Source code for

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
A collection of "vanilla" transforms for IO functions

from typing import Dict, List, Optional, Sequence, Union

import numpy as np
import torch

from monai.config import DtypeLike
from import ImageReader, ITKReader, NibabelReader, NumpyReader, PILReader
from import NiftiSaver
from import PNGSaver
from monai.transforms.transform import Transform
from monai.utils import GridSampleMode, GridSamplePadMode
from monai.utils import ImageMetaKey as Key
from monai.utils import InterpolateMode, ensure_tuple, optional_import

nib, _ = optional_import("nibabel")
Image, _ = optional_import("PIL.Image")

__all__ = ["LoadImage", "SaveImage"]

def switch_endianness(data, old, new):
    If any numpy arrays have `old` (e.g., ">"),
    replace with `new` (e.g., "<").
    if isinstance(data, np.ndarray):
        if data.dtype.byteorder == old:
            data = data.newbyteorder(new)
    elif isinstance(data, tuple):
        data = tuple(switch_endianness(x, old, new) for x in data)
    elif isinstance(data, list):
        data = [switch_endianness(x, old, new) for x in data]
    elif isinstance(data, dict):
        data = {k: switch_endianness(v, old, new) for k, v in data.items()}
    elif isinstance(data, (bool, str, float, int, type(None))):
        raise AssertionError(f"Unknown type: {type(data).__name__}")
    return data

[docs]class LoadImage(Transform): """ Load image file or files from provided path based on reader. Automatically choose readers based on the supported suffixes and in below order: - User specified reader at runtime when call this loader. - Registered readers from the latest to the first in list. - Default readers: (nii, nii.gz -> NibabelReader), (png, jpg, bmp -> PILReader), (npz, npy -> NumpyReader), (others -> ITKReader). """ def __init__( self, reader: Optional[Union[ImageReader, str]] = None, image_only: bool = False, dtype: DtypeLike = np.float32, *args, **kwargs, ) -> None: """ Args: reader: register reader to load image file and meta data, if None, still can register readers at runtime or use the default readers. If a string of reader name provided, will construct a reader object with the `*args` and `**kwargs` parameters, supported reader name: "NibabelReader", "PILReader", "ITKReader", "NumpyReader". image_only: if True return only the image volume, otherwise return image data array and header dict. dtype: if not None convert the loaded image to this data type. args: additional parameters for reader if providing a reader name. kwargs: additional parameters for reader if providing a reader name. Note: The transform returns image data array if `image_only` is True, or a tuple of two elements containing the data array, and the meta data in a dict format otherwise. """ # set predefined readers as default self.readers: List[ImageReader] = [ITKReader(), NumpyReader(), PILReader(), NibabelReader()] if reader is not None: if isinstance(reader, str): supported_readers = { "nibabelreader": NibabelReader, "pilreader": PILReader, "itkreader": ITKReader, "numpyreader": NumpyReader, } reader = reader.lower() if reader not in supported_readers: raise ValueError(f"unsupported reader type: {reader}, available options: {supported_readers}.") self.register(supported_readers[reader](*args, **kwargs)) else: self.register(reader) self.image_only = image_only self.dtype = dtype
[docs] def register(self, reader: ImageReader) -> List[ImageReader]: """ Register image reader to load image file and meta data, latest registered reader has higher priority. Return all the registered image readers. Args: reader: registered reader to load image file and meta data based on suffix, if all registered readers can't match suffix at runtime, use the default readers. """ if not isinstance(reader, ImageReader): raise ValueError(f"reader must be ImageReader object, but got {type(reader)}.") self.readers.append(reader) return self.readers
[docs] def __call__( self, filename: Union[Sequence[str], str], reader: Optional[ImageReader] = None, ): """ Args: filename: path file or file-like object or a list of files. will save the filename to meta_data with key `filename_or_obj`. if provided a list of files, use the filename of first file. reader: runtime reader to load image file and meta data. """ if reader is None or not reader.verify_suffix(filename): for r in reversed(self.readers): if r.verify_suffix(filename): reader = r break if reader is None: raise RuntimeError( f"can not find suitable reader for this file: {filename}. \ Please install dependency libraries: (nii, nii.gz) -> Nibabel, (png, jpg, bmp) -> PIL, \ (npz, npy) -> Numpy, others -> ITK. Refer to the installation instruction: \" ) img = img_array, meta_data = reader.get_data(img) img_array = img_array.astype(self.dtype) if self.image_only: return img_array meta_data[Key.FILENAME_OR_OBJ] = ensure_tuple(filename)[0] # make sure all elements in metadata are little endian meta_data = switch_endianness(meta_data, ">", "<") return img_array, meta_data
[docs]class SaveImage(Transform): """ Save transformed data into files, support NIfTI and PNG formats. It can work for both numpy array and PyTorch Tensor in both pre-transform chain and post transform chain. NB: image should include channel dimension: [B],C,H,W,[D]. Args: output_dir: output image directory. output_postfix: a string appended to all output file names, default to `trans`. output_ext: output file extension name, available extensions: `.nii.gz`, `.nii`, `.png`. resample: whether to resample before saving the data array. if saving PNG format image, based on the `spatial_shape` from metadata. if saving NIfTI format image, based on the `original_affine` from metadata. mode: This option is used when ``resample = True``. Defaults to ``"nearest"``. - NIfTI files {``"bilinear"``, ``"nearest"``} Interpolation mode to calculate output values. See also: - PNG files {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``} The interpolation mode. See also: padding_mode: This option is used when ``resample = True``. Defaults to ``"border"``. - NIfTI files {``"zeros"``, ``"border"``, ``"reflection"``} Padding mode for outside grid values. See also: - PNG files This option is ignored. scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling [0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling. it's used for PNG format only. dtype: data type during 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``. it's used for NIfTI format only. output_dtype: data type for saving data. Defaults to ``np.float32``. it's used for NIfTI format only. save_batch: whether the import image is a batch data, default to `False`. usually pre-transforms run for channel first data, while post-transforms run for batch data. squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false, image will always be saved as (H,W,D,C). it's used for NIfTI format only. data_root_dir: if not empty, it specifies the beginning parts of the input file's absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from `data_root_dir` to preserve folder structure when saving in case there are files in different folders with the same file names. for example: input_file_name: /foo/bar/test1/image.nii, output_postfix: seg output_ext: nii.gz output_dir: /output, data_root_dir: /foo/bar, output will be: /output/test1/image/image_seg.nii.gz """ def __init__( self, output_dir: str = "./", output_postfix: str = "trans", output_ext: str = ".nii.gz", resample: bool = True, mode: Union[GridSampleMode, InterpolateMode, str] = "nearest", padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER, scale: Optional[int] = None, dtype: DtypeLike = np.float64, output_dtype: DtypeLike = np.float32, save_batch: bool = False, squeeze_end_dims: bool = True, data_root_dir: str = "", ) -> None: self.saver: Union[NiftiSaver, PNGSaver] if output_ext in (".nii.gz", ".nii"): self.saver = NiftiSaver( output_dir=output_dir, output_postfix=output_postfix, output_ext=output_ext, resample=resample, mode=GridSampleMode(mode), padding_mode=padding_mode, dtype=dtype, output_dtype=output_dtype, squeeze_end_dims=squeeze_end_dims, data_root_dir=data_root_dir, ) elif output_ext == ".png": self.saver = PNGSaver( output_dir=output_dir, output_postfix=output_postfix, output_ext=output_ext, resample=resample, mode=InterpolateMode(mode), scale=scale, data_root_dir=data_root_dir, ) else: raise ValueError(f"unsupported output extension: {output_ext}.") self.save_batch = save_batch
[docs] def __call__(self, img: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None): """ Args: img: target data content that save into file. meta_data: key-value pairs of meta_data corresponding to the data. """ if self.save_batch: self.saver.save_batch(img, meta_data) else:, meta_data)