ENH: Implemennt ITKWASM_LABEL_IMAGE method

thewtex · Jan 25, 2024 · 199073c · 199073c
1 parent 1ef265d
commit 199073c
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Showing 5 changed files with 73 additions and 89 deletions.
diff --git a/ngff_zarr/methods/_itkwasm.py b/ngff_zarr/methods/_itkwasm.py
@@ -19,10 +19,10 @@ def _itkwasm_blur_and_downsample(
     image_data,
     shrink_factors,
     kernel_radius,
+    smoothing,
 ):
     """Blur and then downsample a given image chunk"""
     import itkwasm
-    from itkwasm_downsample import downsample
 
     # chunk does not have metadata attached, values are ITK defaults
     image = itkwasm.image_from_array(image_data)
@@ -32,9 +32,22 @@ def _itkwasm_blur_and_downsample(
     if any(block_len == 0 for block_len in block_size):
         return None
 
-    downsampled = downsample(
-        image, shrink_factors=shrink_factors, crop_radius=kernel_radius
-    )
+    if smoothing == "gaussian":
+        from itkwasm_downsample import downsample
+
+        downsampled = downsample(
+            image, shrink_factors=shrink_factors, crop_radius=kernel_radius
+        )
+    elif smoothing == "label_image":
+        from itkwasm_downsample import downsample_label_image
+
+        downsampled = downsample_label_image(
+            image, shrink_factors=shrink_factors, crop_radius=kernel_radius
+        )
+    else:
+        msg = f"Unknown smoothing method: {smoothing}"
+        raise ValueError(msg)
+
     return downsampled.data
 
 
@@ -134,8 +147,8 @@ def _downsample_itkwasm_bin_shrink(
     return multiscales
 
 
-def _downsample_itkwasm_gaussian(
-    ngff_image: NgffImage, default_chunks, out_chunks, scale_factors
+def _downsample_itkwasm(
+    ngff_image: NgffImage, default_chunks, out_chunks, scale_factors, smoothing
 ):
     import itkwasm
     from itkwasm_downsample import downsample_bin_shrink, gaussian_kernel_radius
@@ -232,6 +245,7 @@ def _downsample_itkwasm_gaussian(
                     data,
                     shrink_factors=shrink_factors,
                     kernel_radius=kernel_radius,
+                    smoothing=smoothing,
                     dtype=dtype,
                     depth=dict(enumerate(np.flip(kernel_radius))),  # overlap is in tzyx
                     boundary="nearest",
@@ -248,6 +262,7 @@ def _downsample_itkwasm_gaussian(
                 data,
                 shrink_factors=shrink_factors,
                 kernel_radius=kernel_radius,
+                smoothing=smoothing,
                 dtype=dtype,
                 depth=dict(enumerate(np.flip(kernel_radius))),  # overlap is in tzyx
                 boundary="nearest",

diff --git a/ngff_zarr/to_multiscales.py b/ngff_zarr/to_multiscales.py
@@ -22,8 +22,8 @@
     _downsample_itk_gaussian,
 )
 from .methods._itkwasm import (
+    _downsample_itkwasm,
     _downsample_itkwasm_bin_shrink,
-    _downsample_itkwasm_gaussian,
 )
 from .methods._support import _spatial_dims
 from .multiscales import Multiscales
@@ -311,8 +311,12 @@ def to_multiscales(
         method = Methods.DASK_IMAGE_GAUSSIAN
 
     if method is Methods.ITKWASM_GAUSSIAN:
-        images = _downsample_itkwasm_gaussian(
-            ngff_image, default_chunks, out_chunks, scale_factors
+        images = _downsample_itkwasm(
+            ngff_image, default_chunks, out_chunks, scale_factors, "gaussian"
+        )
+    elif method is Methods.ITKWASM_LABEL_IMAGE:
+        images = _downsample_itkwasm(
+            ngff_image, default_chunks, out_chunks, scale_factors, "label_image"
         )
     elif method is Methods.ITKWASM_BIN_SHRINK:
         images = _downsample_itkwasm_bin_shrink(

diff --git a/pyproject.toml b/pyproject.toml
@@ -31,7 +31,7 @@ classifiers = [
 ]
 dependencies = [
     "dask[array]",
-    "itkwasm >= 1.0b166",
+    "itkwasm >= 1.0b167",
     "itkwasm-downsample >= 1.1.0",
     "numpy",
     "platformdirs",

diff --git a/test/_data.py b/test/_data.py
@@ -27,11 +27,6 @@ def input_images():
     )
     result = {}
 
-    # store = DirectoryStore(
-    #     test_data_dir / "input" / "cthead1.zarr", dimension_separator="/"
-    # )
-    # image_ds = xr.open_zarr(store)
-    # image_da = image_ds.cthead1
     image = itk.imread(test_data_dir / "input" / "cthead1.png")
     image_ngff = itk_image_to_ngff_image(image)
     result["cthead1"] = image_ngff
@@ -45,19 +40,9 @@ def input_images():
     image_ngff = itk_image_to_ngff_image(image)
     result["brain_two_components"] = image_ngff
 
-    # store = DirectoryStore(
-    #     test_data_dir / "input" / "small_head.zarr", dimension_separator="/"
-    # )
-    # image_ds = xr.open_zarr(store)
-    # image_da = image_ds.small_head
-    # result["small_head"] = image_da
-
-    # store = DirectoryStore(
-    #     test_data_dir / "input" / "2th_cthead1.zarr",
-    # )
-    # image_ds = xr.open_zarr(store)
-    # image_da = image_ds['2th_cthead1']
-    # result["2th_cthead1"] = image_da
+    image = itk.imread(test_data_dir / "input" / "2th_cthead1.png")
+    image_ngff = itk_image_to_ngff_image(image)
+    result["2th_cthead1"] = image_ngff
 
     return result
 

diff --git a/test/test_to_ngff_zarr_itkwasm.py b/test/test_to_ngff_zarr_itkwasm.py
@@ -44,70 +44,50 @@ def test_gaussian_isotropic_scale_factors(input_images):
 #     verify_against_baseline(dataset_name, baseline_name, multiscale)
 
 
-# def test_gaussian_anisotropic_scale_factors(input_images):
-#     dataset_name = "cthead1"
-#     image = input_images[dataset_name]
-#     scale_factors = [{"x": 2, "y": 4}, {"x": 1, "y": 2}]
-#     multiscale = to_multiscale(image, scale_factors, method=Methods.DASK_IMAGE_GAUSSIAN)
-#     baseline_name = "x2y4_x1y2/DASK_IMAGE_GAUSSIAN"
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
-
-#     dataset_name = "small_head"
-#     image = input_images[dataset_name]
-#     scale_factors = [
-#         {"x": 3, "y": 2, "z": 4},
-#         {"x": 2, "y": 2, "z": 2},
-#         {"x": 1, "y": 2, "z": 1},
-#     ]
-#     multiscale = to_multiscale(image, scale_factors, method=Methods.DASK_IMAGE_GAUSSIAN)
-#     baseline_name = "x3y2z4_x2y2z2_x1y2z1/DASK_IMAGE_GAUSSIAN"
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
-
-
-# def test_label_nearest_isotropic_scale_factors(input_images):
-#     dataset_name = "2th_cthead1"
-#     image = input_images[dataset_name]
-#     baseline_name = "2_4/DASK_IMAGE_NEAREST"
-#     multiscale = to_multiscale(image, [2, 4], method=Methods.DASK_IMAGE_NEAREST)
-#     store_new_image(dataset_name, baseline_name, multiscale)
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
-
-#     dataset_name = "2th_cthead1"
-#     image = input_images[dataset_name]
-#     baseline_name = "2_3/DASK_IMAGE_NEAREST"
-#     multiscale = to_multiscale(image, [2, 3], method=Methods.DASK_IMAGE_NEAREST)
-#     store_new_image(dataset_name, baseline_name, multiscale)
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
-
+def test_gaussian_anisotropic_scale_factors(input_images):
+    dataset_name = "cthead1"
+    image = input_images[dataset_name]
+    scale_factors = [{"x": 2, "y": 2}, {"x": 2, "y": 4}]
+    multiscales = to_multiscales(image, scale_factors, method=Methods.ITKWASM_GAUSSIAN)
+    baseline_name = "x2y2_x2y4/ITKWASM_GAUSSIAN.zarr"
+    store_new_multiscales(dataset_name, baseline_name, multiscales)
+    verify_against_baseline(dataset_name, baseline_name, multiscales)
 
-# def test_label_nearest_anisotropic_scale_factors(input_images):
-#     dataset_name = "2th_cthead1"
-#     image = input_images[dataset_name]
-#     scale_factors = [{"x": 2, "y": 4}, {"x": 1, "y": 2}]
-#     multiscale = to_multiscale(image, scale_factors, method=Methods.DASK_IMAGE_NEAREST)
-#     baseline_name = "x2y4_x1y2/DASK_IMAGE_NEAREST"
-#     store_new_image(dataset_name, baseline_name, multiscale)
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
+    # dataset_name = "small_head"
+    # image = input_images[dataset_name]
+    # scale_factors = [
+    #     {"x": 3, "y": 2, "z": 4},
+    #     {"x": 2, "y": 2, "z": 2},
+    #     {"x": 1, "y": 2, "z": 1},
+    # ]
+    # multiscale = to_multiscale(image, scale_factors, method=Methods.DASK_IMAGE_GAUSSIAN)
+    # baseline_name = "x3y2z4_x2y2z2_x1y2z1/DASK_IMAGE_GAUSSIAN"
+    # verify_against_baseline(dataset_name, baseline_name, multiscale)
 
 
-# def test_label_mode_isotropic_scale_factors(input_images):
-#     dataset_name = "2th_cthead1"
-#     image = input_images[dataset_name]
-#     baseline_name = "2_4/DASK_IMAGE_MODE"
-#     multiscale = to_multiscale(image, [2, 4], method=Methods.DASK_IMAGE_MODE)
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
+def test_label_image_isotropic_scale_factors(input_images):
+    dataset_name = "2th_cthead1"
+    image = input_images[dataset_name]
+    baseline_name = "2_4/ITKWASM_LABEL_IMAGE.zarr"
+    multiscales = to_multiscales(image, [2, 4], method=Methods.ITKWASM_LABEL_IMAGE)
+    store_new_multiscales(dataset_name, baseline_name, multiscales)
+    verify_against_baseline(dataset_name, baseline_name, multiscales)
 
-#     dataset_name = "2th_cthead1"
-#     image = input_images[dataset_name]
-#     baseline_name = "2_3/DASK_IMAGE_MODE"
-#     multiscale = to_multiscale(image, [2, 3], method=Methods.DASK_IMAGE_MODE)
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
+    dataset_name = "2th_cthead1"
+    image = input_images[dataset_name]
+    baseline_name = "2_3/ITKWASM_LABEL_IMAGE.zarr"
+    multiscales = to_multiscales(image, [2, 3], method=Methods.ITKWASM_LABEL_IMAGE)
+    store_new_multiscales(dataset_name, baseline_name, multiscales)
+    verify_against_baseline(dataset_name, baseline_name, multiscales)
 
 
-# def test_label_mode_anisotropic_scale_factors(input_images):
-#     dataset_name = "2th_cthead1"
-#     image = input_images[dataset_name]
-#     scale_factors = [{"x": 2, "y": 4}, {"x": 1, "y": 2}]
-#     multiscale = to_multiscale(image, scale_factors, method=Methods.DASK_IMAGE_MODE)
-#     baseline_name = "x2y4_x1y2/DASK_IMAGE_MODE"
-#     verify_against_baseline(dataset_name, baseline_name, multiscale)
+def test_label_image_anisotropic_scale_factors(input_images):
+    dataset_name = "2th_cthead1"
+    image = input_images[dataset_name]
+    scale_factors = [{"x": 2, "y": 2}, {"x": 2, "y": 4}]
+    multiscales = to_multiscales(
+        image, scale_factors, method=Methods.ITKWASM_LABEL_IMAGE
+    )
+    baseline_name = "x2y4_x2y8/ITKWASM_LABEL_IMAGE.zarr"
+    store_new_multiscales(dataset_name, baseline_name, multiscales)
+    verify_against_baseline(dataset_name, baseline_name, multiscales)