def setUp(self):
if not has_nib:
self.skipTest("nibabel required for test_inverse")
set_determinism(seed=0)
self.all_data = {}
affine = make_rand_affine()
affine[0] *= 2
for size in [10, 11]:
# pad 5 onto both ends so that cropping can be lossless
im_1d = np.pad(np.arange(size), 5)[None]
name = "1D even" if size % 2 == 0 else "1D odd"
self.all_data[name] = {
"image": np.array(im_1d, copy=True),
"label": np.array(im_1d, copy=True),
"other": np.array(im_1d, copy=True),
}
im_2d_fname, seg_2d_fname = [make_nifti_image(i) for i in create_test_image_2d(101, 100)]
im_3d_fname, seg_3d_fname = [make_nifti_image(i, affine) for i in create_test_image_3d(100, 101, 107)]
load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])
self.all_data["2D"] = load_ims({"image": im_2d_fname, "label": seg_2d_fname})
self.all_data["3D"] = load_ims({"image": im_3d_fname, "label": seg_3d_fname})
def setUpClass(cls):
arr = np.random.rand(2, 10, 8, 7)
affine = make_rand_affine()
data = {"i": make_nifti_image(arr, affine)}
loader = LoadImaged("i")
cls.data: MetaTensor = loader(data)
def setUp(self):
if not has_nib:
self.skipTest("nibabel required for test_inverse")
set_determinism(seed=0)
self.all_data = {}
affine = make_rand_affine()
affine[0] *= 2
im_1d = AddChannel()(np.arange(0, 10))
self.all_data["1D"] = {"image": im_1d, "label": im_1d, "other": im_1d}
im_2d_fname, seg_2d_fname = [make_nifti_image(i) for i in create_test_image_2d(101, 100)]
im_3d_fname, seg_3d_fname = [make_nifti_image(i, affine) for i in create_test_image_3d(100, 101, 107)]
load_ims = Compose([LoadImaged(KEYS), AddChanneld(KEYS)])
self.all_data["2D"] = load_ims({"image": im_2d_fname, "label": seg_2d_fname})
self.all_data["3D"] = load_ims({"image": im_3d_fname, "label": seg_3d_fname})
def setUpClass(cls):
arr = np.random.rand(2, 10, 8, 7)
affine = make_rand_affine()
data = {"i": make_nifti_image(arr, affine)}
cls.data = LoadImaged("i")(data)
