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Allow ApplyTransformToPointsd receive a sequence of refer keys #8063

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merged 14 commits into from
Sep 4, 2024
Merged

Allow ApplyTransformToPointsd receive a sequence of refer keys #8063

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0321dc8
enhance ApplyTransformToPointsd
KumoLiu Sep 2, 2024
324d387
Merge remote-tracking branch 'origin/dev' into enhance-applypoints
KumoLiu Sep 2, 2024
dc366e8
Update monai/transforms/utility/dictionary.py
KumoLiu Sep 3, 2024
5713d5a
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 3, 2024
16e2fd2
Update monai/transforms/utility/dictionary.py
KumoLiu Sep 3, 2024
253bec3
add more tests
KumoLiu Sep 3, 2024
96ecc1e
minor fix
KumoLiu Sep 3, 2024
59089b0
address comments
KumoLiu Sep 3, 2024
1a6f9a2
fix format
KumoLiu Sep 3, 2024
3f30d2e
Merge branch 'dev' into enhance-applypoints
KumoLiu Sep 3, 2024
f471cf3
enhance readability
KumoLiu Sep 3, 2024
9521114
Merge branch 'enhance-applypoints' of https://github.com/KumoLiu/MONA…
KumoLiu Sep 3, 2024
850c082
fix mypy
KumoLiu Sep 4, 2024
c71ee0a
Merge branch 'dev' into enhance-applypoints
KumoLiu Sep 4, 2024
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30 changes: 17 additions & 13 deletions monai/transforms/utility/dictionary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1758,8 +1758,9 @@ class ApplyTransformToPointsd(MapTransform, InvertibleTransform):
Args:
keys: keys of the corresponding items to be transformed.
See also: monai.transforms.MapTransform
refer_key: The key of the reference item used for transformation.
It can directly refer to an affine or an image from which the affine can be derived.
refer_keys: The key of the reference item used for transformation.
It can directly refer to an affine or an image from which the affine can be derived. It can also be a
sequence of keys, in which case each refers to the affine applied to the matching points in `keys`.
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dtype: The desired data type for the output.
affine: A 3x3 or 4x4 affine transformation matrix applied to points. This matrix typically originates
from the image. For 2D points, a 3x3 matrix can be provided, avoiding the need to add an unnecessary
Expand All @@ -1782,31 +1783,34 @@ class ApplyTransformToPointsd(MapTransform, InvertibleTransform):
def __init__(
self,
keys: KeysCollection,
refer_key: str | None = None,
refer_keys: KeysCollection | None = None,
dtype: DtypeLike | torch.dtype = torch.float64,
affine: torch.Tensor | None = None,
invert_affine: bool = True,
affine_lps_to_ras: bool = False,
allow_missing_keys: bool = False,
):
MapTransform.__init__(self, keys, allow_missing_keys)
self.refer_key = refer_key
self.refer_keys = ensure_tuple_rep(None, len(self.keys)) if refer_keys is None else ensure_tuple(refer_keys)
if len(self.keys) != len(self.refer_keys):
raise ValueError("refer_keys should have the same length as keys.")
self.converter = ApplyTransformToPoints(
dtype=dtype, affine=affine, invert_affine=invert_affine, affine_lps_to_ras=affine_lps_to_ras
)

def __call__(self, data: Mapping[Hashable, torch.Tensor]):
d = dict(data)
if self.refer_key is not None:
if self.refer_key in d:
refer_data = d[self.refer_key]
else:
raise KeyError(f"The refer_key '{self.refer_key}' is not found in the data.")
else:
refer_data = None
affine = getattr(refer_data, "affine", refer_data)
for key in self.key_iterator(d):
for key, refer_key in self.key_iterator(d, self.refer_keys):
coords = d[key]
affine = None # represents using affine given in constructor
if refer_key is not None:
if refer_key in d:
refer_data = d[refer_key]
else:
raise KeyError(f"The refer_key '{refer_key}' is not found in the data.")

# use the "affine" member of refer_data, or refer_data itself, as the affine matrix
affine = getattr(refer_data, "affine", refer_data)
d[key] = self.converter(coords, affine)
return d

Expand Down
136 changes: 94 additions & 42 deletions tests/test_apply_transform_to_pointsd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,72 +30,90 @@
POINT_3D_WORLD = torch.tensor([[[2, 4, 6], [8, 10, 12]], [[14, 16, 18], [20, 22, 24]]])
POINT_3D_IMAGE = torch.tensor([[[-8, 8, 6], [-2, 14, 12]], [[4, 20, 18], [10, 26, 24]]])
POINT_3D_IMAGE_RAS = torch.tensor([[[-12, 0, 6], [-18, -6, 12]], [[-24, -12, 18], [-30, -18, 24]]])
AFFINE_1 = torch.tensor([[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
AFFINE_2 = torch.tensor([[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]])

TEST_CASES = [
[MetaTensor(DATA_2D, affine=AFFINE_1), POINT_2D_WORLD, None, True, False, POINT_2D_IMAGE], # use image affine
[None, MetaTensor(POINT_2D_IMAGE, affine=AFFINE_1), None, False, False, POINT_2D_WORLD], # use point affine
[None, MetaTensor(POINT_2D_IMAGE, affine=AFFINE_1), AFFINE_1, False, False, POINT_2D_WORLD], # use input affine
[None, POINT_2D_WORLD, AFFINE_1, True, False, POINT_2D_IMAGE], # use input affine
[
MetaTensor(DATA_2D, affine=torch.tensor([[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])),
MetaTensor(DATA_2D, affine=AFFINE_1),
POINT_2D_WORLD,
None,
True,
False,
POINT_2D_IMAGE,
],
True,
POINT_2D_IMAGE_RAS,
], # test affine_lps_to_ras
[MetaTensor(DATA_3D, affine=AFFINE_2), POINT_3D_WORLD, None, True, False, POINT_3D_IMAGE],
["affine", POINT_3D_WORLD, None, True, False, POINT_3D_IMAGE], # use refer_data itself
[
None,
MetaTensor(POINT_2D_IMAGE, affine=torch.tensor([[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])),
MetaTensor(DATA_3D, affine=AFFINE_2),
MetaTensor(POINT_3D_IMAGE, affine=AFFINE_2),
None,
False,
False,
POINT_2D_WORLD,
POINT_3D_WORLD,
],
[MetaTensor(DATA_3D, affine=AFFINE_2), POINT_3D_WORLD, None, True, True, POINT_3D_IMAGE_RAS],
[MetaTensor(DATA_3D, affine=AFFINE_2), POINT_3D_WORLD, None, True, True, POINT_3D_IMAGE_RAS],
]
TEST_CASES_SEQUENCE = [
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[
(MetaTensor(DATA_2D, affine=AFFINE_1), MetaTensor(DATA_3D, affine=AFFINE_2)),
[POINT_2D_WORLD, POINT_3D_WORLD],
None,
MetaTensor(POINT_2D_IMAGE, affine=torch.tensor([[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])),
torch.tensor([[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]),
False,
True,
False,
POINT_2D_WORLD,
],
["image_1", "image_2"],
[POINT_2D_IMAGE, POINT_3D_IMAGE],
], # use image affine
[
MetaTensor(DATA_2D, affine=torch.tensor([[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])),
POINT_2D_WORLD,
(MetaTensor(DATA_2D, affine=AFFINE_1), MetaTensor(DATA_3D, affine=AFFINE_2)),
[POINT_2D_WORLD, POINT_3D_WORLD],
None,
True,
True,
POINT_2D_IMAGE_RAS,
],
["image_1", "image_2"],
[POINT_2D_IMAGE_RAS, POINT_3D_IMAGE_RAS],
], # test affine_lps_to_ras
[
MetaTensor(DATA_3D, affine=torch.tensor([[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]])),
POINT_3D_WORLD,
(None, None),
[MetaTensor(POINT_2D_IMAGE, affine=AFFINE_1), MetaTensor(POINT_3D_IMAGE, affine=AFFINE_2)],
None,
False,
False,
None,
[POINT_2D_WORLD, POINT_3D_WORLD],
], # use point affine
[
(None, None),
[POINT_2D_WORLD, POINT_2D_WORLD],
AFFINE_1,
True,
False,
POINT_3D_IMAGE,
],
["affine", POINT_3D_WORLD, None, True, False, POINT_3D_IMAGE],
None,
[POINT_2D_IMAGE, POINT_2D_IMAGE],
], # use input affine
[
MetaTensor(DATA_3D, affine=torch.tensor([[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]])),
MetaTensor(POINT_3D_IMAGE, affine=torch.tensor([[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]])),
(MetaTensor(DATA_2D, affine=AFFINE_1), MetaTensor(DATA_3D, affine=AFFINE_2)),
[MetaTensor(POINT_2D_IMAGE, affine=AFFINE_1), MetaTensor(POINT_3D_IMAGE, affine=AFFINE_2)],
None,
False,
False,
POINT_3D_WORLD,
],
[
MetaTensor(DATA_3D, affine=torch.tensor([[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]])),
POINT_3D_WORLD,
None,
True,
True,
POINT_3D_IMAGE_RAS,
["image_1", "image_2"],
[POINT_2D_WORLD, POINT_3D_WORLD],
],
]

TEST_CASES_WRONG = [
[POINT_2D_WORLD, True, None],
[POINT_2D_WORLD.unsqueeze(0), False, None],
[POINT_3D_WORLD[..., 0:1], False, None],
[POINT_3D_WORLD, False, torch.tensor([[[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]]])],
[POINT_2D_WORLD, True, None, None],
[POINT_2D_WORLD.unsqueeze(0), False, None, None],
[POINT_3D_WORLD[..., 0:1], False, None, None],
[POINT_3D_WORLD, False, torch.tensor([[[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]]]), None],
[POINT_3D_WORLD, False, None, "image"],
[POINT_3D_WORLD, False, None, []],
]


Expand All @@ -107,10 +125,10 @@ def test_transform_coordinates(self, image, points, affine, invert_affine, affin
"point": points,
"affine": torch.tensor([[1, 0, 0, 10], [0, 1, 0, -4], [0, 0, 1, 0], [0, 0, 0, 1]]),
}
refer_key = "image" if (image is not None and image != "affine") else image
refer_keys = "image" if (image is not None and image != "affine") else image
transform = ApplyTransformToPointsd(
keys="point",
refer_key=refer_key,
refer_keys=refer_keys,
dtype=torch.int64,
affine=affine,
invert_affine=invert_affine,
Expand All @@ -122,11 +140,45 @@ def test_transform_coordinates(self, image, points, affine, invert_affine, affin
invert_out = transform.inverse(output)
self.assertTrue(torch.allclose(invert_out["point"], points))

@parameterized.expand(TEST_CASES_SEQUENCE)
def test_transform_coordinates_sequences(
self, image, points, affine, invert_affine, affine_lps_to_ras, refer_keys, expected_output
):
data = {"image_1": image[0], "image_2": image[1], "point_1": points[0], "point_2": points[1]}
keys = ["point_1", "point_2"]
transform = ApplyTransformToPointsd(
keys=keys,
refer_keys=refer_keys,
dtype=torch.int64,
affine=affine,
invert_affine=invert_affine,
affine_lps_to_ras=affine_lps_to_ras,
)
output = transform(data)

self.assertTrue(torch.allclose(output["point_1"], expected_output[0]))
self.assertTrue(torch.allclose(output["point_2"], expected_output[1]))
invert_out = transform.inverse(output)
self.assertTrue(torch.allclose(invert_out["point_1"], points[0]))

@parameterized.expand(TEST_CASES_WRONG)
def test_wrong_input(self, input, invert_affine, affine):
transform = ApplyTransformToPointsd(keys="point", dtype=torch.int64, invert_affine=invert_affine, affine=affine)
with self.assertRaises(ValueError):
transform({"point": input})
def test_wrong_input(self, input, invert_affine, affine, refer_keys):
if refer_keys == []:
with self.assertRaises(ValueError):
ApplyTransformToPointsd(
keys="point", dtype=torch.int64, invert_affine=invert_affine, affine=affine, refer_keys=refer_keys
)
else:
transform = ApplyTransformToPointsd(
keys="point", dtype=torch.int64, invert_affine=invert_affine, affine=affine, refer_keys=refer_keys
)
data = {"point": input}
if refer_keys == "image":
with self.assertRaises(KeyError):
transform(data)
else:
with self.assertRaises(ValueError):
transform(data)


if __name__ == "__main__":
Expand Down
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