def __init__(
self,
*,
filesystem: JsonableFilesystem,
info_dir: PurePosixPath, #path to dir containing with the info file
scale: PrecomputedChunksScale,
dtype: "np.dtype[Any]",
num_channels: int,
):
self.info_dir = info_dir
self.scale = scale
shape = Shape5D(x=scale.size[0],
y=scale.size[1],
z=scale.size[2],
c=num_channels)
location = Point5D(x=scale.voxel_offset[0],
y=scale.voxel_offset[1],
z=scale.voxel_offset[2])
interval = shape.to_interval5d(offset=location)
chunk_sizes_5d = [
Shape5D(x=cs[0], y=cs[1], z=cs[2], c=num_channels)
for cs in scale.chunk_sizes
]
super().__init__(
filesystem=filesystem,
path=info_dir,
tile_shape=chunk_sizes_5d[0], #FIXME
interval=interval,
dtype=dtype,
)
def __init__(
self,
*,
key: PurePosixPath,
size: Tuple[int, int, int],
resolution: Tuple[int, int, int],
voxel_offset: Tuple[int, int, int],
chunk_sizes: Tuple[Tuple[int, int, int], ...],
encoding: PrecomputedChunksEncoder,
num_channels: int,
):
super().__init__(key=key,
size=size,
resolution=resolution,
voxel_offset=voxel_offset,
chunk_sizes=chunk_sizes,
encoding=encoding)
self.num_channels = num_channels
self.shape = Shape5D(x=self.size[0],
y=self.size[1],
z=self.size[2],
c=num_channels)
self.location = Point5D(x=self.voxel_offset[0],
y=self.voxel_offset[1],
z=self.voxel_offset[2])
self.interval = self.shape.to_interval5d(offset=self.location)
self.chunk_sizes_5d = [
Shape5D(x=cs[0], y=cs[1], z=cs[2], c=num_channels)
for cs in self.chunk_sizes
]
def test_n5_attributes():
attributes = N5DatasetAttributes(
dimensions=Shape5D(x=100, y=200),
blockSize=Shape5D(x=10, y=20),
c_axiskeys="yx",
dataType=np.dtype("uint16").newbyteorder(">"),
compression=GzipCompressor(level=3))
reserialized_attributes = N5DatasetAttributes.from_json_data(
attributes.to_json_data())
assert reserialized_attributes == attributes
assert attributes.to_json_data()["axes"] == ("x", "y")
def test_writing_to_precomputed_chunks():
tmp_path = create_tmp_dir(prefix="test_writing_to_precomputed_chunks")
datasource = ArrayDataSource(data=data, tile_shape=Shape5D(x=10, y=10))
scale = PrecomputedChunksScale.from_datasource(
datasource=datasource,
key=PurePosixPath("my_test_data"),
encoding=RawEncoder())
sink_path = PurePosixPath("mytest.precomputed")
filesystem = OsFs(tmp_path.as_posix())
datasink = PrecomputedChunksScaleSink(
filesystem=filesystem,
info_dir=sink_path,
scale=scale,
dtype=datasource.dtype,
num_channels=datasource.shape.c,
)
creation_result = datasink.create()
if isinstance(creation_result, Exception):
raise creation_result
for tile in datasource.roi.get_datasource_tiles():
datasink.write(tile.retrieve())
precomp_datasource = PrecomputedChunksDataSource(
path=sink_path, filesystem=filesystem, resolution=scale.resolution)
reloaded_data = precomp_datasource.retrieve()
assert reloaded_data == data
def test_retrieve_roi_smaller_than_tile():
# fmt: off
data = Array5D(np.asarray([
[[ 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10],
[ 11, 12, 13, 14, 15],
[ 16, 17, 18, 19, 20]],
[[ 100, 200, 300, 400, 500],
[ 600, 700, 800, 900, 1000],
[1100, 1200, 1300, 1400, 1500],
[1600, 1700, 1800, 1900, 2000]],
]).astype(np.uint32), axiskeys="cyx")
expected_cyx = np.asarray([
[[ 100, 200, 300, 400],
[ 600, 700, 800, 900],
[1100, 1200, 1300, 1400],
[1600, 1700, 1800, 1900]]
])
# fmt: on
path = PurePosixPath(create_n5(data, chunk_size=Shape5D(c=2, y=4, x=4)))
ds = N5DataSource(path=path / "data", filesystem=OsFs("/"))
smaller_than_tile = ds.retrieve(c=1, y=(0, 4), x=(0, 4))
assert np.all(smaller_than_tile.raw("cyx") == expected_cyx)
def test_h5_datasource():
data_2d = Array5D(np.arange(100).reshape(10, 10), axiskeys="yx")
h5_path = create_h5(data_2d, axiskeys_style="vigra", chunk_shape=Shape5D(x=3, y=3))
ds = H5DataSource(outer_path=h5_path, inner_path=PurePosixPath("/data"), filesystem=OsFs("/"))
assert ds.shape == data_2d.shape
assert ds.tile_shape == Shape5D(x=3, y=3)
slc = ds.interval.updated(x=(0, 3), y=(0, 2))
assert (ds.retrieve(slc).raw("yx") == data_2d.cut(slc).raw("yx")).all()
data_3d = Array5D(np.arange(10 * 10 * 10).reshape(10, 10, 10), axiskeys="zyx")
h5_path = create_h5(data_3d, axiskeys_style="vigra", chunk_shape=Shape5D(x=3, y=3))
ds = H5DataSource(outer_path=h5_path, inner_path=PurePosixPath("/data"), filesystem=OsFs("/"))
assert ds.shape == data_3d.shape
assert ds.tile_shape == Shape5D(x=3, y=3)
slc = ds.interval.updated(x=(0, 3), y=(0, 2), z=3)
assert (ds.retrieve(slc).raw("yxz") == data_3d.cut(slc).raw("yxz")).all()
def test_data_roi_get_tiles_can_clamp_to_datasource_tiles():
# fmt: off
data = Array5D(np.asarray([
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
]).astype(np.uint8), axiskeys="yx")
# fmt: on
ds = ArrayDataSource(data=data, tile_shape=Shape5D(x=2, y=2))
data_slice = DataRoi(datasource=ds, x=(1, 4), y=(0, 3))
# fmt: off
dataslice_expected_data = Array5D(np.asarray([
[2, 3, 4],
[7, 8, 9],
[12, 13, 14]
]).astype(np.uint8), axiskeys="yx", location=Point5D.zero(x=1))
# fmt: on
assert data_slice.retrieve() == dataslice_expected_data
# fmt: off
dataslice_expected_slices = [
Array5D(np.asarray([
[1, 2],
[6, 7]
]).astype(np.uint8), axiskeys="yx", location=Point5D.zero()),
Array5D(np.asarray([
[3, 4],
[8, 9],
]).astype(np.uint8), axiskeys="yx", location=Point5D.zero(x=2)),
Array5D(np.asarray([
[11, 12],
[16, 17],
]).astype(np.uint8), axiskeys="yx", location=Point5D.zero(y=2)),
Array5D(np.asarray([
[13, 14],
[18, 19],
]).astype(np.uint8), axiskeys="yx", location=Point5D.zero(x=2, y=2))
]
# fmt: on
expected_slice_dict = {a.interval: a for a in dataslice_expected_slices}
for piece in data_slice.get_datasource_tiles(clamp_to_datasource=True):
expected_data = expected_slice_dict.pop(piece.interval)
assert expected_data == piece.retrieve()
assert len(expected_slice_dict) == 0
def create_h5(array: Array5D, axiskeys_style: str, chunk_shape: Optional[Shape5D] = None, axiskeys: str = "xyztc"):
raw_chunk_shape = (chunk_shape or Shape5D() * 2).clamped(maximum=array.shape).to_tuple(axiskeys)
path = tempfile.mkstemp()[1] + ".h5"
f = h5py.File(path, "w")
ds = f.create_dataset("data", chunks=raw_chunk_shape, data=array.raw(axiskeys))
if axiskeys_style == "dims":
for key, dim in zip(axiskeys, ds.dims):
dim.label = key
elif axiskeys_style == "vigra":
type_flags = {"x": 2, "y": 2, "z": 2, "t": 2, "c": 1}
axistags = [{"key": key, "typeflags": type_flags[key], "resolution": 0, "description": ""} for key in axiskeys]
ds.attrs["axistags"] = json.dumps({"axes": axistags})
else:
raise Exception(f"Bad axiskeys_style: {axiskeys_style}")
return PurePosixPath(path)
def __call__(self, roi: DataRoi) -> FeatureData:
haloed_roi = roi.enlarged(self.halo)
source_data = self.preprocessor(haloed_roi)
step_shape: Shape5D = Shape5D(
c=1,
t=1,
x=1 if self.axis_2d == "x" else source_data.shape.x,
y=1 if self.axis_2d == "y" else source_data.shape.y,
z=1 if self.axis_2d == "z" else source_data.shape.z,
)
out = Array5D.allocate(
interval=roi.updated(c=(roi.c[0] * self.channel_multiplier,
roi.c[1] * self.channel_multiplier)),
dtype=numpy.dtype("float32"),
axiskeys=source_data.axiskeys.replace("c", "") +
"c" # fastfilters puts channel last
)
for data_slice in source_data.split(step_shape):
source_axes = "zyx"
if self.axis_2d:
source_axes = source_axes.replace(self.axis_2d, "")
raw_data: "ndarray[Any, dtype[float32]]" = data_slice.raw(
source_axes).astype(numpy.float32)
raw_feature_data: "ndarray[Any, dtype[float32]]" = self.filter_fn(
raw_data)
feature_data = FeatureData(
raw_feature_data,
axiskeys=source_axes +
"c" if len(raw_feature_data.shape) > len(source_axes) else
source_axes,
location=data_slice.location.updated(c=data_slice.location.c *
self.channel_multiplier))
out.set(feature_data, autocrop=True)
out.setflags(write=False)
return FeatureData(
out.raw(out.axiskeys),
axiskeys=out.axiskeys,
location=out.location,
)
def test_skimage_datasource_tiles():
bs = DataRoi(SkimageDataSource(path=png_image, filesystem=OsFs("/")))
num_checked_tiles = 0
for tile in bs.split(Shape5D(x=2, y=2)):
if tile == Interval5D.zero(x=(0, 2), y=(0, 2)):
expected_raw = raw_0_2x0_2y
elif tile == Interval5D.zero(x=(0, 2), y=(2, 4)):
expected_raw = raw_0_2x2_4y
elif tile == Interval5D.zero(x=(2, 4), y=(0, 2)):
expected_raw = raw_2_4x0_2y
elif tile == Interval5D.zero(x=(2, 4), y=(2, 4)):
expected_raw = raw_2_4x2_4y
elif tile == Interval5D.zero(x=(4, 5), y=(0, 2)):
expected_raw = raw_4_5x0_2y
elif tile == Interval5D.zero(x=(4, 5), y=(2, 4)):
expected_raw = raw_4_5x2_4y
else:
raise Exception(f"Unexpected tile {tile}")
assert (tile.retrieve().raw("yx") == expected_raw).all()
num_checked_tiles += 1
assert num_checked_tiles == 6
def test_n5_datasink():
tmp_path = create_tmp_dir(prefix="test_n5_datasink")
sink = N5DatasetSink(
filesystem=OsFs(tmp_path.as_posix()),
outer_path=PurePosixPath("test_n5_datasink.n5"),
inner_path=PurePosixPath("/data"),
attributes=N5DatasetAttributes(
dimensions=datasource.shape,
blockSize=Shape5D(x=10, y=10),
c_axiskeys=data.axiskeys, #FIXME: double check this
dataType=datasource.dtype,
compression=RawCompressor(),
location=Point5D.zero(x=7, y=13)))
sink_writer = sink.create()
assert not isinstance(sink_writer, Exception)
for tile in DataRoi(datasource).split(sink.tile_shape):
sink_writer.write(tile.retrieve().translated(Point5D.zero(x=7, y=13)))
n5ds = N5DataSource(filesystem=sink.filesystem, path=sink.full_path)
saved_data = n5ds.retrieve()
assert saved_data.location == Point5D.zero(x=7, y=13)
assert saved_data == data
def test_n5_datasource():
# fmt: off
data = Array5D(np.asarray([
[1, 2, 3, 4, 5 ],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20]
]).astype(np.uint8), axiskeys="yx")
# fmt: on
path = PurePosixPath(create_n5(data, chunk_size=Shape5D(x=2, y=2)))
ds = N5DataSource(path=path / "data", filesystem=OsFs("/"))
assert ds.shape == data.shape
# fmt: off
expected_raw_piece = Array5D(np.asarray([
[1, 2, 3],
[6, 7, 8]
]).astype(np.uint8), axiskeys="yx")
# fmt: on
assert ds.retrieve(x=(0, 3), y=(0, 2)) == expected_raw_piece
ds2 = pickle.loads(pickle.dumps(ds))
assert ds2.retrieve(x=(0, 3), y=(0, 2)) == expected_raw_piece
def test_writing_to_offset_precomputed_chunks():
tmp_path = create_tmp_dir(
prefix="test_writing_to_offset_precomputed_chunks")
data_at_1000_1000 = data.translated(
Point5D(x=1000, y=1000) - data.location)
datasource = ArrayDataSource(data=data_at_1000_1000,
tile_shape=Shape5D(x=10, y=10))
scale = PrecomputedChunksScale.from_datasource(
datasource=datasource,
key=PurePosixPath("my_test_data"),
encoding=RawEncoder())
sink_path = PurePosixPath("mytest.precomputed")
filesystem = OsFs(tmp_path.as_posix())
print(f"\n\n will write to '{filesystem.geturl(sink_path.as_posix())}' ")
datasink = PrecomputedChunksScaleSink(
filesystem=filesystem,
info_dir=sink_path,
scale=scale,
num_channels=datasource.shape.c,
dtype=datasource.dtype,
)
creation_result = datasink.create()
if isinstance(creation_result, Exception):
raise creation_result
for tile in datasource.roi.get_datasource_tiles():
datasink.write(tile.retrieve())
precomp_datasource = PrecomputedChunksDataSource(
path=sink_path, filesystem=filesystem, resolution=scale.resolution)
reloaded_data = precomp_datasource.retrieve(
interval=data_at_1000_1000.interval)
assert (reloaded_data.raw("xyz") == data.raw("xyz")).all()
def test_sequence_datasource():
# fmt: off
img1_data = Array5D(np.asarray([
[[100, 101, 102, 103, 104],
[105, 106, 107, 108, 109],
[110, 111, 112, 113, 114],
[115, 116, 117, 118, 119]],
[[120, 121, 122, 123, 124],
[125, 126, 127, 128, 129],
[130, 131, 132, 133, 134],
[135, 136, 137, 138, 139]],
[[140, 141, 142, 143, 144],
[145, 146, 147, 148, 149],
[150, 151, 152, 153, 154],
[155, 156, 157, 158, 159]]
]), axiskeys="cyx")
img2_data = Array5D(np.asarray([
[[200, 201, 202, 203, 204],
[205, 206, 207, 208, 209],
[210, 211, 212, 213, 214],
[215, 216, 217, 218, 219]],
[[220, 221, 222, 223, 224],
[225, 226, 227, 228, 229],
[230, 231, 232, 233, 234],
[235, 236, 237, 238, 239]],
[[240, 241, 242, 243, 244],
[245, 246, 247, 248, 249],
[250, 251, 252, 253, 254],
[255, 256, 257, 258, 259]]
]), axiskeys="cyx")
img3_data = Array5D(np.asarray([
[[300, 301, 302, 303, 304],
[305, 306, 307, 308, 309],
[310, 311, 312, 313, 314],
[315, 316, 317, 318, 319]],
[[320, 321, 322, 323, 324],
[325, 326, 327, 328, 329],
[330, 331, 332, 333, 334],
[335, 336, 337, 338, 339]],
[[340, 341, 342, 343, 344],
[345, 346, 347, 348, 349],
[350, 351, 352, 353, 354],
[355, 356, 357, 358, 359]]
]), axiskeys="cyx")
expected_x_2_4__y_1_3 = Array5D(np.asarray([
[[[107, 108],
[112, 113]],
[[127, 128],
[132, 133]],
[[147, 148],
[152, 153]]],
[[[207, 208],
[212, 213]],
[[227, 228],
[232, 233]],
[[247, 248],
[252, 253]]],
[[[307, 308],
[312, 313]],
[[327, 328],
[332, 333]],
[[347, 348],
[352, 353]]],
]), axiskeys="zcyx")
# fmt: on
slice_x_2_4__y_1_3 = {"x": (2, 4), "y": (1, 3)}
h5_outer_paths = [
# create_n5(img1_data, axiskeys="cyx"),
create_h5(img1_data, axiskeys_style="dims", axiskeys="cyx"),
# create_n5(img2_data, axiskeys="cyx"),
create_h5(img2_data, axiskeys_style="dims", axiskeys="cyx"),
# create_n5(img3_data, axiskeys="cyx"),
create_h5(img3_data, axiskeys_style="dims", axiskeys="cyx"),
]
def stack_h5s(stack_axis: str) -> List[H5DataSource]:
offset = Point5D.zero()
stack: List[H5DataSource] = []
for outer_path in h5_outer_paths:
stack.append(H5DataSource(outer_path=outer_path, inner_path=PurePosixPath("/data"), filesystem=OsFs("/"), location=offset))
offset += Point5D.zero(**{stack_axis: stack[-1].shape[stack_axis]})
return stack
seq_ds = SequenceDataSource(datasources=stack_h5s("z"), stack_axis="z")
assert seq_ds.shape == Shape5D(x=5, y=4, c=3, z=3)
data = seq_ds.retrieve(**slice_x_2_4__y_1_3)
assert (expected_x_2_4__y_1_3.raw("xyzc") == data.raw("xyzc")).all()
seq_ds = SequenceDataSource(datasources=stack_h5s("z"), stack_axis="z")
data = seq_ds.retrieve(**slice_x_2_4__y_1_3)
assert (expected_x_2_4__y_1_3.raw("xyzc") == data.raw("xyzc")).all()
def test_neighboring_tiles():
# fmt: off
arr = Array5D(np.asarray([
[10, 11, 12, 20, 21, 22, 30],
[13, 14, 15, 23, 24, 25, 33],
[16, 17, 18, 26, 27, 28, 36],
[40, 41, 42, 50, 51, 52, 60],
[43, 44, 45, 53, 54, 55, 63],
[46, 47, 48, 56, 57, 58, 66],
[70, 71, 72, 80, 81, 82, 90],
[73, 74, 75, 83, 84, 85, 93],
[76, 77, 78, 86, 87, 88, 96],
[0, 1, 2, 3, 4, 5, 6]], dtype=np.uint8), axiskeys="yx")
ds = SkimageDataSource(path=create_png(arr), filesystem=OsFs("/"))
fifties_slice = DataRoi(ds, x=(3, 6), y=(3, 6))
expected_fifties_slice = Array5D(np.asarray([
[50, 51, 52],
[53, 54, 55],
[56, 57, 58]
]), axiskeys="yx")
# fmt: on
top_slice = DataRoi(ds, x=(3, 6), y=(0, 3))
bottom_slice = DataRoi(ds, x=(3, 6), y=(6, 9))
right_slice = DataRoi(ds, x=(6, 7), y=(3, 6))
left_slice = DataRoi(ds, x=(0, 3), y=(3, 6))
# fmt: off
fifties_neighbor_data = {
top_slice: Array5D(np.asarray([
[20, 21, 22],
[23, 24, 25],
[26, 27, 28]
]), axiskeys="yx"),
right_slice: Array5D(np.asarray([
[60],
[63],
[66]
]), axiskeys="yx"),
bottom_slice: Array5D(np.asarray([
[80, 81, 82],
[83, 84, 85],
[86, 87, 88]
]), axiskeys="yx"),
left_slice: Array5D(np.asarray([
[40, 41, 42],
[43, 44, 45],
[46, 47, 48]
]), axiskeys="yx"),
}
# fmt: on
assert (fifties_slice.retrieve().raw("yx") == expected_fifties_slice.raw("yx")).all()
for neighbor in fifties_slice.get_neighboring_tiles(tile_shape=Shape5D(x=3, y=3)):
try:
expected_slice = fifties_neighbor_data.pop(neighbor)
assert (expected_slice.raw("yx") == neighbor.retrieve().raw("yx")).all()
except KeyError:
print(f"\nWas searching for ", neighbor, "\n")
for k in fifties_neighbor_data.keys():
print("--->>> ", k)
assert len(fifties_neighbor_data) == 0
def test_pixel_classification_workflow():
brushing_applet = BrushingApplet("brushing_applet")
feature_selection_applet = FeatureSelectionApplet(
"feature_selection_applet", datasources=brushing_applet.datasources)
pixel_classifier_applet = PixelClassificationApplet(
"pixel_classifier_applet",
feature_extractors=feature_selection_applet.feature_extractors,
annotations=brushing_applet.annotations)
# wf = PixelClassificationWorkflow(
# feature_selection_applet=feature_selection_applet,
# brushing_applet=brushing_applet,
# pixel_classifier_applet=pixel_classifier_applet,
# predictions_export_applet=predictions_export_applet
# )
# GUI creates a datasource somewhere...
ds = SkimageDataSource(Path("public/images/c_cells_1.png"),
filesystem=OsFs("."),
tile_shape=Shape5D(x=400, y=400))
# GUI creates some feature extractors
feature_selection_applet.feature_extractors.set_value(
[
GaussianSmoothing.from_ilp_scale(scale=0.3, axis_2d="z"),
HessianOfGaussianEigenvalues.from_ilp_scale(scale=0.7,
axis_2d="z"),
],
confirmer=dummy_confirmer)
# GUI creates some annotations
brush_strokes = [
Annotation.interpolate_from_points(
voxels=[Point5D.zero(x=140, y=150),
Point5D.zero(x=145, y=155)],
color=Color(r=np.uint8(0), g=np.uint8(0), b=np.uint8(255)),
raw_data=ds),
Annotation.interpolate_from_points(
voxels=[Point5D.zero(x=238, y=101),
Point5D.zero(x=229, y=139)],
color=Color(r=np.uint8(0), g=np.uint8(0), b=np.uint8(255)),
raw_data=ds),
Annotation.interpolate_from_points(
voxels=[Point5D.zero(x=283, y=87),
Point5D.zero(x=288, y=92)],
color=Color(r=np.uint8(255), g=np.uint8(0), b=np.uint8(0)),
raw_data=ds),
Annotation.interpolate_from_points(
voxels=[Point5D.zero(x=274, y=168),
Point5D.zero(x=256, y=191)],
color=Color(r=np.uint8(255), g=np.uint8(0), b=np.uint8(0)),
raw_data=ds),
]
brushing_applet.annotations.set_value(brush_strokes,
confirmer=dummy_confirmer)
# preds = predictions_export_applet.compute(DataRoi(ds))
classifier = pixel_classifier_applet.pixel_classifier()
executor = HashingExecutor(num_workers=8)
# calculate predictions on an arbitrary data
preds = executor.submit(classifier.compute, ds.roi)
preds.result().as_uint8().show_channels()
# for png_bytes in preds.to_z_slice_pngs():
# path = f"/tmp/junk_test_image_{uuid.uuid4()}.png"
# with open(path, "wb") as outfile:
# outfile.write(png_bytes.getbuffer())
# os.system(f"gimp {path}")
# calculate predictions on just a piece of arbitrary data
exported_tile = executor.submit(
classifier.compute, DataRoi(datasource=ds, x=(100, 200), y=(100, 200)))
exported_tile.result().show_channels()
# wf.save_as(Path("/tmp/blas.ilp"))
#try removing a brush stroke
brushing_applet.annotations.set_value(brush_strokes[1:],
confirmer=dummy_confirmer)
assert tuple(brushing_applet.annotations()) == tuple(brush_strokes[1:])
def parse(cls, group: h5py.Group, raw_data_sources: Mapping[int, "FsDataSource | None"]) -> "IlpPixelClassificationGroup":
LabelColors = ensure_color_list(group, "LabelColors")
LabelNames = ensure_encoded_string_list(group, "LabelNames")
class_to_color: Mapping[np.uint8, Color] = {np.uint8(i): color for i, color in enumerate(LabelColors, start=1)}
label_classes: Dict[Color, Label] = {color: Label(name=name, color=color, annotations=[]) for name, color in zip(LabelNames, LabelColors)}
LabelSets = ensure_group(group, "LabelSets")
for lane_key in LabelSets.keys():
if not lane_key.startswith("labels"):
continue
lane_index = int(lane_key.replace("labels", ""))
lane_label_blocks = ensure_group(LabelSets, lane_key)
if len(lane_label_blocks.keys()) == 0:
continue
raw_data = raw_data_sources.get(lane_index)
if raw_data is None:
raise IlpParsingError(f"No datasource for lane {lane_index:03d}")
for block_name in lane_label_blocks.keys():
if not block_name.startswith("block"):
continue
block = ensure_dataset(lane_label_blocks, block_name)
block_data = block[()]
if not isinstance(block_data, np.ndarray):
raise IlpParsingError("Expected annotation block to contain a ndarray")
raw_axistags = block.attrs.get("axistags")
if not isinstance(raw_axistags, str):
raise IlpParsingError(f"Expected axistags to be a str, found {raw_axistags}")
axistags = AxisTags.fromJSON(raw_axistags)
axiskeys = "".join(axistags.keys())
if "blockSlice" not in block.attrs:
raise IlpParsingError(f"Expected 'blockSlice' in attrs from {block.name}")
blockSlice = block.attrs["blockSlice"]
if not isinstance(blockSlice, str):
raise IlpParsingError(f"Expected 'blockSlice'' to be a str, found {blockSlice}")
# import pydevd; pydevd.settrace()
blockSpans: Sequence[List[str]] = [span_str.split(":") for span_str in blockSlice[1:-1].split(",")]
blockInterval = Interval5D.zero(**{
key: (int(span[0]), int(span[1]))
for key, span in zip(axiskeys, blockSpans)
})
block_5d = Array5D(block_data, axiskeys=axiskeys)
for color_5d in block_5d.unique_colors().split(shape=Shape5D(x=1, c=block_5d.shape.c)):
color_index = np.uint8(color_5d.raw("c")[0])
if color_index == np.uint8(0): # background
continue
color = class_to_color.get(color_index)
if color is None:
raise IlpParsingError(f"Could not find a label color for index {color_index}")
annotation_data: "np.ndarray[Any, np.dtype[np.uint8]]" = block_5d.color_filtered(color=color_5d).raw(axiskeys)
annotation = Annotation(
annotation_data.astype(np.dtype(bool)),
location=blockInterval.start,
axiskeys=axiskeys, # FIXME: what if the user changed the axiskeys in the data source?
raw_data=raw_data,
)
label_classes[color].annotations.append(annotation)
ClassifierFactory = ensure_bytes(group, "ClassifierFactory")
if ClassifierFactory != VIGRA_ILP_CLASSIFIER_FACTORY:
raise IlpParsingError(f"Expecting ClassifierFactory to be pickled ParallelVigraRfLazyflowClassifierFactory, found {ClassifierFactory}")
if "ClassifierForests" in group:
ClassifierForests = ensure_group(group, "ClassifierForests")
forests: List[VigraRandomForest] = []
for forest_key in sorted(ClassifierForests.keys()):
if not forest_key.startswith("Forest"):
continue
forest = VigraRandomForest(group.file.filename, f"{ClassifierForests.name}/{forest_key}")
# forest_bytes = ensure_bytes(ClassifierForests, forest_key)
# forest = h5_bytes_to_vigra_forest(h5_bytes=VigraForestH5Bytes(forest_bytes))
forests.append(forest)
feature_names = ensure_encoded_string_list(ClassifierForests, "feature_names")
feature_extractors, expected_num_channels = cls.ilp_filters_and_expected_num_channels_from_names(feature_names)
classifier = VigraPixelClassifier(
feature_extractors=feature_extractors,
forest_h5_bytes=[vigra_forest_to_h5_bytes(forest) for forest in forests],
num_classes=len([label for label in label_classes.values() if not label.is_empty()]),
num_input_channels=expected_num_channels,
)
else:
classifier = None
return IlpPixelClassificationGroup(
classifier=classifier,
labels=list(label_classes.values()),
)
from ndstructs.point5D import Point5D, Shape5D
from ndstructs.array5D import Array5D
from webilastik.datasink.n5_dataset_sink import N5DatasetSink
from webilastik.datasink.precomputed_chunks_sink import PrecomputedChunksScaleSink
from webilastik.datasource import DataRoi
from webilastik.datasource.array_datasource import ArrayDataSource
from webilastik.datasource.n5_datasource import N5DataSource
from webilastik.datasource.precomputed_chunks_info import PrecomputedChunksScale, RawEncoder
from webilastik.datasource.n5_attributes import GzipCompressor, N5DatasetAttributes, RawCompressor
from webilastik.datasource.precomputed_chunks_datasource import PrecomputedChunksDataSource
data = Array5D(np.arange(20 * 10 * 7).reshape(20, 10, 7), axiskeys="xyz")
data.setflags(write=False)
datasource = ArrayDataSource(data=data, tile_shape=Shape5D(x=10, y=10))
def test_n5_attributes():
attributes = N5DatasetAttributes(
dimensions=Shape5D(x=100, y=200),
blockSize=Shape5D(x=10, y=20),
c_axiskeys="yx",
dataType=np.dtype("uint16").newbyteorder(">"),
compression=GzipCompressor(level=3))
reserialized_attributes = N5DatasetAttributes.from_json_data(
attributes.to_json_data())
assert reserialized_attributes == attributes
assert attributes.to_json_data()["axes"] == ("x", "y")
async def main():
ds = SkimageDataSource(
filesystem=HttpFs(read_url=Url.parse("https://app.ilastik.org/")),
path=Path("api/images/c_cells_1.png"))
async with aiohttp.ClientSession() as session:
print(f"Creating new session--------------")
async with session.post(f"https://app.ilastik.org/api/session",
json={"session_duration": 30}) as response:
response.raise_for_status()
session_data: Dict[str, Any] = await response.json()
session_id = session_data["id"]
print(
f"Done creating session: {json.dumps(session_data)} <<<<<<<<<<<<<<<<<<"
)
session_is_ready = False
for _ in range(10):
response = await session.get(
f"https://app.ilastik.org/api/session/{session_id}")
response.raise_for_status()
session_status = await response.json()
if session_status["status"] == "ready":
session_url = session_status["url"]
break
print(f"Session {session_id} is notready yet")
await asyncio.sleep(2)
else:
raise RuntimeError("Given up waiting on session")
async with session.ws_connect(f"{session_url}/ws") as ws:
asyncio.get_event_loop().create_task(read_server_status(ws))
print("sending some feature extractors=======")
await ws.send_json({
"feature_selection_applet": [
{
"__class__": "GaussianSmoothing",
"sigma": 0.3,
"axis_2d": "z"
},
{
"__class__": "HessianOfGaussianEigenvalues",
"scale": 0.7,
"axis_2d": "z"
},
]
})
print("done sending feature extractors<<<<<")
asyncio.get_event_loop().create_task(read_server_status(ws))
print("sending some annotations=======")
brush_strokes = [
Annotation.interpolate_from_points(voxels=[
Point5D.zero(x=140, y=150),
Point5D.zero(x=145, y=155)
],
color=Color(r=np.uint8(0),
g=np.uint8(255),
b=np.uint8(0)),
raw_data=ds),
Annotation.interpolate_from_points(voxels=[
Point5D.zero(x=238, y=101),
Point5D.zero(x=229, y=139)
],
color=Color(r=np.uint8(0),
g=np.uint8(255),
b=np.uint8(0)),
raw_data=ds),
Annotation.interpolate_from_points(voxels=[
Point5D.zero(x=283, y=87),
Point5D.zero(x=288, y=92)
],
color=Color(r=np.uint8(255),
g=np.uint8(0),
b=np.uint8(0)),
raw_data=ds),
Annotation.interpolate_from_points(voxels=[
Point5D.zero(x=274, y=168),
Point5D.zero(x=256, y=191)
],
color=Color(r=np.uint8(255),
g=np.uint8(0),
b=np.uint8(0)),
raw_data=ds),
]
await ws.send_json(
{"brushing_applet": [a.to_json_data() for a in brush_strokes]})
print("done sending annotations<<<<<")
from base64 import b64encode
encoded_ds: str = b64encode(json.dumps(
ds.to_json_value()).encode("utf8"),
altchars=b'-_').decode("utf8")
response_tasks = {}
for tile in ds.roi.get_tiles(tile_shape=Shape5D(x=256, y=256, c=2),
tiles_origin=Point5D.zero()):
url = f"{session_url}/predictions/raw_data={encoded_ds}/run_id=123456/data/{tile.x[0]}-{tile.x[1]}_{tile.y[0]}-{tile.y[1]}_0-1"
print(f"---> Requesting {url}")
response_tasks[tile] = session.get(url)
for tile, resp in response_tasks.items():
async with resp as response:
print("Status:", response.status)
print("Content-type:", response.headers['content-type'])
tile_bytes = await response.content.read()
print(f"Got predictions<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
raw_data = np.frombuffer(tile_bytes, dtype=np.uint8).reshape(
2, tile.shape.y, tile.shape.x)
a = Array5D(raw_data, axiskeys="cyx")
# a.show_channels()
global finished
finished = True