| """Metadata utilities for the binary coronary segmentation bundle.""" |
|
|
| from typing import Dict, Hashable, Mapping, Union |
|
|
| from monai.config.type_definitions import KeysCollection |
| from monai.data.meta_tensor import MetaTensor |
| from monai.transforms import MapTransform |
|
|
|
|
| class CopyMetadatad(MapTransform): |
| """ |
| Copy MetaData from one MetaTensor to another. |
| |
| Args: |
| keys: The keys that correspond to the MetaTensors to update. |
| reference_key: The key that corresponds to the MetaTensor providing the metadict. |
| allow_missing_keys: Whether to allow missing keys. |
| """ |
|
|
| def __init__( |
| self, |
| keys: KeysCollection, |
| reference_key: KeysCollection, |
| allow_missing_keys: bool = False, |
| ) -> None: |
| super().__init__(keys, allow_missing_keys) |
| self.reference_key = reference_key |
|
|
| def __call__(self, data: Mapping) -> Dict[Hashable, Union[Dict, MetaTensor]]: |
| d = dict(data) |
| ref = d[self.reference_key] |
| for key in self.key_iterator(d): |
| if isinstance(d[key], MetaTensor): |
| d[key].meta = ref.meta.copy() |
| if hasattr(ref, "applied_operations"): |
| d[key].applied_operations = ref.applied_operations.copy() |
| return d |
|
|
