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enh(transform): HDF5 - set values as attributes; generalize affines t…
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…o N transforms (through time axis)
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@oesteban
oesteban committed Sep 19, 2019
1 parent 1663a32 commit f34affc
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Showing 2 changed files with 102 additions and 49 deletions.
8 changes: 4 additions & 4 deletions nibabel/transform/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,10 +78,10 @@ def map_voxels(self, coordinates):
coordinates, axes=1)[:3, ...]

def _to_hdf5(self, group):
group['Type'] = 'image'
group.attrs['Type'] = 'image'
group.attrs['ndim'] = self.ndim
group.create_dataset('affine', data=self.affine)
group.create_dataset('shape', data=self.shape)
group.create_dataset('ndim', data=self.ndim)

def __eq__(self, other):
try:
Expand Down Expand Up @@ -188,8 +188,8 @@ def to_filename(self, filename, fmt='X5'):
'''Store the transform in BIDS-Transforms HDF5 file format (.x5).
'''
with h5py.File(filename, 'w') as out_file:
out_file['Format'] = 'X5'
out_file['Version'] = np.uint16(1)
out_file.attrs['Format'] = 'X5'
out_file.attrs['Version'] = np.uint16(1)
root = out_file.create_group('/0')
self._to_hdf5(root)

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143 changes: 98 additions & 45 deletions nibabel/transform/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,14 +48,19 @@ def __init__(self, matrix=None, reference=None):
'''
if matrix is None:
matrix = np.eye(4)
matrix = [np.eye(4)]

if np.ndim(matrix) == 2:
matrix = [matrix]

self._matrix = np.array(matrix)
assert self._matrix.ndim in (2, 3), 'affine matrix should be 2D or 3D'
assert self._matrix.shape[0] == self._matrix.shape[1], 'affine matrix is not square'
assert self._matrix.ndim == 3, 'affine matrix should be 3D'
assert self._matrix.shape[-2] == self._matrix.shape[-1], 'affine matrix is not square'
super(Affine, self).__init__()

if reference:
if isinstance(reference, str):
reference = loadimg(reference)
self.reference = reference

@property
Expand Down Expand Up @@ -116,28 +121,55 @@ def resample(self, moving, order=3, mode='constant', cval=0.0, prefilter=True,
file=sys.stderr)
reference = moving

nvols = 1
if len(moving.shape) > 3:
nvols = moving.shape[3]

movaff = moving.affine
movingdata = moving.get_data()
if nvols == 1:
movingdata = movingdata[..., np.newaxis]

nmats = self._matrix.shape[0]
if nvols != nmats and nmats > 1:
raise ValueError("""\
The moving image contains {0} volumes, while the transform is defined for \
{1} volumes""".format(nvols, nmats))

singlemat = None
if nmats == 1:
singlemat = np.linalg.inv(movaff).dot(self._matrix[0].dot(reference.affine))

if singlemat is not None and nvols > nmats:
print('Warning: resampling a 4D volume with a single affine matrix',
file=sys.stderr)

# Compose an index to index affine matrix
matrix = np.linalg.inv(moving.affine).dot(self._matrix.dot(reference.affine))
mdata = moving.get_data()
moved = ndi.affine_transform(
mdata, matrix=matrix[:mdata.ndim, :mdata.ndim],
offset=matrix[:mdata.ndim, mdata.ndim],
output_shape=reference.shape, order=order, mode=mode,
cval=cval, prefilter=prefilter)

moved_image = moving.__class__(moved, reference.affine, moving.header)
moved = []
for i in range(nvols):
i2imat = singlemat if singlemat is not None else np.linalg.inv(
movaff).dot(self._matrix[i].dot(reference.affine))
mdata = movingdata[..., i]
moved += [ndi.affine_transform(
mdata, matrix=i2imat[:-1, :-1],
offset=i2imat[:-1, -1],
output_shape=reference.shape, order=order, mode=mode,
cval=cval, prefilter=prefilter)]

moved_image = moving.__class__(
np.squeeze(np.stack(moved, -1)), reference.affine, moving.header)
moved_image.header.set_data_dtype(output_dtype)

return moved_image

def map_point(self, coords, forward=True):
def map_point(self, coords, index=0, forward=True):
coords = np.array(coords)
if coords.shape[0] == self._matrix.shape[0] - 1:
if coords.shape[0] == self._matrix[index].shape[0] - 1:
coords = np.append(coords, [1])
affine = self._matrix if forward else np.linalg.inv(self._matrix)
affine = self._matrix[index] if forward else np.linalg.inv(self._matrix[index])
return affine.dot(coords)[:-1]

def map_voxel(self, index, moving=None):
def map_voxel(self, index, nindex=0, moving=None):
try:
reference = self.reference
except ValueError:
Expand All @@ -152,16 +184,16 @@ def map_voxel(self, index, moving=None):
raise ValueError('Reference and moving spaces are both undefined')

index = np.array(index)
if index.shape[0] == self._matrix.shape[0] - 1:
if index.shape[0] == self._matrix[nindex].shape[0] - 1:
index = np.append(index, [1])

matrix = reference.affine.dot(self._matrix.dot(np.linalg.inv(moving.affine)))
matrix = reference.affine.dot(self._matrix[nindex].dot(np.linalg.inv(moving.affine)))
return tuple(matrix.dot(index)[:-1])

def _to_hdf5(self, x5_root):
x5_root.create_dataset('Transform', data=self._matrix[:3, :])
x5_root.create_dataset('Inverse', data=np.linalg.inv(self._matrix)[:3, :])
x5_root['Type'] = 'affine'
xform = x5_root.create_dataset('Transform', data=self._matrix)
xform.attrs['Type'] = 'affine'
x5_root.create_dataset('Inverse', data=np.linalg.inv(self._matrix))

if self._reference:
self.reference._to_hdf5(x5_root.create_group('Reference'))
Expand All @@ -171,24 +203,26 @@ def to_filename(self, filename, fmt='X5', moving=None):
'''

if fmt.lower() in ['itk', 'ants', 'elastix', 'nifty']:
parameters = LPS.dot(self.matrix.dot(LPS))
parameters = parameters[:3, :3].reshape(-1).tolist() + parameters[:3, 3].tolist()
itkfmt = """\
#Insight Transform File V1.0
#Transform 0
with open(filename, 'w') as f:
f.write('#Insight Transform File V1.0\n')

for i in range(self.matrix.shape[0]):
parameters = LPS.dot(self.matrix[i].dot(LPS))
parameters = parameters[:3, :3].reshape(-1).tolist() + \
parameters[:3, 3].tolist()
itkfmt = """\
#Transform {0}
Transform: MatrixOffsetTransformBase_double_3_3
Parameters: {}
Parameters: {1}
FixedParameters: 0 0 0\n""".format
with open(filename, 'w') as f:
f.write(itkfmt(' '.join(['%g' % p for p in parameters])))
f.write(itkfmt(i, ' '.join(['%g' % p for p in parameters])))
return filename

if fmt.lower() == 'afni':
parameters = LPS.dot(self.matrix.dot(LPS))
parameters = parameters[:3, :].reshape(-1).tolist()
np.savetxt(filename, np.atleast_2d(parameters),
delimiter='\t', header="""\
3dvolreg matrices (DICOM-to-DICOM, row-by-row):""")
parameters = np.swapaxes(LPS.dot(self.matrix.dot(LPS)), 0, 1)
parameters = parameters[:, :3, :].reshape((self.matrix.shape[0], -1))
np.savetxt(filename, parameters, delimiter='\t', header="""\
3dvolreg matrices (DICOM-to-DICOM, row-by-row):""", fmt='%g')
return filename

if fmt.lower() == 'fsl':
Expand All @@ -209,8 +243,15 @@ def to_filename(self, filename, fmt='X5', moving=None):
moving.affine)))

# Compose FSL transform
mat = np.linalg.inv(post.dot(self.matrix.dot(pre)))
np.savetxt(filename, mat, delimiter=' ', fmt='%g')
mat = np.linalg.inv(
np.swapaxes(post.dot(self.matrix.dot(pre)), 0, 1))

if self.matrix.shape[0] > 1:
for i in range(self.matrix.shape[0]):
np.savetxt('%s.%03d' % (filename, i), mat[i],
delimiter=' ', fmt='%g')
else:
np.savetxt(filename, mat[0], delimiter=' ', fmt='%g')
return filename
return super(Affine, self).to_filename(filename, fmt=fmt)

Expand All @@ -222,17 +263,29 @@ def load(filename, fmt='X5', reference=None):
with open(filename) as itkfile:
itkxfm = itkfile.read().splitlines()

parameters = np.fromstring(itkxfm[3].split(':')[-1].strip(), dtype=float, sep=' ')
offset = np.fromstring(itkxfm[4].split(':')[-1].strip(), dtype=float, sep=' ')
if len(parameters) == 12:
matrix = from_matvec(parameters[:9].reshape((3, 3)), parameters[9:])
c_neg = from_matvec(np.eye(3), offset * -1.0)
c_pos = from_matvec(np.eye(3), offset)
matrix = LPS.dot(c_pos.dot(matrix.dot(c_neg.dot(LPS))))

# if fmt.lower() == 'afni':
if 'Insight Transform File' not in itkxfm[0]:
raise ValueError(
"File %s does not look like an ITK transform text file." % filename)

matlist = []
for nxfm in range(len(itkxfm[1:]) // 4):
parameters = np.fromstring(itkxfm[nxfm * 4 + 3].split(':')[-1].strip(),
dtype=float, sep=' ')
offset = np.fromstring(itkxfm[nxfm * 4 + 4].split(':')[-1].strip(),
dtype=float, sep=' ')
if len(parameters) == 12:
matrix = from_matvec(parameters[:9].reshape((3, 3)), parameters[9:])
c_neg = from_matvec(np.eye(3), offset * -1.0)
c_pos = from_matvec(np.eye(3), offset)
matlist.append(LPS.dot(c_pos.dot(matrix.dot(c_neg.dot(LPS)))))
matrix = np.stack(matlist)
# elif fmt.lower() == 'afni':
# parameters = LPS.dot(self.matrix.dot(LPS))
# parameters = parameters[:3, :].reshape(-1).tolist()
elif fmt.lower() in ('x5', 'bids'):
raise NotImplementedError
else:
raise NotImplementedError

if reference and isinstance(reference, str):
reference = loadimg(reference)
Expand Down

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