def test_upsample_multi_channel(tmp_path: Path) -> None:
num_channels = 3
size = (32, 32, 10)
source_data = (
128 * np.random.randn(num_channels, size[0], size[1], size[2])
).astype("uint8")
ds = Dataset(tmp_path / "multi-channel-test", (1, 1, 1))
l = ds.add_layer(
"color",
COLOR_CATEGORY,
dtype_per_channel="uint8",
num_channels=num_channels,
)
mag2 = l.add_mag("2", chunks_per_shard=32)
mag2.write(source_data)
assert np.any(source_data != 0)
l._initialize_mag_from_other_mag("1", mag2, False)
upsample_cube_job(
(mag2.get_view(), l.get_mag("1").get_view(), 0),
[0.5, 0.5, 0.5],
BUFFER_SHAPE,
)
channels = []
for channel_index in range(num_channels):
channels.append(upsample_cube(source_data[channel_index], [2, 2, 2]))
joined_buffer = np.stack(channels)
target_buffer = l.get_mag("1").read()
assert np.any(target_buffer != 0)
assert np.all(target_buffer == joined_buffer)
def convert_zarr(
source_zarr_path: Path,
target_path: Path,
layer_name: str,
data_format: DataFormat,
chunk_size: Vec3Int,
chunks_per_shard: Vec3Int,
is_segmentation_layer: bool = False,
voxel_size: Optional[Tuple[float, float, float]] = (1.0, 1.0, 1.0),
flip_axes: Optional[Union[int, Tuple[int, ...]]] = None,
compress: bool = True,
executor_args: Optional[argparse.Namespace] = None,
) -> MagView:
ref_time = time.time()
f = zarr.open(store=_fsstore_from_path(source_zarr_path), mode="r")
input_dtype = f.dtype
shape = f.shape
if voxel_size is None:
voxel_size = 1.0, 1.0, 1.0
wk_ds = Dataset(target_path, voxel_size=voxel_size, exist_ok=True)
wk_layer = wk_ds.get_or_add_layer(
layer_name,
"segmentation" if is_segmentation_layer else "color",
dtype_per_layer=np.dtype(input_dtype),
num_channels=1,
largest_segment_id=0,
data_format=data_format,
)
wk_layer.bounding_box = BoundingBox((0, 0, 0), shape)
wk_mag = wk_layer.get_or_add_mag("1",
chunk_size=chunk_size,
chunks_per_shard=chunks_per_shard,
compress=compress)
# Parallel chunk conversion
with get_executor_for_args(executor_args) as executor:
largest_segment_id_per_chunk = wait_and_ensure_success(
executor.map_to_futures(
partial(
_zarr_chunk_converter,
source_zarr_path=source_zarr_path,
target_mag_view=wk_mag,
flip_axes=flip_axes,
),
wk_layer.bounding_box.chunk(chunk_size=chunk_size *
chunks_per_shard),
))
if is_segmentation_layer:
largest_segment_id = int(max(largest_segment_id_per_chunk))
cast(SegmentationLayer,
wk_layer).largest_segment_id = largest_segment_id
logger.debug("Conversion of {} took {:.8f}s".format(
source_zarr_path,
time.time() - ref_time))
return wk_mag
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 convert_raw(
source_raw_path: Path,
target_path: Path,
layer_name: str,
input_dtype: str,
shape: Tuple[int, int, int],
data_format: DataFormat,
chunk_size: Vec3Int,
chunks_per_shard: Vec3Int,
order: str = "F",
voxel_size: Optional[Tuple[float, float, float]] = (1.0, 1.0, 1.0),
flip_axes: Optional[Union[int, Tuple[int, ...]]] = None,
compress: bool = True,
executor_args: Optional[argparse.Namespace] = None,
) -> MagView:
assert order in ("C", "F")
time_start(f"Conversion of {source_raw_path}")
if voxel_size is None:
voxel_size = 1.0, 1.0, 1.0
wk_ds = Dataset(target_path, voxel_size=voxel_size, exist_ok=True)
wk_layer = wk_ds.get_or_add_layer(
layer_name,
"color",
dtype_per_layer=np.dtype(input_dtype),
num_channels=1,
data_format=data_format,
)
wk_layer.bounding_box = BoundingBox((0, 0, 0), shape)
wk_mag = wk_layer.get_or_add_mag("1",
chunk_size=chunk_size,
chunks_per_shard=chunks_per_shard,
compress=compress)
# Parallel chunk conversion
with get_executor_for_args(executor_args) as executor:
wait_and_ensure_success(
executor.map_to_futures(
partial(
_raw_chunk_converter,
source_raw_path=source_raw_path,
target_mag_view=wk_mag,
input_dtype=input_dtype,
shape=shape,
order=order,
flip_axes=flip_axes,
),
wk_layer.bounding_box.chunk(chunk_size=chunk_size *
chunks_per_shard),
))
time_stop(f"Conversion of {source_raw_path}")
return wk_mag
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 convert_knossos(
source_path: Path,
target_path: Path,
layer_name: str,
dtype: str,
voxel_size: Tuple[float, float, float],
data_format: DataFormat,
chunk_size: Vec3Int,
chunks_per_shard: Vec3Int,
mag: int = 1,
args: Optional[Namespace] = None,
) -> None:
source_knossos_info = KnossosDatasetInfo(source_path, dtype)
target_dataset = Dataset(target_path, voxel_size, exist_ok=True)
target_layer = target_dataset.get_or_add_layer(
layer_name,
COLOR_CATEGORY,
data_format=data_format,
dtype_per_channel=dtype,
)
with open_knossos(source_knossos_info) as source_knossos:
knossos_cubes = np.array(list(source_knossos.list_cubes()))
if len(knossos_cubes) == 0:
logging.error(
"No input KNOSSOS cubes found. Make sure to pass the path which points to a KNOSSOS magnification (e.g., testdata/knossos/color/1)."
)
exit(1)
min_xyz = knossos_cubes.min(axis=0) * CUBE_EDGE_LEN
max_xyz = (knossos_cubes.max(axis=0) + 1) * CUBE_EDGE_LEN
target_layer.bounding_box = BoundingBox(
Vec3Int(min_xyz), Vec3Int(max_xyz - min_xyz)
)
target_mag = target_layer.get_or_add_mag(
mag, chunk_size=chunk_size, chunks_per_shard=chunks_per_shard
)
with get_executor_for_args(args) as executor:
target_mag.for_each_chunk(
partial(convert_cube_job, source_knossos_info),
chunk_size=chunk_size * chunks_per_shard,
executor=executor,
progress_desc=f"Converting knossos layer {layer_name}",
)
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_downsample_multi_channel(tmp_path: Path) -> None:
num_channels = 3
size = (32, 32, 10)
source_data = (128 * np.random.randn(num_channels, size[0], size[1],
size[2])).astype("uint8")
ds = Dataset(tmp_path / "multi-channel-test", (1, 1, 1))
l = ds.add_layer(
"color",
COLOR_CATEGORY,
dtype_per_channel="uint8",
num_channels=num_channels,
)
mag1 = l.add_mag("1", chunks_per_shard=32)
print("writing source_data shape", source_data.shape)
mag1.write(source_data)
assert np.any(source_data != 0)
mag2 = l._initialize_mag_from_other_mag("2", mag1, False)
downsample_cube_job(
(l.get_mag("1").get_view(), l.get_mag("2").get_view(), 0),
Vec3Int(2, 2, 2),
InterpolationModes.MAX,
BUFFER_SHAPE,
)
channels = []
for channel_index in range(num_channels):
channels.append(
downsample_cube(source_data[channel_index], [2, 2, 2],
InterpolationModes.MAX))
joined_buffer = np.stack(channels)
target_buffer = mag2.read()
assert np.any(target_buffer != 0)
assert np.all(target_buffer == joined_buffer)
def upsample_test_helper(tmp_path: Path, use_compress: bool) -> None:
ds = Dataset(tmp_path, voxel_size=(10.5, 10.5, 5))
layer = ds.add_layer("color", COLOR_CATEGORY)
mag2 = layer.add_mag([2, 2, 2])
offset = Vec3Int(WKW_CUBE_SIZE, 2 * WKW_CUBE_SIZE, 0)
mag2.write(
absolute_offset=offset,
data=(np.random.rand(*BUFFER_SHAPE) * 255).astype(np.uint8),
)
mag1 = layer._initialize_mag_from_other_mag("1-1-2", mag2, use_compress)
source_buffer = mag2.read(
absolute_offset=offset,
size=BUFFER_SHAPE,
)[0]
assert np.any(source_buffer != 0)
upsample_cube_job(
(
mag2.get_view(absolute_offset=offset, size=BUFFER_SHAPE),
mag1.get_view(
absolute_offset=offset,
size=BUFFER_SHAPE,
),
0,
),
[0.5, 0.5, 1.0],
BUFFER_SHAPE,
)
assert np.any(source_buffer != 0)
target_buffer = mag1.read(absolute_offset=offset, size=BUFFER_SHAPE)[0]
assert np.any(target_buffer != 0)
assert np.all(target_buffer == upsample_cube(source_buffer, [2, 2, 1]))
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_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 cubing(
source_path: Path,
target_path: Path,
layer_name: str,
batch_size: Optional[int],
channel_index: Optional[int],
sample_index: Optional[int],
dtype: Optional[str],
target_mag_str: str,
data_format: DataFormat,
chunk_size: Vec3Int,
chunks_per_shard: Vec3Int,
interpolation_mode_str: str,
start_z: int,
skip_first_z_slices: int,
pad: bool,
voxel_size: Tuple[float, float, float],
executor_args: Namespace,
) -> Layer:
source_files = find_source_filenames(source_path)
all_num_x, all_num_y = zip(*[
image_reader.read_dimensions(source_files[i])
for i in range(len(source_files))
])
num_x = max(all_num_x)
num_y = max(all_num_y)
# All images are assumed to have equal channels and samples
num_channels = image_reader.read_channel_count(source_files[0])
num_samples = image_reader.read_sample_count(source_files[0])
num_output_channels = num_channels * num_samples
if channel_index is not None:
# if there is no c axis, but someone meant to only use one channel/sample, set the sample index instead
if sample_index is None and num_channels == 1 and channel_index > 0:
sample_index = channel_index
channel_index = 0
assert (
0 <= channel_index < num_channels
), "Selected channel is invalid. Please check the number of channels in the source file."
num_output_channels = num_samples
if sample_index is not None:
# if no channel axis exists, it is valid to only set the sample index. Set channel index to 0 to avoid confusion
if channel_index is None and num_channels == 1:
channel_index = 0
assert (channel_index is not None
), "Sample index is only valid if a channel index is also set."
assert (
0 <= sample_index < num_samples
), "Selected sample is invalid. Please check the number of samples in the source file."
num_output_channels = 1
num_z_slices_per_file = image_reader.read_z_slices_per_file(
source_files[0])
assert (num_z_slices_per_file == 1 or len(source_files)
== 1), "Multi page TIFF support only for single files"
if num_z_slices_per_file > 1:
num_z = num_z_slices_per_file
else:
num_z = len(source_files)
if dtype is None:
dtype = image_reader.read_dtype(source_files[0])
if batch_size is None:
batch_size = DEFAULT_CHUNK_SIZE.z
target_mag = Mag(target_mag_str)
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_output_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_output_channels,
data_format=data_format,
)
target_layer.bounding_box = target_layer.bounding_box.extended_by(
BoundingBox(
Vec3Int(0, 0, start_z + skip_first_z_slices) * target_mag,
Vec3Int(num_x, num_y, num_z - skip_first_z_slices) * target_mag,
))
target_mag_view = target_layer.get_or_add_mag(
target_mag,
chunks_per_shard=chunks_per_shard,
chunk_size=chunk_size,
)
interpolation_mode = parse_interpolation_mode(interpolation_mode_str,
target_layer.category)
if target_mag != Mag(1):
logging.info(
f"Downsampling the cubed image to {target_mag} in memory with interpolation mode {interpolation_mode}."
)
logging.info("Found source files: count={} size={}x{}".format(
num_z, num_x, num_y))
with get_executor_for_args(executor_args) as executor:
job_args = []
# We iterate over all z sections
for z in range(skip_first_z_slices, num_z, DEFAULT_CHUNK_SIZE.z):
# The z is used to access the source files. However, when writing the data, the `start_z` has to be considered.
max_z = min(num_z, z + DEFAULT_CHUNK_SIZE.z)
# Prepare source files array
if len(source_files) > 1:
source_files_array = source_files[z:max_z]
else:
source_files_array = source_files * (max_z - z)
# Prepare job
job_args.append((
target_mag_view.get_view(
(0, 0, z + start_z),
(num_x, num_y, max_z - z),
),
target_mag,
interpolation_mode,
source_files_array,
batch_size,
pad,
channel_index,
sample_index,
dtype,
target_layer.num_channels,
))
largest_segment_id_per_chunk = wait_and_ensure_success(
executor.map_to_futures(cubing_job, job_args),
progress_desc=f"Cubing from {skip_first_z_slices} to {num_z}",
)
if is_segmentation_layer:
largest_segment_id = max(largest_segment_id_per_chunk)
cast(SegmentationLayer,
target_layer).largest_segment_id = largest_segment_id
# Return layer
return target_layer
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 convert_nifti(
source_nifti_path: Path,
target_path: Path,
layer_name: str,
dtype: str,
voxel_size: Tuple[float, ...],
data_format: DataFormat,
chunk_size: Vec3Int,
chunks_per_shard: Vec3Int,
is_segmentation_layer: bool = False,
bbox_to_enforce: Optional[BoundingBox] = None,
use_orientation_header: bool = False,
flip_axes: Optional[Union[int, Tuple[int, ...]]] = None,
) -> None:
shard_size = chunk_size * chunks_per_shard
time_start(f"Converting of {source_nifti_path}")
source_nifti = nib.load(str(source_nifti_path.resolve()))
if use_orientation_header:
# Get canonical representation of data to incorporate
# encoded transformations. Needs to be flipped later
# to match the coordinate system of WKW.
source_nifti = nib.funcs.as_closest_canonical(source_nifti,
enforce_diag=False)
cube_data = np.array(source_nifti.get_fdata())
category_type: LayerCategoryType = ("segmentation"
if is_segmentation_layer else "color")
logging.debug(f"Assuming {category_type} as layer type for {layer_name}")
if len(source_nifti.shape) == 3:
cube_data = cube_data.reshape((1, ) + source_nifti.shape)
elif len(source_nifti.shape) == 4:
cube_data = np.transpose(cube_data, (3, 0, 1, 2))
else:
logging.warning(
"Converting of {} failed! Too many or too less dimensions".format(
source_nifti_path))
return
if use_orientation_header:
# Flip y and z to transform data into wkw's coordinate system.
cube_data = np.flip(cube_data, (2, 3))
if flip_axes:
cube_data = np.flip(cube_data, flip_axes)
if voxel_size is None:
voxel_size = tuple(map(float, source_nifti.header["pixdim"][:3]))
logging.info(f"Using voxel_size: {voxel_size}")
cube_data = to_target_datatype(cube_data, dtype, is_segmentation_layer)
# everything needs to be padded to
if bbox_to_enforce is not None:
target_topleft = np.array((0, ) + tuple(bbox_to_enforce.topleft))
target_size = np.array((1, ) + tuple(bbox_to_enforce.size))
cube_data = pad_or_crop_to_size_and_topleft(cube_data, target_size,
target_topleft)
# Writing wkw compressed requires files of shape (shard_size, shard_size, shard_size)
# Pad data accordingly
padding_offset = shard_size - np.array(cube_data.shape[1:4]) % shard_size
cube_data = np.pad(
cube_data,
(
(0, 0),
(0, int(padding_offset[0])),
(0, int(padding_offset[1])),
(0, int(padding_offset[2])),
),
)
wk_ds = Dataset(
target_path,
voxel_size=cast(Tuple[float, float, float], voxel_size or (1, 1, 1)),
exist_ok=True,
)
wk_layer = (wk_ds.get_or_add_layer(
layer_name,
category_type,
dtype_per_layer=np.dtype(dtype),
data_format=data_format,
largest_segment_id=int(np.max(cube_data) + 1),
) if is_segmentation_layer else wk_ds.get_or_add_layer(
layer_name,
category_type,
data_format=data_format,
dtype_per_layer=np.dtype(dtype),
))
wk_mag = wk_layer.get_or_add_mag("1",
chunk_size=chunk_size,
chunks_per_shard=chunks_per_shard)
wk_mag.write(cube_data)
time_stop(f"Converting of {source_nifti_path}")