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_url_open_remote(url: str, sample_dataset: wk.Dataset,
sample_bbox: wk.BoundingBox) -> None:
ds = wk.Dataset.open_remote(url, )
assert set(ds.layers.keys()) == {"color", "segmentation"}
data = (ds.get_color_layers()[0].get_finest_mag().read(
absolute_bounding_box=sample_bbox))
assert data.sum() == 122507
assert np.array_equal(
data,
sample_dataset.get_color_layers()[0].get_finest_mag().read(),
)
def test_url_download(url: str, tmp_path: Path, sample_dataset: wk.Dataset,
sample_bbox: wk.BoundingBox) -> None:
ds = wk.Dataset.download(url,
path=tmp_path / "ds",
mags=[wk.Mag(1)],
bbox=sample_bbox)
assert set(ds.layers.keys()) == {"color", "segmentation"}
data = ds.get_color_layers()[0].get_finest_mag().read()
assert data.sum() == 122507
assert np.array_equal(
data,
sample_dataset.get_color_layers()[0].get_finest_mag().read(),
)
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 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 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 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 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 downsample_test_helper(WT1_path: Path, tmp_path: Path, use_compress: bool,
chunk_size: Vec3Int) -> None:
source_path = WT1_path
target_path = tmp_path / "WT1_wkw"
source_ds = Dataset.open(source_path)
target_ds = source_ds.copy_dataset(target_path,
chunk_size=chunk_size,
chunks_per_shard=16)
target_layer = target_ds.get_layer("color")
mag1 = target_layer.get_mag("1")
target_layer.delete_mag("2-2-1") # This is not needed for this test
# The bounding box has to be set here explicitly because the downsampled data is written to a different dataset.
target_layer.bounding_box = source_ds.get_layer("color").bounding_box
mag2 = target_layer._initialize_mag_from_other_mag("2", mag1, use_compress)
# The actual size of mag1 is (4600, 4600, 512).
# To keep this test case fast, we are only downsampling a small part
offset = (4096, 4096, 0)
size = (504, 504, 512)
source_buffer = mag1.read(
absolute_offset=offset,
size=size,
)[0]
assert np.any(source_buffer != 0)
downsample_cube_job(
(
mag1.get_view(absolute_offset=offset, size=size),
mag2.get_view(
absolute_offset=offset,
size=size,
),
0,
),
Vec3Int(2, 2, 2),
InterpolationModes.MAX,
Vec3Int.full(128),
)
assert np.any(source_buffer != 0)
target_buffer = mag2.read(absolute_offset=offset, size=size)[0]
assert np.any(target_buffer != 0)
assert np.all(target_buffer == downsample_cube(source_buffer, [2, 2, 2],
InterpolationModes.MAX))
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 test_main(tmp_path: Path, order: str,
flip_axes: Optional[Tuple[int, int]]) -> None:
raw_file = tmp_path / "input.raw"
input_dtype = "float32"
shape = 64, 128, 256
data = np.arange(np.prod(shape), dtype=input_dtype).reshape(shape,
order=order)
with raw_file.open("wb") as f:
f.write(data.tobytes(order=order))
output_path = tmp_path / "output"
output_path.mkdir()
args_list = [
str(raw_file),
str(output_path),
"--input_dtype",
input_dtype,
"--shape",
",".join(str(i) for i in shape),
"--order",
order,
"--jobs",
"1",
]
if flip_axes is not None:
args_list.extend(
["--flip_axes", ",".join(str(a + 1) for a in flip_axes)])
args = create_parser().parse_args(args_list)
main(args)
dataset = Dataset.open(output_path)
layer = dataset.get_color_layers()[0]
mag_view = layer.get_mag(1)
view = mag_view.get_view()
read_data = view.read()
assert view.size == shape
assert view.get_dtype() == data.dtype
assert np.array_equal(
read_data[0],
data if flip_axes is None else np.flip(data, flip_axes),
)
def test_main(tmp_path: Path, category: str) -> None:
input_folder = tmp_path / "raw_dataset" / category
input_folder.mkdir(parents=True, exist_ok=True)
raw_file = input_folder / "input.tif"
input_dtype = "uint32"
shape = 64, 128, 256
data = np.arange(np.prod(shape), dtype=input_dtype).reshape(shape)
with TiffWriter(raw_file) as tif:
tif.write(data.transpose([2, 1, 0]))
output_path = tmp_path / "output_2"
output_path.mkdir()
args_list = [
str(tmp_path / "raw_dataset"),
str(output_path),
"--jobs",
"1",
"--voxel_size",
"11,11,11",
"--max_mag",
"4",
]
args = create_parser().parse_args(args_list)
cube_with_args(args)
dataset = Dataset.open(output_path)
if category == "color":
layer = dataset.get_color_layers()[0]
else:
layer = dataset.get_segmentation_layers()[0]
mag_view = layer.get_mag(1)
view = mag_view.get_view()
read_data = view.read()
assert view.size == shape
assert view.get_dtype() == data.dtype
assert np.array_equal(
read_data[0],
data,
)
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 export_wkw_as_tiff(args: Namespace) -> None:
setup_logging(args)
mag_view = (Dataset.open(args.source_path).get_layer(
args.layer_name).get_mag(args.mag))
bbox = mag_view.bounding_box if args.bbox is None else args.bbox
logging.info(f"Starting tiff export for bounding box: {bbox}")
if args.tiles_per_dimension is not None:
args.tile_size = [
int(s.strip()) for s in args.tiles_per_dimension.split(",")
]
assert len(args.tile_size) == 2
logging.info(
f"Using tiling with {args.tile_size[0]},{args.tile_size[1]} tiles in the dimensions."
)
args.tile_size[0] = ceil(
bbox.in_mag(mag_view.mag).size.x / args.tile_size[0])
args.tile_size[1] = ceil(
bbox.in_mag(mag_view.mag).size.y / args.tile_size[1])
elif args.tile_size is not None:
args.tile_size = [int(s.strip()) for s in args.tile_size.split(",")]
assert len(args.tile_size) == 2
logging.info(
f"Using tiling with the size of {args.tile_size[0]},{args.tile_size[1]}."
)
args.batch_size = int(args.batch_size)
export_tiff_stack(
mag_view=mag_view,
bbox=bbox,
destination_path=args.destination_path,
name=args.name,
tiling_slice_size=args.tile_size,
batch_size=args.batch_size,
downsample=args.downsample,
args=args,
)
def export_nifti(
source_path: Path,
source_bbox: Optional[BoundingBox],
mag: Mag,
destination_path: Path,
name: str,
padding: Optional[Tuple[int, ...]] = None,
) -> None:
dataset = Dataset.open(source_path)
for layer_name, layer in dataset.layers.items():
logging.info(f"Starting nifti export for bounding box: {source_bbox}")
export_layer_to_nifti(
source_path,
layer.bounding_box if source_bbox is None else source_bbox,
mag,
layer_name,
destination_path,
name + "_" + layer_name,
padding,
)
def export_layer_to_nifti(
source_path: Path,
source_bbox: BoundingBox,
mag: Mag,
layer_name: str,
destination_path: Path,
name: str,
padding: Optional[Tuple[int, ...]] = None,
) -> None:
dataset = Dataset.open(source_path)
layer = dataset.get_layer(layer_name)
mag_layer = layer.get_mag(mag)
is_segmentation_layer = layer.category == SEGMENTATION_CATEGORY
data = mag_layer.read(source_bbox.topleft, source_bbox.size)
data = data.transpose(1, 2, 3, 0)
logging.info(f"Shape with layer {data.shape}")
data = np.array(data)
if is_segmentation_layer and data.max() > 0:
factor = np.iinfo("uint8").max / data.max()
data = data * factor
data = data.astype(np.dtype("uint8"))
if padding:
assert len(padding) == 6, "padding needs 6 values"
padding_per_axis = list(zip(padding[:3], padding[3:]))
padding_per_axis.append((0, 0))
data = np.pad(data, padding_per_axis, mode="constant", constant_values=0)
img = nib.Nifti1Image(data, np.eye(4))
destination_file = str(destination_path.joinpath(name + ".nii"))
logging.info(f"Writing to {destination_file} with shape {data.shape}")
nib.save(img, destination_file)
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 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
f"Number of simultaneous upload processes. Defaults to {DEFAULT_SIMULTANEOUS_UPLOADS}.",
)
parser.add_argument(
"--name",
help=
"Specify a new name for the dataset. Defaults to the name specified in `datasource-properties.json`.",
default=None,
)
add_verbose_flag(parser)
return parser
if __name__ == "__main__":
setup_warnings()
args = create_parser().parse_args()
setup_logging(args)
url = (args.url if args.url is not None else environ.get(
"WK_URL", DEFAULT_WEBKNOSSOS_URL))
token = args.token if args.token is not None else environ.get(
"WK_TOKEN", None)
assert (
token is not None
), f"An auth token needs to be supplied either through the --token command line arg or the WK_TOKEN environment variable. Retrieve your auth token on {url}/auth/token."
with webknossos_context(url=url, token=token):
Dataset.open(args.source_path).upload(new_dataset_name=args.name,
jobs=args.jobs)
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}")
def test_anisotropic_downsampling(sample_wkw_path: Path,
tmp_path: Path) -> None:
copytree(sample_wkw_path, tmp_path)
# We need to delete mag two as it already exists. Then it is replaced by an anisotropic mag.
color_layer = Dataset.open(tmp_path).get_layer("color")
color_layer.delete_mag("2")
check_call(
"python",
"-m",
"wkcuber.downsampling",
"--jobs",
2,
"--from",
1,
"--max",
2,
"--sampling_mode",
"constant_z",
"--buffer_cube_size",
128,
"--layer_name",
"color",
tmp_path,
)
check_call(
"python",
"-m",
"wkcuber.downsampling",
"--jobs",
2,
"--from",
"2-2-1",
"--max",
4,
"--sampling_mode",
"constant_z",
"--buffer_cube_size",
128,
"--layer_name",
"color",
tmp_path,
)
assert (tmp_path / "color" / "2-2-1").exists()
assert (tmp_path / "color" / "4-4-1").exists()
assert count_wkw_files(tmp_path / "color" / "2-2-1") == 1
assert count_wkw_files(tmp_path / "color" / "4-4-1") == 1
check_call(
"python",
"-m",
"wkcuber.downsampling",
"--jobs",
2,
"--from",
"4-4-1",
"--max",
16,
"--buffer_cube_size",
128,
"--layer_name",
"color",
tmp_path,
)
assert (tmp_path / "color" / "8-8-4").exists()
assert (tmp_path / "color" / "16-16-8").exists()
assert count_wkw_files(tmp_path / "color" / "8-8-4") == 1
assert count_wkw_files(tmp_path / "color" / "16-16-8") == 1
type=parse_path,
)
parser.add_argument(
"--no_compression",
help="Use compression, default false",
type=bool,
default=False,
)
add_verbose_flag(parser)
add_distribution_flags(parser)
add_data_format_flags(parser)
return parser
if __name__ == "__main__":
setup_warnings()
args = create_parser().parse_args()
setup_logging(args)
Dataset.open(args.source_path).copy_dataset(
args.target_path,
data_format=args.data_format,
chunk_size=args.chunk_size,
chunks_per_shard=args.chunks_per_shard,
compress=not args.no_compression,
args=args,
)
def compress_mag_inplace(target_path: Path,
layer_name: str,
mag: Mag,
args: Optional[Namespace] = None) -> None:
Dataset.open(target_path).get_layer(layer_name).get_mag(mag).compress(
args=args)
