def test_toFnirt():
def check(got, exp):
tol = dict(atol=1e-5, rtol=1e-5)
assert np.all(np.isclose(got.data, exp.data, **tol))
assert got.src.sameSpace(exp.src)
assert got.ref.sameSpace(exp.ref)
assert got.srcSpace == 'fsl'
assert got.refSpace == 'fsl'
basefield, xform = _random_affine_field()
src = basefield.src
ref = basefield.ref
spaces = it.permutations(('voxel', 'fsl', 'world'), 2)
for from_, to in spaces:
field = nonlinear.convertDeformationSpace(basefield, from_, to)
got = fnirt.toFnirt(field)
check(got, basefield)
src = fslimage.Image(op.join(datadir, 'src'))
ref = fslimage.Image(op.join(datadir, 'ref'))
coef = fnirt.readFnirt(op.join(datadir, 'coefficientfield'), src, ref)
got = fnirt.toFnirt(coef)
check(got, coef)
def test_applyDeformation_altref():
src2ref = affine.compose(
np.random.randint(2, 5, 3),
np.random.randint(1, 10, 3),
np.random.random(3))
ref2src = affine.invert(src2ref)
srcdata = np.random.randint(1, 65536, (10, 10, 10))
refdata = np.random.randint(1, 65536, (10, 10, 10))
src = fslimage.Image(srcdata)
ref = fslimage.Image(refdata, xform=src2ref)
field = _affine_field(src, ref, ref2src, 'world', 'world')
altrefxform = affine.concat(
src2ref,
affine.scaleOffsetXform([1, 1, 1], [5, 0, 0]))
altref = fslimage.Image(refdata, xform=altrefxform)
expect, xf = resample.resampleToReference(
src, altref, matrix=src2ref, order=1, mode='constant', cval=0)
result = nonlinear.applyDeformation(
src, field, ref=altref, order=1, mode='constant', cval=0)
# boundary voxels can get truncated
# (4 is the altref-ref overlap boundary)
expect[4, :, :] = 0
result[4, :, :] = 0
expect = expect[1:-1, 1:-1, 1:-1]
result = result[1:-1, 1:-1, 1:-1]
assert np.all(np.isclose(expect, result))
def test_readWriteNonLinearX5():
datadir = op.join(op.dirname(__file__), 'testdata', 'nonlinear')
dffile = op.join(datadir, 'displacementfield.nii.gz')
srcfile = op.join(datadir, 'src.nii.gz')
reffile = op.join(datadir, 'ref.nii.gz')
src = fslimage.Image(srcfile)
ref = fslimage.Image(reffile)
dfield = fnirt.readFnirt(dffile, src, ref)
wdfield = nonlinear.convertDeformationSpace(dfield, 'world', 'world')
with tempdir.tempdir():
# field must be world->world
with pytest.raises(x5.X5Error):
x5.writeNonLinearX5('nonlinear.x5', dfield)
x5.writeNonLinearX5('nonlinear.x5', wdfield)
gotdfield = x5.readNonLinearX5('nonlinear.x5')
assert gotdfield.src.sameSpace(src)
assert gotdfield.ref.sameSpace(ref)
assert gotdfield.srcSpace == wdfield.srcSpace
assert gotdfield.refSpace == wdfield.refSpace
assert gotdfield.deformationType == wdfield.deformationType
assert np.all(np.isclose(gotdfield.data, wdfield.data))
with h5py.File('nonlinear.x5', 'r') as f:
assert f.attrs['Type'] == 'nonlinear'
_check_metadata(f)
_check_deformation(f['/Transform'], wdfield)
_check_space(f['/A'], ref)
_check_space(f['/B'], src)
def test_linear_altref(seed):
with tempdir.tempdir():
src2ref = affine.scaleOffsetXform([1, 1, 1], [5, 5, 5])
altv2w = affine.scaleOffsetXform([1, 1, 1], [10, 10, 10])
srcdata = np.random.randint(1, 65536, (10, 10, 10))
src = fslimage.Image(srcdata, xform=np.eye(4))
ref = fslimage.Image(src.data, xform=src2ref)
altref = fslimage.Image(src.data, xform=altv2w)
src.save('src')
ref.save('ref')
altref.save('altref')
x5.writeLinearX5('xform.x5', src2ref, src, ref)
fsl_apply_x5.main('src xform.x5 out -r altref'.split())
result = fslimage.Image('out')
expect = np.zeros(srcdata.shape)
expect[:5, :5, :5] = srcdata[5:, 5:, 5:]
assert result.sameSpace(altref)
assert np.all(result.data == expect)
def test_convert_fnirt_coefficient_field():
datadir = op.join(op.dirname(__file__), '..', 'test_transform', 'testdata',
'nonlinear')
srcfile = op.join(datadir, 'src.nii.gz')
reffile = op.join(datadir, 'ref.nii.gz')
cffile = op.join(datadir, 'coefficientfield.nii.gz')
dffile = op.join(datadir, 'displacementfield.nii.gz')
with tempdir.tempdir():
# nii -> x5
fsl_convert_x5.main('fnirt -s {} -r {} {} coef.x5'.format(
srcfile, reffile, cffile).split())
# x5 -> nii
fsl_convert_x5.main('fnirt coef.x5 coef.nii.gz'.split())
src = fslimage.Image(srcfile)
ref = fslimage.Image(reffile)
df = fnirt.readFnirt(dffile, src, ref)
dfnii = fnirt.readFnirt('coef.nii.gz', src, ref)
assert dfnii.sameSpace(df)
assert dfnii.src.sameSpace(src)
assert dfnii.ref.sameSpace(ref)
assert dfnii.srcSpace == df.srcSpace
assert dfnii.refSpace == df.refSpace
assert dfnii.deformationType == 'relative'
diff = np.abs(dfnii.data - df.data)
tols = {'rtol': 1e-5, 'atol': 1e-5}
assert np.all(np.isclose(dfnii.data, df.data, **tols))
def test_sformToFlirtMatrix():
testfile = op.join(datadir, 'test_transform_test_flirtMatrixToSform.txt')
lines = readlines(testfile)
ntests = len(lines) // 18
for i in range(ntests):
tlines = lines[i * 18:i * 18 + 18]
srcShape = [int(w) for w in tlines[0].split()]
srcXform = np.genfromtxt(tlines[1:5])
refShape = [int(w) for w in tlines[5].split()]
refXform = np.genfromtxt(tlines[6:10])
expected = np.genfromtxt(tlines[10:14])
srcXformOvr = np.genfromtxt(tlines[14:18])
srcImg1 = fslimage.Image(np.zeros(srcShape), xform=srcXform)
srcImg2 = fslimage.Image(np.zeros(srcShape), xform=srcXform)
refImg = fslimage.Image(np.zeros(refShape), xform=refXform)
srcImg2.voxToWorldMat = srcXformOvr
result1 = flirt.sformToFlirtMatrix(srcImg1, refImg, srcXformOvr)
result2 = flirt.sformToFlirtMatrix(srcImg2, refImg)
assert np.all(np.isclose(result1, expected))
assert np.all(np.isclose(result2, expected))
def test_convert_fnirt_deformation_field():
datadir = op.join(op.dirname(__file__), '..', 'test_transform', 'testdata',
'nonlinear')
srcfile = op.join(datadir, 'src.nii.gz')
reffile = op.join(datadir, 'ref.nii.gz')
dffile = op.join(datadir, 'displacementfield.nii.gz')
with tempdir.tempdir():
# nii -> x5
fsl_convert_x5.main('fnirt -s {} -r {} {} disp.x5'.format(
srcfile, reffile, dffile).split())
# x5 -> nii
fsl_convert_x5.main('fnirt disp.x5 disp.nii.gz'.split())
src = fslimage.Image(srcfile)
ref = fslimage.Image(reffile)
df = fnirt.readFnirt(dffile, src, ref)
dfnii = fnirt.readFnirt('disp.nii.gz', src, ref)
assert dfnii.src.sameSpace(src)
assert dfnii.ref.sameSpace(ref)
assert dfnii.srcSpace == df.srcSpace
assert dfnii.refSpace == df.refSpace
assert dfnii.deformationType == df.deformationType
assert np.all(np.isclose(dfnii.data, df.data))
def _test_transformCoords(panel, overlayList, displayCtx):
displayCtx = panel.displayCtx
mesh = fslvtk.VTKMesh(op.join(datadir, 'mesh_l_thal.vtk'))
ref = fslimage.Image(op.join(datadir, 'mesh_ref.nii.gz'))
# resample to something other than 1mm^3
# so display/voxel coord systems are different
ref, xform = resample.resampleToPixdims(ref, [1.5, 1.5, 1.5])
ref = fslimage.Image(ref, xform=xform)
overlayList.extend([ref, mesh])
displayCtx.displaySpace = ref
mopts = displayCtx.getOpts(mesh)
ropts = displayCtx.getOpts(ref)
mopts.refImage = ref
# world display mesh
spaces = ['world', 'display', 'mesh', 'voxel']
(xlo, ylo, zlo), (xhi, yhi, zhi) = mesh.bounds
coords = {
'mesh': np.array([xhi - xlo, yhi - ylo, zhi - zlo]),
'world': np.array([3.92563438, 27.8010308, 34.60089011]),
'display': np.array([23.10099983, 32.8390007, 23.02000046]),
'voxel': np.array([15.40066655, 21.89266713, 15.34666697]),
}
for from_, to in it.permutations(coords, 2):
exp = coords[to]
got = mopts.transformCoords(coords[from_], from_, to)
assert np.all(np.isclose(got, exp))
def _test_image_indexed_read4D(threaded):
with tests.testdir() as testdir:
filename = op.join(testdir, 'image.nii.gz')
# Data range grows with volume
nvols = 50
data = np.zeros((50, 50, 50, nvols))
for vol in range(nvols):
data[..., vol] = vol
fslimage.Image(data).save(filename)
img = fslimage.Image(filename,
loadData=False,
calcRange=False,
indexed=True,
threaded=threaded)
# Test reading slice through
# 4D (e.g. voxel time course)
#
# n.b. This is SUPER SLOW!!
voxels = tests.random_voxels((50, 50, 50), 5)
for i, xyz in enumerate(voxels):
x, y, z = [int(v) for v in xyz]
data = img[x, y, z, :]
if threaded:
img.getImageWrapper().getTaskThread().waitUntilIdle()
assert np.all(data == np.arange(nvols))
def test_applyDeformation_worldAligned():
refv2w = affine.scaleOffsetXform([1, 1, 1], [10, 10, 10])
fieldv2w = affine.scaleOffsetXform([2, 2, 2], [10.5, 10.5, 10.5])
src2ref = refv2w
ref2src = affine.invert(src2ref)
srcdata = np.random.randint(1, 65536, (10, 10, 10))
src = fslimage.Image(srcdata)
ref = fslimage.Image(srcdata, xform=src2ref)
field = _affine_field(src, ref, ref2src, 'world', 'world',
shape=(5, 5, 5), fv2w=fieldv2w)
field = nonlinear.DeformationField(
nonlinear.convertDeformationType(field, 'absolute'),
header=field.header,
src=src,
ref=ref,
srcSpace='world',
refSpace='world',
defType='absolute')
expect, xf = resample.resampleToReference(
src, ref, matrix=src2ref, order=1, mode='constant', cval=0)
result = nonlinear.applyDeformation(
src, field, order=1, mode='constant', cval=0)
expect = expect[1:-1, 1:-1, 1:-1]
result = result[1:-1, 1:-1, 1:-1]
assert np.all(np.isclose(expect, result))
def test_readFnirt_defType_intent():
src = op.join(datadir, 'src.nii.gz')
ref = op.join(datadir, 'ref.nii.gz')
coef = op.join(datadir, 'coefficientfield.nii.gz')
disp = op.join(datadir, 'displacementfield.nii.gz')
src = fslimage.Image(src)
ref = fslimage.Image(ref)
field = fnirt.readFnirt(disp, src, ref, defType='absolute')
assert field.deformationType == 'absolute'
field = fnirt.readFnirt(disp, src, ref, defType='relative')
assert field.deformationType == 'relative'
img = nib.load(coef)
img.header['intent_code'] = 0
with tempdir.tempdir():
img.to_filename('field.nii.gz')
with pytest.raises(ValueError):
fnirt.readFnirt('field', src, ref)
field = fnirt.readFnirt('field',
src,
ref,
intent=constants.FSL_CUBIC_SPLINE_COEFFICIENTS)
assert isinstance(field, nonlinear.CoefficientField)
field = fnirt.readFnirt('field',
src,
ref,
intent=constants.FSL_FNIRT_DISPLACEMENT_FIELD)
assert isinstance(field, nonlinear.DeformationField)
def makergb(infile, nchannels):
img = fslimage.Image(infile)
indata = img.data[..., :nchannels]
if nchannels == 3:
dtype = np.dtype([('R', 'uint8'), ('G', 'uint8'), ('B', 'uint8')])
else:
dtype = np.dtype([('R', 'uint8'), ('G', 'uint8'), ('B', 'uint8'),
('A', 'uint8')])
outdata = np.zeros(indata.shape[:3], dtype=dtype)
for cn, ci in zip('RGBA', range(nchannels)):
cd = indata[..., ci].astype(np.float32)
lo, hi = cd.min(), cd.max()
cd = (ci + 1) * 255 * (cd - lo) / (hi - lo)
outdata[cn] = np.round(cd).astype(np.uint8)
img = fslimage.Image(outdata, header=img.header)
name = fslimage.removeExt(op.basename(infile))
name = '{}_makergb_{}'.format(name, nchannels)
img.save(name)
return name
def test_loadMetadata():
with tempdir():
make_image('image.nii.gz')
meta = {'a': 1, 'b': 2}
with open('image.json', 'wt') as f:
json.dump(meta, f)
img = fslimage.Image('image.nii.gz')
gotmeta = fslimage.loadMetadata(img)
assert gotmeta == meta
with tempdir():
imgfile = op.join('data', 'sub-01', 'anat', 'sub-01_T1w.nii.gz')
metafile = op.join('data', 'T1w.json')
os.makedirs(op.dirname(imgfile))
make_image(imgfile)
with open(op.join('data', 'dataset_description.json'), 'wt') as f:
pass
meta = {'a': 1, 'b': 2}
with open(metafile, 'wt') as f:
json.dump(meta, f)
img = fslimage.Image(imgfile)
gotmeta = fslimage.loadMetadata(img)
assert gotmeta == meta
def test_resampleToPixdims():
img = fslimage.Image(make_random_image(dims=(10, 10, 10)))
imglo, imghi = affine.axisBounds(img.shape, img.voxToWorldMat)
oldpix = np.array(img.pixdim, dtype=float)
oldshape = np.array(img.shape, dtype=float)
for i, origin in it.product(range(25), ('centre', 'corner')):
# random pixdims in the range 0.1 - 5.0
newpix = 0.1 + 4.9 * np.random.random(3)
expshape = np.round(oldshape * (oldpix / newpix))
res = resample.resampleToPixdims(img, newpix, origin=origin)
res = fslimage.Image(res[0], xform=res[1])
reslo, reshi = affine.axisBounds(res.shape, res.voxToWorldMat)
resfov = reshi - reslo
expfov = newpix * res.shape
assert np.all(np.isclose(res.shape, expshape))
assert np.all(np.isclose(res.pixdim, newpix))
assert np.all(np.isclose(resfov, expfov, rtol=1e-2, atol=1e-2))
if origin == 'corner':
assert np.all(np.isclose(imglo, reslo))
assert np.all(
np.isclose(reshi, reslo + expfov, rtol=1e-2, atol=1e-2))
def test_readFnirt():
src = op.join(datadir, 'src')
ref = op.join(datadir, 'ref')
coef = op.join(datadir, 'coefficientfield')
disp = op.join(datadir, 'displacementfield')
src = fslimage.Image(src)
ref = fslimage.Image(ref)
coef = fnirt.readFnirt(coef, src, ref)
disp = fnirt.readFnirt(disp, src, ref)
with pytest.raises(ValueError):
fnirt.readFnirt(src, src, ref)
assert isinstance(coef, nonlinear.CoefficientField)
assert isinstance(disp, nonlinear.DeformationField)
assert coef.src.sameSpace(src)
assert coef.ref.sameSpace(ref)
assert disp.src.sameSpace(src)
assert disp.ref.sameSpace(ref)
assert coef.srcSpace == 'fsl'
assert coef.refSpace == 'fsl'
assert disp.srcSpace == 'fsl'
assert disp.refSpace == 'fsl'
def test_coefficientFieldToDeformationField():
nldir = op.join(datadir, 'nonlinear')
src = op.join(nldir, 'src.nii.gz')
ref = op.join(nldir, 'ref.nii.gz')
cf = op.join(nldir, 'coefficientfield.nii.gz')
df = op.join(nldir, 'displacementfield.nii.gz')
dfnp = op.join(nldir, 'displacementfield_no_premat.nii.gz')
src = fslimage.Image(src)
ref = fslimage.Image(ref)
cf = fnirt.readFnirt(cf, src, ref)
rdf = fnirt.readFnirt(df, src, ref)
rdfnp = fnirt.readFnirt(dfnp, src, ref)
adf = nonlinear.convertDeformationType(rdf)
adfnp = nonlinear.convertDeformationType(rdfnp)
rcnv = nonlinear.coefficientFieldToDeformationField(cf)
acnv = nonlinear.coefficientFieldToDeformationField(cf, defType='absolute')
acnvnp = nonlinear.coefficientFieldToDeformationField(cf,
defType='absolute',
premat=False)
rcnvnp = nonlinear.coefficientFieldToDeformationField(cf, premat=False)
tol = dict(atol=1e-5, rtol=1e-5)
assert np.all(np.isclose(rcnv.data, rdf.data, **tol))
assert np.all(np.isclose(acnv.data, adf.data, **tol))
assert np.all(np.isclose(rcnvnp.data, rdfnp.data, **tol))
assert np.all(np.isclose(acnvnp.data, adfnp.data, **tol))
def _test_group(frame, overlayList, displayCtx):
# Currently there is always only one group
group = displayCtx.overlayGroups[0]
img1 = fslimage.Image(op.join(datadir, '3d'))
img2 = fslimage.Image(op.join(datadir, '4d'))
# all new images should be
# added to the overlay group
overlayList.append(img1)
assert img1 in group
# Overlays should be automatically
# removed from the group
del overlayList[:]
assert len(group) == 0
overlayList.append(img1)
overlayList.append(img2)
assert img1 in group
assert img2 in group
del overlayList[0]
assert img1 not in group
assert img2 in group
del overlayList[0]
assert img1 not in group
assert img2 not in group
def test_coefficientField_transform_altref():
# test coordinates (manually determined).
# original ref image is 2mm isotropic,
# resampled is 1mm. Each tuple contains:
#
# (src, ref2mm, ref1mm)
coords = [((18.414, 26.579, 25.599), (11, 19, 11), (22, 38, 22)),
((14.727, 22.480, 20.340), (8, 17, 8), (16, 34, 16)),
((19.932, 75.616, 27.747), (11, 45, 5), (22, 90, 10))]
nldir = op.join(datadir, 'nonlinear')
src = op.join(nldir, 'src.nii.gz')
ref = op.join(nldir, 'ref.nii.gz')
cf = op.join(nldir, 'coefficientfield.nii.gz')
src = fslimage.Image(src)
ref2mm = fslimage.Image(ref)
ref1mm = ref2mm.adjust((1, 1, 1))
cfref2mm = fnirt.readFnirt(cf, src, ref2mm)
cfref1mm = fnirt.readFnirt(cf, src, ref1mm)
for srcc, ref2mmc, ref1mmc in coords:
ref2mmc = cfref2mm.transform(ref2mmc, 'voxel', 'voxel')
ref1mmc = cfref1mm.transform(ref1mmc, 'voxel', 'voxel')
assert np.all(np.isclose(ref2mmc, srcc, 1e-4))
assert np.all(np.isclose(ref1mmc, srcc, 1e-4))
def test_prepareMask():
reg = atlases.registry
reg.rescanAtlases()
probatlas = reg.loadAtlas('harvardoxford-cortical',
indexed=True, loadData=False, calcRange=False)
probsumatlas = reg.loadAtlas('harvardoxford-cortical', loadSummary=True)
lblatlas = reg.loadAtlas('talairach')
for atlas in [probatlas, probsumatlas, lblatlas]:
ashape = list(atlas.shape[:3])
m2shape = [s * 1.5 for s in ashape]
goodmask1 = fslimage.Image(
np.array(np.random.random(ashape), dtype=np.float32),
xform=atlas.voxToWorldMat)
goodmask2, xf = goodmask1.resample(m2shape)
goodmask2 = fslimage.Image(goodmask2, xform=xf)
wrongdims = fslimage.Image(
np.random.random(list(ashape) + [2]))
wrongspace = fslimage.Image(
np.random.random((20, 20, 20)),
xform=transform.concat(atlas.voxToWorldMat, np.diag([2, 2, 2, 1])))
with pytest.raises(atlases.MaskError):
atlas.prepareMask(wrongdims)
with pytest.raises(atlases.MaskError):
atlas.prepareMask(wrongspace)
assert list(atlas.prepareMask(goodmask1).shape) == ashape
assert list(atlas.prepareMask(goodmask2).shape) == ashape
def test_CoefficientField_displacements():
nldir = op.join(datadir, 'nonlinear')
src = op.join(nldir, 'src.nii.gz')
ref = op.join(nldir, 'ref.nii.gz')
cf = op.join(nldir, 'coefficientfield.nii.gz')
df = op.join(nldir, 'displacementfield_no_premat.nii.gz')
src = fslimage.Image(src)
ref = fslimage.Image(ref)
cf = fnirt.readFnirt(cf, src, ref)
df = fnirt.readFnirt(df, src, ref)
ix, iy, iz = ref.shape[:3]
x, y, z = np.meshgrid(np.arange(ix),
np.arange(iy),
np.arange(iz),
indexing='ij')
x = x.flatten()
y = y.flatten()
z = z.flatten()
xyz = np.vstack((x, y, z)).T
disps = cf.displacements(xyz)
disps = disps.reshape(df.shape)
tol = dict(atol=1e-5, rtol=1e-5)
assert np.all(np.isclose(disps, df.data, **tol))
def gen_indices(infile):
basename = fslimage.removeExt(op.basename(infile))
outfile = '{}_indices.nii.gz'.format(basename)
img = fslimage.Image(infile, loadData=False)
shape = img.shape
data = np.arange(np.prod(shape)).reshape(shape)
fslimage.Image(data, header=img.header).save(outfile)
return outfile
def test_CoefficientField_transform():
nldir = op.join(datadir, 'nonlinear')
src = op.join(nldir, 'src.nii.gz')
ref = op.join(nldir, 'ref.nii.gz')
cf = op.join(nldir, 'coefficientfield.nii.gz')
df = op.join(nldir, 'displacementfield.nii.gz')
dfnp = op.join(nldir, 'displacementfield_no_premat.nii.gz')
src = fslimage.Image(src)
ref = fslimage.Image(ref)
cf = fnirt.readFnirt(cf, src, ref)
df = fnirt.readFnirt(df, src, ref)
dfnp = fnirt.readFnirt(dfnp, src, ref)
spaces = ['fsl', 'voxel', 'world']
spaces = list(it.combinations_with_replacement(spaces, 2))
spaces = spaces + [(r, s) for s, r in spaces]
spaces = list(set(spaces))
rx, ry, rz = np.meshgrid(np.arange(ref.shape[0]),
np.arange(ref.shape[1]),
np.arange(ref.shape[2]),
indexing='ij')
rvoxels = np.vstack((rx.flatten(), ry.flatten(), rz.flatten())).T
refcoords = {
'voxel': rvoxels,
'fsl': affine.transform(rvoxels, ref.getAffine('voxel', 'fsl')),
'world': affine.transform(rvoxels, ref.getAffine('voxel', 'world'))
}
srccoords = refcoords['fsl'] + df.data.reshape(-1, 3)
srccoords = {
'voxel': affine.transform(srccoords, src.getAffine('fsl', 'voxel')),
'fsl': srccoords,
'world': affine.transform(srccoords, src.getAffine('fsl', 'world'))
}
srccoordsnp = refcoords['fsl'] + dfnp.data.reshape(-1, 3)
srccoordsnp = {
'voxel': affine.transform(srccoordsnp, src.getAffine('fsl', 'voxel')),
'fsl': srccoordsnp,
'world': affine.transform(srccoordsnp, src.getAffine('fsl', 'world'))
}
tol = dict(atol=1e-5, rtol=1e-5)
for srcspace, refspace in spaces:
got = cf.transform(refcoords[refspace], refspace, srcspace)
gotnp = cf.transform(refcoords[refspace],
refspace,
srcspace,
premat=False)
assert np.all(np.isclose(got, srccoords[srcspace], **tol))
assert np.all(np.isclose(gotnp, srccoordsnp[srcspace], **tol))
def binarise(infile, low, high, scale=1):
basename = fslimage.removeExt(op.basename(infile))
outfile = '{}_binarised_{}_{}_{}.nii.gz'.format(basename, low, high, scale)
img = fslimage.Image(infile)
data = img[:]
binned = ((data > low) & (data < high)).astype(np.uint8) * scale
fslimage.Image(binned, header=img.header).save(outfile)
return outfile
def _test_copyDisplayProperties(panel, overlayList, displayCtx):
displayCtx = panel.displayCtx
img1 = fslimage.Image(np.random.randint(1, 255, (20, 20, 20)))
img2 = fslimage.Image(np.random.randint(1, 255, (20, 20, 20)))
overlayList.extend((img1, img2))
realYield(50)
disp1 = displayCtx.getDisplay(img1)
disp2 = displayCtx.getDisplay(img2)
opts1 = displayCtx.getOpts(img1)
opts2 = displayCtx.getOpts(img2)
disp1.name = 'hurdi hur'
disp1.brightness = 75
disp1.contrast = 25
opts1.cmap = 'blue-lightblue'
realYield(50)
copyoverlay.copyDisplayProperties(displayCtx, img1, img2)
realYield(50)
assert disp2.name == 'hurdi hur'
assert np.isclose(disp2.brightness, 75)
assert np.isclose(disp2.contrast, 25)
assert opts2.cmap.name == 'blue-lightblue'
disp1.name = 'wuzzle wazzle'
opts1.gamma = 0.75
opts1.cmap = 'red-yellow'
copyoverlay.copyDisplayProperties(displayCtx,
img1,
img2,
displayExclude=['name'],
optExclude=['cmap'])
assert disp2.name == 'hurdi hur'
assert np.isclose(opts2.gamma, 0.75)
assert opts2.cmap.name == 'blue-lightblue'
disp1.name = 'walla walla'
opts1.gamma = 0.6
opts1.cmap = 'hot'
copyoverlay.copyDisplayProperties(displayCtx,
img1,
img2,
displayArgs={'name': 'herp derp'},
optArgs={'gamma': 0.2})
assert disp2.name == 'herp derp'
assert np.isclose(opts2.gamma, 0.2)
assert opts2.cmap.name == 'hot'
def roi(fname, roi):
base = fslimage.removeExt(fname)
outfile = '{}_roi_{}_{}_{}_{}_{}_{}'.format(base, *roi)
img = fslimage.Image(fname)
xs, xe, ys, ye, zs, ze = roi
data = img[xs:xe, ys:ye, zs:ze, ...]
img = fslimage.Image(data, header=img.header)
img.save(outfile)
return outfile
def test_applyDeformation_premat():
src2ref = affine.compose(
np.random.randint(2, 5, 3),
np.random.randint(1, 10, 3),
[0, 0, 0])
ref2src = affine.invert(src2ref)
srcdata = np.random.randint(1, 65536, (10, 10, 10))
refdata = np.random.randint(1, 65536, (10, 10, 10))
src = fslimage.Image(srcdata)
ref = fslimage.Image(refdata, xform=src2ref)
field = _affine_field(src, ref, ref2src, 'world', 'world')
# First try a down-sampled version
# of the original source image
altsrc, xf = resample.resample(src, (5, 5, 5), origin='corner')
altsrc = fslimage.Image(altsrc, xform=xf, header=src.header)
expect, xf = resample.resampleToReference(
altsrc, ref, matrix=src2ref, order=1, mode='nearest')
premat = affine.concat(src .getAffine('world', 'voxel'),
altsrc.getAffine('voxel', 'world'))
result = nonlinear.applyDeformation(
altsrc, field, order=1, mode='nearest', premat=premat)
assert np.all(np.isclose(expect, result))
# Now try a down-sampled ROI
# of the original source image
altsrc = roi.roi(src, [(2, 9), (2, 9), (2, 9)])
altsrc, xf = resample.resample(altsrc, (4, 4, 4))
altsrc = fslimage.Image(altsrc, xform=xf, header=src.header)
expect, xf = resample.resampleToReference(
altsrc, ref, matrix=src2ref, order=1, mode='nearest')
premat = affine.concat(src .getAffine('world', 'voxel'),
altsrc.getAffine('voxel', 'world'))
result = nonlinear.applyDeformation(
altsrc, field, order=1, mode='nearest', premat=premat)
assert np.all(np.isclose(expect, result))
# down-sampled and offset ROI
# of the original source image
altsrc = roi.roi(src, [(-5, 8), (-5, 8), (-5, 8)])
altsrc, xf = resample.resample(altsrc, (6, 6, 6))
altsrc = fslimage.Image(altsrc, xform=xf, header=src.header)
expect, xf = resample.resampleToReference(
altsrc, ref, matrix=src2ref, order=1, mode='nearest')
premat = affine.concat(src .getAffine('world', 'voxel'),
altsrc.getAffine('voxel', 'world'))
result = nonlinear.applyDeformation(
altsrc, field, order=1, mode='nearest', premat=premat)
assert np.all(np.isclose(expect, result))
def _test_CopyOverlayAction(panel, overlayList, displayCtx):
img3d = fslimage.Image(np.random.randint(1, 255, (20, 20, 20)))
img4d = fslimage.Image(np.random.randint(1, 255, (20, 20, 20, 20)))
overlayList.extend((img3d, img4d))
class CheckBoxMessageDialog(object):
ShowModal_return = wx.ID_YES
CheckBoxState_return = [False, False, False]
def __init__(self, *a, **kwa):
pass
def ShowModal(self):
return CheckBoxMessageDialog.ShowModal_return
def CheckBoxState(self, cb):
return CheckBoxMessageDialog.CheckBoxState_return[cb]
with mock.patch('fsleyes_widgets.dialog.CheckBoxMessageDialog',
CheckBoxMessageDialog):
act = copyoverlay.CopyOverlayAction(overlayList, displayCtx,
panel.frame)
CheckBoxMessageDialog.ShowModal_return = wx.ID_CANCEL
act()
assert len(overlayList) == 2
displayCtx.selectOverlay(img3d)
CheckBoxMessageDialog.ShowModal_return = wx.ID_YES
act()
assert len(overlayList) == 3
print(overlayList)
copy = overlayList[1]
assert np.all(copy[:] == img3d[:])
overlayList.remove(copy)
displayCtx.selectOverlay(img4d)
act()
assert len(overlayList) == 3
copy = overlayList[2]
assert np.all(copy[:] == img4d[..., 0])
overlayList.remove(copy)
CheckBoxMessageDialog.CheckBoxState_return = [False, False, True]
displayCtx.selectOverlay(img4d)
act()
assert len(overlayList) == 3
copy = overlayList[2]
assert np.all(copy[:] == img4d[:])
overlayList.remove(copy)
def oblique(infile):
basename = fslimage.removeExt(op.basename(infile))
outfile = '{}_oblique.nii.gz'.format(basename)
img = fslimage.Image(infile)
xform = img.getAffine('voxel', 'world')
rot = affine.compose([1, 1, 1], [0, 0, 0], [0, 0, 1])
xform = affine.concat(rot, xform)
img = fslimage.Image(img.data, xform=xform)
img.save(outfile)
return outfile
def _test_image_indexed(threaded):
with tests.testdir() as testdir:
filename = op.join(testdir, 'image.nii.gz')
# Data range grows with volume
data = np.zeros((50, 50, 50, 50))
for vol in range(data.shape[-1]):
data[..., vol] = vol
fslimage.Image(data).save(filename)
img = fslimage.Image(
filename,
loadData=False,
calcRange=False,
threaded=threaded)
# First iteration through the image
start1 = time.time()
for vol in range(data.shape[-1]):
img[..., vol]
if threaded:
img.getImageWrapper().getTaskThread().waitUntilIdle()
assert img.dataRange == (0, vol)
end1 = time.time()
# Double check that indexed_gzip is
# being used (the internal _opener
# attribute is not created until
# after the first data access)
try:
import indexed_gzip as igzip
assert isinstance(img.nibImage.dataobj._opener.fobj,
igzip.IndexedGzipFile)
except ImportError:
pass
# Second iteration through
start2 = time.time()
for vol in range(data.shape[-1]):
img[..., vol]
end2 = time.time()
# Second iteration should be much faster!
assert (end1 - start1) > (end2 - start2)
def _test_screenshot(panel, overlayList, displayCtx, stype, imgfile):
import matplotlib.image as mplimg
import fsleyes.actions.screenshot as screenshot
import fsleyes.views.orthopanel as orthopanel
if isinstance(panel, orthopanel.OrthoPanel):
panel.sceneOpts.showCursor = False
panel.sceneOpts.showLabels = False
img = fslimage.Image(op.join(datadir, imgfile))
overlayList.append(img)
with tempdir():
fname = 'test_screenshot_{}.png'.format(stype)
realYield(100)
idle.idle(screenshot.screenshot, panel, fname)
realYield()
bfname = op.join(datadir, 'test_screenshot_{}.png'.format(stype))
screenshot = mplimg.imread(fname)
benchmark = mplimg.imread(bfname)
result, diff = compare_images(screenshot, benchmark, 50)
print('Comparing {} with {}: {}'.format(fname, bfname, diff))
assert result
