def _update_state(self, s):
self.cached_split_result.update()
self.state.save()
_set_viewer_seeds(s, self.cached_split_result.seeds)
s.layers['unused'].segments = self.state.unused_supervoxels
s.layers['original'].segments = self.cached_split_result.supervoxels
s.layers[
'split-result'].segments = self.cached_split_result.supervoxels
split_result = self.cached_split_result.split_result
if split_result is not None:
self._show_split_result(
s,
cur_eqs=split_result['cur_eqs'],
supervoxel_map=split_result['supervoxel_map'],
)
s.layout = neuroglancer.row_layout([
neuroglancer.LayerGroupViewer(layout='3d',
layers=[
'image', 'original', 'unused',
'inclusive-seeds',
'exclusive-seeds'
]),
neuroglancer.LayerGroupViewer(layout='3d',
layers=[
'image', 'split-result',
'inclusive-seeds',
'exclusive-seeds'
]),
])
def _neuroglancer_link(self):
options = Options.instance()
store_path = Path(options.runs_base_dir).expanduser()
viewer = neuroglancer.Viewer()
with viewer.txn() as s:
train_layers = {}
for i, dataset in enumerate(self.train):
train_layers.update(
dataset._neuroglancer_layers(
exclude_layers=set(train_layers.keys())))
validate_layers = {}
if self.validate is not None:
for i, dataset in enumerate(self.validate):
validate_layers.update(
dataset._neuroglancer_layers(
exclude_layers=set(validate_layers.keys())))
for layer_name, (layer, kwargs) in itertools.chain(
train_layers.items(), validate_layers.items()):
s.layers.append(
name=layer_name,
layer=layer,
**kwargs,
)
s.layout = neuroglancer.row_layout([
neuroglancer.LayerGroupViewer(
layers=list(train_layers.keys())),
neuroglancer.LayerGroupViewer(
layers=list(validate_layers.keys())),
])
return f"http://neuroglancer-demo.appspot.com/#!{json.dumps(viewer.state.to_json())}"
def __init__(self, synapse_path, top_method='min', num_top_partners=10):
with open(synapse_path, 'r') as f:
synapse_data = json.load(f)['data']
self.synapses_by_id, self.synapse_partner_counts = get_synapses_by_id(
synapse_data)
self.top_method = top_method
self.num_top_partners = num_top_partners
dimensions = neuroglancer.CoordinateSpace(
names=['x', 'y', 'z'],
units='nm',
scales=[8, 8, 8],
)
viewer = self.viewer = neuroglancer.Viewer()
viewer.actions.add('select-custom', self._handle_select)
with viewer.config_state.txn() as s:
s.input_event_bindings.data_view['dblclick0'] = 'select-custom'
with viewer.txn() as s:
s.projection_orientation = [
0.63240087, 0.01582051, 0.05692779, 0.77238464
]
s.dimensions = dimensions
s.position = [3000, 3000, 3000]
s.layers['image'] = neuroglancer.ImageLayer(
source=
'precomputed://gs://neuroglancer-public-data/flyem_fib-25/image',
)
s.layers['ground_truth'] = neuroglancer.SegmentationLayer(
source=
'precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
)
s.layers['partners'] = neuroglancer.SegmentationLayer(
source=
'precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
)
s.layers['synapses'] = neuroglancer.LocalAnnotationLayer(
dimensions=dimensions,
linked_segmentation_layer='ground_truth')
s.layout = neuroglancer.row_layout([
neuroglancer.LayerGroupViewer(
layout='xy',
layers=['image', 'ground_truth', 'partners', 'synapses'],
),
neuroglancer.LayerGroupViewer(
layout='3d',
layers=['ground_truth', 'synapses'],
),
neuroglancer.LayerGroupViewer(
layout='3d',
layers=['partners', 'synapses'],
),
])
self.selected_segments = frozenset()
self.viewer.shared_state.add_changed_callback(
lambda: self.viewer.defer_callback(self.on_state_changed))
def _visualize_training(self, run):
# returns a neuroglancer link to visualize snapshots and validations
snapshot_container = self.snapshot_container(run.name)
validation_container = self.validation_container(run.name)
snapshot_zarr = zarr.open(snapshot_container.container)
validation_zarr = zarr.open(validation_container.container)
snapshots = []
validations = []
def generate_groups(container):
def add_element(name, obj):
if isinstance(obj, zarr.hierarchy.Array):
container.append(name)
return add_element
snapshot_zarr.visititems(
lambda name, obj: generate_groups(snapshots)(name, obj)
)
validation_zarr.visititems(
lambda name, obj: generate_groups(validations)(name, obj)
)
viewer = neuroglancer.Viewer()
with viewer.txn() as s:
snapshot_layers = {}
for snapshot in snapshots:
snapshot_layers[snapshot] = ZarrArray.open_from_array_identifier(
snapshot_container.array_identifier(snapshot), name=snapshot
)._neuroglancer_layer()
validation_layers = {}
for validation in validations:
validation_layers[validation] = ZarrArray.open_from_array_identifier(
validation_container.array_identifier(validation), name=validation
)._neuroglancer_layer()
for layer_name, (layer, kwargs) in itertools.chain(
snapshot_layers.items(), validation_layers.items()
):
s.layers.append(
name=layer_name,
layer=layer,
**kwargs,
)
s.layout = neuroglancer.row_layout(
[
neuroglancer.LayerGroupViewer(layers=list(snapshot_layers.keys())),
neuroglancer.LayerGroupViewer(
layers=list(validation_layers.keys())
),
]
)
return f"http://neuroglancer-demo.appspot.com/#!{json.dumps(viewer.state.to_json())}"
def useLayer(self, numLayers):
# Store as ProjectionArray
self.img2 = ProjectionArray(self.img, numLayers)
dimensions = neuroglancer.CoordinateSpace(
names=['z', 'y', 'x'],
units='nm',
scales=[10, 10, 10])
with self.viewer.txn() as s:
s.dimensions = dimensions
s.layers.clear()
s.layers.append(
name='Original Image',
layer=neuroglancer.LocalVolume(
data=self.img,
dimensions=neuroglancer.CoordinateSpace(
names=['c^', 'x', 'y', 'z'],
units=['', 'nm', 'nm', 'nm'],
scales=[1, 10, 10, 10]),
voxel_offset=(0, 0, 0, 0),
),
shader='''
void main() {
emitRGB(vec3(toNormalized(getDataValue(0)),
toNormalized(getDataValue(1)),
toNormalized(getDataValue(2))));
}
''')
s.layers.append(
name='Z-Projection Image',
layer=neuroglancer.LocalVolume(
data=self.img2,
dimensions=neuroglancer.CoordinateSpace(
names=['c^', 'x', 'y', 'z'],
units=['', 'nm', 'nm', 'nm'],
scales=[1, 10, 10, 10]),
voxel_offset=(0, 0, 0, 0),
),
shader='''
void main() {
emitRGB(vec3(toNormalized(getDataValue(0)),
toNormalized(getDataValue(1)),
toNormalized(getDataValue(2))));
}
''')
s.layers['Original Image'] = s.layers[0]
s.layers['Z-Projection Image'] = s.layers[1]
s.layout = neuroglancer.row_layout([
neuroglancer.LayerGroupViewer(layers=['Original Image']),
neuroglancer.LayerGroupViewer(layers=['Z-Projection Image']),
])
viewer.actions.add('my-action', my_action)
with viewer.config_state.txn() as s:
s.input_event_bindings.viewer['keyt'] = 'my-action'
s.status_messages['hello'] = 'Welcome to this example'
with viewer.txn() as s:
s.layout = '3d'
s.projection_scale = 3000
from ipywidgets import Image
screenshot = viewer.screenshot(size=[1000, 1000])
screenshot_image = Image(value=screenshot.screenshot.image)
screenshot_image
with viewer.txn() as s:
s.layout = neuroglancer.row_layout([
neuroglancer.LayerGroupViewer(layers=['image', 'overlay']),
neuroglancer.LayerGroupViewer(layers=['segmentation'])
])
with viewer.txn() as s:
s.layout = neuroglancer.row_layout([
neuroglancer.LayerGroupViewer(layers=['image']),
neuroglancer.LayerGroupViewer(layers=['segmentation'])
])
print(neuroglancer.to_url(viewer.state))
print(viewer)
neuroglancer.stop()
ap = argparse.ArgumentParser()
neuroglancer.cli.add_server_arguments(ap)
args = ap.parse_args()
neuroglancer.cli.handle_server_arguments(args)
viewer = neuroglancer.Viewer()
with viewer.txn() as s:
s.layers['image'] = neuroglancer.ImageLayer(
source=
'precomputed://gs://neuroglancer-public-data/flyem_fib-25/image', )
s.layers['ground_truth'] = neuroglancer.SegmentationLayer(
source=
'precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
)
print(s.layers)
s.layout = neuroglancer.row_layout([
neuroglancer.column_layout([
neuroglancer.LayerGroupViewer(
layers=['image', 'ground_truth']),
neuroglancer.LayerGroupViewer(
layers=['image', 'ground_truth']),
]),
neuroglancer.column_layout([
neuroglancer.LayerGroupViewer(layers=['ground_truth']),
neuroglancer.LayerGroupViewer(layers=['ground_truth']),
]),
])
print(viewer.state)
print(viewer)
webbrowser.open_new(viewer.get_viewer_url())
viewer.actions.add('my-action', my_action)
with viewer.config_state.txn() as s:
s.input_event_bindings.viewer['keyt'] = 'my-action'
s.status_messages['hello'] = 'Welcome to this example'
# with viewer.txn() as s:
# s.layout = '3d'
# s.projection_scale = 3000
"""
from ipywidgets import Image
screenshot = viewer.screenshot(size=[1000, 1000])
screenshot_image = Image(value=screenshot.screenshot.image)
screenshot_image
"""
with viewer.txn() as s:
s.layout = neuroglancer.row_layout(
[neuroglancer.LayerGroupViewer(layers=['image', 'overlay'])])
# neuroglancer.LayerGroupViewer(layers=['segmentation'])])
# with viewer.txn() as s:
# s.layout = neuroglancer.row_layout(
# [neuroglancer.LayerGroupViewer(layers=['image']),
# neuroglancer.LayerGroupViewer(layers=['segmentation'])])
print(neuroglancer.to_url(viewer.state))
print(viewer.state)
print(viewer)
webbrowser.open_new(viewer.get_viewer_url())
def runserver():
event_schedule.enter(30, 1, runserver)
event_schedule.enter(30, 1, runserver())
event_schedule.run()
viewer = neuroglancer.Viewer()
a = np.zeros((3, 100, 100, 100), dtype=np.uint8)
ix, iy, iz = np.meshgrid(*[np.linspace(0, 1, n) for n in a.shape[1:]],
indexing='ij')
a[0, :, :, :] = np.abs(np.sin(4 * (ix + iy))) * 255
a[1, :, :, :] = np.abs(np.sin(4 * (iy + iz))) * 255
a[2, :, :, :] = np.abs(np.sin(4 * (ix + iz))) * 255
with viewer.txn() as s:
s.layers['image'] = neuroglancer.ImageLayer(
source=
'a. nifti://http://127.0.0.1:9000/Users/alex/AlexBadeaMyAtlases/atlases/chass_symmetric3/chass_symmetric3_FA.nii.gz',
)
# s.layers['ground_truth'] = neuroglancer.SegmentationLayer(
# source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
# )
s.layers['overlay'] = neuroglancer.ImageLayer(
# source=neuroglancer.LocalVolume(a, voxel_size=[8, 8, 8], voxel_offset=[3000, 3000, 3000]),
shader="""
void main() {
emitRGB(vec3(toNormalized(getDataValue(0)),
toNormalized(getDataValue(1)),
toNormalized(getDataValue(2))));
}
""", )
s.voxel_coordinates = [3000, 3000, 3000]
num_actions = 0
def my_action(s):
global num_actions
num_actions += 1
with viewer.config_state.txn() as st:
st.status_messages['hello'] = (
'Got action %d: mouse position = %r' %
(num_actions, s.mouse_voxel_coordinates))
print('Got my-action')
print(' Mouse position: %s' % (s.mouse_voxel_coordinates, ))
print(' Layer selected values: %s' % (s.selected_values, ))
viewer.actions.add('my-action', my_action)
with viewer.config_state.txn() as s:
s.input_event_bindings.viewer['keyt'] = 'my-action'
s.status_messages['hello'] = 'Welcome to this example'
# with viewer.txn() as s:
# s.layout = '3d'
# s.projection_scale = 3000
"""
from ipywidgets import Image
screenshot = viewer.screenshot(size=[1000, 1000])
screenshot_image = Image(value=screenshot.screenshot.image)
screenshot_image
"""
with viewer.txn() as s:
s.layout = neuroglancer.row_layout(
[neuroglancer.LayerGroupViewer(layers=['image', 'overlay'])])
# neuroglancer.LayerGroupViewer(layers=['segmentation'])])
# with viewer.txn() as s:
# s.layout = neuroglancer.row_layout(
# [neuroglancer.LayerGroupViewer(layers=['image']),
# neuroglancer.LayerGroupViewer(layers=['segmentation'])])
print(neuroglancer.to_url(viewer.state))
print(viewer.state)
print(viewer)
webbrowser.open_new(viewer.get_viewer_url())
neuroglancer.stop()