def test_itk_reader_multichannel(self):
test_image = np.random.randint(0, 256, size=(256, 224, 3)).astype("uint8")
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.png")
itk_np_view = itk.image_view_from_array(test_image, is_vector=True)
itk.imwrite(itk_np_view, filename)
for flag in (False, True):
result = LoadImage(image_only=True, reader=ITKReader(reverse_indexing=flag))(Path(filename))
test_image = test_image.transpose(1, 0, 2)
np.testing.assert_allclose(result[:, :, 0], test_image[:, :, 0])
np.testing.assert_allclose(result[:, :, 1], test_image[:, :, 1])
np.testing.assert_allclose(result[:, :, 2], test_image[:, :, 2])
def test_register(self):
spatial_size = (32, 64, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image)
itk.imwrite(itk_np_view, filename)
loader = LoadImaged(keys="img", image_only=True)
loader.register(ITKReader())
result = loader({"img": Path(filename)})
self.assertTupleEqual(result["img"].shape, spatial_size[::-1])
def test_itk_reader_multichannel(self):
test_image = np.random.randint(0, 256, size=(256, 256, 3)).astype("uint8")
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.png")
itk_np_view = itk.image_view_from_array(test_image, is_vector=True)
itk.imwrite(itk_np_view, filename)
result, header = LoadImage(reader=ITKReader())(filename)
self.assertTupleEqual(tuple(header["spatial_shape"]), (256, 256))
np.testing.assert_allclose(result[0, :, :], test_image[:, :, 0])
np.testing.assert_allclose(result[1, :, :], test_image[:, :, 1])
np.testing.assert_allclose(result[2, :, :], test_image[:, :, 2])
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 test_register(self):
spatial_size = (32, 64, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image)
itk.imwrite(itk_np_view, filename)
loader = LoadImage(image_only=False)
loader.register(ITKReader())
result, header = loader(filename)
self.assertTupleEqual(tuple(header["spatial_shape"]), spatial_size)
self.assertTupleEqual(result.shape, spatial_size)
def test_load_nifti_multichannel(self):
test_image = np.random.randint(0, 256, size=(31, 64, 16, 2)).astype(np.float32)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image, is_vector=True)
itk.imwrite(itk_np_view, filename)
itk_img = LoadImage(image_only=True, reader=ITKReader())(Path(filename))
self.assertTupleEqual(tuple(itk_img.shape), (16, 64, 31, 2))
nib_image = LoadImage(image_only=True, reader=NibabelReader(squeeze_non_spatial_dims=True))(Path(filename))
self.assertTupleEqual(tuple(nib_image.shape), (16, 64, 31, 2))
np.testing.assert_allclose(itk_img, nib_image, atol=1e-3, rtol=1e-3)
def test_readers(self):
inst = ITKReader()
self.assertIsInstance(inst, ITKReader)
inst = NibabelReader()
self.assertIsInstance(inst, NibabelReader)
inst = NibabelReader(as_closest_canonical=True)
self.assertIsInstance(inst, NibabelReader)
inst = NumpyReader()
self.assertIsInstance(inst, NumpyReader)
inst = NumpyReader(npz_keys="test")
self.assertIsInstance(inst, NumpyReader)
inst = PILReader()
self.assertIsInstance(inst, PILReader)
def test_register(self):
spatial_size = (32, 64, 128)
expected_shape = (128, 64, 32)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image)
itk.imwrite(itk_np_view, filename)
loader = LoadImaged(keys="img")
loader.register(ITKReader(c_order_axis_indexing=True))
result = loader({"img": filename})
self.assertTupleEqual(
tuple(result["img_meta_dict"]["spatial_shape"]),
expected_shape)
self.assertTupleEqual(result["img"].shape, spatial_size)
def test_kwargs(self):
spatial_size = (32, 64, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image)
itk.imwrite(itk_np_view, filename)
loader = LoadImage(image_only=True)
reader = ITKReader(fallback_only=False)
loader.register(reader)
result = loader(filename)
reader = ITKReader()
img = reader.read(filename, fallback_only=False)
result_raw = reader.get_data(img)
result_raw = MetaTensor.ensure_torch_and_prune_meta(*result_raw)
self.assertTupleEqual(result.shape, result_raw.shape)
def test_kwargs(self):
spatial_size = (32, 64, 128)
test_image = np.random.rand(*spatial_size)
with tempfile.TemporaryDirectory() as tempdir:
filename = os.path.join(tempdir, "test_image.nii.gz")
itk_np_view = itk.image_view_from_array(test_image)
itk.imwrite(itk_np_view, filename)
loader = LoadImage(image_only=False)
reader = ITKReader(fallback_only=False)
loader.register(reader)
result, header = loader(filename)
reader = ITKReader()
img = reader.read(filename, fallback_only=False)
result_raw, header_raw = reader.get_data(img)
np.testing.assert_allclose(header["spatial_shape"],
header_raw["spatial_shape"])
self.assertTupleEqual(result.shape, result_raw.shape)
TEST_CASE_4_1 = [ # additional parameter
{
"image_only": False,
"mmap": False
},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_5 = [{
"reader": NibabelReader(mmap=False),
"image_only": False
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_6 = [{
"reader": ITKReader(),
"image_only": True
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_7 = [{
"reader": ITKReader(),
"image_only": False
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_8 = [
{
"reader": ITKReader(),
"image_only": True
},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
"test_image3.nii.gz"
], (3, 128, 128, 128)]
TEST_CASE_4_1 = [ # additional parameter
{
"mmap": False
},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_5 = [{
"reader": NibabelReader(mmap=False)
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_6 = [{"reader": ITKReader()}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_7 = [{"reader": ITKReader()}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_8 = [
{
"reader": ITKReader()
},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_8_1 = [
{
"reader": ITKReader(channel_dim=0)
},
(3, 128, 128, 128),
]
TEST_CASE_4 = [
{"image_only": False},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_5 = [
{"reader": NibabelReader(mmap=False), "image_only": False},
["test_image.nii.gz"],
(128, 128, 128),
]
TEST_CASE_6 = [{"reader": ITKReader(), "image_only": True}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_7 = [{"reader": ITKReader(), "image_only": False}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_8 = [
{"reader": ITKReader(), "image_only": True},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_9 = [
{"reader": ITKReader(), "image_only": False},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
from monai.data import ITKReader
from monai.data.meta_tensor import MetaTensor
from monai.transforms import EnsureChannelFirst, LoadImage
TEST_CASE_1 = [{}, ["test_image.nii.gz"], None]
TEST_CASE_2 = [{}, ["test_image.nii.gz"], -1]
TEST_CASE_3 = [{},
[
"test_image.nii.gz", "test_image2.nii.gz",
"test_image3.nii.gz"
], None]
TEST_CASE_4 = [{"reader": ITKReader()}, ["test_image.nii.gz"], None]
TEST_CASE_5 = [{"reader": ITKReader()}, ["test_image.nii.gz"], -1]
TEST_CASE_6 = [{
"reader": ITKReader()
}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], None]
TEST_CASE_7 = [{
"reader": ITKReader(pixel_type=itk.UC)
}, "tests/testing_data/CT_DICOM", None]
class TestEnsureChannelFirst(unittest.TestCase):
@parameterized.expand([
TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5,
TEST_CASE_4_1 = [ # additional parameter
{
"image_only": False,
"mmap": False
},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_5 = [{
"reader": NibabelReader(mmap=False),
"image_only": False
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_6 = [{
"reader": ITKReader(),
"image_only": True
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_7 = [{
"reader": ITKReader(),
"image_only": False
}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_8 = [
{
"reader": ITKReader(),
"image_only": True
},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
{"reader": "nibabelreader"},
["test_image.mgz", "test_image2.mgz", "test_image3.mgz"],
(3, 128, 128, 128),
]
TEST_CASE_4 = [{}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], (3, 128, 128, 128)]
TEST_CASE_4_1 = [ # additional parameter
{"mmap": False},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_5 = [{"reader": NibabelReader(mmap=False)}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_6 = [{"reader": ITKReader()}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_7 = [{"reader": ITKReader()}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_8 = [
{"reader": ITKReader()},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_8_1 = [
{"reader": ITKReader(channel_dim=0)},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(384, 128, 128),
]
TEST_CASE_7 = [{"image_only": False}, ["test_image.nii.gz"], (128, 128, 128)]
TEST_CASE_8 = [
{"image_only": True},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_9 = [
{"image_only": False},
["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
(3, 128, 128, 128),
]
TEST_CASE_10 = [
{"image_only": False, "reader": ITKReader(pixel_type=itk.UC)},
"tests/testing_data/CT_DICOM",
(4, 16, 16),
]
class TestLoadImage(unittest.TestCase):
@parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])
def test_nibabel_reader(self, input_param, filenames, expected_shape):
test_image = np.random.rand(128, 128, 128)
with tempfile.TemporaryDirectory() as tempdir:
for i, name in enumerate(filenames):
filenames[i] = os.path.join(tempdir, name)
nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])
result = LoadImage(**input_param)(filenames)
