Source code for monai.data.nifti_writer

# 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 Optional, Sequence, Union

import numpy as np
import torch

from monai.data.utils import compute_shape_offset, to_affine_nd
from monai.networks.layers import AffineTransform
from monai.utils import GridSampleMode, GridSamplePadMode, optional_import

nib, _ = optional_import("nibabel")


[docs]def write_nifti( data: np.ndarray, file_name: str, affine: Optional[np.ndarray] = None, target_affine: Optional[np.ndarray] = None, resample: bool = True, output_spatial_shape: Optional[Sequence[int]] = None, 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: """ Write numpy data into NIfTI files to disk. This function converts data into the coordinate system defined by `target_affine` when `target_affine` is specified. If the coordinate transform between `affine` and `target_affine` could be achieved by simply transposing and flipping `data`, no resampling will happen. otherwise this function will resample `data` using the coordinate transform computed from `affine` and `target_affine`. Note that the shape of the resampled `data` may subject to some rounding errors. For example, resampling a 20x20 pixel image from pixel size (1.5, 1.5)-mm to (3.0, 3.0)-mm space will return a 10x10-pixel image. However, resampling a 20x20-pixel image from pixel size (2.0, 2.0)-mm to (3.0, 3.0)-mma space will output a 14x14-pixel image, where the image shape is rounded from 13.333x13.333 pixels. In this case `output_spatial_shape` could be specified so that this function writes image data to a designated shape. When `affine` and `target_affine` are None, the data will be saved with an identity matrix as the image affine. This function assumes the NIfTI dimension notations. Spatially it supports up to three dimensions, that is, H, HW, HWD for 1D, 2D, 3D respectively. When saving multiple time steps or multiple channels `data`, time and/or modality axes should be appended after the first three dimensions. For example, shape of 2D eight-class segmentation probabilities to be saved could be `(64, 64, 1, 8)`. Also, data in shape (64, 64, 8), (64, 64, 8, 1) will be considered as a single-channel 3D image. Args: data: input data to write to file. file_name: expected file name that saved on disk. affine: the current affine of `data`. Defaults to `np.eye(4)` target_affine: before saving the (`data`, `affine`) as a Nifti1Image, transform the data into the coordinates defined by `target_affine`. resample: whether to run resampling when the target affine could not be achieved by swapping/flipping data axes. output_spatial_shape: spatial shape of the output image. This option is used when resample = True. 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``. """ if not isinstance(data, np.ndarray): raise AssertionError("input data must be numpy array.") dtype = dtype or data.dtype sr = min(data.ndim, 3) if affine is None: affine = np.eye(4, dtype=np.float64) affine = to_affine_nd(sr, affine) if target_affine is None: target_affine = affine target_affine = to_affine_nd(sr, target_affine) if np.allclose(affine, target_affine, atol=1e-3): # no affine changes, save (data, affine) results_img = nib.Nifti1Image(data.astype(output_dtype), to_affine_nd(3, target_affine)) nib.save(results_img, file_name) return # resolve orientation start_ornt = nib.orientations.io_orientation(affine) target_ornt = nib.orientations.io_orientation(target_affine) ornt_transform = nib.orientations.ornt_transform(start_ornt, target_ornt) data_shape = data.shape data = nib.orientations.apply_orientation(data, ornt_transform) _affine = affine @ nib.orientations.inv_ornt_aff(ornt_transform, data_shape) if np.allclose(_affine, target_affine, atol=1e-3) or not resample: results_img = nib.Nifti1Image(data.astype(output_dtype), to_affine_nd(3, target_affine)) nib.save(results_img, file_name) return # need resampling affine_xform = AffineTransform( normalized=False, mode=mode, padding_mode=padding_mode, align_corners=align_corners, reverse_indexing=True ) transform = np.linalg.inv(_affine) @ target_affine if output_spatial_shape is None: output_spatial_shape, _ = compute_shape_offset(data.shape, _affine, target_affine) output_spatial_shape_ = list(output_spatial_shape) if data.ndim > 3: # multi channel, resampling each channel while len(output_spatial_shape_) < 3: output_spatial_shape_ = output_spatial_shape_ + [1] spatial_shape, channel_shape = data.shape[:3], data.shape[3:] data_np = data.reshape(list(spatial_shape) + [-1]) data_np = np.moveaxis(data_np, -1, 0) # channel first for pytorch data_torch = affine_xform( torch.as_tensor(np.ascontiguousarray(data_np).astype(dtype)).unsqueeze(0), torch.as_tensor(np.ascontiguousarray(transform).astype(dtype)), spatial_size=output_spatial_shape_[:3], ) data_np = data_torch.squeeze(0).detach().cpu().numpy() data_np = np.moveaxis(data_np, 0, -1) # channel last for nifti data_np = data_np.reshape(list(data_np.shape[:3]) + list(channel_shape)) else: # single channel image, need to expand to have batch and channel while len(output_spatial_shape_) < len(data.shape): output_spatial_shape_ = output_spatial_shape_ + [1] data_torch = affine_xform( torch.as_tensor(np.ascontiguousarray(data).astype(dtype)[None, None]), torch.as_tensor(np.ascontiguousarray(transform).astype(dtype)), spatial_size=output_spatial_shape_[: len(data.shape)], ) data_np = data_torch.squeeze(0).squeeze(0).detach().cpu().numpy() results_img = nib.Nifti1Image(data_np.astype(output_dtype), to_affine_nd(3, target_affine)) nib.save(results_img, file_name) return