Repository: Project-MONAI/MONAI
File: monai/transforms/utils.py (Python)

Code before:
    input_array: NdarrayOrTensor, name: None | str = None, raise_error: bool = False
) -> None:
    """
    Check whether the input array has pending operations, raise an error or warn when it has.

    Args:
        input_array: input array to be checked.
        name: an optional name to be included in the error message.
        raise_error: whether to raise an error, default to False, a warning message will be issued instead.
    """
    if isinstance(input_array, monai.data.MetaTensor) and input_array.pending_operations:
        msg = (
            "The input image is a MetaTensor and has pending operations,\n"
            f"but the function {name or ''} assumes non-lazy input, result may be incorrect."
        )
        if raise_error:
            raise ValueError(msg)
        warnings.warn(msg)


def map_and_generate_sampling_centers(
    label: NdarrayOrTensor,
    spatial_size: Sequence[int] | int,
    num_samples: int,
    label_spatial_shape: Sequence[int],
    indices: Sequence[NdarrayOrTensor],
    num_classes: int | None = None,
    image: NdarrayOrTensor | None = None,
    image_threshold: float = 0.0,
    max_samples_per_class: int | None = None,
    ratios: list[float | int] | None = None,
    rand_state: np.random.RandomState | None = None,
    allow_smaller: bool = False,
    warn: bool = True,
) -> tuple[tuple]:
    """
    Combine "map_classes_to_indices" and "generate_label_classes_crop_centers" functions, return crop center coordinates.
    Call "map_classes_to_indices" if indices is None, pull the shape from the label or image (if label is None), and then call "generate_label_classes_crop_centers".

    Args:
        label: use the label data to get the indices of every class.
        spatial_size: spatial size of the ROIs to be sampled.
        num_samples: total sample centers to be generated.
        label_spatial_shape: spatial shape of the original label data to unravel selected centers.
        indices: sequence of pre-computed foreground indices of every class in 1 dimension.
        num_classes: number of classes for argmax label, not necessary for One-Hot label.
        image: if image is not None, only return the indices of every class that are within the valid
            region of the image (``image > image_threshold``).
        image_threshold: if enabled `image`, use ``image > image_threshold`` to
            determine the valid image content area and select class indices only in this area.
        max_samples_per_class: maximum length of indices in each class to reduce memory consumption.

Review comment (suggestion):
Please consider removing the input `indices: Sequence[NdarrayOrTensor],` or modifying it to `indices: Sequence[NdarrayOrTensor] | None = None,` because the function` map_classes_to_indices `used in the program takes the input` label` and returns` indices`.

Code after:
    input_array: NdarrayOrTensor, name: None | str = None, raise_error: bool = False
) -> None:
    """
    Check whether the input array has pending operations, raise an error or warn when it has.

    Args:
        input_array: input array to be checked.
        name: an optional name to be included in the error message.
        raise_error: whether to raise an error, default to False, a warning message will be issued instead.
    """
    if isinstance(input_array, monai.data.MetaTensor) and input_array.pending_operations:
        msg = (
            "The input image is a MetaTensor and has pending operations,\n"
            f"but the function {name or ''} assumes non-lazy input, result may be incorrect."
        )
        if raise_error:
            raise ValueError(msg)
        warnings.warn(msg)


def map_and_generate_sampling_centers(
    label: NdarrayOrTensor,
    spatial_size: Sequence[int] | int,
    num_samples: int,
    label_spatial_shape: Sequence[int] | None = None,
    num_classes: int | None = None,
    image: NdarrayOrTensor | None = None,
    image_threshold: float = 0.0,
    max_samples_per_class: int | None = None,
    ratios: list[float | int] | None = None,
    rand_state: np.random.RandomState | None = None,
    allow_smaller: bool = False,
    warn: bool = True,
) -> tuple[tuple]:
    """
    Combine "map_classes_to_indices" and "generate_label_classes_crop_centers" functions, return crop center coordinates.
    This calls `map_classes_to_indices` to get indices from `label`, gets the shape from `label_spatial_shape`
    is given otherwise from the labels, calls `generate_label_classes_crop_centers`, and returns its results.

    Args:
        label: use the label data to get the indices of every class.
        spatial_size: spatial size of the ROIs to be sampled.
        num_samples: total sample centers to be generated.
        label_spatial_shape: spatial shape of the original label data to unravel selected centers.
        indices: sequence of pre-computed foreground indices of every class in 1 dimension.
        num_classes: number of classes for argmax label, not necessary for One-Hot label.
        image: if image is not None, only return the indices of every class that are within the valid
            region of the image (``image > image_threshold``).
        image_threshold: if enabled `image`, use ``image > image_threshold`` to
            determine the valid image content area and select class indices only in this area.
        max_samples_per_class: maximum length of indices in each class to reduce memory consumption.