refscan=None)
else:
AllFeatures(reg.estimate, extra_params).run(refscan=None)
# reg.estimate(refscan=None)
print 'estimation complete. Writing to file...'
result = reg.resample(0)
if out_path:
save_image(result, out_path)
return nipy2nifti(result)
def time_space_realign(run_fnames, TR, time_to_space, slice_axis):
run_imgs = [load_image(run) for run in run_fnames]
# Spatio-temporal realigner
R = FmriRealign4d(run_imgs,
tr=TR,
slice_order=time_to_space,
slice_info=(slice_axis, 1))
# Estimate motion within- and between-sessions
R.estimate(refscan=None)
# Save back out
for i, fname in enumerate(run_fnames):
corr_run = R.resample(i)
pth, name = os.path.split(fname)
processed_fname = os.path.join(pth, 'ra' + name)
save_image(corr_run, processed_fname)
def get_mode(seg_stack, out_file):
img = load_image(seg_stack)
new_coord = img[:, :, :, 0].coordmap
data = img.get_data()
mode = np.zeros(data.shape[:3])
for i in xrange(data.shape[0]):
for j in xrange(data.shape[1]):
for k in xrange(data.shape[2]):
u, indices = np.unique(data[i, j, k, :], return_inverse=True)
voxel_mode = u[np.argmax(np.bincount(indices))]
print "mode at {0},{1},{2} = {3}".format(i, j, k, voxel_mode)
mode[i, j, k] = voxel_mode
mode_image = Image(mode, new_coord)
save_image(mode_image, out_file)
return mode
def peelTemplateBrain():
ns=181
nr=217
nc=181
gt_template=np.fromfile('data/phantom_1.0mm_normal_crisp.rawb', dtype=np.ubyte).reshape((ns,nr,nc))
t1_template=np.fromfile('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb', dtype=np.ubyte).reshape((ns,nr,nc))
t2_template=np.fromfile('data/t2/t2_icbm_normal_1mm_pn0_rf0.rawb', dtype=np.ubyte).reshape((ns,nr,nc))
#t1_template*=((1<=gt_template)*(gt_template<=3)+(gt_template==8))
t1_template*=((1<=gt_template)*(gt_template<=3))
t2_template*=((1<=gt_template)*(gt_template<=3))
affine_transform=AffineTransform('ijk', ['aligned-z=I->S','aligned-y=P->A', 'aligned-x=L->R'], np.eye(4))
t1_template=Image(t1_template, affine_transform)
t2_template=Image(t2_template, affine_transform)
nipy.save_image(t1_template,'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz')
nipy.save_image(t2_template,'data/t2/t2_icbm_normal_1mm_pn0_rf0_peeled.nii.gz')
def _run_interface(self, runtime):
from nipy.algorithms.registration import FmriRealign4d as FR4d
all_ims = [load_image(fname) for fname in self.inputs.in_file]
if not isdefined(self.inputs.tr_slices):
TR_slices = None
else:
TR_slices = self.inputs.tr_slices
R = FR4d(all_ims,
tr=self.inputs.tr,
slice_order=self.inputs.slice_order,
tr_slices=TR_slices,
time_interp=self.inputs.time_interp,
start=self.inputs.start)
R.estimate(loops=list(self.inputs.loops),
between_loops=list(self.inputs.between_loops),
speedup=list(self.inputs.speedup))
corr_run = R.resample()
self._out_file_path = []
self._par_file_path = []
for j, corr in enumerate(corr_run):
self._out_file_path.append(
os.path.abspath('corr_%s.nii.gz' %
(split_filename(self.inputs.in_file[j])[1])))
save_image(corr, self._out_file_path[j])
self._par_file_path.append(
os.path.abspath('%s.par' %
(os.path.split(self.inputs.in_file[j])[1])))
mfile = open(self._par_file_path[j], 'w')
motion = R._transforms[j]
# nipy does not encode euler angles. return in original form of
# translation followed by rotation vector see:
# http://en.wikipedia.org/wiki/Rodrigues'_rotation_formula
for i, mo in enumerate(motion):
params = [
'%.10f' % item
for item in np.hstack((mo.translation, mo.rotation))
]
string = ' '.join(params) + '\n'
mfile.write(string)
mfile.close()
return runtime
def main():
try:
DATA_PATH = sys.argv[1]
except IndexError:
raise RuntimeError("Pass data path on command line")
subjects = get_subjects(DATA_PATH)
for name in sorted(subjects):
subject = subjects[name]
print("Smoothing subject " + name)
for run in subject['functionals']:
fname = run['filename']
pth, fpart = os.path.split(fname)
ra_fname = os.path.join(pth, 'ra' + fpart)
sra_fname = os.path.join(pth, 'sra' + fpart)
img = load_image(ra_fname)
save_image(smooth_image(img, 8.), sra_fname)
def save_niftis(self, X):
base_nifti = nipy.load_image(self.base_nifti_file)
if self.pca is not None and self.pca_components:
X = self.pca.inverse_transform(X)
images = []
out_files = []
for i, x in enumerate(X):
image = self.make_image(x, base_nifti, do_pca=False)
out_file = path.join(self.tmp_path, 'tmp_image_%d.nii.gz' % i)
nipy.save_image(image, out_file)
images.append(image)
out_files.append(out_file)
return images, out_files
def main():
try:
DATA_PATH = sys.argv[1]
except IndexError:
raise RuntimeError("Pass data path on command line")
subjects = get_subjects(DATA_PATH)
for name in sorted(subjects):
subject = subjects[name]
print("Smoothing subject " + name)
for run in subject['functionals']:
fname = run['filename']
pth, fpart = os.path.split(fname)
ra_fname = os.path.join(pth, 'ra' + fpart)
sra_fname = os.path.join(pth, 'sra' + fpart)
img = load_image(ra_fname)
save_image(smooth_image(img, 8.), sra_fname)
def generateTestingPair(betaGT):
betaGTRads=np.array(betaGT, dtype=np.float64)
betaGTRads[0:3]=np.copy(np.pi*betaGTRads[0:3]/180.0)
ns=181
nr=217
nc=181
left=np.fromfile('data/t2/t2_icbm_normal_1mm_pn0_rf0.rawb', dtype=np.ubyte).reshape(ns,nr,nc)
left=left.astype(np.float64)
right=np.fromfile('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb', dtype=np.ubyte).reshape(ns,nr,nc)
right=right.astype(np.float64)
right=rcommon.applyRigidTransformation3D(right, betaGTRads)
affine_transform=AffineTransform('ijk', ['aligned-z=I->S','aligned-y=P->A', 'aligned-x=L->R'], np.eye(4))
left=Image(left, affine_transform)
right=Image(right, affine_transform)
nipy.save_image(left,'moving.nii')
nipy.save_image(right,'fixed.nii')
def _run_interface(self, runtime):
all_ims = []
for image in self.inputs.in_file:
im = nb.load(image)
im.affine = im.get_affine()
all_ims.append(im)
if not isdefined(self.inputs.tr_slices):
TR_slices = None
else:
TR_slices = self.inputs.tr_slices
R = FR4d(all_ims, tr=self.inputs.tr,
slice_order=self.inputs.slice_order,
interleaved=self.inputs.interleaved,
tr_slices=TR_slices,
time_interp=self.inputs.time_interp,
start=self.inputs.start)
R.estimate(loops=self.inputs.loops,
between_loops=self.inputs.between_loops,
speedup=self.inputs.speedup)
corr_run = R.resample()
self._out_file_path = []
self._par_file_path = []
for j, corr in enumerate(corr_run):
self._out_file_path.append(os.path.abspath('corr_%s.nii.gz' %
(split_filename(self.inputs.in_file[j])[1])))
save_image(corr, self._out_file_path[j])
self._par_file_path.append(os.path.abspath('%s.par' %
(os.path.split(self.inputs.in_file[j])[1])))
mfile = open(self._par_file_path[j], 'w')
motion = R._transforms[j]
#output a .par file that looks like fsl.mcflirt's .par file
for i, mo in enumerate(motion):
params = ['%.10f' % item for item in np.hstack((mo.rotation,
mo.translation))]
string = ' '.join(params) + '\n'
mfile.write(string)
mfile.close()
return runtime
def _run_interface(self, runtime):
from nipy import save_image, load_image
all_ims = [load_image(fname) for fname in self.inputs.in_file]
if not isdefined(self.inputs.slice_times):
from nipy.algorithms.registration.groupwise_registration import \
SpaceRealign
R = SpaceRealign(all_ims)
else:
from nipy.algorithms.registration import SpaceTimeRealign
R = SpaceTimeRealign(
all_ims,
tr=self.inputs.tr,
slice_times=self.inputs.slice_times,
slice_info=self.inputs.slice_info,
)
R.estimate(refscan=None)
corr_run = R.resample()
self._out_file_path = []
self._par_file_path = []
for j, corr in enumerate(corr_run):
self._out_file_path.append(
os.path.abspath('corr_%s.nii.gz' %
(split_filename(self.inputs.in_file[j])[1])))
save_image(corr, self._out_file_path[j])
self._par_file_path.append(
os.path.abspath('%s.par' %
(os.path.split(self.inputs.in_file[j])[1])))
mfile = open(self._par_file_path[j], 'w')
motion = R._transforms[j]
# nipy does not encode euler angles. return in original form of
# translation followed by rotation vector see:
# http://en.wikipedia.org/wiki/Rodrigues'_rotation_formula
for i, mo in enumerate(motion):
params = [
'%.10f' % item
for item in np.hstack((mo.translation, mo.rotation))
]
string = ' '.join(params) + '\n'
mfile.write(string)
mfile.close()
return runtime
def smooth_mask_nipy(infile, outfile, fwhm=14):
"""uses nipy to smooth an image using gaussian filter of fwhm"""
img = nipy.load_image(infile)
lf = nipy.algorithms.kernel_smooth.LinearFilter(img.coordmap, img.shape,
fwhm)
simg = lf.smooth(img)
outimg = nipy.save_image(simg, outfile)
return outimg
def segment_file(input_filename, output_filename,
model_name, threshold, verbosity,
use_tta):
"""Segment a volume file.
:param input_filename: the input filename.
:param output_filename: the output filename.
:param model_name: the name of model to use.
:param threshold: threshold to apply in predictions (if None,
no threshold will be applied)
:param verbosity: the verbosity level.
:param use_tta: whether it should use TTA (test-time augmentation)
or not.
:return: the output filename.
"""
nii_original = nipy.load_image(input_filename)
pixdim = nii_original.header["pixdim"][3]
target_resample = "0.25x0.25x{:.5f}".format(pixdim)
nii_resampled = resampling.resample_nipy(nii_original, new_size=target_resample, new_size_type='mm',
interpolation='linear', verbose=verbosity)
nii_resampled = nipy2nifti(nii_resampled)
pred_slices = segment_volume(nii_resampled, model_name, threshold,
use_tta)
original_res = "{:.5f}x{:.5f}x{:.5f}".format(
nii_original.header["pixdim"][1],
nii_original.header["pixdim"][2],
nii_original.header["pixdim"][3])
volume_affine = nii_resampled.affine
volume_header = nii_resampled.header
nii_segmentation = nib.Nifti1Image(pred_slices, volume_affine,
volume_header)
nii_segmentation = nifti2nipy(nii_segmentation)
nii_resampled_original = resampling.resample_nipy(nii_segmentation, new_size=original_res, new_size_type='mm',
interpolation='linear', verbose=verbosity)
res_data = nii_resampled_original.get_data()
# Threshold after resampling, only if specified
if threshold is not None:
res_data = threshold_predictions(res_data, 0.5)
nipy.save_image(nii_resampled_original, output_filename)
return output_filename
def tissue_classification(img,mask=None,niters=25,beta=0.5,ngb_size=6,probc=None,probg=None,probw=None):
import numpy as np
from nipy import load_image, save_image
from nipy.core.image.image_spaces import (make_xyz_image,
xyz_affine)
from nipy.algorithms.segmentation import BrainT1Segmentation
import os
# Input image
img = load_image(img)
# Input mask image
mask_img = mask
if mask_img == None:
mask_img = img
else:
mask_img = load_image(mask_img)
# Other optional arguments
#niters = int(get_argument('niters', 25))
#beta = float(get_argument('beta', 0.5))
#ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(), mask=mask, model='5k',
niters=niters, beta=beta, ngb_size=ngb_size)
# Save label image
outfile = os.path.abspath('hard_classif.nii')
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'),
outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if not probc == None and\
not probg == None and\
not probw == None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (probc, probg, probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
def tissue_segmentation(save_path,segmented_image, type, info):
if info=='freesurfer' and type=="GM":
# FreeSurfer gray matter regions
# SupraGMV=LeftCerebralCortex+RightCerebralCortex+SubcorticalGMV
region=np.array([42,3,10,11,12,13,17,18,26,49,50,51,52,53,54,58])
S=nipy.load_image(segmented_image)
name="GM_"+ os.path.basename(segmented_image)
index=np.in1d(S._data, region).reshape(S._data.shape) # coordinates of voxels with values = values from region array
S._data[index]=1 # set voxels values to 1
index=np.logical_not(index) #get coordinates of voxels with values not = values from region array
S._data[index]=0 # set voxels values to 0
nipy.save_image(S, os.path.join(save_path,name))
else:
raise ValueError('Not implemented!')
def _run_interface(self, runtime):
from nipy.algorithms.registration import FmriRealign4d as FR4d
all_ims = [load_image(fname) for fname in self.inputs.in_file]
if not isdefined(self.inputs.tr_slices):
TR_slices = None
else:
TR_slices = self.inputs.tr_slices
R = FR4d(
all_ims,
tr=self.inputs.tr,
slice_order=self.inputs.slice_order,
tr_slices=TR_slices,
time_interp=self.inputs.time_interp,
start=self.inputs.start,
)
R.estimate(
loops=list(self.inputs.loops),
between_loops=list(self.inputs.between_loops),
speedup=list(self.inputs.speedup),
)
corr_run = R.resample()
self._out_file_path = []
self._par_file_path = []
for j, corr in enumerate(corr_run):
self._out_file_path.append(os.path.abspath("corr_%s.nii.gz" % (split_filename(self.inputs.in_file[j])[1])))
save_image(corr, self._out_file_path[j])
self._par_file_path.append(os.path.abspath("%s.par" % (os.path.split(self.inputs.in_file[j])[1])))
mfile = open(self._par_file_path[j], "w")
motion = R._transforms[j]
# nipy does not encode euler angles. return in original form of
# translation followed by rotation vector see:
# http://en.wikipedia.org/wiki/Rodrigues'_rotation_formula
for i, mo in enumerate(motion):
params = ["%.10f" % item for item in np.hstack((mo.translation, mo.rotation))]
string = " ".join(params) + "\n"
mfile.write(string)
mfile.close()
return runtime
def smooth_mask_nipy(infile, outfile, fwhm=14):
"""uses nipy to smooth an image using gaussian filter of fwhm"""
img = nipy.load_image(infile)
lf = nipy.algorithms.kernel_smooth.LinearFilter(img.coordmap,
img.shape,
fwhm)
simg = lf.smooth(img)
outimg = nipy.save_image(simg, outfile)
return outimg
def seg_recovery(files, model_settings):
def get_model(model_setting):
modelname = model_setting['modelname']
axis = model_setting['axis']
loss = model_setting['loss']
postPocess = model_setting['postPocess']
seg_model = segment_model(valfiles=None,
modelname=modelname,
axis=axis,
metric=None,
loss=loss,
postPocess=postPocess)
seg_model.load(model_setting['path'])
return seg_model
for f in files:
img_3d = load_image(TEST_DATA + f)
image = img_3d.get_data()
# image = normlize_data(image)
result_h1 = []
result_h2 = []
for setting in tqdm(model_settings):
seg_model = get_model(setting)
h1, h2 = model_predict(image, seg_model)
result_h1.append(h1)
result_h2.append(h2)
h1 = average(result_h1)
h2 = average(result_h2)
h1 = np.around(h1)
h2 = np.around(h2)
shape = str(image.shape[2])
area = crop_config['3d' + shape]
label = np.zeros_like(image)
label[area['x'][0]:area['x'][1], area['y'][0]:area['y'][1],
area['z1'][0]:area['z1'][1]] = h1
label[area['x'][0]:area['x'][1], area['y'][0]:area['y'][1],
area['z2'][0]:area['z2'][1]] = h2 * 2
img = Image(label, img_3d.coordmap)
save_image(img, OUTPUT + f)
def test_nipy_diagnose():
# Test nipy diagnose script
fimg = load_image(funcfile)
ncomps = 12
with InTemporaryDirectory() as tmpdir:
cmd = ['nipy_diagnose', funcfile,
'--ncomponents={0}'.format(ncomps),
'--out-path=' + tmpdir]
run_command(cmd)
for out_fname in ('components_functional.png',
'pcnt_var_functional.png',
'tsdiff_functional.png',
'vectors_components_functional.npz'):
assert_true(isfile(out_fname))
for out_img in ('max_functional.nii.gz',
'mean_functional.nii.gz',
'min_functional.nii.gz',
'std_functional.nii.gz'):
img = load_image(out_img)
assert_equal(img.shape, fimg.shape[:-1])
del img
pca_img = load_image('pca_functional.nii.gz')
assert_equal(pca_img.shape, fimg.shape[:-1] + (ncomps,))
vecs_comps = np.load('vectors_components_functional.npz')
vec_diff = vecs_comps['slice_mean_diff2'].copy()# just in case
assert_equal(vec_diff.shape, (fimg.shape[-1]-1, fimg.shape[2]))
del pca_img, vecs_comps
with InTemporaryDirectory() as tmpdir:
# Check we can pass in slice and time flags
s0_img = rollimg(fimg, 'k')
save_image(s0_img, 'slice0.nii')
cmd = ['nipy_diagnose', 'slice0.nii',
'--ncomponents={0}'.format(ncomps),
'--out-path=' + tmpdir,
'--time-axis=t',
'--slice-axis=0']
run_command(cmd)
pca_img = load_image('pca_slice0.nii')
assert_equal(pca_img.shape, s0_img.shape[:-1] + (ncomps,))
vecs_comps = np.load('vectors_components_slice0.npz')
assert_almost_equal(vecs_comps['slice_mean_diff2'], vec_diff)
del pca_img, vecs_comps
def test_nipy_diagnose():
# Test nipy diagnose script
fimg = load_image(funcfile)
ncomps = 12
with InTemporaryDirectory() as tmpdir:
cmd = ['nipy_diagnose', funcfile,
'--ncomponents={0}'.format(ncomps),
'--out-path=' + tmpdir]
run_command(cmd)
for out_fname in ('components_functional.png',
'pcnt_var_functional.png',
'tsdiff_functional.png',
'vectors_components_functional.npz'):
assert_true(isfile(out_fname))
for out_img in ('max_functional.nii.gz',
'mean_functional.nii.gz',
'min_functional.nii.gz',
'std_functional.nii.gz'):
img = load_image(out_img)
assert_equal(img.shape, fimg.shape[:-1])
del img
pca_img = load_image('pca_functional.nii.gz')
assert_equal(pca_img.shape, fimg.shape[:-1] + (ncomps,))
vecs_comps = np.load('vectors_components_functional.npz')
vec_diff = vecs_comps['slice_mean_diff2'].copy()# just in case
assert_equal(vec_diff.shape, (fimg.shape[-1]-1, fimg.shape[2]))
del pca_img, vecs_comps
with InTemporaryDirectory() as tmpdir:
# Check we can pass in slice and time flags
s0_img = rollimg(fimg, 'k')
save_image(s0_img, 'slice0.nii')
cmd = ['nipy_diagnose', 'slice0.nii',
'--ncomponents={0}'.format(ncomps),
'--out-path=' + tmpdir,
'--time-axis=t',
'--slice-axis=0']
run_command(cmd)
pca_img = load_image('pca_slice0.nii')
assert_equal(pca_img.shape, s0_img.shape[:-1] + (ncomps,))
vecs_comps = np.load('vectors_components_slice0.npz')
assert_almost_equal(vecs_comps['slice_mean_diff2'], vec_diff)
del pca_img, vecs_comps
def generateTestingPair(betaGT):
betaGTRads = np.array(betaGT, dtype=np.float64)
betaGTRads[0:3] = np.copy(np.pi * betaGTRads[0:3] / 180.0)
ns = 181
nr = 217
nc = 181
left = np.fromfile('data/t2/t2_icbm_normal_1mm_pn0_rf0.rawb',
dtype=np.ubyte).reshape(ns, nr, nc)
left = left.astype(np.float64)
right = np.fromfile('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb',
dtype=np.ubyte).reshape(ns, nr, nc)
right = right.astype(np.float64)
right = rcommon.applyRigidTransformation3D(right, betaGTRads)
affine_transform = AffineTransform(
'ijk', ['aligned-z=I->S', 'aligned-y=P->A', 'aligned-x=L->R'],
np.eye(4))
left = Image(left, affine_transform)
right = Image(right, affine_transform)
nipy.save_image(left, 'moving.nii')
nipy.save_image(right, 'fixed.nii')
def _run_interface(self, runtime):
all_ims = [load_image(fname) for fname in self.inputs.in_file]
if not isdefined(self.inputs.tr_slices):
TR_slices = None
else:
TR_slices = self.inputs.tr_slices
R = FR4d(all_ims, tr=self.inputs.tr,
slice_order=self.inputs.slice_order,
tr_slices=TR_slices,
time_interp=self.inputs.time_interp,
start=self.inputs.start)
R.estimate(loops=self.inputs.loops,
between_loops=self.inputs.between_loops,
speedup=self.inputs.speedup)
corr_run = R.resample()
self._out_file_path = []
self._par_file_path = []
for j, corr in enumerate(corr_run):
self._out_file_path.append(os.path.abspath('corr_%s.nii.gz' %
(split_filename(self.inputs.in_file[j])[1])))
save_image(corr, self._out_file_path[j])
self._par_file_path.append(os.path.abspath('%s.par' %
(os.path.split(self.inputs.in_file[j])[1])))
mfile = open(self._par_file_path[j], 'w')
motion = R._transforms[j]
#output a .par file that looks like fsl.mcflirt's .par file
for i, mo in enumerate(motion):
params = ['%.10f' % item for item in np.hstack((mo.rotation,
mo.translation))]
string = ' '.join(params) + '\n'
mfile.write(string)
mfile.close()
return runtime
def save_niftis(dataset, features, image_dir, base_nifti=None, **kwargs):
"""
Saves a series of niftis.
"""
logger.info("Saving mri images")
spatial_maps = features.spatial_maps
spatial_maps = dataset.get_weights_view(spatial_maps)
for i, feature in features.f.iteritems():
image = dataset.get_nifti(spatial_maps[i], base_nifti=base_nifti)
nipy.save_image(image, path.join(image_dir, "%d.nii.gz" % feature.id))
nifti_files = [
path.join(image_dir, "%d.nii.gz" % feature.id)
for feature in features.f.values()
]
roi_dict = rois.main(nifti_files)
anat_file = ("/export/mialab/users/mindgroup/Data/mrn/"
"mri_extra/ch2better_aligned2EPI.nii")
anat = nipy.load_image(anat_file)
nifti_viewer.save_images(nifti_files, anat, roi_dict, image_dir, **kwargs)
def tissue_segmentation(save_path, segmented_image, type, info):
if info == 'freesurfer' and type == "GM":
# FreeSurfer gray matter regions
# SupraGMV=LeftCerebralCortex+RightCerebralCortex+SubcorticalGMV
region = np.array(
[42, 3, 10, 11, 12, 13, 17, 18, 26, 49, 50, 51, 52, 53, 54, 58])
S = nipy.load_image(segmented_image)
name = "GM_" + os.path.basename(segmented_image)
index = np.in1d(S._data, region).reshape(
S._data.shape
) # coordinates of voxels with values = values from region array
S._data[index] = 1 # set voxels values to 1
index = np.logical_not(
index
) #get coordinates of voxels with values not = values from region array
S._data[index] = 0 # set voxels values to 0
nipy.save_image(S, os.path.join(save_path, name))
else:
raise ValueError('Not implemented!')
def testIntersubjectRigidRegistration(fname0, fname1, level, outfname):
nib_left = nib.load(fname0)
nib_right = nib.load(fname1)
left=nib_left.get_data().astype(np.double).squeeze()
right=nib_right.get_data().astype(np.double).squeeze()
leftPyramid=[i for i in rcommon.pyramid_gaussian_3D(left, level)]
rightPyramid=[i for i in rcommon.pyramid_gaussian_3D(right, level)]
plotSlicePyramidsAxial(leftPyramid, rightPyramid)
print 'Estimation started.'
beta=estimateRigidTransformationMultiscale3D(leftPyramid, rightPyramid)
print 'Estimation finished.'
rcommon.applyRigidTransformation3D(left, beta)
sl=np.array(left.shape)//2
sr=np.array(right.shape)//2
rcommon.overlayImages(left[sl[0],:,:], leftPyramid[0][sr[0],:,:])
rcommon.overlayImages(left[sl[0],:,:], right[sr[0],:,:])
affine_transform=AffineTransform('ijk', ['aligned-z=I->S','aligned-y=P->A', 'aligned-x=L->R'], np.eye(4))
left=Image(left, affine_transform)
nipy.save_image(left,outfname)
return beta
def expandTimepoints(imgFn, baseDir):
"""
Expand an image sequence stored as a .nii.gz file into a collection of
.nii.gz images (where each frame is its own .nii.gz file)
Inputs:
- imgFn: the time series image's filename
- baseDir: the directory in which a new directory
will be created to hold the collection of files
Returns:
- filenames: list of filenames
"""
# load the image
img = load_image(imgFn)
coord = img.coordmap
if not os.path.exists(baseDir + 'timepoints/'):
os.mkdir(baseDir + 'timepoints/')
outDir = baseDir + 'timepoints/'
# pull out the first image from the sequence (timepoint 0)
first = img[:, :, :, 0].get_data()[:, :, :, None]
first_img = Image(first, coord)
# save the first image as 000
save_image(first_img, outDir + str(0).zfill(3) + '.nii.gz')
# build the list of filenames
filenames = [outDir + '000.nii.gz']
# for the remaining images
for i in xrange(1, img.get_data().shape[3], 1):
# pull out the image and save it
tmp = img[:, :, :, i].get_data()[:, :, :, None]
tmp_img = Image(tmp, coord)
outFn = str(i).zfill(3) + '.nii.gz'
save_image(tmp_img, outDir + outFn)
# add the name of the image to the list of filenames
filenames.append(outDir + outFn)
return filenames
def save_niftis(self, X):
'''Save nifti files from array.
Args:
X (numpy.array): array from which to make images.
Returns:
list: list of nifti images.
list: list of output files for images.
'''
base_nifti = nipy.load_image(self.base_nifti_file)
images = []
out_files = []
for i, x in enumerate(X):
image = self.make_image(x, base_nifti)
out_file = path.join(self.tmp_path, 'tmp_image_%d.nii.gz' % i)
nipy.save_image(image, out_file)
images.append(image)
out_files.append(out_file)
return images, out_files
def screen_data_dirnme(in4d, outdir):
"""uses nipy diagnostic code to screen the data for
outlier values and saves results to three images
mean, std, pca, in same dir as original file(s)"""
img = nipy.load_image(in4d)
result = diag.screen(img)
# save mean, std, pca
pth, nme = os.path.split(in4d)
stripnme = nme.split('.')[0]
pcafile = os.path.join(outdir,
'QA-PCA_%s.nii.gz'%(nme))
meanfile = os.path.join(outdir,
'QA-MEAN_%s.nii.gz'%(nme))
stdfile = os.path.join(outdir,
'QA-STD_%s.nii.gz'%(nme))
nipy.save_image(result['mean'], meanfile)
nipy.save_image(result['std'], stdfile)
nipy.save_image(result['pca'], pcafile)
print 'saved: %s\n \t%s\n \t%s\n'%(pcafile, meanfile, stdfile)
#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""This example shows how to create a temporary image to use during processing.
The array is filled with zeros.
"""
import numpy as np
from nipy import load_image, save_image
from nipy.core.api import Image, vox2mni
# create an array of zeros, the shape of your data array
zero_array = np.zeros((91,109,91))
# create an image from our array. The image will be in MNI space
img = Image(zero_array, vox2mni(np.diag([2, 2, 2, 1])))
# save the image to a file
newimg = save_image(img, 'tempimage.nii.gz')
# Example of creating a temporary image file from an existing image with a
# matching coordinate map.
img = load_image('tempimage.nii.gz')
zeroarray = np.zeros(img.shape)
zeroimg = Image(zeroarray, img.coordmap)
newimg = save_image(zeroimg, 'another_tempimage.nii.gz')
else:
mask_img = load_image(mask_img)
# Other optional arguments
niters = int(get_argument('niters', 25))
beta = float(get_argument('beta', 0.5))
ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(), mask=mask, model='5k',
niters=niters, beta=beta, ngb_size=ngb_size)
# Save label image
outfile = 'hard_classif.nii'
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'),
outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if args.probc is not None and \
args.probg is not None and \
args.probw is not None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (args.probc, args.probg, args.probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
from nipy.algorithms.registration import FmriRealign4d
from nipy import load_image, save_image
from nipy.utils import example_data
from os.path import join, split
import sys
import tempfile
# Input images are provided with the nipy-data package
runnames = [example_data.get_filename('fiac','fiac0',run+'.nii.gz') \
for run in ('run1','run2')]
runs = [load_image(run) for run in runnames]
# Spatio-temporal realigner
R = FmriRealign4d(runs, tr=2.5, slice_order='ascending', interleaved=True)
# Estimate motion within- and between-sessions
R.estimate()
# Resample data on a regular space+time lattice using 4d interpolation
corr_runs = R.resample()
# Save images
savedir = tempfile.mkdtemp()
for i in range(len(runs)):
aux = split(runnames[i])
save_image(corr_runs[i], join(savedir, 'ra'+aux[1]))
#resample data
ra_runs = R.resample()
#save resampled images
for i, corrImage in enumerate(ra_runs):
#save realigned image
#trim off .nii or .nii.gz extension
iname = cmdInput.inputs[i].split('.')
if len(iname) > 2 and (iname[-1].lower() == "gz" and iname[-2].lower() == "nii"):
imname = '.'.join(iname[:-2]) + cmdInput.prefix + '.nii.gz' #drop last two dotted pieces: .nii.gz
elif len(iname) > 1 and iname[-1].lower() == "nii":
immname = '.'.join(iname[:-1]) + cmdInput.prefix + '.nii' #drop last dotted piece: .nii
else:
print "Can't determine file name of input properly."
exit(1)
save_image(corrImage, imname)
#save motion estimates, stored in realign object _transforms
motion = R._transforms[i]
#trim off .nii or .nii.gz extension
iname = cmdInput.inputs[i].split('.')
if len(iname) > 2 and (iname[-1].lower() == "gz" and iname[-2].lower() == "nii"):
mname = '.'.join(iname[:-2]) + '.par' #drop last two dotted pieces: .nii.gz
elif len(iname) > 1 and iname[-1].lower() == "nii":
mname = '.'.join(iname[:-1]) + '.par' #drop last dotted piece: .nii
else:
print "Can't determine file name of input properly."
exit(1)
mfile = open(mname, 'w')
J = load_image(target_file)
# Perform affine registration
# The output is an array-like object such that
# np.asarray(T) is a customary 4x4 matrix
print('Setting up registration...')
tic = time.time()
R = HistogramRegistration(I, J, similarity=similarity, interp=interp,
renormalize=renormalize)
T = R.optimize('affine', optimizer=optimizer)
toc = time.time()
print(' Registration time: %f sec' % (toc - tic))
# Resample source image
print('Resampling source image...')
tic = time.time()
#It = resample2(I, J.coordmap, T.inv(), J.shape)
It = resample(I, T.inv(), reference=J)
toc = time.time()
print(' Resampling time: %f sec' % (toc - tic))
# Save resampled source
outroot = source + '_TO_' + target
outimg = outroot + '.nii.gz'
print ('Saving resampled source in: %s' % outimg)
save_image(It, outimg)
# Save transformation matrix
outparams = outroot + '.npy'
np.save(outparams, np.asarray(T))
def space_time_realign(Images,TR=2,numslices=None,SliceTime='asc_alt_2',RefScan=0,Prefix='ra'):
'''
4D simultaneous slice timing and spatial realignment. Adapted from
Alexis Roche's example script, and extend to be used for multiplex
imaging sequences
Inputs:
Images: list of images, input as a list of strings/paths to images
numslices: for non-multiplex sequence, default to be the number of
slices in the image. For multiplex sequence, enter as a tuple,
such that the first element is the number of planes acquired in
parallel between each other, and the second element is the number
of slices of each parallel plane/slab, i.e. (numplanes,numslices)
SliceTime:enter as a string to specify how the slices are ordered.
Choices are the following
1).'ascending': sequential ascending acquisition
2).'descending': sequential descending acquisition
3).'asc_alt_2': ascending interleaved, starting at first slice
4).'asc_alt_2_1': ascending interleaved, starting at the second
slice
5).'desc_alt_2': descending interleaved, starting at last slice
6).'asc_alt_siemens': ascending interleaved, starting at the first
slice if odd number of slices, or second slice if even number
of slices
7).'asc_alt_half': ascending interleaved by half the volume
8).'desc_alt_half': descending interleaved by half the volume
RefScan: reference volume for spatial realignment movement estimation.
Note that scan 0 is the first scan.
Prefix: prefix of the new corrected images. Default is 'ra'
Author: Alexis Roche, 2009.
Edward Cui, February 2014
'''
# Load images
runs = [load_image(run) for run in Images]
# Parse data info
if numslices is None:
numslices = runs[0].shape[2]
numplanes = 1
elif isinstance(numslices,tuple):
(numplanes,numslices) = numslices
else:
numplanes = 1
# Print image info
if numplanes>1:
print('Running multiplex: %s' % numplanes)
print('Number of slices: %s' % numslices)
# Parse slice timing according to the input
slice_timing = getattr(timefuncs,SliceTime)(numslices,TR)
# Repeat the slice timing for multiplex seqquence
slice_timing = np.tile(slice_timing,numplanes)
# Print slice timing info
print('Slice times: %s' % slice_timing)
# Spatio-temporal realigner
R = SpaceTimeRealign(runs, tr=TR, slice_times=slice_timing, slice_info=2)
# Estimate motion within- and between-sessions
print('Estimating motion ...')
R.estimate(refscan=RefScan)
# Resample data on a regular space+time lattice using 4d interpolation
fname=[None]*len(Images) # output images
mfname=[None]*len(Images) # output motion parameter files
print('Saving results ...')
for n in range(len(Images)):
# extract motion parameters
motionparams = np.array([np.concatenate((M.translation,M.rotation),axis=1) for M in R._transforms[n]])
# set motion parameter file name
mfname[n] = os.path.join(os.path.split(Images[n])[0], 'rp_a0001.txt')
# write the motion parameters to file
np.savetxt(mfname[n],motionparams,fmt='%10.7e',delimiter='\t')
# resample data
corr_run = R.resample(n)
# set image name
fname[n] = os.path.join(os.path.split(Images[n])[0], Prefix + os.path.split(Images[n])[1])
# save image
save_image(corr_run, fname[n])
print(fname[n])
return(fname,mfname)
s = R.eval(T0)
sa = R.eval(T0.affine)
assert_almost_equal(s, sa)
# Test 2
T = SplineTransform(I, cp, sigma=5., grid_coords=True, affine=A)
T.param += 1.
s0 = R.eval(T0)
s = R.eval(T)
print(s-s0)
"""
# Optimize spline transform
# T = R.optimize(T0, method='steepest')
###T = R.optimize(T0)
T = T0
###T.param = np.load('spline_param.npy')
# Resample target image
Jt = resample(J, T, reference=I)
save_image(Jt, 'deform_anubis_to_ammon.nii')
# Test 3
"""
ts = t[R._slices+[slice(0,3)]]
tts = T[R._slices]()
"""