Class RandSpatialCropSamplesd

monai/transforms/croppad/dictionary.py:747–831

Dictionary-based version :py:class:`monai.transforms.RandSpatialCropSamples`. Crop image with random size or specific size ROI to generate a list of N samples. It can crop at a random position as center or at the image center. And allows to set the minimum size to limit the randomly

class RandSpatialCropSamplesd(Randomizable, MapTransform, LazyTransform, MultiSampleTrait):
    """
    Dictionary-based version :py:class:`monai.transforms.RandSpatialCropSamples`.
    Crop image with random size or specific size ROI to generate a list of N samples.
    It can crop at a random position as center or at the image center. And allows to set
    the minimum size to limit the randomly generated ROI. Suppose all the expected fields
    specified by `keys` have same shape, and add `patch_index` to the corresponding metadata.
    It will return a list of dictionaries for all the cropped images.

    Note: even `random_size=False`, if a dimension of the expected ROI size is larger than the input image size,
    will not crop that dimension. So the cropped result may be smaller than the expected ROI, and the cropped
    results of several images may not have exactly the same shape.

    This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
    for more information.

    Args:
        keys: keys of the corresponding items to be transformed.
            See also: monai.transforms.MapTransform
        roi_size: if `random_size` is True, it specifies the minimum crop region.
            if `random_size` is False, it specifies the expected ROI size to crop. e.g. [224, 224, 128]
            if a dimension of ROI size is larger than image size, will not crop that dimension of the image.
            If its components have non-positive values, the corresponding size of input image will be used.
            for example: if the spatial size of input data is [40, 40, 40] and `roi_size=[32, 64, -1]`,
            the spatial size of output data will be [32, 40, 40].
        num_samples: number of samples (crop regions) to take in the returned list.
        max_roi_size: if `random_size` is True and `roi_size` specifies the min crop region size, `max_roi_size`
            can specify the max crop region size. if None, defaults to the input image size.
            if its components have non-positive values, the corresponding size of input image will be used.
        random_center: crop at random position as center or the image center.
        random_size: crop with random size or specific size ROI.
            The actual size is sampled from `randint(roi_size, img_size)`.
        allow_missing_keys: don&#x27;t raise exception if key is missing.
        lazy: a flag to indicate whether this transform should execute lazily or not. Defaults to False.

    Raises:
        ValueError: When ``num_samples`` is nonpositive.

    """

    backend = RandSpatialCropSamples.backend

    def __init__(
        self,
        keys: KeysCollection,
        roi_size: Sequence[int] | int,
        num_samples: int,
        max_roi_size: Sequence[int] | int | None = None,
        random_center: bool = True,
        random_size: bool = False,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    ) -> None:
        MapTransform.__init__(self, keys, allow_missing_keys)
        LazyTransform.__init__(self, lazy)
        self.cropper = RandSpatialCropSamples(
            roi_size, num_samples, max_roi_size, random_center, random_size, lazy=lazy
        )