Merge pull request #78 from aeisenbarth/rgb-argument

[WIP] Set rgb argument to auto-detect whether image channels are RGB

CHANGELOG.md

@@ -14,6 +14,10 @@ and this project adheres to [Semantic Versioning][].
 
 -   (Xenium) added `dims` parameter for more control in `xenium_aligned_image()`
 
+### Fixed
+
+-   Passing `rgb=None` to image model parser, leading to 3-4 channel images being interpreted as RGB(A)
+
 ## [0.1.4] - 2024-08-07
 
 ### Changed

pyproject.toml

@@ -25,7 +25,7 @@ dependencies = [
     "anndata",
     "numpy",
     "scanpy",
-    "spatialdata>=0.2.1",
+    "spatialdata>=0.2.2",
     "scikit-image",
     "h5py",
     "joblib",

src/spatialdata_io/readers/_utils/_utils.py

@@ -18,8 +18,8 @@
 
     NDArrayA = NDArray[Any]
 except (ImportError, TypeError):
-    NDArray = np.ndarray  # type: ignore[misc]
-    NDArrayA = np.ndarray  # type: ignore[misc]
+    NDArray = np.ndarray  # type: ignore[misc,unused-ignore]
+    NDArrayA = np.ndarray  # type: ignore[misc,unused-ignore]
 
 
 def _read_counts(

src/spatialdata_io/readers/codex.py

@@ -80,6 +80,7 @@ def codex(
             "images": Image2DModel.parse(
                 image,
                 scale_factors=[2, 2],
+                rgb=None,
             )
         }
         sdata = SpatialData(images=images, shapes={str(region): shapes}, table=table)

src/spatialdata_io/readers/cosmx.py

@@ -200,6 +200,7 @@ def cosmx(
                         "global_only_image": aff,
                     },
                     dims=("y", "x", "c"),
+                    rgb=None,
                     **image_models_kwargs,
                 )
                 images[f"{fov}_image"] = parsed_im

src/spatialdata_io/readers/mcmicro.py

@@ -100,7 +100,7 @@ def mcmicro(
         assert len(samples) == 1
         data = imread(samples[0], **imread_kwargs)
         # , scale_factors=[2, 2]
-        data = Image2DModel.parse(data, transformations=_get_transformation(), **image_models_kwargs)
+        data = Image2DModel.parse(data, transformations=_get_transformation(), rgb=None, **image_models_kwargs)
         images["tma_map"] = data
 
         image_dir = path / McmicroKeys.IMAGES_DIR_TMA
@@ -116,7 +116,7 @@ def mcmicro(
             image_id = core_id
 
         data = imread(sample, **imread_kwargs)
-        data = Image2DModel.parse(data, c_coords=marker_names, **image_models_kwargs)
+        data = Image2DModel.parse(data, c_coords=marker_names, rgb=None, **image_models_kwargs)
         transformations = _get_transformation(
             tma=int(core_id) if tma else None, tma_centroids=tma_centroids, raster_data=data
         )
@@ -131,7 +131,9 @@ def mcmicro(
             raw_name = raw_image.with_name(raw_image.stem).with_suffix("").stem
 
             data = imread(raw_image, **imread_kwargs)
-            images[raw_name] = Image2DModel.parse(data, transformations={raw_name: Identity()}, **image_models_kwargs)
+            images[raw_name] = Image2DModel.parse(
+                data, transformations={raw_name: Identity()}, rgb=None, **image_models_kwargs
+            )
 
     illumination_dir = path / McmicroKeys.ILLUMINATION_DIR
     if illumination_dir.exists():
@@ -143,7 +145,7 @@ def mcmicro(
 
             data = imread(illumination_image, **imread_kwargs)
             images[illumination_name] = Image2DModel.parse(
-                data, transformations={raw_name: Identity()}, **image_models_kwargs
+                data, transformations={raw_name: Identity()}, rgb=None, **image_models_kwargs
             )
 
     samples_labels = list((path / McmicroKeys.LABELS_DIR).glob("*/*" + McmicroKeys.IMAGE_SUFFIX))

src/spatialdata_io/readers/merscope.py

@@ -273,7 +273,7 @@ def _rioxarray_load_merscope(
         dim="c",
     )
 
-    return Image2DModel.parse(im, c_coords=stainings, **image_models_kwargs)
+    return Image2DModel.parse(im, c_coords=stainings, rgb=None, **image_models_kwargs)
 
 
 def _dask_image_load_merscope(
@@ -292,6 +292,7 @@ def _dask_image_load_merscope(
         im,
         dims=("c", "y", "x"),
         c_coords=stainings,
+        rgb=None,
         **image_models_kwargs,
     )
 

src/spatialdata_io/readers/steinbock.py

@@ -97,7 +97,7 @@ def _get_images(
     image_models_kwargs: Mapping[str, Any] = MappingProxyType({}),
 ) -> Union[SpatialImage, MultiscaleSpatialImage]:
     image = imread(path / SteinbockKeys.IMAGES_DIR / f"{sample}{SteinbockKeys.IMAGE_SUFFIX}", **imread_kwargs)
-    return Image2DModel.parse(data=image, transformations={sample: Identity()}, **image_models_kwargs)
+    return Image2DModel.parse(data=image, transformations={sample: Identity()}, rgb=None, **image_models_kwargs)
 
 
 def _get_labels(

src/spatialdata_io/readers/visium.py

@@ -221,6 +221,7 @@ def visium(
                 full_image,
                 scale_factors=[2, 2, 2, 2],
                 transformations={"global": transform_original},
+                rgb=None,
                 **image_models_kwargs,
             )
         else:
@@ -230,13 +231,15 @@ def visium(
         image_hires = imread(path / VisiumKeys.IMAGE_HIRES_FILE, **imread_kwargs).squeeze().transpose(2, 0, 1)
         image_hires = DataArray(image_hires, dims=("c", "y", "x"))
         images[dataset_id + "_hires_image"] = Image2DModel.parse(
-            image_hires, transformations={"downscaled_hires": Identity()}
+            image_hires, transformations={"downscaled_hires": Identity()}, rgb=None
         )
     if (path / VisiumKeys.IMAGE_LOWRES_FILE).exists():
         image_lowres = imread(path / VisiumKeys.IMAGE_LOWRES_FILE, **imread_kwargs).squeeze().transpose(2, 0, 1)
         image_lowres = DataArray(image_lowres, dims=("c", "y", "x"))
         images[dataset_id + "_lowres_image"] = Image2DModel.parse(
-            image_lowres, transformations={"downscaled_lowres": Identity()}
+            image_lowres,
+            transformations={"downscaled_lowres": Identity()},
+            rgb=None,
         )
 
     return SpatialData(images=images, shapes=shapes, table=table)

src/spatialdata_io/readers/xenium.py

@@ -562,7 +562,7 @@ def _get_images(
         # let's add a dummy channel as a temporary workaround.
         image = da.concatenate([image, da.zeros_like(image[0:1])], axis=0)
     return Image2DModel.parse(
-        image, transformations={"global": Identity()}, dims=("c", "y", "x"), **image_models_kwargs
+        image, transformations={"global": Identity()}, dims=("c", "y", "x"), rgb=None, **image_models_kwargs
     )