ENH: Add label image downsampling support

Also switch to DASK_IMAGE_GAUSSIAN for the default intensity
downsampler (better quality).

Author: thewtex

itkwidgets/integrations/__init__.py

@@ -4,7 +4,7 @@
 import itkwasm
 import numpy as np
 import zarr
-from multiscale_spatial_image import MultiscaleSpatialImage, to_multiscale, itk_image_to_multiscale
+from multiscale_spatial_image import MultiscaleSpatialImage, to_multiscale, itk_image_to_multiscale, Methods
 from spatial_image import to_spatial_image, is_spatial_image
 
 import dask
@@ -42,8 +42,12 @@ def _make_multiscale_store(multiscale):
     multiscale.to_zarr(store, compute=True)
     return store
 
-def _get_viewer_image(image):
+def _get_viewer_image(image, label=False):
     min_length = 64
+    if label:
+        method = Methods.DASK_IMAGE_NEAREST
+    else:
+        method = Methods.DASK_IMAGE_GAUSSIAN
     if isinstance(image, MultiscaleSpatialImage):
         return _make_multiscale_store(image)
 
@@ -68,27 +72,27 @@ def _get_viewer_image(image):
                 previous = scale_factor
                 scale_factors.append(scale_factor)
 
-            multiscale = itk_image_to_multiscale(image, scale_factors=scale_factors)
+            multiscale = itk_image_to_multiscale(image, scale_factors=scale_factors, method=method)
             return _make_multiscale_store(multiscale)
 
     if HAVE_VTK:
         import vtk
         if isinstance(image, vtk.vtkImageData):
             spatial_image = vtk_image_to_spatial_image(image)
             scale_factors = _spatial_image_scale_factors(spatial_image, min_length)
-            multiscale = to_multiscale(spatial_image, scale_factors)
+            multiscale = to_multiscale(spatial_image, scale_factors, method=method)
             return _make_multiscale_store(multiscale)
 
     if isinstance(image, dask.array.core.Array):
         spatial_image = to_spatial_image(image)
         scale_factors = _spatial_image_scale_factors(spatial_image, min_length)
-        multiscale = to_multiscale(spatial_image, scale_factors)
+        multiscale = to_multiscale(spatial_image, scale_factors, method=method)
         return _make_multiscale_store(multiscale)
 
     if isinstance(image, zarr.Array):
         spatial_image = to_spatial_image(image)
         scale_factors = _spatial_image_scale_factors(spatial_image, min_length)
-        multiscale = to_multiscale(spatial_image, scale_factors)
+        multiscale = to_multiscale(spatial_image, scale_factors, method=method)
         return _make_multiscale_store(multiscale)
 
     # NGFF Zarr
@@ -100,30 +104,30 @@ def _get_viewer_image(image):
         if isinstance(image, torch.Tensor):
             spatial_image = to_spatial_image(image.numpy())
             scale_factors = _spatial_image_scale_factors(spatial_image, min_length)
-            multiscale = to_multiscale(spatial_image, scale_factors)
+            multiscale = to_multiscale(spatial_image, scale_factors, method=method)
             return _make_multiscale_store(multiscale)
 
     # Todo: preserve dask Array, if present, check if dims are NGFF -> use dims, coords
     # Check if coords are uniform, if not, resample
     if isinstance(image, xr.DataArray):
         if is_spatial_image(image):
             scale_factors = _spatial_image_scale_factors(image, min_length)
-            multiscale = to_multiscale(image, scale_factors)
+            multiscale = to_multiscale(image, scale_factors, method=method)
             return _make_multiscale_store(multiscale)
 
         return xarray_data_array_to_numpy(image)
     if isinstance(image, xr.Dataset):
         da = image[next(iter(image.variables.keys()))]
         if is_spatial_image(da):
             scale_factors = _spatial_image_scale_factors(da, min_length)
-            multiscale = to_multiscale(da, scale_factors)
+            multiscale = to_multiscale(da, scale_factors, method=method)
             return _make_multiscale_store(multiscale)
         return xarray_data_set_to_numpy(image)
 
     if isinstance(image, np.ndarray):
         spatial_image = to_spatial_image(image)
         scale_factors = _spatial_image_scale_factors(spatial_image, min_length)
-        multiscale = to_multiscale(spatial_image, scale_factors)
+        multiscale = to_multiscale(spatial_image, scale_factors, method=method)
         return _make_multiscale_store(multiscale)
     raise RuntimeError("Could not process the viewer image")
 

itkwidgets/viewer.py

@@ -19,6 +19,7 @@
 ]
 
 _viewer_count = 1
+_codecs_registered = False
 
 
 class ViewerRPC:
@@ -79,7 +80,10 @@ async def run(self, ctx):
             render_type = _detect_render_type(data, input_type)
             key = init_key_aliases()[input_type]
             if render_type is RenderType.IMAGE:
-                result = _get_viewer_image(data)
+                if input_type == 'label_image':
+                    result = _get_viewer_image(data, label=True)
+                else:
+                    result = _get_viewer_image(data, label=False)
             elif render_type is RenderType.POINT_SET:
                 result = _get_viewer_point_sets(data)
             if result is None:
@@ -208,7 +212,7 @@ def set_background_color(self, bgColor: List[float]):
     def set_image(self, image: Image):
         render_type = _detect_render_type(image, 'image')
         if render_type is RenderType.IMAGE:
-            image = _get_viewer_image(image)
+            image = _get_viewer_image(image, label=False)
             self.queue_request('setImage', image)
         elif render_type is RenderType.POINT_SET:
             image = _get_viewer_point_sets(image)
@@ -250,7 +254,7 @@ def set_image_volume_sample_distance(self, distance: float):
     def set_label_image(self, label_image: Image):
         render_type = _detect_render_type(label_image, 'image')
         if render_type is RenderType.IMAGE:
-            label_image = _get_viewer_image(label_image)
+            label_image = _get_viewer_image(label_image, label=True)
             self.queue_request('setImage', label_image)
         elif render_type is RenderType.POINT_SET:
             label_image = _get_viewer_point_sets(label_image)

pyproject.toml

@@ -33,13 +33,13 @@ keywords = [
     "webgpu",
 ]
 
-requires-python = ">=3.7"
+requires-python = ">=3.8"
 dependencies = [
     "itkwasm >= 1.0b1",
     "imjoy-rpc >= 0.5.16",
     "imjoy-utils >= 0.1.2",
     "numcodecs",
-    "multiscale_spatial_image >= 0.10.1",
+    "multiscale_spatial_image[dask-image] >= 0.11.1",
     "zarr",
 ]