Class RandSpatialCropSamples

monai/transforms/croppad/array.py:693–773

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. It will return a list of cropped images. Note: even `rand

class RandSpatialCropSamples(Randomizable, TraceableTransform, LazyTransform, MultiSampleTrait):
    """
    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.
    It will return a list of 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:
        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)`.
        lazy: a flag to indicate whether this transform should execute lazily or not. Defaults to False.

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

    """

    backend = RandSpatialCrop.backend

    def __init__(
        self,
        roi_size: Sequence[int] | int,
        num_samples: int,
        max_roi_size: Sequence[int] | int | None = None,
        random_center: bool = True,
        random_size: bool = False,
        lazy: bool = False,
    ) -> None:
        LazyTransform.__init__(self, lazy)
        if num_samples < 1:
            raise ValueError(f"num_samples must be positive, got {num_samples}.")
        self.num_samples = num_samples
        self.cropper = RandSpatialCrop(roi_size, max_roi_size, random_center, random_size, lazy)

    def set_random_state(
        self, seed: int | None = None, state: np.random.RandomState | None = None
    ) -> RandSpatialCropSamples:
        super().set_random_state(seed, state)
        self.cropper.set_random_state(seed, state)
        return self