Apply the transform to `img`.
(self, img: NdarrayOrTensor, randomize: bool = True)
| 721 | self.factor = self.R.uniform(low=self.factors[0], high=self.factors[1]) |
| 722 | |
| 723 | def __call__(self, img: NdarrayOrTensor, randomize: bool = True) -> NdarrayOrTensor: |
| 724 | """ |
| 725 | Apply the transform to `img`. |
| 726 | """ |
| 727 | img = convert_to_tensor(img, track_meta=get_track_meta()) |
| 728 | if randomize: |
| 729 | self.randomize(img) |
| 730 | |
| 731 | if not self._do_transform: |
| 732 | return convert_data_type(img, dtype=self.dtype)[0] |
| 733 | |
| 734 | ret: NdarrayOrTensor |
| 735 | if self.channel_wise: |
| 736 | out = [] |
| 737 | for i, d in enumerate(img): |
| 738 | out_channel = ScaleIntensity(minv=None, maxv=None, factor=self.factor[i], dtype=self.dtype)(d) # type: ignore |
| 739 | out.append(out_channel) |
| 740 | ret = torch.stack(out) # type: ignore |
| 741 | else: |
| 742 | ret = ScaleIntensity(minv=None, maxv=None, factor=self.factor, dtype=self.dtype)(img) |
| 743 | return ret |
| 744 | |
| 745 | |
| 746 | class RandBiasField(RandomizableTransform): |
nothing calls this directly
no test coverage detected