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def run_render(args):
keypoints = load_script(args.script)
num_prefetch_frames = args.prefetch_frames
for keypoint in keypoints:
keypoint['state'].gpu_memory_limit = args.gpu_memory_limit
keypoint['state'].system_memory_limit = args.system_memory_limit
keypoint['state'].concurrent_downloads = args.concurrent_downloads
keypoint['state'].cross_section_background_color = args.cross_section_background_color
viewers = [neuroglancer.Viewer() for _ in range(args.shards)]
for viewer in viewers:
with viewer.config_state.txn() as s:
s.show_ui_controls = False
s.show_panel_borders = False
s.viewer_size = [args.width, args.height]
s.scale_bar_options.scale_factor = args.scale_bar_scale
print('Open the specified URL to begin rendering')
print(viewer)
if args.browser:
webbrowser.open_new(viewer.get_viewer_url())
lock = threading.Lock()
num_frames_written = [0]
fps = args.fps
total_frames = sum(max(1, k['transition_duration'] * fps) for k in keypoints[:-1])
def __init__(self, graph, agglo_id, image_url, segmentation_url, state_path):
self.graph = graph
self.agglo_id = agglo_id
self.image_url = image_url
self.segmentation_url = segmentation_url
self.state = InteractiveState(state_path)
self.cached_split_result = CachedSplitResult(
state=self.state, graph=self.graph, agglo_id=self.agglo_id)
self.agglo_members = set(self.graph.get_agglo_members(agglo_id))
if state_path is not None and os.path.exists(state_path):
self.state.load()
else:
self.state.initialize(self.agglo_members)
viewer = self.viewer = neuroglancer.Viewer()
viewer.actions.add('inclusive-seed', self._add_inclusive_seed)
viewer.actions.add('exclusive-seed', self._add_exclusive_seed)
viewer.actions.add('next-component', self._next_component)
viewer.actions.add('prev-component', self._prev_component)
viewer.actions.add('new-component', self._make_new_component)
viewer.actions.add('exclude-component', self._exclude_component)
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'],
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)
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
b = np.cast[np.uint32](np.floor(np.sqrt((ix - 0.5)**2 + (iy - 0.5)**2 + (iz - 0.5)**2) * 10))
b = np.pad(b, 1, 'constant')
viewer = neuroglancer.Viewer()
dimensions = neuroglancer.CoordinateSpace(
names=['x', 'y', 'z'],
units='nm',
scales=[10, 10, 10])
with viewer.txn() as s:
s.dimensions = dimensions
s.layers.append(
name='a',
layer=neuroglancer.LocalVolume(
data=a,
dimensions=neuroglancer.CoordinateSpace(
names=['c^', 'x', 'y', 'z'],
units=['', 'nm','nm','nm'],
scales=[1, 10, 10, 10]),
voxel_offset=(0, 20, 30, 15),
),
ap = argparse.ArgumentParser()
ap.add_argument(
'-a',
'--bind-address',
help='Bind address for Python web server. Use 127.0.0.1 (the default) to restrict access '
'to browers running on the local machine, use 0.0.0.0 to permit access from remote browsers.')
ap.add_argument(
'--static-content-url', help='Obtain the Neuroglancer client code from the specified URL.')
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(
def __init__(self, filename):
self.filename = filename
self.point_annotation_layer_name = 'false-merges'
self.states = []
self.state_index = None
viewer = self.viewer = neuroglancer.Viewer()
self.other_state_segment_ids = dict()
viewer.actions.add('anno-next-state', lambda s: self.next_state())
viewer.actions.add('anno-prev-state', lambda s: self.prev_state())
viewer.actions.add('anno-save', lambda s: self.save())
viewer.actions.add('anno-show-all', lambda s: self.set_combined_state())
viewer.actions.add('anno-add-segments-from-state',
lambda s: self.add_segments_from_state(s.viewer_state))
with viewer.config_state.txn() as s:
s.input_event_bindings.viewer['pageup'] = 'anno-prev-state'
s.input_event_bindings.viewer['pagedown'] = 'anno-next-state'
s.input_event_bindings.viewer['control+keys'] = 'anno-save'
s.input_event_bindings.viewer['control+keya'] = 'anno-show-all'
viewer.shared_state.add_changed_callback(self.on_state_changed)
def __init__(self):
viewer = self.viewer = neuroglancer.Viewer()
viewer.actions.add('inference', self._do_inference)
self.gt_vol = cloudvolume.CloudVolume(
'https://storage.googleapis.com/neuroglancer-public-data/flyem_fib-25/ground_truth',
mip=0,
bounded=True,
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)
def __init__(self, state_path, bodies, labels, segmentation_url, image_url, num_to_prefetch):
self.state = State(state_path)
self.num_to_prefetch = num_to_prefetch
self.viewer = neuroglancer.Viewer()
self.bodies = bodies
self.state.load()
self.total_voxels = sum(x.num_voxels for x in bodies)
self.cumulative_voxels = np.cumsum([x.num_voxels for x in bodies])
with self.viewer.txn() as s:
s.layers['image'] = neuroglancer.ImageLayer(source=image_url)
s.layers['segmentation'] = neuroglancer.SegmentationLayer(source=segmentation_url)
s.show_slices = False
s.concurrent_downloads = 256
s.gpu_memory_limit = 2 * 1024 * 1024 * 1024
s.layout = '3d'
key_bindings = [
['bracketleft', 'prev-index'],
['bracketright', 'next-index'],
from __future__ import print_function
import webbrowser
import neuroglancer
viewer = neuroglancer.Viewer()
with viewer.txn() as s:
s.layers['image'] = neuroglancer.ImageLayer(
source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/image',
)
def my_action(s):
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'
print(viewer)
webbrowser.open_new(viewer.get_viewer_url())
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',
)