def upsample_mags(
path: Path,
layer_name: Optional[str] = None,
from_mag: Optional[Mag] = None,
target_mag: Mag = Mag(1),
buffer_shape: Optional[Vec3Int] = None,
compress: bool = True,
args: Optional[Namespace] = None,
sampling_mode: Union[str, SamplingModes] = SamplingModes.ANISOTROPIC,
) -> None:
assert layer_name and from_mag or not layer_name and not from_mag, (
"You provided only one of the following "
"parameters: layer_name, from_mag but both "
"need to be set or none. If you don't provide "
"the parameters you need to provide the path "
"argument with the mag and layer to upsample"
" (e.g dataset/color/1)."
)
if not layer_name or not from_mag:
layer_name = path.parent.name
from_mag = Mag(path.name)
path = path.parent.parent
Dataset.open(path).get_layer(layer_name).upsample(
from_mag=from_mag,
finest_mag=target_mag,
compress=compress,
sampling_mode=sampling_mode,
buffer_shape=buffer_shape,
args=args,
)
def test_default_anisotropic_voxel_size(tmp_path: Path) -> None:
ds = Dataset(tmp_path / "default_anisotropic_voxel_size",
voxel_size=(85, 85, 346))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag(1)
mag.write(data=(np.random.rand(10, 20, 30) * 255).astype(np.uint8))
layer.downsample(Mag(1), None, "median", True)
assert sorted(layer.mags.keys()) == [Mag("1"), Mag("2-2-1"), Mag("4-4-1")]
def detect_mag_path(
dataset_path: Path, layer: str, mag: Mag = Mag(1)
) -> Optional[Path]:
layer_path = dataset_path / layer / str(mag)
if layer_path.exists():
return layer_path
layer_path = dataset_path / layer / mag.to_long_layer_name()
if layer_path.exists():
return layer_path
return None
def test_in_mag() -> None:
with pytest.raises(AssertionError):
BoundingBox((1, 2, 3), (9, 9, 9)).in_mag(Mag(2))
with pytest.raises(AssertionError):
BoundingBox((2, 2, 2), (9, 9, 9)).in_mag(Mag(2))
assert BoundingBox(
(2, 2, 2),
(10, 10, 10)).in_mag(Mag(2)) == BoundingBox(topleft=(1, 1, 1),
size=(5, 5, 5))
def test_downsample_with_invalid_mag_list(tmp_path: Path) -> None:
ds = Dataset(tmp_path / "downsample_mag_list", voxel_size=(1, 1, 2))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag(1)
mag.write(data=(np.random.rand(10, 20, 30) * 255).astype(np.uint8))
with pytest.raises(AssertionError):
layer.downsample_mag_list(
from_mag=Mag(1),
target_mags=[Mag(1),
Mag([1, 1, 2]),
Mag([2, 2, 1]),
Mag(2)],
)
def test_downsample_mag_list(tmp_path: Path) -> None:
ds = Dataset(tmp_path / "downsample_mag_list", voxel_size=(1, 1, 2))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag(1)
mag.write(data=(np.random.rand(10, 20, 30) * 255).astype(np.uint8))
target_mags = [Mag([4, 4, 8]),
Mag(2), Mag([32, 32, 8]),
Mag(32)] # unsorted list
layer.downsample_mag_list(from_mag=Mag(1), target_mags=target_mags)
for m in target_mags:
assert m in layer.mags
def test_default_parameter(tmp_path: Path) -> None:
target_path = tmp_path / "downsaple_default"
ds = Dataset(target_path, voxel_size=(1, 1, 1))
layer = ds.add_layer("color",
COLOR_CATEGORY,
dtype_per_channel="uint8",
num_channels=3)
mag = layer.add_mag("2")
mag.write(data=(np.random.rand(3, 10, 20, 30) * 255).astype(np.uint8))
layer.downsample()
# The max_mag is Mag(4) in this case (see test_default_max_mag)
assert sorted(layer.mags.keys()) == [Mag("2"), Mag("4")]
def test_upsampling(tmp_path: Path) -> None:
ds = Dataset(tmp_path, voxel_size=(1, 1, 1))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag([4, 4, 2])
mag.write(
absolute_offset=(10 * 4, 20 * 4, 40 * 2),
data=(np.random.rand(46, 45, 27) * 255).astype(np.uint8),
)
layer.upsample(
from_mag=Mag([4, 4, 2]),
finest_mag=Mag(1),
compress=False,
sampling_mode=SamplingModes.ANISOTROPIC,
buffer_edge_len=64,
args=None,
)
def cube_with_args(args: Namespace) -> None:
if args.isotropic is not None:
raise DeprecationWarning(
"The flag 'isotropic' is deprecated. Consider using '--sampling_mode isotropic' instead."
)
auto_detect_and_run_conversion(args)
layer_path_to_mags: Dict[Path, List[Mag]] = detect_present_mags(args.target_path)
if not args.no_compress:
for (layer_path, mags) in layer_path_to_mags.items():
layer_name = layer_path.name
for mag in mags:
compress_mag_inplace(args.target_path, layer_name, mag, args)
for (layer_path, mags) in layer_path_to_mags.items():
layer_name = layer_path.name
mags.sort()
downsample_mags(
path=args.target_path,
layer_name=layer_name,
from_mag=mags[-1],
max_mag=None if args.max_mag is None else Mag(args.max_mag),
interpolation_mode="default",
compress=not args.no_compress,
sampling_mode=args.sampling_mode,
args=args,
)
refresh_metadata(args.target_path)
def detect_segmentation_layer(
dataset_path: Path,
layer_name: str,
max_id: int,
compute_max_id: bool = False,
exact_bounding_box: Optional[dict] = None,
) -> dict:
layer_info = detect_standard_layer(
dataset_path, layer_name, exact_bounding_box, category="segmentation"
)
layer_info["mappings"] = detect_mappings(dataset_path, layer_name)
layer_info["largestSegmentId"] = max_id
if compute_max_id:
logging.info("Computing max id of layer={}".format(layer_name))
# Computing the current largest segment id
# This may take very long due to IO load
layer_path = str(detect_mag_path(dataset_path, layer_name, Mag(1)))
with wkw.Dataset.open(layer_path) as dataset:
bbox = layer_info["boundingBox"]
layer_info["largestSegmentId"] = int(
np.max(
dataset.read(
bbox["topLeft"], [bbox["width"], bbox["height"], bbox["depth"]]
)
)
)
logging.info(
"Max id of layer={} is {}".format(
layer_name, layer_info["largestSegmentId"]
)
)
return layer_info
def detect_bbox(dataset_path: Path, layer: str, mag: Mag = Mag(1)) -> Optional[dict]:
# Detect the coarse bounding box of a dataset by iterating
# over the WKW cubes
layer_path = detect_mag_path(dataset_path, layer, mag)
if layer_path is None:
return None
cubes_list = [parse_cube_file_name(f) for f in layer_path.rglob("*/*/*.wkw")]
if len(cubes_list) == 0:
return None
xs, ys, zs = list(zip(*cubes_list))
min_x, min_y, min_z = min(xs), min(ys), min(zs)
max_x, max_y, max_z = max(xs), max(ys), max(zs)
cubeLength = detect_cubeLength(dataset_path, layer, mag)
if cubeLength is None:
return None
return {
"topLeft": [min_x * cubeLength, min_y * cubeLength, min_z * cubeLength],
"width": (1 + max_x - min_x) * cubeLength,
"height": (1 + max_y - min_y) * cubeLength,
"depth": (1 + max_z - min_z) * cubeLength,
}
def main(args: argparse.Namespace) -> None:
source_path = args.source_path
if source_path.is_dir():
logger.error("source_path is not a file")
return
executor_args = get_executor_args(args)
mag_view = convert_raw(
source_path,
args.target_path,
args.layer_name,
args.input_dtype,
args.shape,
args.data_format,
args.chunk_size,
args.chunks_per_shard,
args.order,
args.voxel_size,
args.flip_axes,
not args.no_compress,
executor_args=executor_args,
)
mag_view.layer.downsample(
from_mag=mag_view.mag,
coarsest_mag=None if args.max_mag is None else Mag(args.max_mag),
interpolation_mode=args.interpolation_mode,
compress=not args.no_compress,
sampling_mode=args.sampling_mode,
args=executor_args,
)
def main(args: argparse.Namespace) -> None:
source_path = args.source_path
if not source_path.is_dir():
logger.error("source_path is not a directory")
return
executor_args = get_executor_args(args)
mag_view = convert_zarr(
source_path,
args.target_path,
layer_name=args.layer_name,
data_format=args.data_format,
chunk_size=args.chunk_size,
chunks_per_shard=args.chunks_per_shard,
is_segmentation_layer=args.is_segmentation_layer,
voxel_size=args.voxel_size,
flip_axes=args.flip_axes,
compress=not args.no_compress,
executor_args=executor_args,
)
mag_view.layer.downsample(
from_mag=mag_view.mag,
coarsest_mag=None if args.max_mag is None else Mag(args.max_mag),
interpolation_mode=args.interpolation_mode,
compress=not args.no_compress,
sampling_mode=args.sampling_mode,
args=executor_args,
)
def detect_num_channels(dataset_path: Path, layer: str, mag: Mag = Mag(1)) -> int:
layer_path = detect_mag_path(dataset_path, layer, mag)
if layer_path is not None:
with wkw.Dataset.open(str(layer_path)) as dataset:
return dataset.header.num_channels
raise RuntimeError(
f"Failed to detect numChannels (for {dataset_path}, {layer}, {mag}) because the layer_path is None"
)
def detect_cubeLength(dataset_path: Path, layer: str, mag: Mag = Mag(1)) -> int:
layer_path = detect_mag_path(dataset_path, layer, mag)
if layer_path is not None:
with wkw.Dataset.open(str(layer_path)) as dataset:
return dataset.header.block_len * dataset.header.file_len
raise RuntimeError(
f"Failed to detect the cube length (for {dataset_path}, {layer}, {mag}) because the layer_path is None"
)
def test_downsample_2d(tmp_path: Path) -> None:
ds = Dataset(tmp_path / "downsample_compressed", voxel_size=(1, 1, 2))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag(1, chunk_size=8, chunks_per_shard=8)
# write 2D data with all values set to "123"
mag.write(data=(np.ones((100, 100, 1)) * 123).astype(np.uint8))
with pytest.warns(Warning):
# This call produces a warning because only the mode "CONSTANT_Z" makes sense for 2D data.
layer.downsample(
from_mag=Mag(1),
coarsest_mag=Mag(2),
sampling_mode=SamplingModes.
ISOTROPIC, # this mode is intentionally not "CONSTANT_Z" for this test
)
assert Mag("2-2-1") in layer.mags
assert np.all(layer.get_mag(
Mag("2-2-1")).read() == 123) # The data is not darkened
def test_upsampling(sample_wkw_path: Path, tmp_path: Path,
tiff_mag_2_reference_path: Path) -> None:
copytree(sample_wkw_path, tmp_path)
color_layer = Dataset.open(tmp_path).get_layer("color")
color_layer.delete_mag("1")
color_layer.bounding_box = color_layer.bounding_box.align_with_mag(
Mag("2"), ceil=True)
check_call(
"python",
"-m",
"wkcuber.upsampling",
"--jobs",
2,
"--from_mag",
"2-2-2",
"--target_mag",
1,
"--buffer_cube_size",
1024,
"--layer_name",
"color",
tmp_path,
)
color_layer = Dataset.open(tmp_path).get_layer("color")
color_layer.delete_mag("2")
check_call(
"python",
"-m",
"wkcuber.downsampling",
"--jobs",
2,
"--from_mag",
1,
"--max",
2,
"--sampling_mode",
"isotropic",
"--buffer_cube_size",
256,
"--layer_name",
"color",
"--interpolation_mode",
"nearest",
tmp_path,
)
assert (Dataset.open(tmp_path).get_layer("color").get_mag("2").bounding_box
) == (Dataset.open(tiff_mag_2_reference_path).get_layer(
"color").get_mag("2").bounding_box)
assert (Dataset.open(tmp_path).get_layer("color").get_mag(
"2").content_is_equal(
Dataset.open(tiff_mag_2_reference_path).get_layer("color").get_mag(
"2")))
def test_align_with_mag_floored() -> None:
assert BoundingBox(
(1, 1, 1),
(10, 10, 10)).align_with_mag(Mag(2)) == BoundingBox(topleft=(2, 2, 2),
size=(8, 8, 8))
assert BoundingBox(
(1, 1, 1),
(9, 9, 9)).align_with_mag(Mag(2)) == BoundingBox(topleft=(2, 2, 2),
size=(8, 8, 8))
assert BoundingBox(
(1, 1, 1),
(9, 9, 9)).align_with_mag(Mag(4)) == BoundingBox(topleft=(4, 4, 4),
size=(4, 4, 4))
assert BoundingBox(
(1, 2, 3),
(9, 9, 9)).align_with_mag(Mag(2)) == BoundingBox(topleft=(2, 2, 4),
size=(8, 8, 8))
def test_align_with_mag_ceiled() -> None:
assert BoundingBox((1, 1, 1), (10, 10, 10)).align_with_mag(
Mag(2), ceil=True) == BoundingBox(topleft=(0, 0, 0), size=(12, 12, 12))
assert BoundingBox(
(1, 1, 1),
(9, 9, 9)).align_with_mag(Mag(2),
ceil=True) == BoundingBox(topleft=(0, 0, 0),
size=(10, 10, 10))
assert BoundingBox(
(1, 1, 1),
(9, 9, 9)).align_with_mag(Mag(4),
ceil=True) == BoundingBox(topleft=(0, 0, 0),
size=(12, 12, 12))
assert BoundingBox(
(1, 2, 3),
(9, 9, 9)).align_with_mag(Mag(2),
ceil=True) == BoundingBox(topleft=(0, 2, 2),
size=(10, 10, 10))
def export_wkw_as_nifti(args: Namespace) -> None:
setup_logging(args)
export_nifti(
source_path=args.source_path,
source_bbox=args.source_bbox,
mag=Mag(args.mag),
destination_path=args.destination_path,
name=args.name,
padding=args.padding,
)
def detect_dtype(dataset_path: Path, layer: str, mag: Mag = Mag(1)) -> str:
layer_path = detect_mag_path(dataset_path, layer, mag)
if layer_path is not None:
with wkw.Dataset.open(str(layer_path)) as dataset:
voxel_size = dataset.header.voxel_type
num_channels = dataset.header.num_channels
if voxel_size == np.uint8 and num_channels > 1:
return "uint" + str(8 * num_channels)
else:
return convert_dtype_to_element_class(voxel_size)
raise RuntimeError(
f"Failed to detect dtype (for {dataset_path}, {layer}, {mag}) because the layer_path is None"
)
def sample_wkw_path() -> Path:
ds_path = TESTDATA_DIR / "tiff_wkw"
if ds_path.exists():
rmtree(ds_path)
check_call(
[
"python",
"-m",
"wkcuber.cubing",
"--jobs",
"2",
"--voxel_size",
"1,1,1",
str(TESTDATA_DIR / "tiff"),
str(ds_path),
]
)
copytree(
TESTDATA_DIR / "tiff" / "datasource-properties.wkw-fixture.json",
ds_path / PROPERTIES_FILE_NAME,
)
Dataset.open(ds_path).get_layer("color").downsample_mag(Mag(1), Mag(2))
return ds_path
def detect_present_mags(target_path: Path) -> Dict[Path, List[Mag]]:
layer_path_to_mags: Dict[Path, List[Mag]] = dict()
layer_paths = list([p for p in target_path.iterdir() if p.is_dir()])
for layer_p in layer_paths:
layer_path_to_mags.setdefault(layer_p, list())
mag_paths = list([p for p in layer_p.iterdir() if p.is_dir()])
for mag_p in mag_paths:
try:
mag = Mag(mag_p.name)
except (AssertionError, ValueError) as _:
continue
layer_path_to_mags[layer_p].append(mag)
return layer_path_to_mags
def test_downsample_compressed(tmp_path: Path) -> None:
ds = Dataset(tmp_path / "downsample_compressed", voxel_size=(1, 1, 2))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag(1, chunk_size=8, chunks_per_shard=8)
mag.write(data=(np.random.rand(80, 240, 15) * 255).astype(np.uint8))
assert not mag._is_compressed()
mag.compress()
assert mag._is_compressed()
layer.downsample(
from_mag=Mag(1),
coarsest_mag=Mag(
4
), # Setting max_mag to "4" covers an edge case because the z-dimension (15) has to be rounded
)
# Note: this test does not check if the data is correct. This is already covered by other test cases.
assert len(layer.mags) == 3
assert Mag("1") in layer.mags.keys()
assert Mag("2-2-1") in layer.mags.keys()
assert Mag("4-4-2") in layer.mags.keys()
def test_default_max_mag() -> None:
assert calculate_default_coarsest_mag(dataset_size=(65536, 65536,
65536)) == Mag(1024)
assert calculate_default_coarsest_mag(dataset_size=(4096, 4096,
4096)) == Mag(64)
assert calculate_default_coarsest_mag(dataset_size=(131072, 262144,
262144)) == Mag(4096)
assert calculate_default_coarsest_mag(dataset_size=(32768, 32768,
32768)) == Mag(512)
assert calculate_default_coarsest_mag(dataset_size=(16384, 65536,
65536)) == Mag(1024)
assert calculate_default_coarsest_mag(dataset_size=(16384, 65536,
16384)) == Mag(1024)
assert calculate_default_coarsest_mag(dataset_size=(256, 256,
256)) == Mag([4, 4, 4])
def downsample_mags(
path: Path,
layer_name: Optional[str] = None,
from_mag: Optional[Mag] = None,
max_mag: Optional[Mag] = None,
interpolation_mode: str = "default",
buffer_shape: Optional[Vec3Int] = None,
compress: bool = True,
args: Optional[Namespace] = None,
sampling_mode: Union[str, SamplingModes] = SamplingModes.ANISOTROPIC,
force_sampling_scheme: bool = False,
) -> None:
"""
Argument `path` expects the directory containing the dataset.
Argument `layer_name` expects the name of the layer (color or segmentation).
Argument `from_mag` expects the resolution to base downsampling on.
For the other parameters see the CLI help or `Layer.downsample` and `Layer.downsampling_mag`.
"""
assert layer_name and from_mag or not layer_name and not from_mag, (
"You provided only one of the following "
"parameters: layer_name, from_mag but both "
"need to be set or none. If you don't provide "
"the parameters you need to provide the path "
"argument with the mag and layer to downsample"
" (e.g dataset/color/1).")
if not layer_name or not from_mag:
layer_name = path.parent.name
from_mag = Mag(path.name)
path = path.parent.parent
assert layer_name is not None # for mypy
assert from_mag is not None # for mypy
Dataset.open(path).get_layer(layer_name).downsample(
from_mag=from_mag,
coarsest_mag=max_mag,
interpolation_mode=interpolation_mode,
compress=compress,
sampling_mode=sampling_mode,
buffer_shape=buffer_shape,
force_sampling_scheme=force_sampling_scheme,
args=args,
)
def test_export_nifti_file(tmp_path: Path) -> None:
destination_path = tmp_path / f"{DS_NAME}_nifti"
destination_path.mkdir()
bbox = BoundingBox((100, 100, 10), (100, 500, 50))
bbox_dict = bbox.to_config_dict()
args_list = [
"--source_path",
str(SOURCE_PATH),
"--destination_path",
str(destination_path),
"--name",
"test_export",
"--source_bbox",
bbox.to_csv(),
"--mag",
"1",
]
export_wkw_as_nifti_from_arg_list(args_list)
wk_ds = Dataset.open(SOURCE_PATH)
for layer_name, layer in wk_ds.layers.items():
correct_image = layer.get_mag(Mag(1)).read(bbox_dict["topleft"],
bbox_dict["size"])
# nifti is transposed
correct_image = correct_image.transpose(1, 2, 3, 0)
correct_image = np.squeeze(correct_image)
nifti_path = destination_path.joinpath(f"test_export_{layer_name}.nii")
assert nifti_path.is_file(
), f"Expected a nifti to be written at: {nifti_path}."
nifti = nib.load(str(nifti_path))
test_image = np.array(nifti.get_fdata())
assert np.array_equal(correct_image, test_image), (
f"The nifti file {nifti_path} that was written is not "
f"equal to the original wkw_file.")
def test_downsample_mag_list_with_only_setup_mags(tmp_path: Path) -> None:
ds = Dataset(tmp_path / "downsample_mag_list", voxel_size=(1, 1, 2))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag = layer.add_mag(1)
mag.write(data=(np.random.rand(10, 20, 30) * 255).astype(np.uint8))
target_mags = [Mag([4, 4, 8]),
Mag(2), Mag([32, 32, 8]),
Mag(32)] # unsorted list
layer.downsample_mag_list(from_mag=Mag(1),
target_mags=target_mags,
only_setup_mags=True)
for m in target_mags:
assert np.all(
layer.get_mag(m).read() == 0), "The mags should be empty."
layer.downsample_mag_list(from_mag=Mag(1),
target_mags=target_mags,
allow_overwrite=True)
for m in target_mags:
assert m in layer.mags
def tile_cubing(
target_path: Path,
layer_name: str,
batch_size: int,
input_path_pattern: str,
voxel_size: Tuple[int, int, int],
args: Optional[Namespace] = None,
) -> None:
decimal_lengths = get_digit_counts_for_dimensions(input_path_pattern)
(
min_dimensions,
max_dimensions,
arbitrary_file,
file_count,
) = detect_interval_for_dimensions(input_path_pattern, decimal_lengths)
if not arbitrary_file:
logging.error(
f"No source files found. Maybe the input_path_pattern was wrong. You provided: {input_path_pattern}"
)
return
# Determine tile size from first matching file
tile_width, tile_height = image_reader.read_dimensions(arbitrary_file)
num_z = max_dimensions["z"] - min_dimensions["z"] + 1
num_x = (max_dimensions["x"] - min_dimensions["x"] + 1) * tile_width
num_y = (max_dimensions["y"] - min_dimensions["y"] + 1) * tile_height
x_offset = min_dimensions["x"] * tile_width
y_offset = min_dimensions["y"] * tile_height
num_channels = image_reader.read_channel_count(arbitrary_file)
logging.info("Found source files: count={} with tile_size={}x{}".format(
file_count, tile_width, tile_height))
if args is None or not hasattr(args, "dtype") or args.dtype is None:
dtype = image_reader.read_dtype(arbitrary_file)
else:
dtype = args.dtype
target_ds = Dataset(target_path, voxel_size=voxel_size, exist_ok=True)
is_segmentation_layer = layer_name == "segmentation"
if is_segmentation_layer:
target_layer = target_ds.get_or_add_layer(
layer_name,
SEGMENTATION_CATEGORY,
dtype_per_channel=dtype,
num_channels=num_channels,
largest_segment_id=0,
)
else:
target_layer = target_ds.get_or_add_layer(
layer_name,
COLOR_CATEGORY,
dtype_per_channel=dtype,
num_channels=num_channels,
)
bbox = BoundingBox(
Vec3Int(x_offset, y_offset, min_dimensions["z"]),
Vec3Int(num_x, num_y, num_z),
)
if target_layer.bounding_box.volume() == 0:
# If the layer is empty, we want to set the bbox directly because extending it
# would mean that the bbox would always start at (0, 0, 0)
target_layer.bounding_box = bbox
else:
target_layer.bounding_box = target_layer.bounding_box.extended_by(bbox)
target_mag_view = target_layer.get_or_add_mag(
Mag(1), block_len=DEFAULT_CHUNK_SIZE.z)
with get_executor_for_args(args) as executor:
job_args = []
# Iterate over all z batches
for z_batch in get_regular_chunks(min_dimensions["z"],
max_dimensions["z"],
DEFAULT_CHUNK_SIZE.z):
# The z_batch always starts and ends at a multiple of DEFAULT_CHUNK_SIZE.z.
# However, we only want the part that is inside the bounding box
z_batch = range(
max(list(z_batch)[0], target_layer.bounding_box.topleft.z),
min(
list(z_batch)[-1] + 1,
target_layer.bounding_box.bottomright.z),
)
z_values = list(z_batch)
job_args.append((
target_mag_view.get_view(
(x_offset, y_offset, z_values[0]),
(num_x, num_y, len(z_values)),
),
z_values,
input_path_pattern,
batch_size,
(tile_width, tile_height, num_channels),
min_dimensions,
max_dimensions,
decimal_lengths,
dtype,
num_channels,
))
largest_segment_id_per_chunk = wait_and_ensure_success(
executor.map_to_futures(tile_cubing_job, job_args),
f"Tile cubing layer {layer_name}",
)
if is_segmentation_layer:
largest_segment_id = max(largest_segment_id_per_chunk)
cast(SegmentationLayer,
target_layer).largest_segment_id = largest_segment_id
def detect_resolutions(dataset_path: Path, layer: str) -> Generator[Mag, None, None]:
for mag in (dataset_path / layer).iterdir():
try:
yield Mag(mag.name)
except ValueError:
logging.info("ignoring {} as resolution".format(mag))
