Update reader.py to v0.3

.isort.cfg

@@ -1,5 +1,5 @@
 [settings]
-known_third_party = cv2,dask,numpy,pytest,scipy,setuptools,skimage,vispy,zarr
+known_third_party = dask,numpy,pytest,scipy,setuptools,skimage,vispy,zarr
 multi_line_output=6
 include_trailing_comma=False
 force_grid_wrap=0

environment.yml

@@ -8,9 +8,7 @@ dependencies:
   - ipython
   - mypy
   - omero-py
-  - opencv
   - pip
-  - py-opencv
   - pytest
   - requests
   - s3fs

ome_zarr/format.py

@@ -13,6 +13,7 @@ def format_implementations() -> Iterator["Format"]:
     """
     Return an instance of each format implementation, newest to oldest.
     """
+    yield FormatV03()
     yield FormatV02()
     yield FormatV01()
 
@@ -124,4 +125,15 @@ def init_store(self, path: str, mode: str = "r") -> FSStore:
         return store
 
 
-CurrentFormat = FormatV02
+class FormatV03(FormatV02):  # inherits from V02 to avoid code duplication
+    """
+    Changelog: variable number of dimensions (up to 5),
+    introduce axes field in multiscales (June 2021)
+    """
+
+    @property
+    def version(self) -> str:
+        return "0.3"
+
+
+CurrentFormat = FormatV03

ome_zarr/reader.py

@@ -262,10 +262,17 @@ def matches(zarr: ZarrLocation) -> bool:
     def __init__(self, node: Node) -> None:
         super().__init__(node)
 
+        axes_values = {"t", "c", "z", "y", "x"}
         try:
             multiscales = self.lookup("multiscales", [])
-            version = multiscales[0].get("version", "0.1")
+            version = multiscales[0].get(
+                "version", "0.1"
+            )  # should this be matched with Format.version?
             datasets = multiscales[0]["datasets"]
+            # axes field was introduced in 0.3, before all data was 5d
+            axes = tuple(multiscales[0].get("axes", ["t", "c", "z", "y", "x"]))
+            if len(set(axes) - axes_values) > 0:
+                raise RuntimeError(f"Invalid axes names: {set(axes) - axes_values}")
             datasets = [d["path"] for d in datasets]
             self.datasets: List[str] = datasets
             LOGGER.info("datasets %s", datasets)
@@ -274,14 +281,13 @@ def __init__(self, node: Node) -> None:
             return  # EARLY EXIT
 
         for resolution in self.datasets:
-            # data.shape is (t, c, z, y, x) by convention
             data: da.core.Array = self.array(resolution, version)
             chunk_sizes = [
                 str(c[0]) + (" (+ %s)" % c[-1] if c[-1] != c[0] else "")
                 for c in data.chunks
             ]
             LOGGER.info("resolution: %s", resolution)
-            LOGGER.info(" - shape (t, c, z, y, x) = %s", data.shape)
+            LOGGER.info(" - shape %s = %s", axes, data.shape)
             LOGGER.info(" - chunks =  %s", chunk_sizes)
             LOGGER.info(" - dtype = %s", data.dtype)
             node.data.append(data)

ome_zarr/scale.py

@@ -9,11 +9,15 @@
 from dataclasses import dataclass
 from typing import Callable, Iterator, List
 
-import cv2
 import numpy as np
 import zarr
 from scipy.ndimage import zoom
-from skimage.transform import downscale_local_mean, pyramid_gaussian, pyramid_laplacian
+from skimage.transform import (
+    downscale_local_mean,
+    pyramid_gaussian,
+    pyramid_laplacian,
+    resize,
+)
 
 from .io import parse_url
 
@@ -123,19 +127,21 @@ def __create_group(
 
     def nearest(self, base: np.ndarray) -> List[np.ndarray]:
         """
-        Downsample using :func:`cv2.resize`.
+        Downsample using :func:`skimage.transform.resize`.
 
         The :const:`cvs2.INTER_NEAREST` interpolation method is used.
         """
         return self._by_plane(base, self.__nearest)
 
     def __nearest(self, plane: np.ndarray, sizeY: int, sizeX: int) -> np.ndarray:
         """Apply the 2-dimensional transformation."""
-        return cv2.resize(
+        return resize(
             plane,
-            dsize=(sizeY // self.downscale, sizeX // self.downscale),
-            interpolation=cv2.INTER_NEAREST,
-        )
+            output_shape=(sizeY // self.downscale, sizeX // self.downscale),
+            order=0,
+            preserve_range=True,
+            anti_aliasing=False,
+        ).astype(plane.dtype)
 
     def gaussian(self, base: np.ndarray) -> List[np.ndarray]:
         """Downsample using :func:`skimage.transform.pyramid_gaussian`."""

setup.py

@@ -25,7 +25,6 @@ def read(fname):
 install_requires += (["scikit-image"],)
 install_requires += (["toolz"],)
 install_requires += (["vispy"],)
-install_requires += (["opencv-contrib-python-headless"],)
 
 
 setup(

tests/test_ome_zarr.py

@@ -9,7 +9,7 @@
 
 def log_strings(idx, t, c, z, y, x, ct, cc, cz, cy, cx, dtype):
     yield f"resolution: {idx}"
-    yield f" - shape (t, c, z, y, x) = ({t}, {c}, {z}, {y}, {x})"
+    yield f" - shape ('t', 'c', 'z', 'y', 'x') = ({t}, {c}, {z}, {y}, {x})"
     yield f" - chunks =  ['{ct}', '{cc}', '{cz}', '{cx}', '{cy}']"
     yield f" - dtype = {dtype}"