(self, data: Mapping[Hashable, np.ndarray])
| 285 | return signal |
| 286 | |
| 287 | def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.ndarray]: |
| 288 | d: dict = dict(data) |
| 289 | for key in self.key_iterator(d): |
| 290 | if key == "image": |
| 291 | image = d[key] |
| 292 | tmp_image = image[0 : 0 + self.number_intensity_ch, ...] |
| 293 | guidance = d[self.guidance] |
| 294 | for key_label in guidance.keys(): |
| 295 | # Getting signal based on guidance |
| 296 | signal = self._get_signal(image, guidance[key_label]) |
| 297 | tmp_image = np.concatenate([tmp_image, signal], axis=0) |
| 298 | if isinstance(d[key], MetaTensor): |
| 299 | d[key].array = tmp_image |
| 300 | else: |
| 301 | d[key] = tmp_image |
| 302 | return d |
| 303 | else: |
| 304 | print("This transform only applies to image key") |
| 305 | return d |
| 306 | |
| 307 | |
| 308 | class FindAllValidSlicesDeepEditd(MapTransform): |
nothing calls this directly
no test coverage detected