[segmetnation.data] Add refuge database

src/mednet/libs/segmentation/config/data/refuge/cup.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""REFUGE dataset for Optic Cup Segmentation (default protocol).
+
+* Configuration resolution: 1632 x 1632 (after resizing and padding)
+* Reference (including split): [REFUGE-2018]_
+* See :py:mod:`deepdraw.data.refuge` for dataset details
+"""
+
+from mednet.libs.segmentation.config.data.refuge.datamodule import DataModule
+
+datamodule = DataModule("default.json", target_type="cup")

src/mednet/libs/segmentation/config/data/refuge/datamodule.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""DREFUGE for Optic Disc and Cup Segmentation."""
+
+import os
+import pathlib
+
+import PIL.Image
+import pkg_resources
+from mednet.libs.common.data.datamodule import CachingDataModule
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
+from mednet.libs.segmentation.data.typing import (
+    SegmentationRawDataLoader as _SegmentationRawDataLoader,
+)
+from torchvision import tv_tensors
+from torchvision.transforms.functional import to_tensor
+
+from ....utils.rc import load_rc
+
+CONFIGURATION_KEY_DATADIR = "datadir." + (__name__.rsplit(".", 2)[-2])
+"""Key to search for in the configuration file for the root directory of this
+database."""
+
+
+class SegmentationRawDataLoader(_SegmentationRawDataLoader):
+    """A specialized raw-data-loader for the drishtigs1 dataset.
+
+    Parameters
+    ----------
+    target_type
+        Indicate whether to use the "cup" or "disc" target.
+    """
+
+    datadir: pathlib.Path
+    """This variable contains the base directory where the database raw data is
+    stored."""
+
+    def __init__(self, target_type: str):
+        self.datadir = pathlib.Path(
+            load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
+        )
+
+        self._pkg_path = pathlib.Path(
+            pkg_resources.resource_filename(__name__, "masks")
+        )
+
+        self.target_type = target_type
+
+    def sample(self, sample: tuple[str, str, str]) -> Sample:
+        """Load a single image sample from the disk.
+
+        Parameters
+        ----------
+        sample
+            A tuple containing path suffixes to the sample image, target, and mask
+            to be loaded, within the dataset root folder.
+
+        Returns
+        -------
+            The sample representation.
+        """
+
+        image = to_tensor(PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB"))
+
+        if self.target_type == "disc":
+            target = to_tensor(
+                PIL.Image.open(self.datadir / sample[1])
+                .convert(mode="RGB", dither=None)
+                .convert("L")
+                .point(lambda p: p <= 150, mode="1")
+            )
+        elif self.target_type == "cup":
+            target = to_tensor(
+                PIL.Image.open(self.datadir / sample[1])
+                .convert(mode="RGB", dither=None)
+                .convert("L")
+                .point(lambda p: p <= 100, mode="1")
+            )
+        else:
+            raise ValueError(
+                f"Target type {self.target_type} is not an option. Available options are 'cup' and 'disc'."
+            )
+
+        mask = to_tensor(
+            PIL.Image.open(self._pkg_path / sample[2]).convert(mode="1", dither=None)
+        )
+
+        tensor = tv_tensors.Image(image)
+        target = tv_tensors.Image(target)
+        mask = tv_tensors.Mask(mask)
+
+        return tensor, dict(target=target, mask=mask, name=sample[0])  # type: ignore[arg-type]
+
+
+class DataModule(CachingDataModule):
+    """REFUGE for Optic Disc and Cup Segmentation.
+
+    The dataset consists of 1200 color fundus photographs, created for a MICCAI
+    challenge. The goal of the challenge is to evaluate and compare automated
+    algorithms for glaucoma detection and optic disc/cup segmentation on a common
+    dataset of retinal fundus images.
+
+    * Reference (including train/dev/test split): [REFUGE-2018]_
+    * Protocols ``optic-disc`` and ``cup``:
+
+    * Training samples:
+
+        * 400
+        * includes optic-disc and cup labels
+        * includes label: glaucomatous and non-glaucomatous
+        * original resolution: 2056 x 2124
+
+    * Validation samples:
+
+        * 400
+        * includes optic-disc and cup labels
+        * original resolution: 1634 x 1634
+
+    * Test samples:
+
+        * 400
+        * includes optic-disc and cup labels
+        * includes label: glaucomatous and non-glaucomatous
+        * original resolution:
+
+    Parameters
+    ----------
+    split_filename
+        Name of the .json file containing the split to load.
+    target_type
+        Indicate whether to use the "cup" or "disc" target.
+    """
+
+    def __init__(self, split_filename: str, target_type: str):
+        assert __package__ is not None
+        super().__init__(
+            database_split=make_split(__package__, split_filename),
+            raw_data_loader=SegmentationRawDataLoader(target_type),
+            database_name=__package__.rsplit(".", 1)[1],
+            split_name=pathlib.Path(split_filename).stem,
+        )

src/mednet/libs/segmentation/config/data/refuge/disc.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""REFUGE dataset for Optic Disc Segmentation (default protocol).
+
+* Configuration resolution: 1632 x 1632 (after resizing and padding)
+* Reference (including split): [REFUGE-2018]_
+* See :py:mod:`deepdraw.data.refuge` for dataset details
+"""
+
+from mednet.libs.segmentation.config.data.refuge.datamodule import DataModule
+
+datamodule = DataModule("default.json", target_type="disc")