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)