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def _start_flood_fill(self, pos):
self._stop_flood_fill()
inf_results = zarr.zeros(
self.gt_vol.bounds.to_list()[3:], chunks=(64, 64, 64), dtype=np.uint8)
inf_volume = neuroglancer.LocalVolume(
data=inf_results, dimensions=self.dimensions)
with self.viewer.txn() as s:
s.layers['points'] = neuroglancer.LocalAnnotationLayer(self.dimensions)
s.layers['inference'] = neuroglancer.ImageLayer(
source=inf_volume,
shader='''
void main() {
float v = toNormalized(getDataValue(0));
vec4 rgba = vec4(0,0,0,0);
if (v != 0.0) {
rgba = vec4(colormapJet(v), 1.0);
}
emitRGBA(rgba);
}
''',
)
self.flood_fill_event = threading.Event()
t = threading.Thread(
target=self._do_flood_fill,
kwargs=dict(
viewer.actions.add('exclude-all-but-component', self._exclude_all_but_component)
key_bindings = [
['bracketleft', 'prev-component'],
['bracketright', 'next-component'],
['at:dblclick0', 'exclude-component'],
['at:shift+mousedown2', 'exclude-all-but-component'],
['at:control+mousedown0', 'inclusive-seed'],
['at:shift+mousedown0', 'exclusive-seed'],
['enter', 'new-component'],
]
with viewer.txn() as s:
s.layers.append(
name='image',
layer=neuroglancer.ImageLayer(source=self.image_url),
)
s.layers.append(
name='original',
layer=neuroglancer.SegmentationLayer(
source=self.segmentation_url,
segments=self.agglo_members,
),
)
s.layers.append(
name='unused',
layer=neuroglancer.SegmentationLayer(source=self.segmentation_url,
),
visible=False,
)
s.layers.append(
name='split-result',
bounded=True,
progress=False,
provenance={})
viewer.actions.add('start-fill', self._start_fill_action)
viewer.actions.add('stop-fill', self._stop_fill_action)
self.dimensions = neuroglancer.CoordinateSpace(
names=['x', 'y', 'z'],
units='nm',
scales=[8, 8, 8],
)
with viewer.config_state.txn() as s:
s.input_event_bindings.data_view['shift+mousedown0'] = 'start-fill'
s.input_event_bindings.data_view['keyt'] = 'stop-fill'
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',
)
s.layers['ground_truth'].visible = False
self.flood_fill_event = None
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='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['overlay'] = neuroglancer.ImageLayer(
source=neuroglancer.LocalVolume(
a,
dimensions=neuroglancer.CoordinateSpace(
scales=[1, 8, 8, 8],
units=['', 'nm', 'nm', 'nm'],
names=['c^', 'x', 'y', 'z']),
voxel_offset=[0, 3000, 3000, 3000]),
shader="""
void main() {
emitRGB(vec3(toNormalized(getDataValue(0)),
toNormalized(getDataValue(1)),
toNormalized(getDataValue(2))));
}
""",
)
s.voxel_coordinates = [3000, 3000, 3000]
args = ap.parse_args()
if args.bind_address:
neuroglancer.set_server_bind_address(args.bind_address)
if args.static_content_url:
neuroglancer.set_static_content_source(url=args.static_content_url)
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='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['overlay'] = neuroglancer.ImageLayer(
source=neuroglancer.LocalVolume(
a,
dimensions=neuroglancer.CoordinateSpace(
scales=[1, 8, 8, 8],
units=['', 'nm', 'nm', 'nm'],
names=['c^', 'x', 'y', 'z']),
voxel_offset=[0, 3000, 3000, 3000]),
shader="""
void main() {
emitRGB(vec3(toNormalized(getDataValue(0)),
def display_split_result(graph, agglo_id, cur_eqs, supervoxel_map, split_seeds, image_url,
segmentation_url):
agglo_members = set(graph.get_agglo_members(agglo_id))
state = neuroglancer.ViewerState()
state.layers.append(name='image', layer=neuroglancer.ImageLayer(source=image_url))
state.layers.append(
name='original',
layer=neuroglancer.SegmentationLayer(
source=segmentation_url,
segments=agglo_members,
),
visible=False,
)
state.layers.append(
name='isolated-supervoxels',
layer=neuroglancer.SegmentationLayer(
source=segmentation_url,
segments=set(x for x, seeds in six.viewitems(supervoxel_map) if len(seeds) > 1),
),
visible=False,
)
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'],
),
progress=False,
provenance={})
self.dimensions = neuroglancer.CoordinateSpace(
names=['x', 'y', 'z'],
units='nm',
scales=self.gt_vol.resolution,
)
self.inf_results = zarr.zeros(
self.gt_vol.bounds.to_list()[3:], chunks=(64, 64, 64), dtype=np.uint8)
self.inf_volume = neuroglancer.LocalVolume(
data=self.inf_results, dimensions=self.dimensions)
with viewer.config_state.txn() as s:
s.input_event_bindings.data_view['shift+mousedown0'] = 'inference'
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',
)
s.layers['ground_truth'].visible = False
s.layers['inference'] = neuroglancer.ImageLayer(
source=self.inf_volume,
shader='''
void main() {