Method __call__

monai/transforms/croppad/array.py:1353–1394

Args: img: input data to crop samples from based on the ratios of every class, assumes `img` is a channel-first array. label: the label image that is used for finding indices of every class, if None, use `self.label`. image: optional image

(
        self,
        img: torch.Tensor,
        label: torch.Tensor | None = None,
        image: torch.Tensor | None = None,
        indices: list[NdarrayOrTensor] | None = None,
        randomize: bool = True,
        lazy: bool | None = None,
    )

        return False

    def __call__(
        self,
        img: torch.Tensor,
        label: torch.Tensor | None = None,
        image: torch.Tensor | None = None,
        indices: list[NdarrayOrTensor] | None = None,
        randomize: bool = True,
        lazy: bool | None = None,
    ) -> list[torch.Tensor]:
        """
        Args:
            img: input data to crop samples from based on the ratios of every class, assumes `img` is a
                channel-first array.
            label: the label image that is used for finding indices of every class, if None, use `self.label`.
            image: optional image data to help select valid area, can be same as `img` or another image array.
                use ``image > image_threshold`` to select the centers only in valid region. if None, use `self.image`.
            indices: list of indices for every class in the image, used to randomly select crop centers.
            randomize: whether to execute the random operations, default to `True`.
            lazy: a flag to override the lazy behaviour for this call, if set. Defaults to None.
        """
        if image is None:
            image = self.image
        if randomize:
            if label is None:
                label = self.label
            self.randomize(label, indices, image)
        results: list[torch.Tensor] = []
        if self.centers is not None:
            img_shape = img.peek_pending_shape() if isinstance(img, MetaTensor) else img.shape[1:]
            roi_size = fall_back_tuple(self.spatial_size, default=img_shape)
            lazy_ = self.lazy if lazy is None else lazy
            for i, center in enumerate(self.centers):
                cropper = SpatialCrop(roi_center=tuple(center), roi_size=roi_size, lazy=lazy_)
                cropped = cropper(img)
                if get_track_meta():
                    ret_: MetaTensor = cropped  # type: ignore
                    ret_.meta[Key.PATCH_INDEX] = i
                    ret_.meta["crop_center"] = center
                    self.push_transform(ret_, replace=True, lazy=lazy_)
                results.append(cropped)

        return results


class ResizeWithPadOrCrop(InvertibleTransform, LazyTransform):