Source code for monai.data.nifti_saver

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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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


[docs]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, output_dtype: Optional[np.dtype] = np.float32, ) -> 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``. output_dtype: data type for saving data. Defaults to ``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.output_dtype = output_dtype self._data_index = 0
[docs] 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}" # change data shape to be (channel, h, w, d) while len(data.shape) < 4: data = np.expand_dims(data, -1) # change data to "channel last" format and write to nifti format file 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, output_dtype=self.output_dtype, )
[docs] 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): # save a batch of files self.save(data, {k: meta_data[k][i] for k in meta_data} if meta_data else None)