Source code for mednet.data.segment.iostar

# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
"""IOSTAR (training set) for vessel and optic-disc segmentation.

The IOSTAR vessel segmentation dataset includes 30 images with a resolution of
1024×1024 pixels. All the vessels in this dataset are annotated by a group of
experts working in the field of retinal image analysis. Additionally the
dataset includes annotations for the optic disc and the artery/vein ratio.

* Reference: [IOSTAR-2016]_
* Original resolution (height x width): 1024×1024
* Split reference: [MEYER-2017]_
* Protocol ``vessel``:

  * Training samples: 20 (including labels and masks)
  * Test samples: 10 (including labels and masks)

* Protocol ``optic-disc``:

  * Training samples: 20 (including labels and masks)
  * Test samples: 10 (including labels and masks)

This module contains the base declaration of common data modules and raw-data
loaders for this database. All configured splits inherit from this definition.
"""

import importlib.resources.abc
import os
import pathlib

import PIL.Image
from torchvision import tv_tensors
from torchvision.transforms.functional import to_tensor

from ...models.transforms import crop_image_to_mask
from ...utils.rc import load_rc
from ..datamodule import CachingDataModule
from ..split import JSONDatabaseSplit
from .typing import Sample, SegmentationRawDataLoader

DATABASE_SLUG = __name__.rsplit(".", 1)[-1]
"""Pythonic name to refer to this database."""

CONFIGURATION_KEY_DATADIR = "datadir." + DATABASE_SLUG
"""Key to search for in the configuration file for the root directory of this
database."""



[docs]
class RawDataLoader(SegmentationRawDataLoader):
    """A specialized raw-data-loader for the iostar dataset."""

    datadir: pathlib.Path
    """This variable contains the base directory where the database raw data is
    stored."""

    def __init__(self):
        self.datadir = pathlib.Path(
            load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
        )


[docs]
    def sample(self, sample: tuple[str, str, str | None]) -> Sample:
        """Load 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
        -------
            The sample representation.
        """

        image = to_tensor(PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB"))

        target = to_tensor(
            PIL.Image.open(self.datadir / sample[1]).convert(mode="1", dither=None)
        )

        assert sample[2] is not None
        mask = to_tensor(
            PIL.Image.open(self.datadir / sample[2]).convert(mode="1", dither=None)
        )

        image = tv_tensors.Image(crop_image_to_mask(image, mask))
        target = tv_tensors.Mask(crop_image_to_mask(target, mask))
        mask = tv_tensors.Mask(crop_image_to_mask(mask, mask))

        return dict(image=image, target=target, mask=mask), dict(name=sample[0])  # type: ignore[arg-type]





[docs]
class DataModule(CachingDataModule):
    """IOSTAR (training set) for vessel and optic-disc segmentation.

    Parameters
    ----------
    split_path
        Path or traversable (resource) with the JSON split description to load.
    """

    def __init__(self, split_path: pathlib.Path | importlib.resources.abc.Traversable):
        super().__init__(
            database_split=JSONDatabaseSplit(split_path),
            raw_data_loader=RawDataLoader(),
            database_name=DATABASE_SLUG,
            split_name=split_path.name.rsplit(".", 2)[0],
            task="segmentation",
        )