# 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.
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# 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.config import DtypeLike
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: Union[Sequence[int], np.ndarray, None] = None,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: DtypeLike = np.float64,
output_dtype: DtypeLike = 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.
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 output_spatial_shape is not None else []
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