[segmentation] Add chasedb1 database

src/mednet/libs/segmentation/config/data/chasedb1/second-annotator.json

+{
+ "train": [
+  [
+   "Image_11L.jpg",
+   "Image_11L_2ndHO.png",
+   "Image_11L.png"
+  ],
+  [
+   "Image_11R.jpg",
+   "Image_11R_2ndHO.png",
+   "Image_11R.png"
+  ],
+  [
+   "Image_12L.jpg",
+   "Image_12L_2ndHO.png",
+   "Image_12L.png"
+  ],
+  [
+   "Image_12R.jpg",
+   "Image_12R_2ndHO.png",
+   "Image_12R.png"
+  ],
+  [
+   "Image_13L.jpg",
+   "Image_13L_2ndHO.png",
+   "Image_13L.png"
+  ],
+  [
+   "Image_13R.jpg",
+   "Image_13R_2ndHO.png",
+   "Image_13R.png"
+  ],
+  [
+   "Image_14L.jpg",
+   "Image_14L_2ndHO.png",
+   "Image_14L.png"
+  ],
+  [
+   "Image_14R.jpg",
+   "Image_14R_2ndHO.png",
+   "Image_14R.png"
+  ]
+ ],
+ "test": [
+  [
+   "Image_01L.jpg",
+   "Image_01L_2ndHO.png",
+   "Image_01L.png"
+  ],
+  [
+   "Image_01R.jpg",
+   "Image_01R_2ndHO.png",
+   "Image_01R.png"
+  ],
+  [
+   "Image_02L.jpg",
+   "Image_02L_2ndHO.png",
+   "Image_02L.png"
+  ],
+  [
+   "Image_02R.jpg",
+   "Image_02R_2ndHO.png",
+   "Image_02R.png"
+  ],
+  [
+   "Image_03L.jpg",
+   "Image_03L_2ndHO.png",
+   "Image_03L.png"
+  ],
+  [
+   "Image_03R.jpg",
+   "Image_03R_2ndHO.png",
+   "Image_03R.png"
+  ],
+  [
+   "Image_04L.jpg",
+   "Image_04L_2ndHO.png",
+   "Image_04L.png"
+  ],
+  [
+   "Image_04R.jpg",
+   "Image_04R_2ndHO.png",
+   "Image_04R.png"
+  ],
+  [
+   "Image_05L.jpg",
+   "Image_05L_2ndHO.png",
+   "Image_05L.png"
+  ],
+  [
+   "Image_05R.jpg",
+   "Image_05R_2ndHO.png",
+   "Image_05R.png"
+  ],
+  [
+   "Image_06L.jpg",
+   "Image_06L_2ndHO.png",
+   "Image_06L.png"
+  ],
+  [
+   "Image_06R.jpg",
+   "Image_06R_2ndHO.png",
+   "Image_06R.png"
+  ],
+  [
+   "Image_07L.jpg",
+   "Image_07L_2ndHO.png",
+   "Image_07L.png"
+  ],
+  [
+   "Image_07R.jpg",
+   "Image_07R_2ndHO.png",
+   "Image_07R.png"
+  ],
+  [
+   "Image_08L.jpg",
+   "Image_08L_2ndHO.png",
+   "Image_08L.png"
+  ],
+  [
+   "Image_08R.jpg",
+   "Image_08R_2ndHO.png",
+   "Image_08R.png"
+  ],
+  [
+   "Image_09L.jpg",
+   "Image_09L_2ndHO.png",
+   "Image_09L.png"
+  ],
+  [
+   "Image_09R.jpg",
+   "Image_09R_2ndHO.png",
+   "Image_09R.png"
+  ],
+  [
+   "Image_10L.jpg",
+   "Image_10L_2ndHO.png",
+   "Image_10L.png"
+  ],
+  [
+   "Image_10R.jpg",
+   "Image_10R_2ndHO.png",
+   "Image_10R.png"
+  ]
+ ]
+}

src/mednet/libs/segmentation/config/data/chasedb1/second_annotator.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""CHASE-DB1 dataset for Vessel Segmentation."""
+
+from mednet.libs.segmentation.config.data.chasedb1.datamodule import DataModule
+
+datamodule = DataModule("second-annotator.json")

src/mednet/libs/segmentation/tests/test_chasedb1.py

+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Tests for chasedb1 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",
+    [
+        ("first-annotator", dict(train=8, test=20)),
+        ("second-annotator", dict(train=8, test=20)),
+    ],
+    ids=id_function,  # just changes how pytest prints it
+)
+def test_protocol_consistency(
+    database_checkers,
+    split: str,
+    lengths: dict[str, int],
+):
+    from mednet.libs.segmentation.config.data.chasedb1.datamodule import (
+        make_split,
+    )
+
+    database_checkers.check_split(
+        make_split(f"{split}.json"),
+        lengths=lengths,
+    )
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.chasedb1")
+def test_database_check():
+    from mednet.libs.segmentation.scripts.database import check
+
+    runner = CliRunner()
+    result = runner.invoke(check, ["chasedb1"])
+    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.chasedb1")
+@pytest.mark.parametrize(
+    "dataset",
+    [
+        "train",
+        "test",
+    ],
+)
+@pytest.mark.parametrize(
+    "name",
+    [
+        "first_annotator",
+        "second_annotator",
+    ],
+)
+def test_loading(database_checkers, name: str, dataset: str):
+    datamodule = importlib.import_module(
+        f".{name}",
+        "mednet.libs.segmentation.config.data.chasedb1",
+    ).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.chasedb1")
+def test_raw_transforms_image_quality(database_checkers, datadir):
+    reference_histogram_file = str(
+        datadir
+        / "histograms/raw_data/histograms_chasedb1_first_annotator.json",
+    )
+
+    datamodule = importlib.import_module(
+        ".first_annotator",
+        "mednet.libs.segmentation.config.data.chasedb1",
+    ).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.chasedb1")
+@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}_chasedb1_first_annotator.json",
+    )
+
+    datamodule = importlib.import_module(
+        ".first_annotator",
+        "mednet.libs.segmentation.config.data.chasedb1",
+    ).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,
+    )