[segmentation] Add cxr8 database

src/mednet/libs/segmentation/config/data/cxr8/datamodule.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""ChestX-ray8: Hospital-scale Chest X-ray Database."""
+
+import os
+import pathlib
+
+import numpy as np
+import PIL.Image
+import torch
+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."""
+
+CONFIGURATION_KEY_IDIAP_FILESTRUCTURE = (
+    (__name__.rsplit(".", 2)[-2]) + ".idiap_folder_structure"
+)
+"""Key to search for in the configuration file indicating if the loader should
+use standard or idiap-based file organisation structure.
+
+It causes the 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).
+"""
+
+
+class SegmentationRawDataLoader(_SegmentationRawDataLoader):
+    """A specialized raw-data-loader for the cxr8 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))
+        )
+        self.idiap_file_organisation = load_rc().get(
+            CONFIGURATION_KEY_IDIAP_FILESTRUCTURE,
+            False,
+        )
+
+    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.
+        """
+
+        file_path = pathlib.Path(sample[0])
+        label_path = pathlib.Path(sample[1])
+
+        if self.idiap_file_organisation:
+            sample_parts = sample[0].split("/", 1)
+            file_path = pathlib.Path(
+                sample_parts[0] + "/" + sample_parts[1][:5] + "/" + sample_parts[1]
+            )
+
+            label_parts = sample[1].split("/", 1)
+            label_path = pathlib.Path(
+                pathlib.Path(
+                    label_parts[0] + "/" + label_parts[1][:5] + "/" + label_parts[1]
+                )
+            )
+
+        tensor = to_tensor(PIL.Image.open(self.datadir / file_path).convert(mode="RGB"))
+
+        target = np.array(
+            PIL.Image.open(
+                pathlib.Path(str(self.datadir) + "-segmentations") / label_path
+            )
+        )
+        target = np.where(target == 255, 0, target)
+        target = to_tensor(PIL.Image.fromarray(np.array(target > 0)))
+
+        tensor = tv_tensors.Image(tensor)
+        target = tv_tensors.Image(target)
+        mask = tv_tensors.Mask(torch.ones_like(target))
+
+        return tensor, dict(target=target, mask=mask, name=sample[0])  # type: ignore[arg-type]
+
+
+class DataModule(CachingDataModule):
+    """The database contains a total  of 112120 images. Image size for each X-ray is
+    1024 x 1024. One set of mask annotations is available for all images.
+
+    * Reference: [CXR8-2017]_
+    * Original resolution (height x width): 1024 x 1024
+    * Configuration resolution: 256 x 256 (after rescaling)
+    * Split reference: [GAAL-2020]_
+    * Protocol ``default``:
+
+    * Training samples: 78484 (including labels)
+    * Validation samples: 11212 (including labels)
+    * Test samples: 22424 (including labels)
+
+    Parameters
+    ----------
+    split_filename
+        Name of the .json file containing the split to load.
+    """
+
+    def __init__(self, split_filename: str):
+        assert __package__ is not None
+        super().__init__(
+            database_split=make_split(__package__, split_filename),
+            raw_data_loader=SegmentationRawDataLoader(),
+            database_name=__package__.rsplit(".", 1)[1],
+            split_name=pathlib.Path(split_filename).stem,
+        )

src/mednet/libs/segmentation/config/data/cxr8/default.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""CXR8 dataset for Vessel Segmentation (default protocol).
+
+* Split reference: [CXR8-2004]_
+* This configuration resolution: 544 x 544 (center-crop)
+* See :py:mod:`deepdraw.data.cxr8` for dataset details
+* This dataset offers a second-annotator comparison for the test set only
+"""
+
+from mednet.libs.segmentation.config.data.cxr8.datamodule import DataModule
+
+datamodule = DataModule("default.json")

src/mednet/libs/segmentation/tests/test_cxr8.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Tests for cxr8 dataset."""
+
+import importlib
+
+import pytest
+from click.testing import CliRunner
+
+
+def id_function(val):
+    if isinstance(val, dict):
+        return str(val)
+    return repr(val)
+
+
+@pytest.mark.parametrize(
+    "split,lengths",
+    [
+        ("default", dict(train=78484, validation=11212, test=22424)),
+    ],
+    ids=id_function,  # just changes how pytest prints it
+)
+def test_protocol_consistency(
+    database_checkers,
+    split: str,
+    lengths: dict[str, int],
+):
+    from mednet.libs.common.data.split import make_split
+
+    database_checkers.check_split(
+        make_split("mednet.libs.segmentation.config.data.cxr8", f"{split}.json"),
+        lengths=lengths,
+    )
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+def test_database_check():
+    from mednet.libs.segmentation.scripts.database import check
+
+    runner = CliRunner()
+    result = runner.invoke(check, ["--limit=10", "cxr8"])
+    assert (
+        result.exit_code == 0
+    ), f"Exit code {result.exit_code} != 0 -- Output:\n{result.output}"
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+@pytest.mark.parametrize(
+    "dataset",
+    [
+        "train",
+        "test",
+    ],
+)
+@pytest.mark.parametrize(
+    "name",
+    [
+        "default",
+    ],
+)
+def test_loading(database_checkers, name: str, dataset: str):
+    datamodule = importlib.import_module(
+        f".{name}",
+        "mednet.libs.segmentation.config.data.cxr8",
+    ).datamodule
+
+    datamodule.model_transforms = []  # should be done before setup()
+    datamodule.setup("predict")  # sets up all datasets
+
+    loader = datamodule.predict_dataloader()[dataset]
+
+    limit = 3  # limit load checking
+    for batch in loader:
+        if limit == 0:
+            break
+        database_checkers.check_loaded_batch(
+            batch,
+            batch_size=1,
+            color_planes=3,
+            expected_num_targets=1,
+        )
+        limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+def test_raw_transforms_image_quality(database_checkers, datadir):
+    reference_histogram_file = str(
+        datadir / "histograms/raw_data/histograms_cxr8_default.json",
+    )
+
+    datamodule = importlib.import_module(
+        ".default",
+        "mednet.libs.segmentation.config.data.cxr8",
+    ).datamodule
+
+    datamodule.model_transforms = []
+    datamodule.setup("predict")
+
+    database_checkers.check_image_quality(datamodule, reference_histogram_file)
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+@pytest.mark.parametrize(
+    "model_name",
+    ["lwnet"],
+)
+def test_model_transforms_image_quality(database_checkers, datadir, model_name):
+    reference_histogram_file = str(
+        datadir / f"histograms/models/histograms_{model_name}_cxr8_default.json",
+    )
+
+    datamodule = importlib.import_module(
+        ".default",
+        "mednet.libs.segmentation.config.data.cxr8",
+    ).datamodule
+
+    model = importlib.import_module(
+        f".{model_name}",
+        "mednet.libs.segmentation.config.models",
+    ).model
+
+    datamodule.model_transforms = model.model_transforms
+    datamodule.setup("predict")
+
+    database_checkers.check_image_quality(
+        datamodule,
+        reference_histogram_file,
+        compare_type="statistical",
+        pearson_coeff_threshold=0.005,
+    )