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# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
import importlib.resources
import os
import PIL.Image
from torchvision.transforms.functional import to_tensor
from ...utils.rc import load_rc
from ..datamodule import CachingDataModule
from ..split import JSONDatabaseSplit
from ..typing import DatabaseSplit
from ..typing import RawDataLoader as _BaseRawDataLoader
from ..typing import Sample
class RawDataLoader(_BaseRawDataLoader):
"""A specialized raw-data-loader for the NIH CXR-14 dataset.
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Attributes
----------
datadir
This variable contains the base directory where the database raw data
is stored.
idiap_file_organisation
This variable will be ``True``, if the user has set the configuration
parameter ``nih_cxr14.idiap_file_organisation`` in the global
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configuration file. It will cause internal loader to search for files
in a slightly different folder structure, that was adapted to Idiap's
requirements (number of files per folder to be less than 10k).
"""
datadir: str
idiap_file_organisation: bool
def __init__(self):
rc = load_rc()
self.datadir = rc.get("datadir.nih_cxr14", os.path.realpath(os.curdir))
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self.idiap_file_organisation = rc.get(
"nih_cxr14.idiap_folder_structure", False
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)
def sample(self, sample: tuple[str, list[int]]) -> Sample:
"""Loads a single image sample from the disk.
Parameters
----------
sample:
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing the
sample label.
Returns
-------
sample
The sample representation
"""
file_path = sample[0] # default
if self.idiap_file_organisation:
# for folder lookup efficiency, data is split into subfolders
# each original file is on the subfolder `f[:5]/f`, where f
# is the original file basename
basename = os.path.basename(sample[0])
file_path = os.path.join(
os.path.dirname(sample[0]),
basename[:5],
basename,
)
# N.B.: NIH CXR-14 images are encoded as color PNGs
image = PIL.Image.open(os.path.join(self.datadir, file_path))
tensor = to_tensor(image)
# use the code below to view generated images
# from torchvision.transforms.functional import to_pil_image
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
def label(self, sample: tuple[str, list[int]]) -> list[int]:
"""Loads a single image sample label from the disk.
Parameters
----------
sample:
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing the
sample label.
Returns
-------
labels
The integer labels associated with the sample
"""
return sample[1]
def make_split(basename: str) -> DatabaseSplit:
"""Returns a database split for the NIH CXR-14 database."""
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return JSONDatabaseSplit(
importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
)
class DataModule(CachingDataModule):
"""NIH CXR14 (relabeled) datamodule for computer-aided diagnosis.
This dataset was extracted from the clinical PACS database at the National
Institutes of Health Clinical Center (USA) and represents 60% of all their
radiographs. It contains labels for 14 common radiological signs in this
order: cardiomegaly, emphysema, effusion, hernia, infiltration, mass,
nodule, atelectasis, pneumothorax, pleural thickening, pneumonia, fibrosis,
edema and consolidation. This is the relabeled version created in the
CheXNeXt study.
* Reference: [NIH-CXR14-2017]_
* Original resolution (height x width): 1024 x 1024
* Labels: [CHEXNEXT-2018]_
* Split reference: [CHEXNEXT-2018]_
* Protocol ``default``:
* Training samples: 98637
* Validation samples: 6350
* Test samples: 4355
* Output image:
* Transforms:
* Load raw PNG with :py:mod:`PIL`
* Final specifications
* RGB, encoded as a 3-plane image, 8 bits
* Square (1024x1024 px)
"""
def __init__(self, split_filename: str):
super().__init__(
database_split=make_split(split_filename),
raw_data_loader=RawDataLoader(),
)