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| from typing import Dict, Optional, Union |
|
|
| import numpy as np |
| import torch |
|
|
| from monai.data.nifti_writer import write_nifti |
| from monai.data.utils import create_file_basename |
| from monai.utils import GridSampleMode, GridSamplePadMode |
|
|
|
|
| class NiftiSaver: |
| """ |
| Save the data as NIfTI file, it can support single data content or a batch of data. |
| Typically, the data can be segmentation predictions, call `save` for single data |
| or call `save_batch` to save a batch of data together. If no meta data provided, |
| use index from 0 as the filename prefix. |
| """ |
|
|
| def __init__( |
| self, |
| output_dir: str = "./", |
| output_postfix: str = "seg", |
| output_ext: str = ".nii.gz", |
| resample: bool = True, |
| mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR, |
| padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, |
| align_corners: bool = False, |
| dtype: Optional[np.dtype] = np.float64, |
| ) -> None: |
| """ |
| Args: |
| output_dir: output image directory. |
| output_postfix: a string appended to all output file names. |
| output_ext: output file extension name. |
| resample: whether to resample before saving the data array. |
| mode: {``"bilinear"``, ``"nearest"``} |
| This option is used when ``resample = True``. |
| Interpolation mode to calculate output values. Defaults to ``"bilinear"``. |
| See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample |
| padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``} |
| This option is used when ``resample = True``. |
| Padding mode for outside grid values. Defaults to ``"border"``. |
| See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample |
| align_corners: Geometrically, we consider the pixels of the input as squares rather than points. |
| See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample |
| dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision. |
| If None, use the data type of input data. To be compatible with other modules, |
| the output data type is always ``np.float32``. |
| """ |
| self.output_dir = output_dir |
| self.output_postfix = output_postfix |
| self.output_ext = output_ext |
| self.resample = resample |
| self.mode: GridSampleMode = GridSampleMode(mode) |
| self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode) |
| self.align_corners = align_corners |
| self.dtype = dtype |
| self._data_index = 0 |
|
|
| def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None: |
| """ |
| Save data into a Nifti file. |
| The meta_data could optionally have the following keys: |
| |
| - ``'filename_or_obj'`` -- for output file name creation, corresponding to filename or object. |
| - ``'original_affine'`` -- for data orientation handling, defaulting to an identity matrix. |
| - ``'affine'`` -- for data output affine, defaulting to an identity matrix. |
| - ``'spatial_shape'`` -- for data output shape. |
| |
| When meta_data is specified, the saver will try to resample batch data from the space |
| defined by "affine" to the space defined by "original_affine". |
| |
| If meta_data is None, use the default index (starting from 0) as the filename. |
| |
| Args: |
| data: target data content that to be saved as a NIfTI format file. |
| Assuming the data shape starts with a channel dimension and followed by spatial dimensions. |
| meta_data: the meta data information corresponding to the data. |
| |
| See Also |
| :py:meth:`monai.data.nifti_writer.write_nifti` |
| """ |
| filename = meta_data["filename_or_obj"] if meta_data else str(self._data_index) |
| self._data_index += 1 |
| original_affine = meta_data.get("original_affine", None) if meta_data else None |
| affine = meta_data.get("affine", None) if meta_data else None |
| spatial_shape = meta_data.get("spatial_shape", None) if meta_data else None |
|
|
| if torch.is_tensor(data): |
| data = data.detach().cpu().numpy() |
| filename = create_file_basename(self.output_postfix, filename, self.output_dir) |
| filename = f"{filename}{self.output_ext}" |
| |
| while len(data.shape) < 4: |
| data = np.expand_dims(data, -1) |
| |
| data = np.moveaxis(data, 0, -1) |
| write_nifti( |
| data, |
| file_name=filename, |
| affine=affine, |
| target_affine=original_affine, |
| resample=self.resample, |
| output_spatial_shape=spatial_shape, |
| mode=self.mode, |
| padding_mode=self.padding_mode, |
| align_corners=self.align_corners, |
| dtype=self.dtype, |
| ) |
|
|
| def save_batch(self, batch_data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None: |
| """ |
| Save a batch of data into Nifti format files. |
| |
| Spatially it supports up to three dimensions, that is, H, HW, HWD for |
| 1D, 2D, 3D respectively (with resampling supports for 2D and 3D only). |
| |
| When saving multiple time steps or multiple channels `batch_data`, |
| time and/or modality axes should be appended after the batch dimensions. |
| For example, the shape of a batch of 2D eight-class |
| segmentation probabilities to be saved could be `(batch, 8, 64, 64)`; |
| in this case each item in the batch will be saved as (64, 64, 1, 8) |
| NIfTI file (the third dimension is reserved as a spatial dimension). |
| |
| Args: |
| batch_data: target batch data content that save into NIfTI format. |
| meta_data: every key-value in the meta_data is corresponding to a batch of data. |
| """ |
| for i, data in enumerate(batch_data): |
| self.save(data, {k: meta_data[k][i] for k in meta_data} if meta_data else None) |
|
|
