🆕 Add Support for QuPath Compatible Output

tests/engines/test_engine_abc.py

@@ -66,7 +66,7 @@ def test_incorrect_output_type() -> None:
 
     with pytest.raises(
         TypeError,
-        match=r".*output_type must be 'dict' or 'zarr' or 'annotationstore*",
+        match=r".*output_type must be 'dict' or 'zarr', 'qupath' or 'annotationstore*",
     ):
         _ = eng.run(
             images=np.zeros((10, 224, 224, 3), dtype=np.uint8),
@@ -77,6 +77,38 @@ def test_incorrect_output_type() -> None:
         )
 
 
+def test_incorrect_output_type_save_dir() -> None:
+    """Test EngineABC for None output_type and output type zarr/annotationstore."""
+    pretrained_model = "alexnet-kather100k"
+
+    # Test engine run without ioconfig
+    eng = TestEngineABC(model=pretrained_model)
+
+    with pytest.raises(
+        ValueError,
+        match=r".*Please provide save_dir for output_type=zarr*",
+    ):
+        _ = eng.run(
+            images=np.zeros((10, 224, 224, 3), dtype=np.uint8),
+            device=device,
+            patch_mode=True,
+            ioconfig=None,
+            output_type="zarr",
+        )
+
+    with pytest.raises(
+        ValueError,
+        match=r".*Please provide save_dir for output_type=annotationstore*",
+    ):
+        _ = eng.run(
+            images=np.zeros((10, 224, 224, 3), dtype=np.uint8),
+            device=device,
+            patch_mode=True,
+            ioconfig=None,
+            output_type="annotationstore",
+        )
+
+
 def test_pretrained_ioconfig() -> None:
     """Test EngineABC initialization with pretrained model name in the toolbox."""
     pretrained_model = "alexnet-kather100k"
@@ -369,48 +401,6 @@ def test_patch_pred_zarr_store(track_tmp_path: pytest.TempPathFactory) -> None:
     )
     assert Path.exists(out), "Zarr output file does not exist"
 
-    eng = TestEngineABC(model="alexnet-kather100k")
-    with pytest.raises(
-        ValueError,
-        match=r".*Patch output must contain coordinates.",
-    ):
-        _ = eng.run(
-            images=np.zeros((10, 224, 224, 3), dtype=np.uint8),
-            labels=list(range(10)),
-            device=device,
-            save_dir=save_dir,
-            overwrite=True,
-            output_type="AnnotationStore",
-        )
-
-    with pytest.raises(
-        ValueError,
-        match=r".*Patch output must contain coordinates.",
-    ):
-        _ = eng.run(
-            images=np.zeros((10, 224, 224, 3), dtype=np.uint8),
-            labels=list(range(10)),
-            device=device,
-            save_dir=save_dir,
-            overwrite=True,
-            output_type="AnnotationStore",
-            class_dict={0: "class0", 1: "class1"},
-        )
-
-    with pytest.raises(
-        ValueError,
-        match=r".*Patch output must contain coordinates.",
-    ):
-        _ = eng.run(
-            images=np.zeros((10, 224, 224, 3), dtype=np.uint8),
-            labels=list(range(10)),
-            device=device,
-            save_dir=save_dir,
-            overwrite=True,
-            output_type="AnnotationStore",
-            scale_factor=(2.0, 2.0),
-        )
-
 
 def test_get_dataloader(sample_svs: Path) -> None:
     """Test the get_dataloader function."""

tests/engines/test_ioconfig.py

@@ -1,8 +1,9 @@
 """Tests for IOconfig."""
 
+import numpy as np
 import pytest
 
-from tiatoolbox.models import ModelIOConfigABC
+from tiatoolbox.models import IOSegmentorConfig, ModelIOConfigABC
 
 
 def test_validation_error_io_config() -> None:
@@ -21,3 +22,100 @@ def test_validation_error_io_config() -> None:
             input_resolutions=[{"units": "level", "resolution": 1.0}],
             patch_input_shape=(224, 224),
         )
+
+
+def test_scale_to_highest_mpp() -> None:
+    """Mpp → min(old_vals) / old_vals."""
+    resolutions = [
+        {"units": "mpp", "resolution": 0.25},
+        {"units": "mpp", "resolution": 0.5},
+    ]
+    result = ModelIOConfigABC.scale_to_highest(resolutions, units="mpp")
+
+    expected = np.array([1.0, 0.5])  # 0.25 / [0.25, 0.5]
+    np.testing.assert_allclose(result, expected)
+
+
+def test_scale_to_highest_mpp_reversed_order() -> None:
+    """Ensure order is preserved even when resolutions are reversed."""
+    resolutions = [
+        {"units": "mpp", "resolution": 0.5},
+        {"units": "mpp", "resolution": 0.25},
+    ]
+    result = ModelIOConfigABC.scale_to_highest(resolutions, units="mpp")
+
+    expected = np.array([0.5, 1.0])  # 0.25 / [0.5, 0.25]
+    np.testing.assert_allclose(result, expected)
+
+
+def test_scale_to_highest_baseline() -> None:
+    """Baseline → identity."""
+    resolutions = [
+        {"units": "baseline", "resolution": 2.0},
+        {"units": "baseline", "resolution": 4.0},
+    ]
+    result = ModelIOConfigABC.scale_to_highest(resolutions, units="baseline")
+
+    expected = [2.0, 4.0]
+    assert result == expected
+
+
+def test_scale_to_highest_power() -> None:
+    """Power → old_vals / max(old_vals)."""
+    resolutions = [
+        {"units": "power", "resolution": 10},
+        {"units": "power", "resolution": 5},
+    ]
+    result = ModelIOConfigABC.scale_to_highest(resolutions, units="power")
+
+    expected = np.array([1.0, 0.5])  # [10, 5] / 10
+    np.testing.assert_allclose(result, expected)
+
+
+def test_scale_to_highest_invalid_units() -> None:
+    """Test ModelIOConfigABC for unknown units."""
+    resolutions = [{"units": "mpp", "resolution": 1.0}]
+    with pytest.raises(ValueError, match="Unknown units"):
+        ModelIOConfigABC.scale_to_highest(resolutions, units="unknown")
+
+
+def test_modelio_to_baseline_without_save_resolution() -> None:
+    """Test ModelIOConfigABC when save_resolution is None.
+
+    Ensure ModelIOConfigABC.to_baseline does NOT add or convert
+    save_resolution when it is None.
+
+    """
+    cfg = ModelIOConfigABC(
+        input_resolutions=[{"units": "mpp", "resolution": 0.5}],
+        output_resolutions=[{"units": "mpp", "resolution": 1.0}],
+        patch_input_shape=(224, 224),
+        stride_shape=(224, 224),
+    )
+
+    new_cfg = cfg.to_baseline()
+
+    # save_resolution should not appear in the new config
+    assert not hasattr(new_cfg, "save_resolution") or new_cfg.save_resolution is None
+
+
+def test_ios_to_baseline_without_save_resolution() -> None:
+    """Test IOSegmentorConfig when save_resolution is None.
+
+    Ensure IOSegmentorConfig.to_baseline leaves save_resolution=None
+    when no save_resolution is provided.
+
+    """
+    cfg = IOSegmentorConfig(
+        input_resolutions=[{"units": "mpp", "resolution": 0.5}],
+        output_resolutions=[{"units": "mpp", "resolution": 1.0}],
+        patch_input_shape=(224, 224),
+        patch_output_shape=(112, 112),
+        stride_shape=(224, 224),
+        save_resolution=None,
+    )
+
+    new_cfg = cfg.to_baseline()
+
+    # save_resolution should remain None after conversion
+    assert new_cfg.save_resolution is None