| """Segmentation related io (file cannot be named as io). |
| |
| https://stackoverflow.com/questions/26569828/pycharm-py-initialize-cant-initialize-sys-standard-streams |
| """ |
| from __future__ import annotations |
|
|
| from pathlib import Path |
|
|
| import numpy as np |
| import SimpleITK as sitk |
|
|
| from imgx.datasets.save import save_image |
|
|
|
|
| def save_segmentation_prediction( |
| label_pred: np.ndarray, |
| uids: list[str], |
| out_dir: Path | None, |
| tfds_dir: Path, |
| reference_suffix: str = "mask_preprocessed", |
| output_suffix: str = "mask_pred", |
| ) -> None: |
| """Save segmentation predictions. |
| |
| Args: |
| label_pred: (num_samples, ...), the values are integers. |
| uids: (num_samples,). |
| out_dir: output directory. |
| tfds_dir: directory saving preprocessed images and labels. |
| reference_suffix: suffix of reference image. |
| output_suffix: suffix of output image. |
| """ |
| if out_dir is None: |
| return |
| if label_pred.ndim == 3 and np.max(label_pred) > 1: |
| raise ValueError( |
| f"Prediction values should be 0 or 1, but " |
| f"max value is {np.max(label_pred)}. " |
| f"Multi-class segmentation for 2D images are not supported." |
| ) |
| if label_pred.ndim not in [3, 4]: |
| raise ValueError( |
| f"Prediction should be 3D or 4D with num_samples axis, but {label_pred.ndim}D is given." |
| ) |
| file_suffix = "nii.gz" if label_pred.ndim == 4 else "png" |
| out_dir.mkdir(parents=True, exist_ok=True) |
| for i, uid in enumerate(uids): |
| reference_image = sitk.ReadImage(tfds_dir / f"{uid}_{reference_suffix}.{file_suffix}") |
| save_image( |
| image=label_pred[i, ...], |
| reference_image=reference_image, |
| out_path=out_dir / f"{uid}_{output_suffix}.{file_suffix}", |
| dtype=np.uint8, |
| ) |
|
|
