def save_script(script_path, keypoints):
temp_path = script_path + '.tmp'
with open(temp_path, 'w') as f:
for x in keypoints:
f.write(neuroglancer.to_url(x['state']) + '\n')
f.write(str(x['transition_duration']) + '\n')
os.rename(temp_path, script_path)
def save_script(script_path, keypoints):
temp_path = script_path + '.tmp'
with open(temp_path, 'w') as f:
for x in keypoints:
f.write(neuroglancer.to_url(x['state']) + '\n')
f.write(str(x['transition_duration']) + '\n')
os.rename(temp_path, script_path)
def run_batch(args, graph):
for path in args.split_seeds:
split_seeds = load_split_seeds(path)
split_result = do_split(graph=graph, split_seeds=split_seeds, agglo_id=args.agglo_id)
state = display_split_result(
graph=graph,
split_seeds=split_seeds,
image_url=args.image_url,
segmentation_url=args.segmentation_url,
**split_result)
print('<p><a href="%s">%s</a></p>' % (neuroglancer.to_url(state), path))
def run_batch(args, graph):
for path in args.split_seeds:
split_seeds = load_split_seeds(path)
split_result = do_split(graph=graph, split_seeds=split_seeds, agglo_id=args.agglo_id)
state = display_split_result(
graph=graph,
split_seeds=split_seeds,
image_url=args.image_url,
segmentation_url=args.segmentation_url,
**split_result)
print('<p><a href="%s">%s</a></p>' % (neuroglancer.to_url(state), path))
def save_script(script_path, keypoints):
temp_path = script_path + '.tmp'
with open(temp_path, 'w') as f:
for x in keypoints:
f.write(neuroglancer.to_url(x['state']) + '\n')
f.write(str(x['transition_duration']) + '\n')
if hasattr(os, 'replace'):
# Only available on Python3
os.replace(temp_path, script_path)
else:
# Fails on Windows if script_path already exists
os.rename(temp_path, script_path)
def dataset_view(datasetname):
dataset = DataSet.query.filter(DataSet.name == datasetname).first_or_404()
state = neuroglancer.ViewerState()
state.layers['img'] = neuroglancer.ImageLayer(source='precomputed://' +
dataset.image_source)
if dataset.pychunkedgraph_viewer_source is not None:
state.layers['seg'] = neuroglancer.SegmentationLayer(
source='graphene://' + dataset.pychunkedgraph_viewer_source)
else:
state.layers['seg'] = neuroglancer.SegmentationLayer(
source='precomputed://' + dataset.flat_segmentation_source)
state.layers['ann'] = neuroglancer.AnnotationLayer()
state.layout = "xy-3d"
ng_url = neuroglancer.to_url(state,
prefix=current_app.config['NEUROGLANCER_URL'])
return render_template('dataset.html',
dataset=dataset,
ng_url=ng_url,
version=__version__)
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)
anno = Annotator(args.filename)
for url in args.add_segments_from_url:
anno.add_segments_from_state(url)
if args.print_sets:
print(repr(anno.get_sets()))
if args.print_combined_state:
print(neuroglancer.to_url(anno.make_combined_state()))
if args.print_summary:
print('<html>')
print('<h1>%s</h1>' % args.filename)
print('<a href="%s">Neuroglancer</a><br/>' %
neuroglancer.to_url(anno.make_combined_state()))
print(repr(anno.get_sets()))
print('</html>')
else:
print(anno.get_viewer_url())
if not args.no_webbrowser:
anno.show()
import neuroglancer
import numpy as np
import argparse
from pathlib import Path
viewer = neuroglancer.Viewer()
print(viewer)
parser = argparse.ArgumentParser()
parser.add_argument("nifti_path", type=Path)
p = parser.parse_args()
with viewer.txn() as s:
s.layers['image'] = neuroglancer.ImageLayer(
#source='nifti://http://127.0.0.1:9000/B51315_T1_masked.nii.gz',
source='nifti://http://127.0.0.1:9000/B51325_invivoAPOE1_labels.nii.gz'
)
webbrowser.open_new(viewer.get_viewer_url())
print(neuroglancer.to_url(viewer.state))
print(viewer.state)
print(viewer)
neuroglancer.stop()
def print_combined_state_url(self):
print(neuroglancer.to_url(self.make_combined_state()))
def url(self):
return neuroglancer.to_url(self.state, prefix=self.base)
def render_on_neuroglancer(mesh_ids,
dff,
id_column='mesh_ids',
pos_col="center_of_mass",
center=[96809, 136521, 540]):
#r= requests.get(blank_state_basil)
# our base viewer configuration
base_json = {
"layers": [{
"source":
"precomputed://gs://neuroglancer-fafb-data/fafb_v14/fafb_v14_orig",
"type": "image",
"name": "fafb_v14"
}, {
"source":
"precomputed://gs://neuroglancer-fafb-data/fafb_v14/fafb_v14_clahe",
"type": "image",
"name": "fafb_v14_clahe",
"visible": False
}, {
"type": "segmentation",
"mesh":
"precomputed://gs://neuroglancer-fafb-data/elmr-data/FAFBNP.surf/mesh",
"selectedAlpha": 0.78,
"notSelectedAlpha": 0.1,
"segments": ["22", "59"],
"skeletonRendering": {
"mode2d": "lines_and_points",
"mode3d": "lines"
},
"name": "neuropil-regions-surface"
}],
"navigation": {
"pose": {
"position": {
"voxelSize": [4, 4, 40],
"voxelCoordinates": [124416, 67072, 3531.5]
}
},
"zoomFactor": 4
},
"perspectiveOrientation": [
0.0033587864600121975, -0.005099608097225428,
-0.0007366925710812211, 0.9999811053276062
],
"perspectiveZoom":
10761.580012248845,
"layout":
"3d"
}
viewer = neuroglancer.Viewer()
viewer.set_state(base_json)
with viewer.txn() as s:
if len(mesh_ids) > 0:
ind = np.where(dff['mesh_id'] == mesh_ids[0])[0][0]
center = np.asarray(dff[pos_col][ind]) / [4, 4, 40]
s.navigation.pose.position.voxelCoordinates = center
seg_layer = s.layers['neuropil-regions-surface']
seg_layer.selectedAlpha = 0.78
seg_layer.notSelectedAlpha = 0.1
seg_layer.segments = mesh_ids
return (neuroglancer.to_url(
viewer.state, prefix='https://neuromancer-seung-import.appspot.com/'))
'--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)
anno = Annotator(args.filename)
for url in args.add_segments_from_url:
anno.add_segments_from_state(url)
if args.print_sets:
print(repr(anno.get_sets()))
if args.print_combined_state:
print(neuroglancer.to_url(anno.make_combined_state()))
if args.print_summary:
print('<html>')
print('<h1>%s</h1>' % args.filename)
print(
'<a href="%s">Neuroglancer</a><br/>' % neuroglancer.to_url(anno.make_combined_state()))
print(repr(anno.get_sets()))
print('</html>')
else:
print(anno.get_viewer_url())
if not args.no_webbrowser:
anno.show()
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()
def print_combined_state_url(self):
print(neuroglancer.to_url(self.make_combined_state()))
action='store_true',
help='Prints a neuroglancer link for the combined state.')
ap.add_argument(
'--print-summary',
action='store_true',
help='Prints a neuroglancer link for the combined state.')
args = ap.parse_args()
anno = Annotator(args.filename)
for url in args.add_segments_from_url:
anno.add_segments_from_state(url)
if args.print_sets:
print(repr(anno.get_sets()))
if args.print_combined_state:
anno.print_combined_state_url()
if args.print_summary:
print('<html>')
print('<h1>%s</h1>' % args.filename)
print(
'<a href="%s">Neuroglancer</a><br/>' % neuroglancer.to_url(anno.make_combined_state()))
print(repr(anno.get_sets()))
print('</html>')
else:
print(anno.get_viewer_url())
if not args.no_webbrowser:
anno.show()
