Add zarr test

.gitignore

@@ -137,3 +137,4 @@ dmypy.json
 
 # Generated test images
 *.tif
+*.zarr

qubalab/images/bioio_server.py

@@ -35,7 +35,7 @@ def __init__(self, path: str, scene: int = 0, detect_resolutions=True, bioio_kwa
         """
         super().__init__(**kwargs)
         self._path = path
-        self._reader = BioImage(path, dask_tiles=True, **bioio_kwargs)
+        self._reader = BioImage(path, **bioio_kwargs)
         self._scene = scene
         self._detect_resolutions = detect_resolutions
 

tests/images/test_bioio_server.py

@@ -2,7 +2,7 @@
 from qubalab.images.bioio_server import BioIOServer
 from qubalab.images.region_2d import Region2D
 from qubalab.images.metadata.pixel_calibration import PixelCalibration, PixelLength
-from ..res import multi_resolution_uint8_3channels, single_resolution_float_5d, single_resolution_rgb_image
+from ..res import multi_resolution_uint8_3channels, single_resolution_float_5d, single_resolution_float_5d_zarr, single_resolution_rgb_image
 
 
 def test_uint8_3channels_image_name():
@@ -217,6 +217,116 @@ def test_read_float_5d_image_with_dask():
     bioio_server.close()
 
 
+
+## start
+
+def test_float_5d_zarr_image_name():
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+
+    name = bioio_server.metadata.name
+
+    assert name == single_resolution_float_5d_zarr.get_name()
+
+    bioio_server.close()
+
+
+def test_float_5d_zarr_image_shapes():
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+
+    shapes = bioio_server.metadata.shapes
+
+    assert shapes == single_resolution_float_5d_zarr.get_shapes()
+
+    bioio_server.close()
+
+
+def test_float_5d_zarr_image_pixel_calibration():
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+
+    pixel_calibration = bioio_server.metadata.pixel_calibration
+
+    assert pixel_calibration == PixelCalibration(
+        PixelLength(single_resolution_float_5d_zarr.get_pixel_size_x_y_in_micrometers()),      # The BioIO library does not currently handle unit attachment
+        PixelLength(single_resolution_float_5d_zarr.get_pixel_size_x_y_in_micrometers()),
+        PixelLength(single_resolution_float_5d_zarr.get_pixel_size_x_y_in_micrometers())
+    )
+
+    bioio_server.close()
+
+
+def test_float_5d_zarr_image_is_not_rgb():
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+
+    is_rgb = bioio_server.metadata.is_rgb
+
+    assert not(is_rgb)
+
+    bioio_server.close()
+
+
+def test_float_5d_zarr_image_dtype():
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+
+    dtype = bioio_server.metadata.dtype
+
+    assert dtype == single_resolution_float_5d_zarr.get_dtype()
+
+    bioio_server.close()
+
+
+def test_float_5d_zarr_image_downsamples():
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+
+    downsamples = bioio_server.metadata.downsamples
+
+    assert downsamples == single_resolution_float_5d_zarr.get_downsamples()
+
+    bioio_server.close()
+
+
+def test_read_float_5d_zarr_image():
+    full_resolution = single_resolution_float_5d_zarr.get_shapes()[0]
+    z = int(full_resolution.z / 2)
+    t = int(full_resolution.t / 2)
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+    expected_pixels = np.array(
+        [[[single_resolution_float_5d_zarr.get_pixel_value(x, y, c, z, t)
+            for x in range(full_resolution.x)]
+            for y in range(full_resolution.y)]
+            for c in range(full_resolution.c)],
+        single_resolution_float_5d_zarr.get_dtype()
+    )
+
+    image = bioio_server.read_region(
+        1,
+        Region2D(width=bioio_server.metadata.width, height=bioio_server.metadata.height, z=z, t=t)
+    )
+
+    np.testing.assert_array_equal(image, expected_pixels)
+
+    bioio_server.close()
+
+
+def test_read_float_5d_zarr_image_with_dask():
+    full_resolution = single_resolution_float_5d_zarr.get_shapes()[0]
+    bioio_server = BioIOServer(single_resolution_float_5d_zarr.get_path())
+    expected_pixels = np.array(
+        [[[[[single_resolution_float_5d_zarr.get_pixel_value(x, y, c, z, t)
+            for x in range(full_resolution.x)]
+            for y in range(full_resolution.y)]
+            for z in range(full_resolution.z)]
+            for c in range(full_resolution.c)]
+            for t in range(full_resolution.t)],
+        single_resolution_float_5d_zarr.get_dtype()
+    )
+
+    image = bioio_server.level_to_dask(0).compute()
+
+    np.testing.assert_array_equal(image, expected_pixels)
+
+    bioio_server.close()
+
+
 def test_rgb_image_name():
     bioio_server = BioIOServer(single_resolution_rgb_image.get_path())
 

tests/res/single_resolution_float_5d_zarr.py

@@ -0,0 +1,60 @@
+import bioio.writers
+import os
+import shutil
+import numpy as np
+from bioio_base.types import PhysicalPixelSizes
+from qubalab.images.metadata.image_shape import ImageShape
+
+
+def get_name() -> str:
+    return "single_resolution_float_5d.ome.zarr"
+
+
+def get_path() -> str:
+    return os.path.realpath(os.path.join(os.path.realpath(__file__), os.pardir, get_name()))
+
+
+def get_shapes() -> tuple[ImageShape, ...]:
+    return (ImageShape(32, 16, 10, 5, 15),)
+
+
+def get_pixel_size_x_y_in_micrometers() -> float:
+    return 0.5
+
+
+def get_dtype():
+    return np.float64
+
+
+def get_downsamples() -> tuple[float, ...]:
+    return tuple([get_shapes()[0].x / shape.x for shape in get_shapes()])
+
+
+def get_pixel_value(x: int, y: int, c: int, z: int, t: int) -> int:
+    return pixels[t, c, z, y, x]
+
+
+def _get_pixels() -> np.array:
+    return np.random.rand(get_shapes()[0].t, get_shapes()[0].c, get_shapes()[0].z, get_shapes()[0].y, get_shapes()[0].x)
+
+
+def _write_image(pixels: np.array):
+    ## zarr writer fails if dir exists
+    if os.path.exists(get_path()) and os.path.isdir(get_path()):
+        shutil.rmtree(get_path())
+
+    zarr = bioio.writers.OmeZarrWriter(get_path())
+    zarr.write_image(
+        image_data = pixels,
+        image_name = "single_resolution_float_5d",
+        channel_names = ["Channel " + str(i) for i in range(get_shapes()[0].c)],
+        channel_colors = [i for i in range(get_shapes()[0].c)],
+        physical_pixel_sizes = PhysicalPixelSizes(
+            X = get_pixel_size_x_y_in_micrometers(),
+            Y = get_pixel_size_x_y_in_micrometers(),
+            Z = get_pixel_size_x_y_in_micrometers())
+    )
+
+
+pixels = _get_pixels()
+_write_image(pixels)