Update cucim.skimage API to match scikit-image 0.19

benchmarks/skimage/cucim_exposure_bench.py

@@ -112,13 +112,13 @@ def set_args(self, dtype):
 for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
     ndim = len(shape)
 
-    multichannel = shape[-1] in [3, 4]
+    channel_axis = -1 if shape[-1] in [3, 4] else None
 
     B = MatchHistogramBench(
         function_name="match_histograms",
         shape=shape,
         dtypes=dtypes,
-        fixed_kwargs=dict(multichannel=multichannel),
+        fixed_kwargs=dict(channel_axis=channel_axis),
         var_kwargs=dict(),
         module_cpu=skimage.exposure,
         module_gpu=cucim.skimage.exposure,

benchmarks/skimage/cucim_feature_bench.py

@@ -74,7 +74,7 @@ def set_args(self, dtype):
             continue
 
         if function_name == "multiscale_basic_features":
-            fixed_kwargs["multichannel"] = shape[-1] == 3
+            fixed_kwargs["channel_axis"] = -1 if shape[-1] == 3 else None
             if ndim == 3 and shape[-1] != 3:
                 # Omit texture=True case to avoid excessive GPU memory usage
                 var_kwargs["texture"] = [False]

benchmarks/skimage/cucim_filters_bench.py

@@ -128,15 +128,17 @@
             var_kwargs["frequency"] = [f for f in var_kwargs["frequency"] if f >= 0.1]
 
         if function_name == "median":
-            selems = []
+            footprints = []
             ndim = len(shape)
-            selem_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
-            for selem_size in [3, 5, 7, 9]:
-                selems.append(np.ones((selem_size,) * ndim, dtype=bool))
-            var_kwargs["selem"] = selems
+            footprint_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
+            for footprint_size in [3, 5, 7, 9]:
+                footprints.append(
+                    np.ones((footprint_size,) * ndim, dtype=bool)
+                )
+            var_kwargs["footprint"] = footprints
 
         if function_name in ["gaussian", "unsharp_mask"]:
-            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+            fixed_kwargs["channel_axis"] = -1 if shape[-1] == 3 else None
 
         B = ImageBench(
             function_name=function_name,

benchmarks/skimage/cucim_measure_bench.py

@@ -246,15 +246,17 @@ def set_args(self, dtype):
             fixed_kwargs["dst"] = (shape[0] - 32, shape[1] + 9)
 
         if function_name == "median":
-            selems = []
+            footprints = []
             ndim = len(shape)
-            selem_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
-            for selem_size in [3, 5, 7, 9]:
-                selems.append(np.ones((selem_sizes,) * ndim, dtype=bool))
-            var_kwargs["selem"] = selems
+            footprint_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
+            for footprint_size in [3, 5, 7, 9]:
+                footprints.append(
+                    np.ones((footprint_sizes,) * ndim, dtype=bool)
+                )
+            var_kwargs["footprint"] = footprints
 
         if function_name in ["gaussian", "unsharp_mask"]:
-            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+            fixed_kwargs["channel_axis"] = -1 if shape[-1] == 3 else None
 
         B = FiltersBench(
             function_name=function_name,

benchmarks/skimage/cucim_metrics_bench.py

@@ -68,7 +68,7 @@ def set_args(self, dtype):
             continue
 
         if function_name in ["structural_similarity"]:
-            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+            fixed_kwargs["channel_axis"] = -1 if shape[-1] == 3 else None
 
         B = MetricsBench(
             function_name=function_name,

benchmarks/skimage/cucim_morphology_bench.py

@@ -23,7 +23,7 @@ def __init__(
         self,
         function_name,
         shape,
-        selem=None,
+        footprint=None,
         dtypes=[bool],
         fixed_kwargs={},
         index_str="",
@@ -32,9 +32,9 @@ def __init__(
         module_gpu=cucim.skimage.morphology,
     ):
 
-        array_kwargs = dict(selem=selem)
-        if "selem" in fixed_kwargs:
-            raise ValueError("fixed_kwargs cannot contain 'selem'")
+        array_kwargs = dict(footprint=footprint)
+        if "footprint" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'footprint'")
         fixed_kwargs = copy.deepcopy(fixed_kwargs)
         fixed_kwargs.update(array_kwargs)
 
@@ -110,7 +110,7 @@ def set_args(self, dtype):
         all_results = pickle.load(f)
 else:
     all_results = pd.DataFrame()
-dtypes_grey = [np.float32]
+dtypes_gray = [np.float32]
 
 
 for function_name, fixed_kwargs, var_kwargs, allow_nd in [
@@ -155,13 +155,13 @@ def set_args(self, dtype):
 
         for connectivity in range(1, ndim + 1):
             index_str = f"conn={connectivity}"
-            selem = ndi.generate_binary_structure(ndim, connectivity)
+            footprint = ndi.generate_binary_structure(ndim, connectivity)
 
             B = BinaryMorphologyBench(
                 function_name=function_name,
                 shape=shape,
                 dtypes=[bool],
-                selem=selem,
+                footprint=footprint,
                 fixed_kwargs={},
                 var_kwargs=var_kwargs,
                 index_str=index_str,
@@ -213,7 +213,7 @@ def set_args(self, dtype):
     ("black_tophat", dict(), dict(), False, True),
     # greyreconstruct.py
     ("reconstruction", dict(), dict(), False, True),
-    # selem.py
+    # footprints.py
     # OMIT the functions from this file (each creates a structuring element)
 ]:
 
@@ -235,15 +235,17 @@ def set_args(self, dtype):
             var_kwargs["frequency"] = [f for f in var_kwargs["frequency"] if f >= 0.1]
 
         if function_name == "median":
-            selems = []
+            footprints = []
             ndim = len(shape)
-            selem_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
-            for selem_size in [3, 5, 7, 9]:
-                selems.append(np.ones((selem_sizes,) * ndim, dtype=bool))
-            var_kwargs["selem"] = selems
+            footprint_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
+            for footprint_size in [3, 5, 7, 9]:
+                footprints.append(
+                    np.ones((footprint_sizes,) * ndim, dtype=bool)
+                )
+            var_kwargs["footprint"] = footprints
 
         if function_name in ["gaussian", "unsharp_mask"]:
-            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+            fixed_kwargs["channel_axis"] = -1 if shape[-1] == 3 else None
 
         if function_name == "reconstruction":
             TestClass = ReconstructionBench
@@ -253,7 +255,7 @@ def set_args(self, dtype):
         B = TestClass(
             function_name=function_name,
             shape=shape,
-            dtypes=dtypes_grey,
+            dtypes=dtypes_gray,
             fixed_kwargs=fixed_kwargs,
             var_kwargs=var_kwargs,
             module_cpu=skimage.morphology,

benchmarks/skimage/cucim_restoration_bench.py

@@ -127,7 +127,7 @@ def set_args(self, dtype):
             continue
 
         if function_name == "denoise_tv_chambolle":
-            fixed_kwargs["multichannel"] = shape[-1] == 3
+            fixed_kwargs["channel_axis"] = -1 if shape[-1] == 3 else None
 
         if function_name == "calibrate_denoiser":
             denoise_class = CalibratedDenoiseBench
@@ -152,7 +152,7 @@ def set_args(self, dtype):
     # deconvolution.py
     ("wiener", dict(balance=100.0), dict(), False, False),
     ("unsupervised_wiener", dict(), dict(), False, False),
-    ("richardson_lucy", dict(), dict(iterations=[5]), False, True),
+    ("richardson_lucy", dict(), dict(num_iter=[5]), False, True),
 ]:
 
     for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:

benchmarks/skimage/cucim_segmentation_bench.py

@@ -140,14 +140,14 @@ def set_args(self, dtype):
     (
         "morphological_geodesic_active_contour",
         dict(),
-        dict(iterations=[16], init_level_set=["checkerboard", "disk"]),
+        dict(num_iter=[16], init_level_set=["checkerboard", "disk"]),
         False,
         False,
     ),
     (
         "morphological_chan_vese",
         dict(),
-        dict(iterations=[16], init_level_set=["checkerboard", "disk"]),
+        dict(num_iter=[16], init_level_set=["checkerboard", "disk"]),
         False,
         False,
     ),

benchmarks/skimage/cucim_transform_bench.py

@@ -27,6 +27,13 @@
         True,
         True,
     ),  # scale handled in loop below
+    (
+        "resize_local_mean",
+        dict(preserve_range=True),
+        {},
+        True,
+        True,
+    ),  # scale handled in loop below
     (
         "rescale",
         dict(preserve_range=True),
@@ -87,15 +94,15 @@
         ndim_spatial = ndim - 1 if shape[-1] == 3 else ndim
 
         if function_name in ["rescale", "warp_polar", "pyramid_gaussian", "pyramid_laplacian"]:
-            fixed_kwargs["multichannel"] = ndim_spatial < ndim
+            fixed_kwargs["channel_axis"] = -1 if ndim_spatial < ndim else None
 
         function_is_generator = function_name in ["pyramid_gaussian", "pyramid_laplacian"]
 
-        if function_name in ["rescale", "resize"]:
+        if function_name in ["rescale", "resize", "resize_local_mean"]:
             scales = [0.75, 1.25]
             if function_name == "rescale":
                 var_kwargs["scale"] = [(s,) * ndim_spatial for s in scales]
-            elif function_name == "resize":
+            elif function_name.startswith("resize"):
                 out_shapes = [[int(s_ * s) for s_ in shape] for s in scales]
                 if ndim_spatial < ndim:
                     # don't resize along channels dimension

conda/environments/env.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
 dependencies:
   - cupy>=9
-  - scikit-image=0.18.1
+  - scikit-image>=0.18.1
   - openslide
   - zlib
   - jpeg

conda/recipes/cucim/meta.yaml

@@ -36,7 +36,7 @@ requirements:
     - cupy >=9,<11.0.0a0
     - numpy 1.18
     - scipy
-    - scikit-image 0.18.1
+    - scikit-image >=0.18.1,<0.20.0a0
   run:
     - {{ pin_compatible('cudatoolkit', max_pin='x', min_pin='x') }}
     - python {{ python_version }}.*
@@ -45,7 +45,7 @@ requirements:
     - cupy >=9,<11.0.0a0
     - {{ pin_compatible('numpy') }}
     - scipy
-    - scikit-image 0.18.1
+    - scikit-image >=0.18.1,<0.20.0a0
     # - openslide # skipping here but benchmark binary would needs openslide library
 
 tests:                                 # [linux64]

python/cucim/setup.cfg

@@ -21,16 +21,18 @@ ignore =
     W504
     # whitespace before :
     E203
+    # f-string is missing placeholders
+    F541
 exclude = .tox,.eggs,ci/templates,build,dist,.git,__pycache__,doc/conf.py,doc/sphinxext,build,dist,__init__.py
 per-file-ignores =
     setup.py:F821
     versioneer.py:W605
     src/localtest.py:E127
     src/cucim/skimage/__init__.py:F401
-    src/cucim/skimage/util/tests/test_shape.py:E201,E202,E241
     src/cucim/skimage/measure/tests/test_block.py:E201,E202,E241
-    src/cucim/skimage/transform/tests/test_warps.py:E201,E202,E241,W605
     src/cucim/skimage/transform/_geometric.py:E201,E202,E241
+    src/cucim/skimage/transform/tests/test_warps.py:E201,E202,E241,W605
+    src/cucim/skimage/util/tests/test_shape.py:E201,E202,E241
     src/cucim/core/operations/expose/transform.py:F401
 
 [tool:pytest]

python/cucim/src/cucim/__init__.py

@@ -37,22 +37,41 @@
 # If cucim.clara package is imported first, you may see the following error when running on CUDA 10.x (#44)
 #   python3: Relink `/usr/lib/x86_64-linux-gnu/libnccl.so.2.8.3' with `/lib/x86_64-linux-gnu/librt.so.1' for IFUNC symbol `clock_gettime'
 #   Segmentation fault
+submodules = []
+submod_attrs = {}
+
 try:
     import cupy
     _is_cupy_available = True
+    submodules += ['core', 'skimage']
 except ImportError:
     pass
 
 try:
     from .clara import CuImage, __version__, cli
     _is_clara_available = True
+    submodules += ['clara']
+    submod_attrs['clara'] = ['CuImage', 'cli']
 except ImportError:
     from ._version import get_versions
     __version__ = get_versions()['version']
     del get_versions
     del _version
 
 
+from .skimage._shared import lazy
+
+__getattr__, __lazy_dir__, _ = lazy.attach(
+    __name__,
+    submodules,
+    submod_attrs,
+)
+
+
+def __dir__():
+    return __lazy_dir__() + ['__version__', 'is_available']
+
+
 def is_available(module_name: str = "") -> bool:
     """Check if a specific module is available.
 

python/cucim/src/cucim/skimage/__init__.py

@@ -12,6 +12,8 @@
 -----------
 color
     Color space conversion.
+data
+    Test images and example data.
 exposure
     Image intensity adjustment, e.g., histogram equalization, etc.
 feature
@@ -58,7 +60,35 @@
 
 """
 
-# All skimage root imports go here
-from .util.dtype import (dtype_limits, img_as_bool, img_as_float,
-                         img_as_float32, img_as_float64, img_as_int,
-                         img_as_ubyte, img_as_uint)
+from ._shared import lazy
+
+submodules = [
+    'color',
+    'data',
+    'exposure',
+    'feature',
+    'filters',
+    'measure',
+    'metrics',
+    'morphology',
+    'registration',
+    'restoration',
+    'segmentation',
+    'transform',
+    'util',
+]
+
+
+__getattr__, __lazy_dir__, _ = lazy.attach(
+    __name__,
+    submodules,
+    {'util.dtype': ['dtype_limits', 'img_as_bool', 'img_as_float',
+                    'img_as_float32', 'img_as_float64', 'img_as_int',
+                    'img_as_ubyte', 'img_as_uint'],
+     'util.lookfor': ['lookfor'],
+    }
+)
+
+
+def __dir__():
+    return __lazy_dir__()

python/cucim/src/cucim/skimage/_shared/_dependency_checks.py

@@ -0,0 +1,11 @@
+from .version_requirements import is_installed
+
+has_mpl = is_installed("matplotlib", ">=3.0.3")
+if has_mpl:
+    try:
+        # will fail with
+        #    ImportError: Failed to import any qt binding
+        # if only matplotlib-base is installed
+        from matplotlib import pyplot  # noqa
+    except ImportError:
+        has_mpl = False