def __init__(self, imgfile, datafile=None, subject=None, group=None, convert=True):
self.name = imgfile.split('/')[-1].split('.')[0]
self.json = imageutils.img_to_json(imgfile)
# If image is in Analyze format, write NIFTI copy
if convert and re.match('img', imgfile):
img = NiftiImage(imgfile)
img.save('%s.nii.gz' % self.name)
def __init__(self, dims=(91, 109, 91)):
self.dims = dims
resources = os.path.join(os.path.dirname(__file__), 'resources')
self.mask_img = NiftiImage(
os.path.join(resources, 'images',
'scalped_avg152T1_graymatter_smoothed.img'))
self.atlas = NiftiImage(
os.path.join(resources, 'images', 'MNI152_T1_2mm_brain.nii.gz'))
self.full = np.float64(self.mask_img.data.ravel())
self.in_mask = (self.full != 0)
self.ind_in_mask = self.full
self.ind_in_mask[self.in_mask] = range(np.sum(self.in_mask))
def __init__(self,
imgfile,
datafile=None,
subject=None,
group=None,
convert=True):
self.name = imgfile.split('/')[-1].split('.')[0]
self.json = imageutils.img_to_json(imgfile)
# If image is in Analyze format, write NIFTI copy
if convert and re.match('img', imgfile):
img = NiftiImage(imgfile)
img.save('%s.nii.gz' % self.name)
def handle_arg(arg):
"""Helper which would read in NiftiImage if necessary
"""
if isinstance(arg, basestring):
arg = NiftiImage(arg)
argshape = arg.data.shape
# Assure that we have 3D (at least)
if len(argshape)<3:
arg.data = arg.data.reshape((1,)*(3-len(argshape)) + argshape)
if isinstance(arg, N.ndarray):
if len(arg.shape) != 3:
raise ValueError, "For now just handling 3D volumes"
return arg
def handle_arg(arg):
"""Helper which would read in NiftiImage if necessary
"""
if isinstance(arg, basestring):
arg = NiftiImage(arg)
argshape = arg.data.shape
# Assure that we have 3D (at least)
if len(argshape) < 3:
arg.data = arg.data.reshape((1, ) * (3 - len(argshape)) +
argshape)
if isinstance(arg, np.ndarray):
if len(arg.shape) != 3:
raise ValueError, "For now just handling 3D volumes"
return arg
def __init__(self, under_image, over_image):
"""
Provide the underlay and overlay NiftiImages. Can also
provide filename strings.
Example:
stat = OverlayMap('anat.nii.gz','stat.nii.gz')
"""
# we've got traits
HasTraits.__init__(self)
# load in the image
if isinstance(under_image, NiftiImage):
# use it
self.__under_image = under_image
elif isinstance(under_image, str):
# load from file
self.__under_image = NiftiImage(under_image)
else:
raise ValueError("under_image must be a NiftiImage or a file.")
# TODO: set the extent and spacing of the under image
# set the over data
if isinstance(over_image, str):
# load from file
over_image = NiftiImage(over_image)
if isinstance(over_image, NiftiImage):
# TODO: make sure it matches the dims of under image
# TODO: set the extent
# save just the dat
self.__over_image = over_image.data.T
elif isinstance(over_image, np.ndarray):
# just set it
# assumes it matches the dims and extent of the under image
self.__over_image = over_image
else:
raise ValueError("over_image must be a NiftiImage, ndarray, or file.")
self.__over_image = np.ma.masked_invalid(self.__over_image)
self.configure_traits()
pass
def _load_images(self):
# shortcut
imagefile = self.header.images.imagefile
#self.nlevels = len(self._levels_by_id)
# Set offset if defined in XML file
# XXX: should just take one from the qoffset... now that one is
# defined... this origin might be misleading actually
self._origin = np.array((0, 0, 0))
if imagefile.attrib.has_key('offset'):
self._origin = np.array(
[int(x) for x in imagefile.get('offset').split(',')])
# Load the image file which has labels
if self._force_image_file is not None:
imagefilename = self._force_image_file
else:
imagefilename = imagefile.text
imagefilename = reuse_absolute_path(self._filename, imagefilename)
try:
self._image = NiftiImage(imagefilename)
except RuntimeError, e:
raise RuntimeError, \
" Cannot open file %s due to %s" % (imagefilename, e)
def getImage(self,filename):
if str(filename).endswith('nii') or str(filename).endswith('nii.gz'):
nim = NiftiImage(str(filename))
shape = nim.data.shape
img_data = nim.data
miny = np.amin(img_data)
arr = img_data.astype(float)
arr -= miny
maxy = np.amax(arr)
arr = arr / (maxy/2)
arr -= 1
arr = sk.img_as_ubyte(arr)
img_data = arr
elif str(filename).endswith('hdr'):
# Use the header to figure out the shape of the data
# then load the raw data and reshape the array
img_data = np.frombuffer(open(str(filename).replace('.hdr', '.dat'), 'rb').read(),
np.uint8)\
.reshape((shape[2], shape[1], shape[0]))
return (img_data,shape)
def load_datadb_tutorial_data(path=os.path.join(
pymvpa_datadbroot, 'tutorial_data', 'tutorial_data', 'data'),
roi='brain'):
"""Loads the block-design demo dataset from PyMVPA dataset DB.
Parameters
----------
path : str
Path of the directory containing the dataset files.
roi : str or int or tuple or None
Region Of Interest to be used for masking the dataset. If a string is
given a corresponding mask image from the demo dataset will be used
(mask_<str>.nii.gz). If an int value is given, the corresponding ROI
is determined from the atlas image (mask_hoc.nii.gz). If a tuple is
provided it may contain int values that a processed as explained
before, but the union of a ROIs is taken to produce the final mask.
If None, no masking is performed.
"""
from nifti import NiftiImage
from mvpa.datasets.mri import fmri_dataset
from mvpa.misc.io import SampleAttributes
if roi is None:
mask = None
elif isinstance(roi, str):
mask = os.path.join(path, 'mask_' + roi + '.nii.gz')
elif isinstance(roi, int):
nimg = NiftiImage(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = nimg.data == roi
mask = NiftiImage(tmpmask.astype(int), nimg.header)
elif isinstance(roi, tuple) or isinstance(roi, list):
nimg = NiftiImage(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = np.zeros(nimg.data.shape, dtype='bool')
for r in roi:
tmpmask = np.logical_or(tmpmask, nimg.data == r)
mask = NiftiImage(tmpmask.astype(int), nimg.header)
else:
raise ValueError("Got something as mask that I cannot handle.")
attr = SampleAttributes(os.path.join(path, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(path, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=mask)
return ds
def handler(points, mr, gofscale, gof, sigma):
from pdf2py import readwrite
from meg import density
from mri import transform
from scipy import ndimage
from nifti import NiftiImage
from numpy import float32, int16, array
report = {}
fids = eval(mr.description)
lpa = fids[0]
rpa = fids[1]
nas = fids[2]
# self.points = array([[0,0,0],[10,0,0],[0,20,0]])#DEBUG-----------------
xyz = transform.meg2mri(lpa, rpa, nas, dipole=points)
# readwrite.writedata(xyz, os.path.dirname(mripath)+'/'+'xyz')
print "lpa, rpa, nas", lpa, rpa, nas
print mr.pixdim
# do some scaling of the dips using the GOF as a weight.
VoxDim = mr.voxdim[::-1]
xyzscaled = (xyz / VoxDim).T
print xyzscaled
d = density.calc(xyz)
gofscale = float32(gofscale)
print "gofscale", gofscale
s = gof - gofscale
sf = (1 / (1 - gofscale)) * s
ds = d * sf
# apply a 1D gaussian filter
z = density.val2img(mr.data, ds, xyzscaled)
# sigma = float32(self.sigmaval.GetValue())
print "sigma", sigma
# sigma = 3
print "filtering 1st dimension"
f = ndimage.gaussian_filter1d(z, sigma * 1 / VoxDim[0], axis=0)
print "filtering 2nd dimension"
f = ndimage.gaussian_filter1d(f, sigma * 1 / VoxDim[1], axis=1)
print "filtering 3rd dimension"
f = ndimage.gaussian_filter1d(f, sigma * 1 / VoxDim[2], axis=2)
scaledf = int16((z.max() / f.max()) * f * 1000)
print "writing nifti output image"
overlay = NiftiImage(int16(scaledf))
overlay.setDescription(mr.description)
overlay.setFilename(mr.filename + "dd")
overlay.setQForm(mr.getQForm())
return overlay
def save(self, image):
nifti_image = NiftiImage(image.data)
spacing = image.spacing.tolist()
spacing.reverse()
nifti_image.pixdim = spacing
# logging.warning("Image direction and origin will not be saved")
nifti_image.save(self._filename)
# Save gradient direction file if saving NIfTI
if "mr_diffusion_sequence" in image.metadata:
gradients = [[], [], []]
b_values = []
for diffusion in image.metadata["mr_diffusion_sequence"]:
gradient = diffusion.diffusion_gradient_direction_sequence[
0].diffusion_gradient_orientation
b_value = diffusion.diffusion_bvalue
for index, value in enumerate(gradient):
gradients[index].append(str(value))
b_values.append(str(b_value))
gradients = "\n".join(
[" ".join(direction) for direction in gradients])
b_values = " ".join(b_values)
base_name = os.path.splitext(self._filename)[0]
if base_name.endswith(".nii"):
base_name = os.path.splitext(base_name)[0]
gradients_file = open("{0}.bvec".format(base_name), "w")
gradients_file.write(gradients)
gradients_file.close()
bvalues_file = open("{0}.bval".format(base_name), "w")
bvalues_file.write(b_values)
bvalues_file.close()
def load_data(self, index=0):
# pynifti does not appear to have a hook in nifti.clib.nifti_image_read,
# nor is it possible to feed it a buffer filled with the content of the
# file
if self._report_progress is not None:
self._report_progress(0.)
image = NiftiImage(self._filename)
data = image.data
if self._report_progress is not None:
self._report_progress(1.)
while data.shape[0] == 1 and len(data.shape) > 3:
data = data.reshape(data.shape[1:])
return data
def LoadVolumeData(self, data_set):
if data_set.endswith('nii') or data_set.endswith('nii.gz'):
try:
from nifti import NiftiImage
except ImportError:
print "Apparently you don't have PyNIfTI installed, see http://www.siafoo.net/snippet/310 for instructions"
exit(1)
nim = NiftiImage(data_set)
img_data = nim.data
img_header = nim.header
print nim.data.shape
if len(nim.data.shape) == 3: # single volume
pass
elif len(nim.data.shape) == 4: # multiple volume
alldata = numpy.array(nim.data[0])
print alldata.shape
for i in range(1, len(nim.data)):
#self.addVol( nim.data[i] )
#alldata = numpy.append( alldata, nim.data[i], axis=0)
alldata = numpy.add(alldata, nim.data[i])
print alldata.shape
img_data = alldata
elif len(nim.data.shape) == 5: # tensor field volume
alldata = numpy.array(nim.data[0][0])
print alldata.shape
for i in range(1, len(nim.data)):
#self.addVol( nim.data[i] )
#alldata = numpy.append( alldata, nim.data[i][0], axis=0)
alldata = numpy.add(alldata, nim.data[i][0])
print alldata.shape
img_data = alldata
elif data_set.endswith('hdr'):
# Use the header to figure out the shape of the data
# then load the raw data and reshape the array
shape = [int(x) for x in open(data_set).readline().split()]
img_data = numpy.frombuffer(open(data_set.replace('.hdr', '.dat'), 'rb').read(),
numpy.uint8)\
.reshape((shape[2], shape[1], shape[0]))
self.addVol(img_data, img_header)
return 0
def run():
if not 'screenshots' in os.listdir('./'):
os.mkdir('./screenshots')
#load Matlab data
# import scipy.io
# mri = scipy.io.loadmat('brainweb_128.mat')
# activity = scipy.io.loadmat('L.mat')
# v1 = uint16( activity['L']*(2**16*(1.0/dynrange)/activity['L'].max()) )
# v2 = uint16( mri['t1_128']*(2**16/mri['t1_128'].max()) )
#load Nifty data
from nifti import NiftiImage
v1 = NiftiImage('./activity_128.nii').data
v2 = 0*v1
#create volume renderer and initialize it
V = VolumeRender((N,N,N),(512,512))
V.show()
V.set_volume1(v1)
V.set_volume2(v2)
#set visualization parameters
V.set_density1(0.05)
V.set_brightness1(5.4)
V.set_transferOffset1(-0.02)
V.set_transferScale1(1.27)
V.set_density2(0.46)
V.set_brightness2(0.5)
V.set_transferOffset2(0.06)
V.set_transferScale2(1.31)
sleep(10)
for im_frame in range(126):
v1b = double(v1)
v1b[:,im_frame:im_frame+thickness,:] = dynrange*double(v1[:,im_frame:im_frame+thickness,:])
V.set_volume1(uint16(v1b))
V.dump_screenshot("./screenshots/example2_%d.png"%im_frame)
while 1:
pass
def LoadVolumeData(self, data_set):
if data_set.endswith('nii') or data_set.endswith('nii.gz'):
try:
from nifti import NiftiImage
except ImportError:
print "Apparently you don't have PyNIfTI installed, see http://www.siafoo.net/snippet/310 for instructions"
exit(1)
nim = NiftiImage(data_set)
img_data = nim.data
elif data_set.endswith('hdr'):
# Use the header to figure out the shape of the data
# then load the raw data and reshape the array
shape = [int(x) for x in open(data_set).readline().split()]
img_data = numpy.frombuffer(open(data_set.replace('.hdr', '.dat'), 'rb').read(),
numpy.uint8)\
.reshape((shape[2], shape[1], shape[0]))
img = vtkImageImportFromArray()
img.SetArray(img_data)
return img
def _load_images(self):
resolution = self._resolution
header = self.header
images = header.images
# Load present images
# XXX might be refactored to avoid duplication of
# effort with PyMVPAAtlas
ni_image = None
resolutions = []
if self._force_image_file is None:
imagefile_candidates = [
reuse_absolute_path(self._filename,
i.imagefile.text,
force=True) for i in images
]
else:
imagefile_candidates = [self._force_image_file]
for imagefilename in imagefile_candidates:
try:
ni_image_ = NiftiImage(imagefilename, load=False)
except RuntimeError, e:
raise RuntimeError, " Cannot open file " + imagefilename
resolution_ = ni_image_.pixdim[0]
if resolution is None:
# select this one if the best
if ni_image is None or \
resolution_ < ni_image.pixdim[0]:
ni_image = ni_image_
self._image_file = imagefilename
else:
if resolution_ == resolution:
ni_image = ni_image_
self._image_file = imagefilename
break
else:
resolutions += [resolution_]
Group(Item('scene', editor=SceneEditor(scene_class=MayaviScene),
height=500, width=500, show_label=False)),
Group(
Group(Item('over_low', label="Lower Thresh"),
Item('over_hi', label="Upper Thresh"),
label="Overlay Properties",
show_border=True),
),
Group(
HGroup(Item('x_visible'),
Item('y_visible'),
Item('z_visible'),
Item('colormap'),
label="Plane visibility + colormap",
show_border=True),
),
),
resizable=True,
title='Overlay Viewer')
if __name__ == "__main__":
# let's try it out
# XXX: This will break without files there
stat = OverlayMap(under_image=NiftiImage('/home/thorsten/struct_brain.nii.gz'),
over_image=NiftiImage('/home/thorsten/struct_brain.nii.gz'))
#stat = OverlayMap(under_image=NiftiImage('TT_icbm452.nii.gz'),
# over_image=NiftiImage('stats.nii.gz'))
def run():
if not 'screenshots' in os.listdir('./'):
os.mkdir('./screenshots')
#load Matlab data
# import scipy.io
# mri = scipy.io.loadmat('brainweb_128.mat')
# activity = scipy.io.loadmat('L.mat')
# v1 = uint16( activity['L']*(2**16*(1.0/dynrange)/activity['L'].max()) )
# v2 = uint16( mri['t1_128']*(2**16/mri['t1_128'].max()) )
#load Nifty data
from nifti import NiftiImage
v1 = NiftiImage('./activity_128.nii').data
v2 = 0 * v1
#create volume renderer and initialize it
V = VolumeRender((N, N, N), (512, 512))
V.show()
V.set_volume1(v1)
V.set_volume2(v2)
#set visualization parameters
V.set_density1(0.05)
V.set_brightness1(7.1)
V.set_transferOffset1(-0.04)
V.set_transferScale1(1.1)
V.set_density2(0.05)
V.set_brightness2(0.3)
V.set_transferOffset2(0.05)
V.set_transferScale2(1)
#visualize a dynamic scene and save screenshots
N_frames = N * image_steps
d_rotation_x = 0.0
d_rotation_y = 0.5
d_zoom = 0.004
im_frame_prev = 0
frame = 0
t_frame = 0
while 1:
frame += 1
if frame < 1460:
t_frame += 1
if frame < 150 + 250:
V.rotate(d_rotation_x, d_rotation_y)
if (frame > 150 + 150 and frame < 150 + 500):
V.zoom(d_zoom)
if frame > 150 + 250:
V.rotate(d_rotation_x / 4, d_rotation_y / 4)
im_frame = t_frame / image_steps
if not im_frame == im_frame_prev:
im_frame_prev = im_frame
if (im_frame < (N - thickness)):
v1b = double(v1)
v1b[:, :,
im_frame:im_frame + thickness] = dynrange * double(
v1[:, :, im_frame:im_frame + thickness])
#v1b = v1b*(2**16/v1b.max())
V.set_volume1(uint16(v1b))
if frame > 1500:
t_frame -= 1
im_frame = t_frame / image_steps
if not im_frame == im_frame_prev:
im_frame_prev = im_frame
if (im_frame < (N - thickness)):
v1b = double(v1)
v1b[:, :,
im_frame:im_frame + thickness] = dynrange * double(
v1[:, :, im_frame:im_frame + thickness])
#v1b = v1b*(2**16/v1b.max())
V.set_volume1(uint16(v1b))
if (frame > 2550 + 100):
break
elif (frame > 2550 + 75):
V.zoom(-d_zoom * 3.5)
elif frame > 2550:
V.rotate(d_rotation_x, d_rotation_y)
V.zoom(-d_zoom * 3.5)
V.dump_screenshot("./screenshots/%d.png" % frame)
sleep(0.003)
while 1:
pass
from nifti import NiftiImage
from datetime import datetime
prima=datetime.now()
#FLAGS
#############################
background_threshold=25 #
reverse_YAxis=True #
#############################
nim=NiftiImage("cervello")
print "Processing '%s'" %nim.filename,
nim.load()
d=nim.extent #genearatin a 4-uple containing the dimension of nifti image (x,y,z,time)
O=(d[0]/2,d[1]/2)
print "(%dx%dx%d)\n" %(d[0],d[1],d[2])
print "--------------------------------------------------------------------------------"
print
bb=nim.bbox
#ASSUMING IMAGE HAS TIME SIZE=1 -> program will work just on first istant on 4-dimensional images
print "\tLeft Pole\tRight Pole\tAverage Pole"
for z in range(bb[0][0],bb[0][1]) : #bottom-up scroll
y=bb[1][0]
found=False
pl.show()
plotter.fig.plotter = plotter
return plotter.fig
if __name__ == "__main__":
# for easy debugging
import os
from mvpa.base import cfg
impath = os.path.join('datadb', 'tutorial_data')
plot_lightbox(
#background = NiftiImage('%s/anat.nii.gz' % impath),
background='%s/anat.nii.gz' % impath,
background_mask=None,
overlay=NiftiImage('%s/bold.nii.gz' % impath).data.squeeze()[0],
overlay_mask='%s/mask_brain.nii.gz' % impath,
#
do_stretch_colors=False,
add_colorbar=True,
cmap_bg='gray',
cmap_overlay='hot', # YlOrRd_r # pl.cm.autumn
#
fig=None,
# vlim describes value limits
# clim color limits (same by default)
vlim=[100, None],
#vlim_type = 'symneg_z',
interactive=True,
#
#nrows = 2,
def write_nifti_file(settings, basename):
"""Write data <basename>.nii."""
nim = NiftiImage(get_data(settings))
filename = basename + '.nii'
print 'Writing data to the nifti file ' + filename + '.'
nim.save(filename)
def write_nifti_file(settings, basename):
"""Write data <basename>.nii."""
nim = NiftiImage(get_data(settings))
filename = basename+'.nii'
print 'Writing data to the nifti file '+filename+'.'
nim.save(filename)