def _cmp(self, filename, shape, ch_shape, reader_1, reader_2, outname,
ext):
data_dict = {"img": filename}
keys = data_dict.keys()
xforms = Compose(
[LoadImaged(keys, reader=reader_1, ensure_channel_first=True)])
img_dict = xforms(data_dict) # load dicom with itk
self.assertTupleEqual(img_dict["img"].shape, ch_shape)
self.assertTupleEqual(
tuple(img_dict[PostFix.meta("img")]["spatial_shape"]), shape)
with tempfile.TemporaryDirectory() as tempdir:
save_xform = SaveImageD(keys,
meta_keys=PostFix.meta("img"),
output_dir=tempdir,
squeeze_end_dims=False,
output_ext=ext)
save_xform(img_dict) # save to nifti
new_xforms = Compose(
[LoadImaged(keys, reader=reader_2),
EnsureChannelFirstD(keys)])
out = new_xforms({"img":
os.path.join(tempdir,
outname)}) # load nifti with itk
self.assertTupleEqual(out["img"].shape, ch_shape)
self.assertTupleEqual(
tuple(out[PostFix.meta("img")]["spatial_shape"]), shape)
if "affine" in img_dict[PostFix.meta("img")] and "affine" in out[
PostFix.meta("img")]:
np.testing.assert_allclose(
img_dict[PostFix.meta("img")]["affine"],
out[PostFix.meta("img")]["affine"],
rtol=1e-3)
np.testing.assert_allclose(out["img"], img_dict["img"], rtol=1e-3)
def test_channel_dim(self):
spatial_size = (32, 64, 3, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
nib.save(nib.Nifti1Image(test_image, affine=np.eye(4)), filename)
loader = LoadImaged(keys="img", image_only=True)
loader.register(ITKReader(channel_dim=2))
t = Compose([EnsureChannelFirstD("img"), FromMetaTensord("img")])
result = t(loader({"img": filename}))
self.assertTupleEqual(result["img"].shape, (3, 32, 64, 128))
def test_channel_dim(self):
spatial_size = (32, 64, 3, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
nib.save(nib.Nifti1Image(test_image, affine=np.eye(4)), filename)
loader = LoadImaged(keys="img")
loader.register(ITKReader(channel_dim=2))
result = EnsureChannelFirstD("img")(loader({"img": filename}))
self.assertTupleEqual(tuple(result["img_meta_dict"]["spatial_shape"]), (32, 64, 128))
self.assertTupleEqual(result["img"].shape, (3, 32, 64, 128))
def _cmp(self, filename, ch_shape, reader_1, reader_2, outname, ext):
data_dict = {"img": filename}
keys = data_dict.keys()
xforms = Compose([
LoadImaged(keys,
reader=reader_1,
ensure_channel_first=True,
image_only=True)
])
img_dict = xforms(data_dict) # load dicom with itk
self.assertTupleEqual(img_dict["img"].shape, ch_shape)
with tempfile.TemporaryDirectory() as tempdir:
save_xform = SaveImageD(keys,
output_dir=tempdir,
squeeze_end_dims=False,
output_ext=ext)
save_xform(img_dict) # save to nifti
new_xforms = Compose([
LoadImaged(keys, reader=reader_2, image_only=True),
EnsureChannelFirstD(keys),
FromMetaTensord(keys),
ToMetaTensord(keys),
])
out = new_xforms({"img":
os.path.join(tempdir,
outname)}) # load nifti with itk
self.assertTupleEqual(out["img"].shape, ch_shape)
def is_identity(x):
return (x == torch.eye(x.shape[0])).all()
if not is_identity(img_dict["img"].affine) and not is_identity(
out["img"].affine):
assert_allclose(img_dict["img"].affine,
out["img"].affine,
rtol=1e-3)
assert_allclose(out["img"], img_dict["img"], rtol=1e-3)