polygon_query() support for images

src/spatialdata/_core/query/spatial_query.py

@@ -617,7 +617,14 @@ def _(
 
 
 def _polygon_query(
-    sdata: SpatialData, polygon: Polygon, target_coordinate_system: str, filter_table: bool, shapes: bool, points: bool
+    sdata: SpatialData,
+    polygon: Polygon,
+    target_coordinate_system: str,
+    filter_table: bool,
+    shapes: bool,
+    points: bool,
+    images: bool,
+    labels: bool,
 ) -> SpatialData:
     from spatialdata._core.query._utils import circles_to_polygons
     from spatialdata._core.query.relational_query import _filter_table_by_elements
@@ -669,11 +676,32 @@ def _polygon_query(
             set_transformation(ddf, transformation, target_coordinate_system)
             new_points[points_name] = ddf
 
-    if filter_table:
+    new_images = {}
+    if images:
+        for images_name, im in sdata.images.items():
+            min_x, min_y, max_x, max_y = polygon.bounds
+            cropped = bounding_box_query(
+                im,
+                min_coordinate=[min_x, min_y],
+                max_coordinate=[max_x, max_y],
+                axes=("x", "y"),
+                target_coordinate_system=target_coordinate_system,
+            )
+            new_images[images_name] = cropped
+    if labels:
+        for labels_name, l in sdata.labels.items():
+            _ = labels_name
+            _ = l
+            raise NotImplementedError(
+                "labels=True is not implemented yet. If you encounter this error please open an "
+                "issue and we will prioritize the implementation."
+            )
+
+    if filter_table and sdata.table is not None:
         table = _filter_table_by_elements(sdata.table, {"shapes": new_shapes, "points": new_points})
     else:
         table = sdata.table
-    return SpatialData(shapes=new_shapes, points=new_points, table=table)
+    return SpatialData(shapes=new_shapes, points=new_points, images=new_images, table=table)
 
 
 # this function is currently excluded from the API documentation. TODO: add it after the refactoring
@@ -684,6 +712,8 @@ def polygon_query(
     filter_table: bool = True,
     shapes: bool = True,
     points: bool = True,
+    images: bool = True,
+    labels: bool = True,
 ) -> SpatialData:
     """
     Query a spatial data object by a polygon, filtering shapes and points.
@@ -725,14 +755,21 @@ def polygon_query(
             filter_table=filter_table,
             shapes=shapes,
             points=points,
+            images=images,
+            labels=labels,
         )
     # TODO: the performance for this case can be greatly improved by using the geopandas queries only once, and not
     #  in a loop as done preliminarily here
-    if points:
-        raise NotImplementedError(
-            "points=True is not implemented when querying by multiple polygons. If you encounter this error, please"
-            " open an issue on GitHub and we will prioritize the implementation."
+    if points or images or labels:
+        logger.warning(
+            "Spatial querying of images, points and labels is not implemented when querying by multiple polygons "
+            'simultaneously. You can silence this warning by setting "points=False, images=False, labels=False". If '
+            "you need this implementation please open an issue on GitHub and we will prioritize the implementation."
         )
+        points = False
+        images = False
+        labels = False
+
     sdatas = []
     for polygon in tqdm(polygons):
         try:
@@ -744,6 +781,8 @@ def polygon_query(
                 filter_table=False,
                 shapes=shapes,
                 points=points,
+                images=images,
+                labels=labels,
             )
             sdatas.append(queried_sdata)
         except ValueError as e:

tests/core/query/test_spatial_query.py

@@ -4,6 +4,7 @@
 import pytest
 from anndata import AnnData
 from multiscale_spatial_image import MultiscaleSpatialImage
+from shapely import Polygon
 from spatial_image import SpatialImage
 from spatialdata import SpatialData
 from spatialdata._core.query.spatial_query import (
@@ -379,7 +380,11 @@ def test_polygon_query_shapes(sdata_query_aggregation):
     circle_pol = circle.buffer(sdata["by_circles"].radius.iloc[0])
 
     queried = polygon_query(
-        values_sdata, polygons=polygon, target_coordinate_system="global", shapes=True, points=False
+        values_sdata,
+        polygons=polygon,
+        target_coordinate_system="global",
+        shapes=True,
+        points=False,
     )
     assert len(queried["values_polygons"]) == 4
     assert len(queried["values_circles"]) == 4
@@ -432,11 +437,34 @@ def test_polygon_query_spatial_data(sdata_query_aggregation):
     assert len(queried.table) == 8
 
 
-@pytest.mark.skip
-def test_polygon_query_image2d():
-    # single image case
-    # multiscale case
-    pass
+@pytest.mark.parametrize("n_channels", [1, 2, 3])
+def test_polygon_query_image2d(n_channels: int):
+    original_image = np.zeros((n_channels, 10, 10))
+    # y: [5, 9], x: [0, 4] has value 1
+    original_image[:, 5::, 0:5] = 1
+    image_element = Image2DModel.parse(original_image)
+    image_element_multiscale = Image2DModel.parse(original_image, scale_factors=[2, 2])
+
+    polygon = Polygon([(3, 3), (3, 7), (5, 3)])
+    for image in [image_element, image_element_multiscale]:
+        # bounding box: y: [5, 10[, x: [0, 5[
+        image_result = polygon_query(
+            SpatialData(images={"my_image": image}),
+            polygons=polygon,
+            target_coordinate_system="global",
+        )["my_image"]
+        expected_image = original_image[:, 3:7, 3:5]  # c dimension is preserved
+        if isinstance(image, SpatialImage):
+            assert isinstance(image, SpatialImage)
+            np.testing.assert_allclose(image_result, expected_image)
+        elif isinstance(image, MultiscaleSpatialImage):
+            assert isinstance(image_result, MultiscaleSpatialImage)
+            v = image_result["scale0"].values()
+            assert len(v) == 1
+            xdata = v.__iter__().__next__()
+            np.testing.assert_allclose(xdata, expected_image)
+        else:
+            raise ValueError("Unexpected type")
 
 
 @pytest.mark.skip