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Add crop_to_bbox function to restore nnUNet compatibility #3

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Add crop_to_bbox function to restore nnUNet compatibility #3

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9 changes: 8 additions & 1 deletion acvl_utils/cropping_and_padding/bounding_boxes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,11 +44,18 @@ def bounding_box_to_slice(bounding_box: List[List[int]]):
return tuple([slice(*i) for i in bounding_box])


def crop_to_bbox(array: np.ndarray, bounding_box: List[List[int]]):
assert len(bounding_box) == len(array.shape), f"Dimensionality of bbox and array do not match. bbox has length " \
f"{len(bounding_box)} while array has dimension {len(array.shape)}"
slicer = bounding_box_to_slice(bounding_box)
return array[slicer]


def get_bbox_from_mask(mask: np.ndarray) -> List[List[int]]:
"""
this implementation uses less ram than the np.where one and is faster as well IF we expect the bounding box to
be close to the image size. If it's not it's likely slower!

bbox is returned so that you can just do slice(minzidx, maxzidx) to retrieve the object of interest with nothing cut off

:param mask:
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