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feat: ✨ Generalize get_viewer util #200

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merged 4 commits into from
Mar 20, 2024
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feat: ✨ Generalize get_viewer util #200

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feat: ✨ Generalize get_viewer util
rhoadesScholar Mar 20, 2024
34f4b7f
Merge branch 'dev/main' into dev/get_viewer
rhoadesScholar Mar 20, 2024
0d24e2a
Merge branch 'dev/get_viewer' of github.com:janelia-cellmap/dacapo in…
rhoadesScholar Mar 20, 2024
7f44619
feat: ✨ Update synthetic example notebook.
rhoadesScholar Mar 20, 2024
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112 changes: 52 additions & 60 deletions dacapo/examples/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,51 +19,71 @@


def get_viewer(
raw_array: gp.Array | Array | ZarrArray,
labels_array: gp.Array | Array | ZarrArray,
pred_array: Optional[gp.Array | Array | ZarrArray] = None,
pred_labels_array: Optional[gp.Array | Array | ZarrArray] = None,
width: int = 1500,
height: int = 600,
) -> IFrame:
arrays = {
"raw": raw_array,
"labels": labels_array,
}
if pred_array is not None:
arrays["pred"] = pred_array
if pred_labels_array is not None:
arrays["pred_labels"] = pred_labels_array

data = {}
voxel_sizes = {}
for name, array in arrays.items():
arrays: dict, width: int = 1500, height: int = 600, headless: bool = True
) -> neuroglancer.Viewer | IFrame:
for name, array_data in arrays.items():
array = array_data["array"]
if hasattr(array, "to_ndarray"):
data[name] = array.to_ndarray()
arrays[name]["array"] = array.to_ndarray()
else:
data[name] = array.data
arrays[name]["array"] = array.data
if hasattr(array, "voxel_size"):
voxel_sizes[name] = array.voxel_size
arrays[name]["voxel_sizes"] = array.voxel_size
else:
voxel_sizes[name] = array.spec.voxel_size
arrays[name]["voxel_sizes"] = array.spec.voxel_size

neuroglancer.set_server_bind_address("0.0.0.0")
viewer = neuroglancer.Viewer()
with viewer.txn() as state:
state.showSlices = False
add_seg_layer(state, "labels", data["labels"], voxel_sizes["labels"])
for name, array_data in arrays.items():
meshes = "meshes" in array_data and array_data["meshes"]
is_seg = "is_seg" in array_data and array_data["is_seg"]
if is_seg:
add_seg_layer(
state, name, array_data["array"], array_data["voxel_sizes"], meshes
)
else:
add_scalar_layer(
state, name, array_data["array"], array_data["voxel_sizes"]
)

add_scalar_layer(state, "raw", data["raw"], voxel_sizes["raw"])
if headless:
return viewer
else:
return IFrame(src=viewer, width=width, height=height)

if "pred" in data:
add_scalar_layer(state, "pred", data["pred"], voxel_sizes["pred"])

if "pred_labels" in data:
add_seg_layer(
state, "pred_labels", data["pred_labels"], voxel_sizes["pred_labels"]
)
def add_seg_layer(state, name, data, voxel_size, meshes=False):
if meshes:
kwargs = {"segments": np.unique(data[data > 0])}
else:
kwargs = {}
state.layers[name] = neuroglancer.SegmentationLayer(
# segments=[str(i) for i in np.unique(data[data > 0])], # this line will cause all objects to be selected and thus all meshes to be generated...will be slow if lots of high res meshes
source=neuroglancer.LocalVolume(
data=data,
dimensions=neuroglancer.CoordinateSpace(
names=["z", "y", "x"],
units=["nm", "nm", "nm"],
scales=voxel_size,
),
),
**kwargs,
)


return IFrame(src=viewer, width=width, height=height)
def add_scalar_layer(state, name, data, voxel_size):
state.layers[name] = neuroglancer.ImageLayer(
source=neuroglancer.LocalVolume(
data=data,
dimensions=neuroglancer.CoordinateSpace(
names=["z", "y", "x"],
units=["nm", "nm", "nm"],
scales=voxel_size,
),
),
)


class NeuroglancerRunViewer:
Expand Down Expand Up @@ -214,31 +234,3 @@ def update_with_new_validation_if_possible(self):
self.most_recent_iteration,
validation_dataset.name,
)


def add_seg_layer(state, name, data, voxel_size):
state.layers[name] = neuroglancer.SegmentationLayer(
# segments=[str(i) for i in np.unique(data[data > 0])], # this line will cause all objects to be selected and thus all meshes to be generated...will be slow if lots of high res meshes
source=neuroglancer.LocalVolume(
data=data,
dimensions=neuroglancer.CoordinateSpace(
names=["z", "y", "x"],
units=["nm", "nm", "nm"],
scales=voxel_size,
),
),
segments=np.unique(data[data > 0]),
)


def add_scalar_layer(state, name, data, voxel_size):
state.layers[name] = neuroglancer.ImageLayer(
source=neuroglancer.LocalVolume(
data=data,
dimensions=neuroglancer.CoordinateSpace(
names=["z", "y", "x"],
units=["nm", "nm", "nm"],
scales=voxel_size,
),
),
)
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