def eval_spharm_grid(self, vendor, coeffs):
'''
We evaluate the spherical harmonics on a less sampled grid.
This is a spacetime vs accuracy tradeoff.
'''
# init the grid first
if not self.fovmin:
fovmin = globals.siemens_fovmin
else:
fovmin = self.fovmin
if not self.fovmax:
fovmax = globals.siemens_fovmax
else:
fovmax = self.fovmax
if not self.numpoints:
numpoints = globals.siemens_numpoints
else:
numpoints = self.numpoints
# convert to mm
fovmin = fovmin * 1000.
fovmax = fovmax * 1000.
# the grid in meters. this is needed for spherical harmonics
vec = np.linspace(fovmin, fovmax, numpoints)
gvx, gvy, gvz = utils.meshgrid(vec, vec, vec)
# mm
cf = (fovmax - fovmin) / numpoints
# deduce the transformation from rcs to grid
g_rcs2xyz = np.array( [[0, cf, 0, fovmin],
[cf, 0, 0, fovmin],
[0, 0, cf, fovmin],
[0, 0, 0, 1]], dtype=np.float32 )
# get the grid to rcs transformation also
g_xyz2rcs = np.linalg.inv(g_rcs2xyz)
# indices into the gradient displacement vol
gr, gc, gs = utils.meshgrid(np.arange(numpoints), np.arange(numpoints),
np.arange(numpoints), dtype=np.float32)
log.info('Evaluating spherical harmonics')
log.info('on a ' + str(numpoints) + '^3 grid')
log.info('with extents ' + str(fovmin) + 'mm to ' + str(fovmax) + 'mm')
gvxyz = CV(gvx, gvy, gvz)
_dv, _dxyz = eval_spherical_harmonics(coeffs, vendor, gvxyz)
return CV(_dv.x, _dv.y, _dv.z), CV(gr, gc, gs), g_xyz2rcs
def _get_anchor_boxes(self, input_size):
'''Compute anchor boxes for each feature map.
Args:
input_size: (tensor) model input size of (w,h).
Returns:
boxes: (list) anchor boxes for each feature map. Each of size [#anchors,4],
where #anchors = fmw * fmh * #anchors_per_cell
'''
num_fms = len(self.anchor_areas)
fm_sizes = [(input_size/pow(2.,i+3)).ceil() for i in range(num_fms)] # p3 -> p7 feature map sizes
boxes = []
for i in range(num_fms):
fm_size = fm_sizes[i]
grid_size = input_size / fm_size
fm_w, fm_h = int(fm_size[0]), int(fm_size[1])
xy = meshgrid(fm_w,fm_h) + 0.5 # [fm_h*fm_w, 2]
xy = (xy*grid_size).view(fm_h,fm_w,1,2).expand(fm_h,fm_w,9,2)
wh = self.anchor_wh[i].view(1,1,9,2).expand(fm_h,fm_w,9,2)
box = torch.cat([xy,wh], 3) # [x,y,w,h]
boxes.append(box.view(-1,4))
return torch.cat(boxes, 0)
def get_anchor_boxes(self, input_size):
'''
Compute anchor boxes for each feature map
Args:
input_size: (tensor) model input size of (z, h, w).
Returns:
boxes: (tensor) anchor boxes for each feature map. Each of size [#anchors, 6],
where #anchors = fm_z * fm_h * fm_w * #anchors_per_cell
'''
fm_sizes = [(input_size / pow(2, i + 1)).ceil() for i in range(self.num_levels)]
# Compute every size in p3
boxes = []
for i in range(self.num_levels):
fm_size = fm_sizes[i]
grid_size = (input_size / fm_size).floor()
fm_d, fm_h, fm_w = int(fm_size[0]), int(fm_size[1]), int(fm_size[2])
zyx = meshgrid(fm_d, fm_h, fm_w) + 0.5 # [fm_d * fm_h * fm_w, 3]
zyx = (zyx * grid_size).view(fm_d, fm_h, fm_w, 1, 3).expand(fm_d, fm_h, fm_w, 9, 3)
dhw = self.anchor_edges[i].view(1, 1, 1, 9, 3).expand(fm_d, fm_h, fm_w, 9, 3)
box = torch.cat([zyx, dhw], 4) # (fm_d, fm_h, fm_w, 9, 6)
boxes.append(box.view(-1, 6)) # (num_levels, fm_d*fm_h*fm_w*9, 6)
return torch.cat(boxes, 0) # (num_levels*fm_d*fm_h*fm_w*9, 6) -> (36864, 6)
def non_linear_unwarp_siemens(self, volshape, dv, dxyz, m_rcs2lai,
m_rcs2lai_nohalf, g_xyz2rcs):
''' Performs the crux of the unwarping.
It's agnostic to Siemens or GE and uses more functions to
do the processing separately.
Needs self.vendor, self.coeffs, self.warp, self.nojac to be set
Parameters
----------
vxyz : CoordsVector (namedtuple) contains np.array
has 3 elements x,y and z each representing the grid coordinates
dxyz : CoordsVector (namedtuple)
differential coords vector
Returns
-------
TODO still vague what to return
vwxyz : CoordsVector (namedtuple) contains np.array
x,y and z coordinates of the unwarped coordinates
vjacmult_lps : np.array
the jacobian multiplier (determinant)
'''
log.info('Evaluating the jacobian multiplier')
nr, nc, ns = self.vol.shape[:3]
if not self.nojac:
jim2 = np.zeros((nr, nc), dtype=np.float32)
vjacdet_lpsw = np.zeros((nr, nc), dtype=np.float32)
if dxyz == 0:
vjacdet_lps = 1
else:
vjacdet_lps = eval_siemens_jacobian_mult(dv, dxyz)
# essentially pre-allocating everything
out = np.zeros((nr, nc, ns), dtype=np.float32)
fullWarp = np.zeros((nr, nc, ns, 3), dtype=np.float32)
vjacout = np.zeros((nr, nc, ns), dtype=np.float32)
im2 = np.zeros((nr, nc), dtype=np.float32)
dvx = np.zeros((nr, nc), dtype=np.float32)
dvy = np.zeros((nr, nc), dtype=np.float32)
dvz = np.zeros((nr, nc), dtype=np.float32)
im_ = np.zeros((nr, nc), dtype=np.float32)
# init jacobian temp image
vc, vr = utils.meshgrid(np.arange(nc), np.arange(nr))
log.info('Unwarping slice by slice')
# for every slice
for s in xrange(ns):
# pretty print
sys.stdout.flush()
if (s + 1) % 10 == 0:
print s + 1,
else:
print '.',
# hopefully, free memory
gc.collect()
# init to 0
dvx.fill(0.)
dvy.fill(0.)
dvz.fill(0.)
im_.fill(0.)
vs = np.ones(vr.shape) * s
vrcs = CV(vr, vc, vs)
vxyz = utils.transform_coordinates(vrcs, m_rcs2lai_nohalf)
vrcsg = utils.transform_coordinates(vxyz, g_xyz2rcs)
ndimage.interpolation.map_coordinates(dv.x,
vrcsg,
output=dvx,
order=self.order)
ndimage.interpolation.map_coordinates(dv.y,
vrcsg,
output=dvy,
order=self.order)
ndimage.interpolation.map_coordinates(dv.z,
vrcsg,
output=dvz,
order=self.order)
# new locations of the image voxels in XYZ ( LAI ) coords
#dvx.fill(0.)
#dvy.fill(0.)
#dvz.fill(0.)
vxyzw = CV(x=vxyz.x + self.polarity * dvx,
y=vxyz.y + self.polarity * dvy,
z=vxyz.z + self.polarity * dvz)
# convert the locations got into RCS indices
vrcsw = utils.transform_coordinates(vxyzw,
np.linalg.inv(m_rcs2lai))
# map the internal voxel coordinates to FSL scaled mm coordinates
pixdim1 = float((subprocess.Popen(
['fslval', self.name, 'pixdim1'],
stdout=subprocess.PIPE).communicate()[0]).strip())
pixdim2 = float((subprocess.Popen(
['fslval', self.name, 'pixdim2'],
stdout=subprocess.PIPE).communicate()[0]).strip())
pixdim3 = float((subprocess.Popen(
['fslval', self.name, 'pixdim3'],
stdout=subprocess.PIPE).communicate()[0]).strip())
dim1 = float((subprocess.Popen(
['fslval', self.name, 'dim1'],
stdout=subprocess.PIPE).communicate()[0]).strip())
outputOrient = subprocess.Popen(
['fslorient', self.name],
stdout=subprocess.PIPE).communicate()[0]
if outputOrient.strip() == 'NEUROLOGICAL':
# if neurological then flip x coordinate (both here in premat and later in postmat)
m_vox2fsl = np.array(
[[-1.0 * pixdim1, 0.0, 0.0, pixdim1 *
(dim1 - 1)], [0.0, pixdim2, 0.0, 0.0],
[0.0, 0.0, pixdim3, 0.0], [0.0, 0.0, 0.0, 1.0]],
dtype=np.float)
else:
m_vox2fsl = np.array(
[[pixdim1, 0.0, 0.0, 0.0], [0.0, pixdim2, 0.0, 0.0],
[0.0, 0.0, pixdim3, 0.0], [0.0, 0.0, 0.0, 1.0]],
dtype=np.float)
vfsl = utils.transform_coordinates(vrcsw, m_vox2fsl)
#im_ = utils.interp3(self.vol, vrcsw.x, vrcsw.y, vrcsw.z)
ndimage.interpolation.map_coordinates(self.vol,
vrcsw,
output=im_,
order=self.order)
# find NaN voxels, and set them to 0
im_[np.where(np.isnan(im_))] = 0.
im_[np.where(np.isinf(im_))] = 0.
im2[vr, vc] = im_
#img = nib.Nifti1Image(dvx,np.eye(4))
#nib.save(img,"x"+str(s).zfill(3)+".nii.gz")
#img = nib.Nifti1Image(dvy,np.eye(4))
#nib.save(img,"y"+str(s).zfill(3)+".nii.gz")
#img = nib.Nifti1Image(dvz,np.eye(4))
#nib.save(img,"z"+str(s).zfill(3)+".nii.gz")
# Multiply the intensity with the Jacobian det, if needed
if not self.nojac:
vjacdet_lpsw.fill(0.)
jim2.fill(0.)
# if polarity is negative, the jacobian is also inversed
if self.polarity == -1:
vjacdet_lps = 1. / vjacdet_lps
ndimage.interpolation.map_coordinates(vjacdet_lps,
vrcsg,
output=vjacdet_lpsw,
order=self.order)
vjacdet_lpsw[np.where(np.isnan(vjacdet_lpsw))] = 0.
vjacdet_lpsw[np.where(np.isinf(vjacdet_lpsw))] = 0.
jim2[vr, vc] = vjacdet_lpsw
im2 = im2 * jim2
vjacout[..., s] = jim2
fullWarp[..., s, 0] = vfsl.x
fullWarp[..., s, 1] = vfsl.y
fullWarp[..., s, 2] = vfsl.z
out[..., s] = im2
print
img = nib.Nifti1Image(fullWarp, self.m_rcs2ras)
nib.save(img, "fullWarp_abs.nii.gz")
# return image and the jacobian
return out, vjacout
def run(self):
'''
'''
#pdb.set_trace()
# define polarity based on the warp requested
self.polarity = 1.
if self.warp:
self.polarity = -1.
# transform RAS-coordinates into LAI-coordinates
m_ras2lai = np.array([[-1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]], dtype=np.float)
m_rcs2lai = np.dot(m_ras2lai, self.m_rcs2ras)
# indices of image volume
nr, nc, ns = self.vol.shape[:3]
vc3, vr3, vs3 = utils.meshgrid(np.arange(nr), np.arange(nc), np.arange(ns), dtype=np.float32)
vrcs = CV(x=vr3, y=vc3, z=vs3)
vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
# account for half-voxel shift in R and C directions
halfvox = np.zeros((4, 4))
halfvox[0, 3] = m_rcs2lai[0, 0] / 2.0
halfvox[1, 3] = m_rcs2lai[1, 1] / 2.0
m_rcs2lai = m_rcs2lai + halfvox
# extract rotational and scaling parts of the transformation matrix
# ignore the translation part
r_rcs2lai = np.eye(4, 4)
r_rcs2lai[:3, :3] = m_rcs2lai[:3, :3]
# Jon Polimeni:
# Since partial derivatives in Jacobian Matrix are differences
# that depend on the ordering of the elements of the 3D array, the
# coordinates may increase in the opposite direction from the array
# indices, in which case the differential element should be negative.
# The differentials can be determined by mapping a vector of 1s
# through rotation and scaling, where any mirror
# will impose a negation
ones = CV(1., 1., 1.)
dxyz = utils.transform_coordinates_old(ones, r_rcs2lai)
# # convert image vol coordinates from RAS to LAI
# vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
# # compute new coordinates and the jacobian determinant
# # TODO still not clear about what to return
# self.out, self.vjacmult_lps = self.non_linear_unwarp(vxyz, dxyz,
# m_rcs2lai)
# for each slice
'''
for slice in xrange(ns):
sys.stdout.flush()
if (slice + 1) % 10 == 0:
print(slice + 1),
else:
print('.'),
# we are doing it slice by slice
vs = np.ones(vr.shape) * slice
vrcs2d = CV(vr, vc, slice)
# rcs2lai
vxyz2d = utils.transform_coordinates(vrcs2d, m_rcs2lai)
# compute new coordinates and the jacobian determinant
moddv, modxyz = eval_spherical_harmonics(self.coeffs, self.vendor, vxyz2d)
dvx[..., slice] = moddv.x
dvy[..., slice] = moddv.y
dvz[..., slice] = moddv.z
'''
print
# Evaluate spherical harmonics on a smaller grid
dv, grcs, g_xyz2rcs = self.eval_spharm_grid(self.vendor, self.coeffs)
# do the nonlinear unwarp
self.out, self.vjacmult_lps = self.non_linear_unwarp(vxyz, grcs, dv, dxyz,
m_rcs2lai, g_xyz2rcs)
def save_disagg_to_csv(nrml_disaggregation, output_dir, plot):
"""
Save disaggregation matrices to multiple .csv files.
"""
metadata, matrices = parse_nrml_disaggregation_file(nrml_disaggregation)
skip_keys = ('Mag', 'Dist', 'Lon', 'Lat', 'Eps', 'TRT')
base_header = ','.join(
'%s=%s' % (key, value) for key, value in metadata.items()
if value is not None and key not in skip_keys
)
for disag_type, (poe, iml, matrix) in matrices.items():
header = '# %s,poe=%s,iml=%s\n' % (base_header, poe, iml)
if disag_type == 'Mag,Lon,Lat':
matrix = numpy.swapaxes(matrix, 0, 1)
matrix = numpy.swapaxes(matrix, 1, 2)
disag_type = 'Lon,Lat,Mag'
variables = disag_type
axis = [metadata[v] for v in variables]
header += ','.join(v for v in variables)
header += ',poe'
# compute axis mid points
axis = [(ax[: -1] + ax[1:]) / 2. if ax.dtype == float
else ax for ax in axis]
values = None
if len(axis) == 1:
values = numpy.array([axis[0], matrix.flatten()]).T
else:
try:
grids = numpy.meshgrid(*axis, indexing='ij')
except:
grids = utils.meshgrid(*axis, indexing='ij')
values = [g.flatten() for g in grids]
values.append(matrix.flatten())
values = numpy.array(values).T
output_file = '%s/%s.csv' % (output_dir, '_'.join(disag_type))
f = open(output_file, 'w')
f.write(header+'\n')
numpy.savetxt(f, values, fmt='%s', delimiter=',')
f.close()
if plot:
call(['gmtset', 'LABEL_OFFSET=0.6c'])
if disag_type == 'Mag':
plot_1d_hist(output_file, 'Magnitude', '')
elif disag_type == 'Dist':
plot_1d_hist(output_file, 'JB distance', '')
elif disag_type == 'TRT':
ntrt = metadata['TRT'].size
bin_edges = range(ntrt)
annotation_file = open("annotation.dat", 'w')
for i in range(ntrt):
annotation_file.write(
"%s %s %s %s %s %s %s\n" % (
bin_edges[i],
numpy.max(matrix) + 0.05 * numpy.max(matrix),
12, 0.0, 0, 'MC', metadata['TRT'][i]))
annotation_file.close()
plot_1d_hist(output_file, 'Tectonic Region',
'', annotation_file.name)
elif disag_type == 'Mag,Dist':
plot_2d_hist(output_file, 'Magnitude', 'JB distance', '')
elif disag_type == 'Lon,Lat':
plot_2d_hist(output_file, 'Longitude', 'Latitude', '')
elif disag_type == 'Mag,Dist,Eps':
plot_3d_hist(
output_file, 'Magnitude', 'Distance', 'Epsilon', '')
elif disag_type == 'Lon,Lat,Eps':
plot_3d_hist(
output_file, 'Longitude', 'Latitude', 'Epsilon', '')
elif disag_type == 'Lon,Lat,Mag':
plot_3d_hist(
output_file, 'Longitude', 'Latitude', 'Magnitude', '')
elif disag_type == 'Lon,Lat,TRT':
plot_3d_hist(
output_file, 'Longitude', 'Latitude', '', '')
def non_linear_unwarp_siemens(self, volshape, dv, dxyz, m_rcs2lai, g_xyz2rcs):
''' Performs the crux of the unwarping.
It's agnostic to Siemens or GE and uses more functions to
do the processing separately.
Needs self.vendor, self.coeffs, self.warp, self.nojac to be set
Parameters
----------
vxyz : CoordsVector (namedtuple) contains np.array
has 3 elements x,y and z each representing the grid coordinates
dxyz : CoordsVector (namedtuple)
differential coords vector
Returns
-------
TODO still vague what to return
vwxyz : CoordsVector (namedtuple) contains np.array
x,y and z coordinates of the unwarped coordinates
vjacmult_lps : np.array
the jacobian multiplier (determinant)
'''
log.info('Evaluating the jacobian multiplier')
nr, nc, ns = self.vol.shape[:3]
if not self.nojac:
jim2 = np.zeros((nr, nc), dtype=np.float32)
vjacdet_lpsw = np.zeros((nr, nc), dtype=np.float32)
if dxyz == 0:
vjacdet_lps = 1
else:
vjacdet_lps = eval_siemens_jacobian_mult(dv, dxyz)
# essentially pre-allocating everything
out = np.zeros((nr, nc, ns), dtype=np.float32)
fullWarp = np.zeros((nr, nc, ns, 3), dtype=np.float32)
vjacout = np.zeros((nr, nc, ns), dtype=np.float32)
im2 = np.zeros((nr, nc), dtype=np.float32)
dvx = np.zeros((nr, nc), dtype=np.float32)
dvy = np.zeros((nr, nc), dtype=np.float32)
dvz = np.zeros((nr, nc), dtype=np.float32)
im_ = np.zeros((nr, nc), dtype=np.float32)
# init jacobian temp image
vc, vr = utils.meshgrid(np.arange(nc), np.arange(nr))
log.info('Unwarping slice by slice')
# for every slice
for s in xrange(ns):
# pretty print
sys.stdout.flush()
if (s+1) % 10 == 0:
print s+1,
else:
print '.',
# hopefully, free memory
gc.collect()
# init to 0
dvx.fill(0.)
dvy.fill(0.)
dvz.fill(0.)
im_.fill(0.)
vs = np.ones(vr.shape) * s
vrcs = CV(vr, vc, vs)
vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
vrcsg = utils.transform_coordinates(vxyz, g_xyz2rcs)
ndimage.interpolation.map_coordinates(dv.x,
vrcsg,
output=dvx,
order=self.order)
ndimage.interpolation.map_coordinates(dv.y,
vrcsg,
output=dvy,
order=self.order)
ndimage.interpolation.map_coordinates(dv.z,
vrcsg,
output=dvz,
order=self.order)
# new locations of the image voxels in XYZ ( LAI ) coords
#dvx.fill(0.)
#dvy.fill(0.)
#dvz.fill(0.)
vxyzw = CV(x=vxyz.x + self.polarity * dvx,
y=vxyz.y + self.polarity * dvy,
z=vxyz.z + self.polarity * dvz)
# convert the locations got into RCS indices
vrcsw = utils.transform_coordinates(vxyzw,
np.linalg.inv(m_rcs2lai))
# map the internal voxel coordinates to FSL scaled mm coordinates
pixdim1=float((subprocess.Popen(['fslval', self.name,'pixdim1'], stdout=subprocess.PIPE).communicate()[0]).strip())
pixdim2=float((subprocess.Popen(['fslval', self.name,'pixdim2'], stdout=subprocess.PIPE).communicate()[0]).strip())
pixdim3=float((subprocess.Popen(['fslval', self.name,'pixdim3'], stdout=subprocess.PIPE).communicate()[0]).strip())
dim1=float((subprocess.Popen(['fslval', self.name,'dim1'], stdout=subprocess.PIPE).communicate()[0]).strip())
outputOrient=subprocess.Popen(['fslorient', self.name], stdout=subprocess.PIPE).communicate()[0]
if outputOrient.strip() == 'NEUROLOGICAL':
# if neurological then flip x coordinate (both here in premat and later in postmat)
m_vox2fsl = np.array([[-1.0*pixdim1, 0.0, 0.0, pixdim1*(dim1-1)],
[0.0, pixdim2, 0.0, 0.0],
[0.0, 0.0, pixdim3, 0.0],
[0.0, 0.0, 0.0, 1.0]], dtype=np.float)
else:
m_vox2fsl = np.array([[pixdim1, 0.0, 0.0, 0.0],
[0.0, pixdim2, 0.0, 0.0],
[0.0, 0.0, pixdim3, 0.0],
[0.0, 0.0, 0.0, 1.0]], dtype=np.float)
vfsl = utils.transform_coordinates(vrcsw, m_vox2fsl)
#im_ = utils.interp3(self.vol, vrcsw.x, vrcsw.y, vrcsw.z)
ndimage.interpolation.map_coordinates(self.vol,
vrcsw,
output=im_,
order=self.order)
# find NaN voxels, and set them to 0
im_[np.where(np.isnan(im_))] = 0.
im_[np.where(np.isinf(im_))] = 0.
im2[vr, vc] = im_
#img = nib.Nifti1Image(dvx,np.eye(4))
#nib.save(img,"x"+str(s).zfill(3)+".nii.gz")
#img = nib.Nifti1Image(dvy,np.eye(4))
#nib.save(img,"y"+str(s).zfill(3)+".nii.gz")
#img = nib.Nifti1Image(dvz,np.eye(4))
#nib.save(img,"z"+str(s).zfill(3)+".nii.gz")
# Multiply the intensity with the Jacobian det, if needed
if not self.nojac:
vjacdet_lpsw.fill(0.)
jim2.fill(0.)
# if polarity is negative, the jacobian is also inversed
if self.polarity == -1:
vjacdet_lps = 1. / vjacdet_lps
ndimage.interpolation.map_coordinates(vjacdet_lps,
vrcsg,
output=vjacdet_lpsw,
order=self.order)
vjacdet_lpsw[np.where(np.isnan(vjacdet_lpsw))] = 0.
vjacdet_lpsw[np.where(np.isinf(vjacdet_lpsw))] = 0.
jim2[vr, vc] = vjacdet_lpsw
im2 = im2 * jim2
vjacout[..., s] = jim2
fullWarp[...,s,0]=vfsl.x
fullWarp[...,s,1]=vfsl.y
fullWarp[...,s,2]=vfsl.z
out[..., s] = im2
print
img=nib.Nifti1Image(fullWarp,self.m_rcs2ras)
nib.save(img,"fullWarp_abs.nii.gz")
# return image and the jacobian
return out, vjacout