Merge pull request #15 from rhornb/dev

Fix 2D table error

Author: lorenzocerrone

pyproject.toml

@@ -65,7 +65,7 @@ dev = [
 # https://docs.astral.sh/ruff
 [tool.ruff]
 line-length = 88
-target-version = "py311"
+target-version = "py39"
 src = ["src"]
 
 # https://docs.astral.sh/ruff/rules
@@ -89,6 +89,7 @@ select = [
 ignore = [
     "D401", # First line should be in imperative mood (remove to opt in)
     "D415", # First line should end with a period (remove to opt in)
+    "B008",
 ]
 
 [tool.ruff.lint.per-file-ignores]

src/ilastik_tasks/ilastik_pixel_classification_segmentation.py

@@ -23,7 +23,7 @@
 import json
 import logging
 import os
-from typing import Any, Union
+from typing import Any, Optional
 
 import anndata as ad
 import dask.array as da
@@ -83,7 +83,7 @@ def segment_ROI(
     foreground_class: int = 0,
     threshold: float = 0.5,
     min_size: int = 15,
-    label_dtype: Union[np.dtype, None] = None,
+    label_dtype: Optional[np.dtype] = None,
     relabeling: bool = True,
 ) -> np.ndarray:
     """Run the Ilastik model on a single ROI.
@@ -189,8 +189,8 @@ def ilastik_pixel_classification_segmentation(
         default_factory=IlastikChannel2InputModel
     ),
     input_ROI_table: str = "FOV_ROI_table",
-    output_ROI_table: Union[str, None] = None,
-    output_label_name: Union[str, None] = None,
+    output_ROI_table: Optional[str] = None,
+    output_label_name: Optional[str] = None,
     use_masks: bool = True,
     # Ilastik-related arguments
     ilastik_model: str,
@@ -503,6 +503,24 @@ def ilastik_pixel_classification_segmentation(
             preprocessing_kwargs=preprocessing_kwargs,
         )
 
+        # Make 3D in case of 2D data
+        # Check that the shape of the new label image matches the expected shape
+        expected_shape = (
+            e_z - s_z,
+            e_y - s_y,
+            e_x - s_x,
+        )
+        logger.info(f"Expected shape: {expected_shape}")
+        if new_label_img.shape != expected_shape:
+            try:
+                new_label_img = np.broadcast_to(new_label_img, expected_shape)
+            except Exception as err:
+                raise ValueError(
+                    f"Shape mismatch: {new_label_img.shape} != {expected_shape} "
+                    "Between the segmented label image and expected shape in "
+                    "the zarr array."
+                ) from err
+
         if output_ROI_table:
             bbox_df = array_to_bounding_box_table(
                 new_label_img,
@@ -519,21 +537,6 @@ def ilastik_pixel_classification_segmentation(
                     f"{len(overlap_list)} bounding-box pairs overlap"
                 )
 
-        # Check that the shape of the new label image matches the expected shape
-        expected_shape = (
-            e_z - s_z,
-            e_y - s_y,
-            e_x - s_x,
-        )
-        if new_label_img.shape != expected_shape:
-            try:
-                new_label_img = da.broadcast_to(new_label_img, expected_shape)
-            except:
-                raise ValueError(
-                    f"Shape mismatch: {new_label_img.shape} != {expected_shape} "
-                    "Between the segmented label image and expected shape in the zarr array."
-                )
-
         # Compute and store 0-th level to disk
         da.array(new_label_img).to_zarr(
             url=mask_zarr,

tests/data/pixel_classifier_3D_single_channel.ilp

@@ -12,8 +12,6 @@
     IlastikChannel2InputModel,
 )
 
-# TODO: add 2D testdata
-
 
 @pytest.fixture(scope="function")
 def ome_zarr_3d_url(tmp_path: Path, testdata_path: Path) -> str:
@@ -80,7 +78,9 @@ def test_ilastik_pixel_classification_segmentation_task_3D_single_channel(
         channel=IlastikChannel1InputModel(label="DAPI_2"),
         channel2=IlastikChannel2InputModel(label=None),
         ilastik_model=str(ilastik_model),
+        input_ROI_table="well_ROI_table",
         output_label_name="test_label",
+        output_ROI_table="test_ROI_table",
         relabeling=True,
     )
 
@@ -93,6 +93,7 @@ def test_ilastik_pixel_classification_segmentation_task_3D_single_channel(
             channel=IlastikChannel1InputModel(label="DAPI_2"),
             channel2=IlastikChannel2InputModel(label="ECadherin_2"),
             ilastik_model=str(ilastik_model),
+            input_ROI_table="well_ROI_table",
             output_label_name="test_label",
             relabeling=True,
         )
@@ -130,6 +131,7 @@ def test_ilastik_pixel_classification_segmentation_task_2D_single_channel(
         channel2=IlastikChannel2InputModel(label=None),
         ilastik_model=str(ilastik_model),
         output_label_name="test_label",
+        output_ROI_table="test_ROI_table",
         relabeling=True,
     )