[3/3] Add make_stitched_labels (materialise stitched labels)

docs/api.md

@@ -151,6 +151,7 @@ See the {doc}`extensibility guide </extensibility>` for how to implement a custo
     experimental.tl.TilingQCParams
     experimental.tl.assign_stitch_groups
     experimental.tl.StitchParams
+    experimental.im.make_stitched_labels
     experimental.pl.tiling_qc
     experimental.im.fit_stain_reference
     experimental.im.apply_stain_normalization

docs/release/notes-dev.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+- Add {func}`squidpy.experimental.im.make_stitched_labels` to materialise a stitched labels element (and an optional collapsed table) from an {func}`squidpy.experimental.tl.assign_stitch_groups` result, completing the tile-cut stitching workflow.
 - Fix {func}`squidpy.tl.var_by_distance` behaviour when providing {mod}`numpy` arrays of coordinates as anchor point.
 - Update :attr:`squidpy.pl.var_by_distance` to show multiple variables on same plot.
   [@LLehner](https://github.com/LLehner)

src/squidpy/experimental/im/__init__.py

@@ -15,6 +15,7 @@
     apply_stain_normalization,
     fit_stain_reference,
 )
+from ._stitched_labels import make_stitched_labels
 
 __all__ = [
     "BackgroundDetectionParams",
@@ -26,6 +27,7 @@
     "apply_stain_normalization",
     "detect_tissue",
     "fit_stain_reference",
+    "make_stitched_labels",
     "make_tiles",
     "make_tiles_from_spots",
     "qc_image",

src/squidpy/experimental/tl/_tiling_qc.py

@@ -28,7 +28,7 @@
 
 import math
 from collections.abc import Mapping
-from dataclasses import asdict, dataclass, fields
+from dataclasses import asdict, dataclass
 from typing import Any, Literal
 
 import anndata as ad
@@ -54,6 +54,7 @@
 )
 from squidpy.experimental.tl._tiling_stitch import _STITCH_COLUMNS, _STITCH_PARAM_KEYS, StitchParams
 from squidpy.experimental.utils._labels import resolve_labels_array
+from squidpy.experimental.utils._params import resolve_params
 
 __all__ = ["TilingQCParams", "calculate_tiling_qc"]
 
@@ -92,23 +93,13 @@ def __post_init__(self) -> None:
 
 
 _QC_DEFAULTS = TilingQCParams()
-_QC_FIELDS = frozenset(f.name for f in fields(TilingQCParams))
 
 
 def _resolve_qc_params(qc_params: TilingQCParams | Mapping[str, Any] | None) -> TilingQCParams:
     """Normalise the ``tiling_qc_params`` argument to a :class:`TilingQCParams` instance."""
     if qc_params is None:
         return _QC_DEFAULTS
-    if isinstance(qc_params, TilingQCParams):
-        return qc_params
-    if isinstance(qc_params, Mapping):
-        unknown = set(qc_params) - _QC_FIELDS
-        if unknown:
-            raise ValueError(
-                f"Unknown `tiling_qc_params` field(s): {sorted(unknown)}; expected from {sorted(_QC_FIELDS)}."
-            )
-        return TilingQCParams(**qc_params)
-    raise TypeError(f"`tiling_qc_params` must be TilingQCParams, Mapping, or None; got {type(qc_params).__name__}.")
+    return resolve_params(qc_params, TilingQCParams, label="`tiling_qc_params`")
 
 
 # Standard consistency factor sd ~ 1.4826 x MAD for normal distributions.

src/squidpy/experimental/tl/_tiling_stitch.py

@@ -31,12 +31,11 @@
 from scipy.ndimage import binary_closing
 from scipy.sparse import csr_matrix
 from scipy.sparse.csgraph import connected_components
+from skimage.measure import find_contours, regionprops
 from skimage.measure import label as cc_label
-from skimage.measure import regionprops
 from skimage.morphology import disk as morph_disk
 from spatialdata._logging import logger as logg
 
-from squidpy.experimental.utils._geometry import equivalent_diameter, largest_contour
 from squidpy.experimental.utils._labels import iter_chunked_regionprops, resolve_labels_array
 from squidpy.experimental.utils._params import resolve_params
 
@@ -302,10 +301,12 @@ def _extract_cut_edges(
         if not cell_mask.any():
             continue
         outlier_crops[lid] = cell_mask
+        # 1px zero-pad so cells filling their bbox still trace a closed contour.
         mask = np.pad(cell_mask.astype(np.float32), 1, mode="constant", constant_values=0)
-        contour = largest_contour(mask)
-        if contour is None:
+        contours = find_contours(mask, 0.5)
+        if not contours:  # degenerate mask traces nothing; skip it
             continue
+        contour = max(contours, key=len)
         contour_global = contour.copy()
         contour_global[:, 0] += min_r - 1
         contour_global[:, 1] += min_c - 1
@@ -315,7 +316,7 @@ def _extract_cut_edges(
         cy = float(ys.mean()) + min_r - 1
         cx = float(xs.mean()) + min_c - 1
         area = float(mask.sum())
-        eq_diameter = equivalent_diameter(area)
+        eq_diameter = float(np.sqrt(4 * area / np.pi))  # diameter of the equal-area circle
         min_len = max(min_edge_length, min_edge_length_ratio * eq_diameter)
 
         # find_contours places level set 0.5 outside the integer pixel boundary.