x_scale_nm, y_scale_nm, z_scale_nm = 5000, 5000, 10000
""" Handle the different steps """
if step == 'step0':
print("step 0")
volume_size = (x_dim, y_dim, z_dim)
resolution = (x_scale_nm, y_scale_nm, z_scale_nm)
vol = make_info_file(volume_size=volume_size,
layer_dir=layer_dir,
resolution=resolution,
atlas_type='Princeton')
elif step == 'step1':
print("step 1")
# Find the individual z planes in the full_sizedatafld - these are the blended images at the raw resolution
sorted_files = sorted(all_slices)
vol = CloudVolume(f'file://{layer_dir}')
done_files = set([int(z) for z in os.listdir(progress_dir)])
all_files = set(range(vol.bounds.minpt.z, vol.bounds.maxpt.z + 1))
to_upload = [int(z) for z in list(all_files.difference(done_files))]
to_upload.sort()
print(f"Have {len(to_upload)} planes to upload")
with ProcessPoolExecutor(max_workers=16) as executor:
executor.map(process_slice, to_upload)
elif step == 'step2': # downsampling
print("step 2")
vol = CloudVolume(f'file://{layer_dir}')
tasks = make_downsample_tasks(vol, mip_start=0, num_mips=4)
with LocalTaskQueue(parallel=18) as tq:
tq.insert_all(tasks)
def DeleteSkeletonFilesTask(cloudpath: str,
prefix: str,
skel_dir: Optional[str] = None):
cv = CloudVolume(cloudpath, skel_dir=skel_dir)
cf = CloudFiles(cv.skeleton.meta.layerpath)
cf.delete(cf.list(prefix=prefix))
class ReadPrecomputedOperator(OperatorBase):
def __init__(self,
volume_path: str,
mip: int = 0,
expand_margin_size=(0, 0, 0),
fill_missing: bool = False,
validate_mip: int = None,
blackout_sections: bool = None,
dry_run: bool = False,
name: str = 'cutout'):
super().__init__(name=name)
self.volume_path = volume_path
self.mip = mip
self.expand_margin_size = expand_margin_size
self.fill_missing = fill_missing
self.validate_mip = validate_mip
self.blackout_sections = blackout_sections
self.dry_run = dry_run
if blackout_sections:
with Storage(volume_path) as stor:
self.blackout_section_ids = stor.get_json(
'blackout_section_ids.json')['section_ids']
verbose = (logging.getLogger().getEffectiveLevel() <= 30)
self.vol = CloudVolume(self.volume_path,
bounded=False,
fill_missing=self.fill_missing,
progress=verbose,
mip=self.mip,
cache=False,
green_threads=True)
def __call__(self, output_bbox):
chunk_slices = tuple(
slice(s.start - m, s.stop + m)
for s, m in zip(output_bbox.to_slices(), self.expand_margin_size))
if self.dry_run:
input_bbox = Bbox.from_slices(chunk_slices)
return Chunk.from_bbox(input_bbox)
logging.info('cutout {} from {}'.format(chunk_slices[::-1],
self.volume_path))
# always reverse the indexes since cloudvolume use x,y,z indexing
chunk = self.vol[chunk_slices[::-1]]
chunk = np.asarray(chunk)
# the cutout is fortran ordered, so need to transpose and make it C order
chunk = chunk.transpose()
# we can delay this transpose later
# actually we do not need to make it contiguous
# chunk = np.ascontiguousarray(chunk)
# if the channel number is 1, squeeze it as 3d array
# this should not be neccessary
# TODO: remove this step and use 4D array all over this package.
# always use 4D array will simplify some operations
voxel_offset = tuple(s.start for s in chunk_slices)
if chunk.shape[0] == 1:
chunk = np.squeeze(chunk, axis=0)
else:
voxel_offset = (0, ) + voxel_offset
chunk = Chunk(chunk,
voxel_offset=voxel_offset,
voxel_size=tuple(self.vol.resolution[::-1]))
if self.blackout_sections:
chunk = self._blackout_sections(chunk)
if self.validate_mip:
self._validate_chunk(chunk)
return chunk
def _blackout_sections(self, chunk):
"""
make some sections black.
this was normally used for the section with bad alignment.
The ConvNet was supposed to handle them better with black image.
TODO: make this function as a separate operator
"""
# current code only works with 3d image
assert chunk.ndim == 3, "current code assumes that the chunk is 3D image."
for z in self.blackout_section_ids:
z0 = z - chunk.voxel_offset[0]
if z0 >= 0 and z0 < chunk.shape[0]:
chunk[z0, :, :] = 0
return chunk
def _validate_chunk(self, chunk):
"""
check that all the input voxels was downloaded without black region
We have found some black regions in previous inference run,
so hopefully this will solve the problem.
"""
if chunk.ndim == 4 and chunk.shape[0] > 1:
chunk = chunk[0, :, :, :]
validate_vol = CloudVolume(self.volume_path,
bounded=False,
fill_missing=self.fill_missing,
progress=False,
mip=self.validate_mip,
cache=False,
green_threads=True)
chunk_mip = self.mip
logging.info('validate chunk in mip {}'.format(self.validate_mip))
assert self.validate_mip >= chunk_mip
# only use the region corresponds to higher mip level
# clamp the surrounding regions in XY plane
# this assumes that the input dataset was downsampled starting from the
# beginning offset in the info file
voxel_offset = chunk.voxel_offset
# factor3 follows xyz order in CloudVolume
factor3 = np.array([
2**(self.validate_mip - chunk_mip), 2
**(self.validate_mip - chunk_mip), 1
],
dtype=np.int32)
clamped_offset = tuple(go + f - (go - vo) % f for go, vo, f in zip(
voxel_offset[::-1], self.vol.voxel_offset, factor3))
clamped_stop = tuple(
go + s - (go + s - vo) % f
for go, s, vo, f in zip(voxel_offset[::-1], chunk.shape[::-1],
vol.voxel_offset, factor3))
clamped_slices = tuple(
slice(o, s) for o, s in zip(clamped_offset, clamped_stop))
clamped_bbox = Bbox.from_slices(clamped_slices)
clamped_input = chunk.cutout(clamped_slices[::-1])
# transform to xyz order
clamped_input = np.transpose(clamped_input)
# get the corresponding bounding box for validation
validate_bbox = self.vol.bbox_to_mip(clamped_bbox,
mip=chunk_mip,
to_mip=self.validate_mip)
#validate_bbox = clamped_bbox // factor3
# downsample the input using avaraging
# keep the z as it is since the mip only applies to xy plane
# recursivly downsample the input
# if we do it directly, the downsampled input will not be the same with the recursive one
# because of the rounding error of integer division
for _ in range(self.validate_mip - chunk_mip):
clamped_input = downsample_with_averaging(clamped_input, (2, 2, 1))
# validation by template matching
assert validate_by_template_matching(clamped_input)
validate_input = validate_vol[validate_bbox.to_slices()]
if validate_input.shape[3] == 1:
validate_input = np.squeeze(validate_input, axis=3)
# use the validate input to check the downloaded input
assert np.alltrue(validate_input == clamped_input)
def uploadskeletons(skelsource, skelseglist, skelnamelist, path):
"""Upload skeleton (of cloudvolume class) to a local server.
Parameters
----------
skelsource : List containing cloud volume skeletons
skelseglist : List containing the segids(skid)
skelnamelist : List containing the names of skeletons
path : path to the local data server
Returns
-------
cv : cloudvolume class object
"""
info = {
"@type":
"neuroglancer_skeletons",
"transform":
skelsource[0].transform.flatten(),
"vertex_attributes": [{
"id": "radius",
"data_type": "float32",
"num_components": 1
}],
"scales":
"um"
}
path = 'file://' + path + '/precomputed'
cv = CloudVolume(path, info=info)
# prepare for info file
cv.skeleton.meta.info['@type'] = 'neuroglancer_skeletons'
cv.skeleton.meta.info['transform'] = skelsource[0].transform.flatten()
cv.skeleton.meta.info['vertex_attributes'] = [{
'id': 'radius',
'data_type': 'float32',
'num_components': 1
}]
del cv.skeleton.meta.info['sharding']
del cv.skeleton.meta.info['spatial_index']
cv.skeleton.meta.info['segment_properties'] = 'seg_props'
cv.skeleton.meta.commit_info()
files = [
os.path.join(cv.skeleton.meta.skeleton_path, str(skel.id))
for skel in skelsource
]
for fileidx in range(len(files)):
fullfilepath = files[fileidx]
fullfilepath = os.path.join(cv.basepath, os.path.basename(path),
fullfilepath)
uploadskel = Skeleton(vertices=skelsource[fileidx].vertices,
edges=skelsource[fileidx].edges)
print(fullfilepath)
with open(fullfilepath, 'wb') as f:
f.write(uploadskel.to_precomputed())
segfilepath = os.path.join(cv.basepath, os.path.basename(path),
cv.skeleton.meta.skeleton_path, 'seg_props')
if not os.path.exists(segfilepath):
os.makedirs(segfilepath)
print('creating:', segfilepath)
allsegproplist = []
for segid in skelseglist:
segpropdict = {}
segpropdict['id'] = segid
segpropdict['type'] = 'label'
segpropdict['values'] = skelnamelist
allsegproplist.append(segpropdict)
seginfo = {
"@type": "neuroglancer_segment_properties",
"inline": {
"ids": skelseglist,
"properties": allsegproplist
}
}
segfile = os.path.join(segfilepath, 'info')
with open(segfile, 'w') as segfile:
json.dump(seginfo, segfile)
return cv
def segment(args):
"""Run segmentation on contiguous block of affinities from CV
Args:
args: ArgParse object from main
"""
bbox_start = Vec(*args.bbox_start)
bbox_size = Vec(*args.bbox_size)
chunk_size = Vec(*args.chunk_size)
bbox = Bbox(bbox_start, bbox_start + bbox_size)
src_cv = CloudVolume(args.src_path,
fill_missing=True,
parallel=args.parallel)
info = CloudVolume.create_new_info(
num_channels=1,
layer_type='segmentation',
data_type='uint64',
encoding='raw',
resolution=src_cv.info['scales'][args.mip]['resolution'],
voxel_offset=bbox_start,
chunk_size=chunk_size,
volume_size=bbox_size,
mesh='mesh_mip_{}_err_{}'.format(args.mip,
args.max_simplification_error))
dst_cv = CloudVolume(args.dst_path, info=info, parallel=args.parallel)
dst_cv.provenance.description = 'ws+agg using waterz'
dst_cv.provenance.processing.append({
'method': {
'task': 'watershed+agglomeration',
'src_path': args.src_path,
'dst_path': args.dst_path,
'mip': args.mip,
'shape': bbox_size.tolist(),
'bounds': [
bbox.minpt.tolist(),
bbox.maxpt.tolist(),
],
},
'by': args.owner,
'date': strftime('%Y-%m-%d%H:%M %Z'),
})
dst_cv.provenance.owners = [args.owner]
dst_cv.commit_info()
dst_cv.commit_provenance()
if args.segment:
print('Downloading affinities')
aff = src_cv[bbox.to_slices()]
aff = np.transpose(aff, (3, 0, 1, 2))
aff = np.ascontiguousarray(aff, dtype=np.float32)
thresholds = [args.threshold]
print('Starting ws+agg')
seg_gen = waterz.agglomerate(aff, thresholds)
seg = next(seg_gen)
print('Deleting affinities')
del aff
print('Uploading segmentation')
dst_cv[bbox.to_slices()] = seg
if args.mesh:
print('Starting meshing')
with LocalTaskQueue(parallel=args.parallel) as tq:
tasks = tc.create_meshing_tasks(
layer_path=args.dst_path,
mip=args.mip,
shape=args.chunk_size,
simplification=True,
max_simplification_error=args.max_simplification_error,
progress=True)
tq.insert_all(tasks)
tasks = tc.create_mesh_manifest_tasks(layer_path=args.dst_path,
magnitude=args.magnitude)
tq.insert_all(tasks)
print("Meshing complete")
app.logger.error('Error: {}'.format(e))
return {'error': str(e)}
app.logger.debug('Locations queried: {}'.format(str(locs)))
if locs.shape[0] > config.MaxLocations:
err = {
'error':
'Max number of locations ({}) exceeded'.format(config.MaxLocations)
}
return make_response(jsonify(err), 400)
try:
seg_ids = process.get_multiple_ids(locs,
vol,
max_workers=config.MaxWorkers)
except BaseException:
tb = traceback.format_exc()
app.logger.error('Error: {}'.format(tb))
return make_response(jsonify({'error': str(tb)}), 500)
return jsonify(seg_ids.tolist())
started = datetime.datetime.now()
vol = CloudVolume(**config.CloudVolumeKwargs)
if __name__ == "__main__":
app.run(host='0.0.0.0')
def create_skeleton_layer(s3_bucket, skel_res, img_dims, num_res=7):
"""Creates segmentation layer for skeletons
Arguments:
s3_bucket {str} -- path to precomputed skeleton destination
skel_res {list} -- x,y,z dimensions of highest res voxel size (nm)
img_dims {list} -- x,y,z voxel dimensions of tiff images
Keyword Arguments:
num_res {int} -- number of image resolutions to be downsampled
Returns:
vol {cloudvolume.CloudVolume} -- CloudVolume to upload skeletons to
"""
# create cloudvolume info
info = CloudVolume.create_new_info(
num_channels=1,
layer_type="segmentation",
data_type="uint64", # Channel images might be 'uint8'
encoding="raw", # raw, jpeg, compressed_segmentation, fpzip, kempressed
# Voxel scaling, units are in nanometers
resolution=skel_res,
voxel_offset=[0, 0, 0], # x,y,z offset in voxels from the origin
# Pick a convenient size for your underlying chunk representation
# Powers of two are recommended, doesn't need to cover image exactly
chunk_size=[int(i / 4) for i in img_dims],
# chunk_size=[128, 128, 64], # units are voxels
volume_size=[i * 2**(num_res - 1)
for i in img_dims], # units are voxels
skeletons="skeletons",
)
skel_info = {
"@type":
"neuroglancer_skeletons",
"transform": [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],
"vertex_attributes": [
{
"id": "radius",
"data_type": "float32",
"num_components": 1
},
{
"id": "vertex_types",
"data_type": "float32",
"num_components": 1
},
{
"id": "vertex_color",
"data_type": "float32",
"num_components": 4
},
],
}
# get cloudvolume info
vol = CloudVolume(s3_bucket, info=info, parallel=True)
[vol.add_scale((2**i, 2**i, 2**i)) for i in range(num_res)] # num_res - 1
vol.commit_info()
# upload skeleton info to /skeletons/ dir
with storage.SimpleStorage(vol.cloudpath) as stor:
stor.put_json(str(Path("skeletons") / "info"), skel_info)
return vol
def analyze_synapse(
segmentation_vol, vc_vol, sj_vol, mask_vol, mask_mip,
output_dir, chunk_size, overlap, offset, size):
'''
segmentaion, vc, sj are by default at same mip level
'''
vc_thresh = 5
sj_thresh = 5
chunk_size = np.array(chunk_size)
overlap = np.array(overlap)
if mpi_rank == 0:
os.makedirs(output_dir, exist_ok=True)
cv_args = dict(progress=False, parallel=False, fill_missing=True, bounded=False)
seg_cv = CloudVolume('file://%s' % segmentation_vol, mip=0, **cv_args)
vc_cv = CloudVolume('file://%s' % vc_vol, mip=0, **cv_args)
sj_cv = CloudVolume('file://%s' % sj_vol, mip=0, **cv_args)
mask_cv = CloudVolume('file://%s' % mask_vol, mip=mask_mip, **cv_args)
if offset is None or size is None:
union_bb = Bbox.intersection(seg_cv.meta.bounds(0), vc_cv.meta.bounds(0))
offset = union_bb.minpt
size = union_bb.size3()
offset = np.array(offset)
size = np.array(size)
print(offset, size)
union_bb = Bbox(offset, offset + size)
print(union_bb)
bbs = get_chunk_bboxes(union_bb, chunk_size, overlap)
print(len(bbs))
all_inds = np.arange(len(bbs))
np.random.shuffle(all_inds)
sub_inds = np.array_split(all_inds, mpi_size)
# sub_bbs = np.array_split(bbs, mpi_size)
else:
seg_cv = None
vc_cv = None
sj_cv = None
mask_cv = None
bbs = None
sub_inds = None
# sub_bbs = None
seg_cv = mpi_comm.bcast(seg_cv, 0)
vc_cv = mpi_comm.bcast(vc_cv, 0)
sj_cv = mpi_comm.bcast(sj_cv, 0)
mask_cv = mpi_comm.bcast(mask_cv, 0)
# sub_bbs = mpi_comm.scatter(sub_bbs, 0)
bbs = mpi_comm.bcast(bbs, 0)
sub_inds = mpi_comm.scatter(sub_inds, 0)
padding = overlap // 2
all_vc_dfs = []
all_syn_dfs = []
# for ind, bb in tqdm(enumerate(sub_bbs), total=len(sub_bbs), desc='iterate bbs'):
for ind in tqdm(sub_inds, total=len(sub_inds), desc='iterate bbs'):
bb = bbs[ind]
bb = Bbox(bb.minpt + padding, bb.maxpt - padding)
offset = bb.minpt
seg_chunk = np.array(seg_cv[bb])[..., 0]
vc_chunk = np.array(vc_cv[bb])[..., 0]
sj_chunk = np.array(sj_cv[bb])[..., 0]
mask_chunk = np.array(mask_cv[bb])[..., 0]
if np.logical_or.reduce(seg_chunk.ravel()) == 0:
continue
vc_df, vc_labels = find_vc_fast(
seg_chunk, mask_chunk, vc_chunk, offset,
vc_thresh=vc_thresh, size_thresh=100)
if vc_df is None:
continue
all_vc_dfs.append(vc_df)
pre_synapse_df, sj_labels = find_sj(
vc_df, seg_chunk, vc_labels, mask_chunk, sj_chunk, offset,
pad=(3, 3, 2), border_thickness=(3, 3, 2), min_sj_size=60,
max_neighbor_count=3)
if pre_synapse_df is None:
continue
synapse_df, sj_psd_annos = find_post_syn(pre_synapse_df, seg_chunk, sj_labels,
offset, rad=(20, 20, 3), max_angle=60.0, border_thickness=(3, 3, 2))
if len(synapse_df):
cube_df_path = os.path.join(output_dir, 'synapse_%d_%d_%d.csv' % (offset[0], offset[1], offset[2]))
synapse_df = synapse_df.set_index(['pre_seg_id', 'post_seg_id'])
synapse_df.to_csv(cube_df_path)
all_syn_dfs.append(synapse_df)
mpi_comm.barrier()
logging.warning('rank %d reached', mpi_rank)
all_vc_dfs = mpi_comm.reduce(all_vc_dfs, MPI.SUM, 0)
all_syn_dfs = mpi_comm.reduce(all_syn_dfs, MPI.SUM, 0)
if mpi_rank == 0:
all_vc_df = pd.concat(all_vc_dfs)
vc_out_path = os.path.join(output_dir, 'vc.csv')
all_vc_df.to_csv(vc_out_path)
all_syn_df = pd.concat(all_syn_dfs)
syn_out_path = os.path.join(output_dir, 'synapse.csv')
all_syn_df.to_csv(syn_out_path)
import shutil
import gzip
import json
from cloudvolume import CloudVolume, chunks, Storage, PrecomputedSkeleton
from cloudvolume.storage import SimpleStorage
from cloudvolume.lib import mkdir, Bbox, Vec
from cloudvolume.skeletonservice import SkeletonDecodeError
info = CloudVolume.create_new_info(
num_channels=1, # Increase this number when we add more tests for RGB
layer_type='segmentation',
data_type='uint16',
encoding='raw',
resolution=[1,1,1],
voxel_offset=(0,0,0),
skeletons=True,
volume_size=(100, 100, 100),
chunk_size=(64, 64, 64),
)
def test_skeletons():
# Skeleton of my initials
# z=0: W ; z=1 S
vertices = np.array([
[ 0, 1, 0 ],
[ 1, 0, 0 ],
[ 1, 1, 0 ],
def test_cache_validity():
image = np.zeros(shape=(128,128,128,1), dtype=np.uint8)
dirpath = '/tmp/cloudvolume/caching-validity-' + str(TEST_NUMBER)
layer_path = 'file://' + dirpath
vol = create_volume_from_image(
image=image,
offset=(1,1,1),
layer_path=layer_path,
layer_type='image',
resolution=(1,1,1),
encoding='raw'
)
vol.cache.enabled = True
vol.cache.flush()
vol.commit_info()
def test_with_mock_cache_info(info, shoulderror):
finfo = os.path.join(vol.cache.path, 'info')
with open(finfo, 'w') as f:
f.write(json.dumps(info))
if shoulderror:
try:
CloudVolume(vol.layer_cloudpath, cache=True)
except ValueError:
pass
else:
assert False
else:
CloudVolume(vol.layer_cloudpath, cache=True)
test_with_mock_cache_info(vol.info, shoulderror=False)
info = vol.info.copy()
info['scales'][0]['size'][0] = 666
test_with_mock_cache_info(info, shoulderror=False)
test_with_mock_cache_info({ 'zomg': 'wow' }, shoulderror=True)
def tiny_change(key, val):
info = vol.info.copy()
info[key] = val
test_with_mock_cache_info(info, shoulderror=True)
tiny_change('type', 'zoolander')
tiny_change('data_type', 'uint32')
tiny_change('num_channels', 2)
tiny_change('mesh', 'mesh')
def scale_change(key, val, mip=0):
info = vol.info.copy()
info['scales'][mip][key] = val
test_with_mock_cache_info(info, shoulderror=True)
scale_change('voxel_offset', [ 1, 2, 3 ])
scale_change('resolution', [ 1, 2, 3 ])
scale_change('encoding', 'npz')
vol.cache.flush()
# Test no info file at all
CloudVolume(vol.layer_cloudpath, cache=True)
vol.cache.flush()
def myfunction(arg1):
return arg1
import networkx as nx
import numpy as np
# pip install numpy tifffile cloud-volume
import numpy as np
import tifffile
from cloudvolume import CloudVolume
vol = CloudVolume("s3://open-neurodata/kasthuri/kasthuri11/image",
mip=0,
use_https=True)
# load data into numpy array
cutout = vol[11264:11776, 13312:13824, 912:928]
# save cutout as TIFF
tifffile.imwrite("data.tiff", data=np.transpose(cutout))
print(nx.info(graph))
graph2 = nx.Graph(graph)
nx.average_clustering(graph2)
#global efficiency
ugraph = nx.to_undirected(graph)
nx.global_efficiency(ugraph)
def test_cloud_access():
vol = CloudVolume('gs://seunglab-test/test_v0/image')
vol = CloudVolume('s3://seunglab-test/test_dataset/image')
def test_boss_download():
vol = CloudVolume('gs://seunglab-test/test_v0/image')
bossvol = CloudVolume('boss://automated_testing/test_v0/image')
vimg = vol[:,:,:5]
bimg = bossvol[:,:,:5]
assert np.all(bimg == vimg)
assert bimg.dtype == vimg.dtype
vol.bounded = False
vol.fill_missing = True
bossvol.bounded = False
bossvol.fill_missing = True
assert np.all(vol[-100:100,-100:100,-10:10] == bossvol[-100:100,-100:100,-10:10])
# BOSS using a different algorithm for creating downsamples
# so hard to compare 1:1 w/ pixels.
bossvol.bounded = True
bossvol.fill_missing = False
bossvol.mip = 1
bimg = bossvol[:,:,5:6]
assert np.any(bimg > 0)
def _serial_processing(
self,
seg_ids,
ngl,
num_verts,
start_vert,
include_neighborhood,
write=False,
batch_size=None,
file_path=None,
):
"""Core code which actually extracts features."""
voxel_dict = {}
counter = 0
batch_id = 0
for seg_id in seg_ids:
if self.segment_url is None:
segment = ngl.cv.skeleton.get(seg_id)
else:
cv_skel = CloudVolume(self.segment_url)
segment = cv_skel.skeleton.get(seg_id)
if num_verts is not None and num_verts <= len(segment.vertices):
if num_verts <= len(segment.vertices):
verts = segment.vertices[start_vert:num_verts]
else:
warnings.warn(
UserWarning(
f"Number of vertices {num_verts} greater than total vertices {len(segment.vertices)}. Defaulting to max len."
)
)
verts = segment.vertices[start_vert:]
else:
verts = segment.vertices[start_vert:]
start_vert = 0
for v_id, vertex in enumerate(verts):
start = time.time()
img, bounds, voxel = ngl.pull_voxel(seg_id, v_id, self.size)
img_off = ngl.pull_bounds_img(bounds + self.offset)
end = time.time()
self.download_time += end - start
start = time.time()
features = self._convert_to_features(img)
features_off = self._convert_to_features(img_off)
end = time.time()
self.conversion_time += end - start
voxel_dict[counter] = {
**{"Segment": int(seg_id), "Vertex": int(v_id), "Label": 1},
**features,
}
counter += 1
voxel_dict[counter] = {
**{"Segment": int(seg_id), "Vertex": int(v_id), "Label": 0},
**features_off,
}
counter += 1
if write:
if counter % batch_size == 0 or (counter + 1) % batch_size == 0:
df = pd.DataFrame.from_dict(voxel_dict, "index")
path = (
file_path
+ str(batch_id * batch_size)
+ "_"
+ str((batch_id + 1) * batch_size)
+ "_"
+ str(seg_id)
+ "_"
+ str(v_id)
+ ".feather"
)
start = time.time()
feather.write_dataframe(df, path)
end = time.time()
self.write_time += end - start
voxel_dict = {}
batch_id += 1
if file_path is None:
if write:
if not (counter % batch_size == 0 or (counter + 1) % batch_size == 0):
df = pd.DataFrame.from_dict(voxel_dict, "index")
path = (
file_path
+ str(batch_id * batch_size)
+ "_"
+ str(counter)
+ "_"
+ str(seg_id)
+ "_"
+ str(v_id)
+ ".feather"
)
feather.write_dataframe(df, path)
else:
df = pd.DataFrame.from_dict(voxel_dict, "index")
return df
def execute(self): vol = CloudVolume(self.cloudpath, self.mip, non_aligned_writes=self.non_aligned_writes) bounds = Bbox(self.offset, self.shape + self.offset) bounds = Bbox.clamp(bounds, vol.bounds) img = np.zeros(bounds.size3(), dtype=vol.dtype) + self.value vol[bounds] = img
def test_skeletons():
# Skeleton of my initials
# z=0: W ; z=1 S
vertices = np.array([
[ 0, 1, 0 ],
[ 1, 0, 0 ],
[ 1, 1, 0 ],
[ 2, 0, 0 ],
[ 2, 1, 0 ],
[ 0, 0, 1 ],
[ 1, 0, 1 ],
[ 1, 1, 1 ],
[ 0, 1, 1 ],
[ 0, 2, 1 ],
[ 1, 2, 1 ],
], np.float32)
edges = np.array([
[0, 1],
[1, 2],
[2, 3],
[3, 4],
[4, 5],
[5, 6],
[6, 7],
[7, 8],
[8, 9],
[9, 10],
[10, 11],
], dtype=np.uint32)
radii = np.array([
1.0,
2.5,
3.0,
4.1,
1.2,
5.6,
2000.123123,
15.33332221,
8128.124,
-1,
1824.03
], dtype=np.float32)
vol = CloudVolume('file:///tmp/cloudvolume/test-skeletons', info=info)
vol.skeleton.upload(segid=1, vertices=vertices, edges=edges, radii=radii)
skel = vol.skeleton.get(1)
assert skel.id == 1
assert np.all(skel.vertices == vertices)
assert np.all(skel.edges == edges)
assert np.all(skel.radii == radii)
assert np.all(skel.vertex_types == 0)
assert vol.skeleton.path == 'skeletons'
with SimpleStorage('file:///tmp/cloudvolume/test-skeletons/') as stor:
rawskel = stor.get_file('skeletons/1')
assert len(rawskel) == 8 + 11 * (12 + 8 + 4 + 1)
stor.delete_file('skeletons/1')
try:
vol.skeleton.get(5)
assert False
except SkeletonDecodeError:
pass
def execute(self): # This could be made more sophisticated using exists vol = CloudVolume(self.cloudpath, self.mip, fill_missing=False) bounds = Bbox(self.offset, self.shape + self.offset) bounds = Bbox.clamp(bounds, vol.bounds) image = vol[bounds]
def large_local_to_cloud(data_path,
cloud_path,
begin=None,
end=None,
dtype=None,
multi=False,
z_step=64,
layer_type=None,
chunk_size=(64, 64, 64),
resolution=None,
scale=0):
'''
when data is a tiffstack above RAM limit
layer_type: 'image' or 'segmentation'
resolution: tuple of 3
'''
if not begin and not end:
S, L = check_stack_len(data_path) # start and length
else:
S, L = begin, end - begin
print(S, L)
first_slice = dxchange.read_tiff(data_path)
X, Y = first_slice.shape
#volume_size = (L,X,Y)
volume_size = (X, Y, L)
if not dtype:
data_type = first_slice.dtype
else:
data_type = dtype
data_generator = large_data_generator(data_path, S, S + L, z_step,
data_type, multi)
if not os.path.exists(cloud_path):
os.makedirs(cloud_path)
info = CloudVolume.create_new_info(
1,
layer_type=layer_type,
data_type=str(data_type),
encoding='raw',
chunk_size=chunk_size,
resolution=list(resolution),
voxel_offset=(0, 0, 0),
volume_size=volume_size,
)
info = build_pyramid_info(info, scale)
if layer_type == 'segmentation':
info['mesh'] = 'mesh'
pprint(info)
with open(os.path.join(cloud_path, 'info'), 'w') as f:
json.dump(info, f)
print('>>', L, z_step)
for i, data in tqdm(data_generator, total=L // z_step):
curr_z_start = i
curr_z_step = z_step
for j in range(0, scale):
vol = CloudVolume('file://' + cloud_path, mip=j,
compress='') # Basic Example
x, y, z = vol.volume_size
if j == 0 and i + curr_z_step >= z:
curr_z_step = z - curr_z_start
if j > 0:
data = data[::2, ::2, ::2]
data = data[0:x, 0:y, 0:z]
vol[:, :, curr_z_start:curr_z_start +
curr_z_step] = data[:, :, :curr_z_step]
curr_z_start //= 2
curr_z_step //= 2
return
def remap_segmentation(cv,
chunk_x,
chunk_y,
chunk_z,
mip=2,
overlap_vx=1,
time_stamp=None,
progress=False):
ws_cv = CloudVolume(cv.meta.cloudpath,
mip=mip,
progress=progress,
fill_missing=cv.fill_missing)
mip_diff = mip - cv.meta.watershed_mip
mip_chunk_size = np.array(cv.meta.graph_chunk_size,
dtype=np.int) / np.array(
[2**mip_diff, 2**mip_diff, 1])
mip_chunk_size = mip_chunk_size.astype(np.int)
offset = Vec(chunk_x, chunk_y, chunk_z) * mip_chunk_size
bbx = Bbox(offset, offset + mip_chunk_size + overlap_vx)
if cv.meta.chunks_start_at_voxel_offset:
bbx += ws_cv.voxel_offset
bbx = Bbox.clamp(bbx, ws_cv.bounds)
seg = ws_cv[bbx][..., 0]
if not np.any(seg):
return seg
sv_remapping, unsafe_dict = get_lx_overlapping_remappings(
cv,
chunk_x,
chunk_y,
chunk_z,
time_stamp=time_stamp,
progress=progress)
seg = fastremap.mask_except(seg, list(sv_remapping.keys()), in_place=True)
fastremap.remap(seg,
sv_remapping,
preserve_missing_labels=True,
in_place=True)
for unsafe_root_id in tqdm(unsafe_dict.keys(),
desc="Unsafe Relabel",
disable=(not progress)):
bin_seg = seg == unsafe_root_id
if np.sum(bin_seg) == 0:
continue
l2_edges = []
cc_seg = cc3d.connected_components(bin_seg)
for i_cc in range(1, np.max(cc_seg) + 1):
bin_cc_seg = cc_seg == i_cc
overlaps = []
overlaps.extend(np.unique(seg[-2, :, :][bin_cc_seg[-1, :, :]]))
overlaps.extend(np.unique(seg[:, -2, :][bin_cc_seg[:, -1, :]]))
overlaps.extend(np.unique(seg[:, :, -2][bin_cc_seg[:, :, -1]]))
overlaps = np.unique(overlaps)
linked_l2_ids = overlaps[np.in1d(overlaps,
unsafe_dict[unsafe_root_id])]
if len(linked_l2_ids) == 0:
seg[bin_cc_seg] = 0
elif len(linked_l2_ids) == 1:
seg[bin_cc_seg] = linked_l2_ids[0]
else:
seg[bin_cc_seg] = linked_l2_ids[0]
for i_l2_id in range(len(linked_l2_ids) - 1):
for j_l2_id in range(i_l2_id + 1, len(linked_l2_ids)):
l2_edges.append(
[linked_l2_ids[i_l2_id], linked_l2_ids[j_l2_id]])
if len(l2_edges) > 0:
g = nx.Graph()
g.add_edges_from(l2_edges)
ccs = nx.connected_components(g)
for cc in ccs:
cc_ids = np.sort(list(cc))
seg[np.in1d(seg, cc_ids[1:]).reshape(seg.shape)] = cc_ids[0]
return seg
def create_sharded_skeleton_merge_tasks(layer_path,
dust_threshold,
tick_threshold,
shard_index_bytes=2**13,
minishard_index_bytes=2**15,
minishard_index_encoding='gzip',
data_encoding='gzip',
max_cable_length=None,
spatial_index_db=None):
cv = CloudVolume(layer_path,
progress=True,
spatial_index_db=spatial_index_db)
cv.mip = cv.skeleton.meta.mip
# 17 sec to download for pinky100
all_labels = cv.skeleton.spatial_index.query(cv.bounds * cv.resolution)
(shard_bits, minishard_bits, preshift_bits) = \
compute_shard_params_for_hashed(
num_labels=len(all_labels),
shard_index_bytes=int(shard_index_bytes),
minishard_index_bytes=int(minishard_index_bytes),
)
spec = ShardingSpecification(
type='neuroglancer_uint64_sharded_v1',
preshift_bits=preshift_bits,
hash='murmurhash3_x86_128',
minishard_bits=minishard_bits,
shard_bits=shard_bits,
minishard_index_encoding=minishard_index_encoding,
data_encoding=data_encoding,
)
cv.skeleton.meta.info['sharding'] = spec.to_dict()
cv.skeleton.meta.commit_info()
# rebuild b/c sharding changes the skeleton source
cv = CloudVolume(layer_path,
progress=True,
spatial_index_db=spatial_index_db)
cv.mip = cv.skeleton.meta.mip
# perf: ~36k hashes/sec
shardfn = lambda lbl: cv.skeleton.reader.spec.compute_shard_location(
lbl).shard_number
shard_labels = defaultdict(list)
for label in tqdm(all_labels, desc="Hashes"):
shard_labels[shardfn(label)].append(label)
cf = CloudFiles(cv.skeleton.meta.layerpath, progress=True)
files = ((str(shardno) + '.labels', labels)
for shardno, labels in shard_labels.items())
cf.put_jsons(files,
compress="gzip",
cache_control="no-cache",
total=len(shard_labels))
cv.provenance.processing.append({
'method': {
'task': 'ShardedSkeletonMergeTask',
'cloudpath': layer_path,
'mip': cv.skeleton.meta.mip,
'dust_threshold': dust_threshold,
'tick_threshold': tick_threshold,
'max_cable_length': max_cable_length,
'preshift_bits': preshift_bits,
'minishard_bits': minishard_bits,
'shard_bits': shard_bits,
},
'by': operator_contact(),
'date': strftime('%Y-%m-%d %H:%M %Z'),
})
cv.commit_provenance()
return [
ShardedSkeletonMergeTask(layer_path,
shard_no,
dust_threshold,
tick_threshold,
max_cable_length=max_cable_length)
for shard_no in shard_labels.keys()
]
class NumpyToNeuroglancer():
viewer = None
def __init__(self,
animal,
volume,
scales,
layer_type,
data_type,
num_channels=1,
chunk_size=[256, 256, 128],
offset=[0, 0, 0]):
self.volume = volume
self.scales = scales
self.layer_type = layer_type
self.data_type = data_type
self.chunk_size = chunk_size
self.precomputed_vol = None
self.offset = offset
self.starting_points = None
self.animal = animal
self.num_channels = num_channels
def add_annotation_point():
...
def init_precomputed(self,
path,
volume_size,
starting_points=None,
progress_id=None):
info = CloudVolume.create_new_info(
num_channels=self.num_channels,
layer_type=self.layer_type, # 'image' or 'segmentation'
data_type=self.data_type, #
encoding=
'raw', # other options: 'jpeg', 'compressed_segmentation' (req. uint32 or uint64)
resolution=self.scales, # Size of X,Y,Z pixels in nanometers,
voxel_offset=self.offset, # values X,Y,Z values in voxels
chunk_size=self.chunk_size, # rechunk of image X,Y,Z in voxels
volume_size=volume_size, # X,Y,Z size in voxels
)
self.starting_points = starting_points
self.progress_id = progress_id
self.precomputed_vol = CloudVolume(f'file://{path}',
mip=0,
info=info,
compress=True,
progress=False)
self.precomputed_vol.commit_info()
self.precomputed_vol.commit_provenance()
def init_volume(self, path):
info = CloudVolume.create_new_info(
num_channels=self.volume.shape[2]
if len(self.volume.shape) > 2 else 1,
layer_type=self.layer_type,
data_type=self.
data_type, # str(self.volume.dtype), # Channel images might be 'uint8'
encoding=
'raw', # raw, jpeg, compressed_segmentation, fpzip, kempressed
resolution=self.scales, # Voxel scaling, units are in nanometers
voxel_offset=self.offset, # x,y,z offset in voxels from the origin
chunk_size=self.chunk_size, # units are voxels
volume_size=self.volume.
shape[:3], # e.g. a cubic millimeter dataset
)
self.precomputed_vol = CloudVolume(f'file://{path}',
mip=0,
info=info,
compress=True,
progress=False)
self.precomputed_vol.commit_info()
#self.precomputed_vol[:, :, :] = self.volume[:, :, :]
def add_segment_properties(self, segment_properties):
if self.precomputed_vol is None:
raise NotImplementedError(
'You have to call init_precomputed before calling this function.'
)
self.precomputed_vol.info['segment_properties'] = 'names'
self.precomputed_vol.commit_info()
segment_properties_path = os.path.join(
self.precomputed_vol.layer_cloudpath.replace('file://', ''),
'names')
os.makedirs(segment_properties_path, exist_ok=True)
info = {
"@type": "neuroglancer_segment_properties",
"inline": {
"ids": [str(number) for number, label in segment_properties],
"properties": [{
"id":
"label",
"type":
"label",
"values":
[str(label) for number, label in segment_properties]
}]
}
}
with open(os.path.join(segment_properties_path, 'info'), 'w') as file:
json.dump(info, file, indent=2)
def add_rechunking(self, outpath, downsample, chunks=None):
if self.precomputed_vol is None:
raise NotImplementedError(
'You have to call init_precomputed before calling this function.'
)
cpus, _ = get_cpus()
tq = LocalTaskQueue(parallel=cpus)
outpath = f'file://{outpath}'
if chunks is None:
chunks = calculate_chunks(downsample, 0)
tasks = tc.create_transfer_tasks(self.precomputed_vol.layer_cloudpath,
dest_layer_path=outpath,
chunk_size=chunks,
skip_downsamples=True)
tq.insert(tasks)
tq.execute()
def add_downsampled_volumes(self, chunk_size=[128, 128, 64], num_mips=4):
if self.precomputed_vol is None:
raise NotImplementedError(
'You have to call init_precomputed before calling this function.'
)
_, cpus = get_cpus()
tq = LocalTaskQueue(parallel=cpus)
tasks = tc.create_downsampling_tasks(
self.precomputed_vol.layer_cloudpath,
preserve_chunk_size=False,
num_mips=num_mips,
chunk_size=chunk_size,
compress=True)
tq.insert(tasks)
tq.execute()
def add_segmentation_mesh(self, shape=[448, 448, 448], mip=0):
if self.precomputed_vol is None:
raise NotImplementedError(
'You have to call init_precomputed before calling this function.'
)
_, cpus = get_cpus()
tq = LocalTaskQueue(parallel=cpus)
tasks = tc.create_meshing_tasks(
self.precomputed_vol.layer_cloudpath,
mip=mip,
max_simplification_error=40,
shape=shape,
compress=True) # The first phase of creating mesh
tq.insert(tasks)
tq.execute()
# It should be able to incoporated to above tasks, but it will give a weird bug. Don't know the reason
tasks = tc.create_mesh_manifest_tasks(
self.precomputed_vol.layer_cloudpath
) # The second phase of creating mesh
tq.insert(tasks)
tq.execute()
def process_simple_slice(self, file_key):
index, infile = file_key
print(index, infile)
try:
image = Image.open(infile)
except:
print('Could not open', infile)
width, height = image.size
array = np.array(image, dtype=self.data_type, order='F')
array = array.reshape((1, height, width)).T
self.precomputed_vol[:, :, index] = array
touchfile = os.path.join(self.progress_dir, os.path.basename(infile))
touch(touchfile)
image.close()
return
def process_mesh(self, file_key):
index, infile = file_key
if os.path.exists(
os.path.join(self.progress_dir, os.path.basename(infile))):
print(f"Section {index} already processed, skipping ")
return
img = io.imread(infile)
labels = [[v - 8, v - 1] for v in range(9, 256, 8)]
arr = np.copy(img)
for label in labels:
mask = (arr >= label[0]) & (arr <= label[1])
arr[mask] = label[1]
arr[arr > 248] = 255
img = arr.T
del arr
self.precomputed_vol[:, :,
index] = img.reshape(img.shape[0], img.shape[1],
1)
touchfile = os.path.join(self.progress_dir, os.path.basename(infile))
touch(touchfile)
del img
return
def process_coronal_slice(self, file_key):
index, infile = file_key
if os.path.exists(
os.path.join(self.progress_dir, os.path.basename(infile))):
print(f"Slice {index} already processed, skipping ")
return
img = io.imread(infile)
starty, endy, startx, endx = self.starting_points
#img = np.rot90(img, 2)
#img = np.flip(img)
img = img[starty:endy, startx:endx]
img = img.reshape(img.shape[0], img.shape[1], 1)
#print(index, infile, img.shape, img.dtype, self.precomputed_vol.dtype, self.precomputed_vol.shape)
self.precomputed_vol[:, :, index] = img
touchfile = os.path.join(self.progress_dir, os.path.basename(infile))
touch(touchfile)
del img
return
def process_image(self, file_key):
index, infile = file_key
basefile = os.path.basename(infile)
#completed = file_processed(self.animal, self.progress_id, basefile)
completed = False
if completed:
print(f"Section {index} already processed, skipping ")
return
img = io.imread(infile, img_num=0)
img = img.reshape(self.num_channels, img.shape[0], img.shape[1]).T
self.precomputed_vol[:, :, index] = img
#set_file_completed(self.animal, self.progress_id, basefile)
del img
return
def process_3channel(self, file_key):
index, infile = file_key
basefile = os.path.basename(infile)
completed = file_processed(self.animal, self.progress_id, basefile)
if completed:
print(f"Section {index} already processed, skipping ")
return
img = io.imread(infile, img_num=0)
img = img.reshape(img.shape[0], img.shape[1], 1, img.shape[2])
img = np.rot90(img, 1)
img = np.flipud(img)
self.precomputed_vol[:, :, index] = img
set_file_completed(self.animal, self.progress_id, basefile)
del img
return
def add_volume(self, volume, layer_name=None, clear_layer=False):
if self.viewer is None:
self.viewer = neuroglancer.Viewer()
if layer_name is None:
layer_name = f'{self.layer_type}_{self.scales}'
source = neuroglancer.LocalVolume(
data=volume,
dimensions=neuroglancer.CoordinateSpace(names=['x', 'y', 'z'],
units='nm',
scales=self.scales),
voxel_offset=self.offset)
if self.layer_type == 'segmentation':
layer = neuroglancer.SegmentationLayer(source=source)
else:
layer = neuroglancer.ImageLayer(source=source)
with self.viewer.txn() as s:
if clear_layer:
s.layers.clear()
s.layers[layer_name] = layer
print(f'A new layer named {layer_name} is added to:')
print(self.viewer)
def preview(self, layer_name=None, clear_layer=False):
self.add_volume(self.volume,
layer_name=layer_name,
clear_layer=clear_layer)
def create_skeletonizing_tasks(cloudpath,
mip,
shape=Vec(512, 512, 512),
teasar_params={
'scale': 10,
'const': 10
},
info=None,
object_ids=None,
mask_ids=None,
fix_branching=True,
fix_borders=True,
fix_avocados=False,
fill_holes=False,
dust_threshold=1000,
progress=False,
parallel=1,
fill_missing=False,
sharded=False,
spatial_index=True,
synapses=None,
num_synapses=None):
"""
Assign tasks with one voxel overlap in a regular grid
to be densely skeletonized. The default shape (512,512,512)
was designed to work within 6 GB of RAM on average at parallel=1
but can exceed this amount for certain objects such as glia.
4 GB is usually OK.
When this run completes, you'll follow up with create_skeleton_merge_tasks
to postprocess the generated fragments into single skeletons.
WARNING: If you are processing hundreds of millions of labels or more and
are using Cloud Storage this can get expensive ($8 per million labels typically
accounting for fragment generation and postprocessing)! This scale is when
the experimental sharded format generator becomes crucial to use.
cloudpath: cloudvolume path
mip: which mip level to skeletonize
For a 4x4x40 dataset, mip 3 is good. Mip 4 starts introducing
artifacts like snaking skeletons along the edge of thin objects.
teasar_params:
NOTE: see github.com/seung-lab/kimimaro for an updated list
see https://github.com/seung-lab/kimimaro/wiki/Intuition-for-Setting-Parameters-const-and-scale
for help with setting these parameters.
NOTE: DBF = Distance from Boundary Field (i.e. euclidean distance transform)
scale: float, multiply invalidation radius by distance from boundary
const: float, add this physical distance to the invalidation radius
soma_detection_threshold: if object has a DBF value larger than this,
root will be placed at largest DBF value and special one time invalidation
will be run over that root location (see soma_invalidation scale)
expressed in chosen physical units (i.e. nm)
pdrf_scale: scale factor in front of dbf, used to weight DBF over euclidean distance (higher to pay more attention to dbf)
pdrf_exponent: exponent in dbf formula on distance from edge, faster if factor of 2 (default 16)
soma_invalidation_scale: the 'scale' factor used in the one time soma root invalidation (default .5)
soma_invalidation_const: the 'const' factor used in the one time soma root invalidation (default 0)
(units in chosen physical units (i.e. nm))
info: supply your own info file
object_ids: mask out all but these ids if specified
mask_ids: mask out these ids if specified
fix_branching: Trades speed for quality of branching at forks. You'll
almost always want this set to True.
fix_borders: Allows trivial merging of single overlap tasks. You'll only
want to set this to false if you're working on single or non-overlapping
volumes.
dust_threshold: don't skeletonize labels smaller than this number of voxels
as seen by a single task.
progress: show a progress bar
parallel: number of processes to deploy against a single task. parallelizes
over labels, it won't speed up a single complex label. You can be slightly
more memory efficient using a single big task with parallel than with seperate
tasks that add up to the same volume. Unless you know what you're doing, stick
with parallel=1 for cloud deployments.
fill_missing: passthrough to CloudVolume, fill missing image tiles with zeros
instead of throwing an error if True.
sharded: (bool) if true, output a single mapbuffer dict containing all skeletons
in a task, which will serve as input to a sharded format generator. You don't
want this unless you know what you're doing. If False, generate a skeleton fragment
file per a label for later agglomeration using the SkeletonMergeTask.
spatial_index: (bool) Concurrently generate a json file that describes which
labels were skeletonized in a given task. This makes it possible to query for
skeletons by bounding box later on using CloudVolume.
synapses: If provided, after skeletonization of a label is complete, draw
additional paths to one of the nearest voxels to synapse centroids.
(x,y,z) centroid is specified in physical coordinates.
Iterable yielding ((x,y,z),segid,swc_label)
num_synapses: If synapses is an iterator, you must provide the total number of synapses.
"""
shape = Vec(*shape)
vol = CloudVolume(cloudpath, mip=mip, info=info)
kdtree, labelsmap = None, None
if synapses:
centroids, kdtree, labelsmap = synapses_in_space(synapses,
N=num_synapses)
if not 'skeletons' in vol.info:
vol.info['skeletons'] = 'skeletons_mip_{}'.format(mip)
vol.commit_info()
if spatial_index:
if 'spatial_index' not in vol.skeleton.meta.info or not vol.skeleton.meta.info[
'spatial_index']:
vol.skeleton.meta.info['spatial_index'] = {}
vol.skeleton.meta.info['@type'] = 'neuroglancer_skeletons'
vol.skeleton.meta.info['spatial_index']['resolution'] = tuple(
vol.resolution)
vol.skeleton.meta.info['spatial_index']['chunk_size'] = tuple(
shape * vol.resolution)
vol.skeleton.meta.info['mip'] = int(mip)
vol.skeleton.meta.info['vertex_attributes'] = vol.skeleton.meta.info[
'vertex_attributes'][:1]
vol.skeleton.meta.commit_info()
will_postprocess = bool(np.any(vol.bounds.size3() > shape))
bounds = vol.bounds.clone()
class SkeletonTaskIterator(FinelyDividedTaskIterator):
def task(self, shape, offset):
bbox_synapses = None
if synapses:
bbox_synapses = self.synapses_for_bbox(shape, offset)
return SkeletonTask(
cloudpath=cloudpath,
shape=(shape +
1).clone(), # 1px overlap on the right hand side
offset=offset.clone(),
mip=mip,
teasar_params=teasar_params,
will_postprocess=will_postprocess,
info=info,
object_ids=object_ids,
mask_ids=mask_ids,
fix_branching=fix_branching,
fix_borders=fix_borders,
fix_avocados=fix_avocados,
dust_threshold=dust_threshold,
progress=progress,
parallel=parallel,
fill_missing=bool(fill_missing),
sharded=bool(sharded),
spatial_index=bool(spatial_index),
spatial_grid_shape=shape.clone(
), # used for writing index filenames
synapses=bbox_synapses,
)
def synapses_for_bbox(self, shape, offset):
"""
Returns { seigd: [ ((x,y,z), swc_label), ... ]
where x,y,z are in voxel coordinates with the
origin set to the bottom left corner of this cutout.
"""
bbox = Bbox(offset, shape + offset) * vol.resolution
center = bbox.center()
diagonal = Vec(*((bbox.maxpt - center)))
pts = [
centroids[i, :]
for i in kdtree.query_ball_point(center, diagonal.length())
]
pts = [
tuple(Vec(*pt, dtype=int)) for pt in pts if bbox.contains(pt)
]
synapses = defaultdict(list)
for pt in pts:
for label, swc_label in labelsmap[pt]:
voxel_pt = Vec(*pt,
dtype=np.float32) / vol.resolution - offset
synapses[label].append(
(tuple(voxel_pt.astype(int)), swc_label))
return synapses
def on_finish(self):
vol.provenance.processing.append({
'method': {
'task': 'SkeletonTask',
'cloudpath': cloudpath,
'mip': mip,
'shape': shape.tolist(),
'dust_threshold': dust_threshold,
'teasar_params': teasar_params,
'object_ids': object_ids,
'mask_ids': mask_ids,
'will_postprocess': will_postprocess,
'fix_branching': fix_branching,
'fix_borders': fix_borders,
'fix_avocados': fix_avocados,
'progress': progress,
'parallel': parallel,
'fill_missing': bool(fill_missing),
'sharded': bool(sharded),
'spatial_index': bool(spatial_index),
'synapses': bool(synapses),
},
'by':
operator_contact(),
'date':
strftime('%Y-%m-%d %H:%M %Z'),
})
vol.commit_provenance()
return SkeletonTaskIterator(bounds, shape)
def uploadshardedskeletons(skelsource, skelseglist, skelnamelist, path):
"""Upload sharded skeletons to a local server.
Parameters
----------
skelsource : List containing cloud volume skeletons
skelseglist : List containing the segids(skid)
skelnamelist : List containing the names of skeletons
path : path to the local data server
Returns
-------
cv : cloudvolume class object
"""
info = {
"@type":
"neuroglancer_skeletons",
"transform":
skelsource[0].transform.flatten(),
"vertex_attributes": [{
"id": "radius",
"data_type": "float32",
"num_components": 1
}],
"scales":
"um"
}
path = 'file://' + path + '/precomputed'
cv = CloudVolume(path, info=info)
# prepare for info file
cv.skeleton.meta.info['@type'] = 'neuroglancer_skeletons'
cv.skeleton.meta.info['transform'] = skelsource[0].transform.flatten()
cv.skeleton.meta.info['vertex_attributes'] = [{
'id': 'radius',
'data_type': 'float32',
'num_components': 1
}]
# prepare sharding info
spec = ShardingSpecification(
'neuroglancer_uint64_sharded_v1',
preshift_bits=9,
hash='murmurhash3_x86_128',
minishard_bits=6,
shard_bits=15,
minishard_index_encoding='raw',
data_encoding='raw',
)
cv.skeleton.meta.info['sharding'] = spec.to_dict()
cv.skeleton.meta.info['segment_properties'] = 'seg_props'
cv.skeleton.meta.commit_info()
precomputedskels = {}
for skelidx in range(len(skelsource)):
skelid = int(skelsource[skelidx].id)
skel = Skeleton(skelsource[skelidx].vertices,
edges=skelsource[skelidx].edges,
segid=skelid,
extra_attributes=[{
"id": "radius",
"data_type": "float32",
"num_components": 1,
}]).physical_space()
precomputedskels[skelid] = skel.to_precomputed()
shardfiles = spec.synthesize_shards(precomputedskels)
shardedfilepath = os.path.join(cv.basepath, os.path.basename(path),
cv.skeleton.meta.skeleton_path)
for fname in shardfiles.keys():
with open(shardedfilepath + '/' + fname, 'wb') as f:
f.write(shardfiles[fname])
segfilepath = os.path.join(cv.basepath, os.path.basename(path),
cv.skeleton.meta.skeleton_path, 'seg_props')
if not os.path.exists(segfilepath):
os.makedirs(segfilepath)
print('creating:', segfilepath)
allsegproplist = []
for segid in skelseglist:
segpropdict = {}
segpropdict['id'] = segid
segpropdict['type'] = 'label'
segpropdict['values'] = skelnamelist
allsegproplist.append(segpropdict)
seginfo = {
"@type": "neuroglancer_segment_properties",
"inline": {
"ids": skelseglist,
"properties": allsegproplist
}
}
segfile = os.path.join(segfilepath, 'info')
with open(segfile, 'w') as segfile:
json.dump(seginfo, segfile)
return cv
sliceArgs.append(slice(startSlice, startSlice + 1))
else:
sliceArgs.append(slice(None))
sliceArgs = tuple(sliceArgs)
return arr[sliceArgs]
def generateThumbnailsVolume(volume, x_out, y_out, z_out, isColor=False):
sliceX = obtainCenterSlice(volume, 0, isColor)
sliceY = obtainCenterSlice(volume, 1, isColor)
sliceZ = obtainCenterSlice(volume, 2, isColor)
generateThumbnailsSlices(sliceX[0], sliceY[:, 0], sliceZ[:, :, 0], x_out,
y_out, z_out)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("input_path")
parser.add_argument("x_out")
parser.add_argument("y_out")
parser.add_argument("z_out")
p = parser.parse_args()
volume = CloudVolume(
urlPrefix + str(p.input_path), progress=True, fill_missing=True
) #TODO: fill_missing = True? Or allow thumbnails to crash?
#We should probably reduce the thumbnail dimensions rather than fill_missing.
generateThumbnailsVolume(volume, p.x_out, p.y_out, p.z_out)
def execute(self):
vol = CloudVolume(
self.cloudpath,
mip=self.mip,
info=self.info,
cdn_cache=False,
parallel=self.parallel,
fill_missing=self.fill_missing,
)
bbox = Bbox.clamp(self.bounds, vol.bounds)
index_bbox = Bbox.clamp(self.index_bounds, vol.bounds)
path = skeldir(self.cloudpath)
path = os.path.join(self.cloudpath, path)
all_labels = vol[bbox.to_slices()]
all_labels = all_labels[:, :, :, 0]
if self.mask_ids:
all_labels = fastremap.mask(all_labels, self.mask_ids)
extra_targets_after = {}
if self.synapses:
extra_targets_after = kimimaro.synapses_to_targets(
all_labels, self.synapses)
skeletons = kimimaro.skeletonize(
all_labels,
self.teasar_params,
object_ids=self.object_ids,
anisotropy=vol.resolution,
dust_threshold=self.dust_threshold,
progress=self.progress,
fix_branching=self.fix_branching,
fix_borders=self.fix_borders,
fix_avocados=self.fix_avocados,
fill_holes=self.fill_holes,
parallel=self.parallel,
extra_targets_after=extra_targets_after.keys(),
)
for segid, skel in skeletons.items():
skel.vertices[:] += bbox.minpt * vol.resolution
if self.synapses:
for segid, skel in skeletons.items():
terminal_nodes = skel.vertices[skel.terminals()]
for i, vert in enumerate(terminal_nodes):
vert = vert / vol.resolution - self.bounds.minpt
vert = tuple(np.round(vert).astype(int))
if vert in extra_targets_after.keys():
skel.vertex_types[i] = extra_targets_after[vert]
# neuroglancer doesn't support int attributes
strip_integer_attributes(skeletons.values())
if self.sharded:
self.upload_batch(vol, path, index_bbox, skeletons)
else:
self.upload_individuals(vol, path, bbox, skeletons)
if self.spatial_index:
self.upload_spatial_index(vol, path, index_bbox, skeletons)
def transfer_to(self,
cloudpath,
bbox,
mip,
block_size=None,
compress=True,
compress_level=None):
"""
Transfer files from one storage location to another, bypassing
volume painting. This enables using a single CloudVolume instance
to transfer big volumes. In some cases, gsutil or aws s3 cli tools
may be more appropriate. This method is provided for convenience. It
may be optimized for better performance over time as demand requires.
cloudpath (str): path to storage layer
bbox (Bbox object): ROI to transfer
mip (int): resolution level
block_size (int): number of file chunks to transfer per I/O batch.
compress (bool): Set to False to upload as uncompressed
"""
from cloudvolume import CloudVolume
if mip is None:
mip = self.config.mip
bbox = Bbox.create(bbox, self.meta.bounds(mip))
realized_bbox = bbox.expand_to_chunk_size(
self.meta.chunk_size(mip), offset=self.meta.voxel_offset(mip))
realized_bbox = Bbox.clamp(realized_bbox, self.meta.bounds(mip))
if bbox != realized_bbox:
raise exceptions.AlignmentError(
"Unable to transfer non-chunk aligned bounding boxes. Requested: {}, Realized: {}"
.format(bbox, realized_bbox))
default_block_size_MB = 50 # MB
chunk_MB = self.meta.chunk_size(mip).rectVolume() * np.dtype(
self.meta.dtype).itemsize * self.meta.num_channels
if self.meta.layer_type == 'image':
# kind of an average guess for some EM datasets, have seen up to 1.9x and as low as 1.1
# affinites are also images, but have very different compression ratios. e.g. 3x for kempressed
chunk_MB /= 1.3
else: # segmentation
chunk_MB /= 100.0 # compression ratios between 80 and 800....
chunk_MB /= 1024.0 * 1024.0
if block_size:
step = block_size
else:
step = int(default_block_size_MB // chunk_MB) + 1
try:
destvol = CloudVolume(cloudpath, mip=mip)
except exceptions.InfoUnavailableError:
destvol = CloudVolume(cloudpath,
mip=mip,
info=self.meta.info,
provenance=self.meta.provenance.serialize())
destvol.commit_info()
destvol.commit_provenance()
except exceptions.ScaleUnavailableError:
destvol = CloudVolume(cloudpath)
for i in range(len(destvol.scales) + 1, len(self.meta.scales)):
destvol.scales.append(self.meta.scales[i])
destvol.commit_info()
destvol.commit_provenance()
num_blocks = np.ceil(
self.meta.bounds(mip).volume() /
self.meta.chunk_size(mip).rectVolume()) / step
num_blocks = int(np.ceil(num_blocks))
cloudpaths = chunknames(bbox,
self.meta.bounds(mip),
self.meta.key(mip),
self.meta.chunk_size(mip),
protocol=self.meta.path.protocol)
pbar = tqdm(
desc='Transferring Blocks of {} Chunks'.format(step),
unit='blocks',
disable=(not self.config.progress),
total=num_blocks,
)
with pbar:
with Storage(self.meta.cloudpath) as src_stor:
with Storage(cloudpath) as dest_stor:
for _ in range(num_blocks, 0, -1):
srcpaths = list(itertools.islice(cloudpaths, step))
files = src_stor.get_files(srcpaths)
files = [(f['filename'], f['content']) for f in files]
dest_stor.put_files(
files=files,
compress=compress,
compress_level=compress_level,
content_type=tx.content_type(destvol),
)
pbar.update()
def _validate_chunk(self, chunk):
"""
check that all the input voxels was downloaded without black region
We have found some black regions in previous inference run,
so hopefully this will solve the problem.
"""
if chunk.ndim == 4 and chunk.shape[0] > 1:
chunk = chunk[0, :, :, :]
validate_vol = CloudVolume(self.volume_path,
bounded=False,
fill_missing=self.fill_missing,
progress=False,
mip=self.validate_mip,
cache=False,
green_threads=True)
chunk_mip = self.mip
logging.info('validate chunk in mip {}'.format(self.validate_mip))
assert self.validate_mip >= chunk_mip
# only use the region corresponds to higher mip level
# clamp the surrounding regions in XY plane
# this assumes that the input dataset was downsampled starting from the
# beginning offset in the info file
voxel_offset = chunk.voxel_offset
# factor3 follows xyz order in CloudVolume
factor3 = np.array([
2**(self.validate_mip - chunk_mip), 2
**(self.validate_mip - chunk_mip), 1
],
dtype=np.int32)
clamped_offset = tuple(go + f - (go - vo) % f for go, vo, f in zip(
voxel_offset[::-1], self.vol.voxel_offset, factor3))
clamped_stop = tuple(
go + s - (go + s - vo) % f
for go, s, vo, f in zip(voxel_offset[::-1], chunk.shape[::-1],
vol.voxel_offset, factor3))
clamped_slices = tuple(
slice(o, s) for o, s in zip(clamped_offset, clamped_stop))
clamped_bbox = Bbox.from_slices(clamped_slices)
clamped_input = chunk.cutout(clamped_slices[::-1])
# transform to xyz order
clamped_input = np.transpose(clamped_input)
# get the corresponding bounding box for validation
validate_bbox = self.vol.bbox_to_mip(clamped_bbox,
mip=chunk_mip,
to_mip=self.validate_mip)
#validate_bbox = clamped_bbox // factor3
# downsample the input using avaraging
# keep the z as it is since the mip only applies to xy plane
# recursivly downsample the input
# if we do it directly, the downsampled input will not be the same with the recursive one
# because of the rounding error of integer division
for _ in range(self.validate_mip - chunk_mip):
clamped_input = downsample_with_averaging(clamped_input, (2, 2, 1))
# validation by template matching
assert validate_by_template_matching(clamped_input)
validate_input = validate_vol[validate_bbox.to_slices()]
if validate_input.shape[3] == 1:
validate_input = np.squeeze(validate_input, axis=3)
# use the validate input to check the downloaded input
assert np.alltrue(validate_input == clamped_input)
def _upload_output(self):
vol = CloudVolume(self.output_layer_path, compress='gzip', fill_missing=True,
bounded=False, autocrop=True, mip=self.image_mip, progress=True)
output_slices = self.output_bounds.to_slices()
self.output = np.transpose(self.output)
vol[output_slices[::-1]+(slice(0,self.output.shape[-1]),)] = self.output
def __init__(self,
output_path: str,
output_format: str,
mip: int = None,
voxel_size: tuple = None,
simplification_factor: int = 100,
max_simplification_error: int = 8,
dust_threshold: int = None,
ids: set = None,
manifest: bool = False,
name: str = 'meshing',
verbose: bool = True):
"""
Parameters
------------
output_path:
path to store mesh files
output_format:
format of output {'ply', 'obj', 'precomputed'}
voxel_size:
size of voxels
simplification_factor:
mesh simplification factor.
max_simplification_error:
maximum tolerance error of meshing.
dust_threshold:
do not mesh tiny objects with voxel number less than threshold
ids:
only mesh the selected segmentation ids, other segments will not be meshed.
manifest:
create manifest files or not. This should
not be True if you are only doing meshing for a segmentation chunk.
name:
operator name.
verbose:
print out informations or not.
Note that some functions are adopted from igneous.
"""
super().__init__(name=name, verbose=verbose)
self.simplification_factor = simplification_factor
self.max_simplification_error = max_simplification_error
# zmesh use fortran order, translate zyx to xyz
self.output_path = output_path
self.output_format = output_format
self.dust_threshold = dust_threshold
self.ids = ids
self.manifest = manifest
if manifest:
assert output_format == 'precomputed'
mesh_path = output_path
if output_format == 'precomputed':
# adjust the mesh path according to info
vol = CloudVolume(self.output_path, mip)
info = vol.info
if 'mesh' not in info:
# add mesh to info and update it
info['mesh'] = 'mesh_err_{}'.format(max_simplification_error)
vol.info = info
vol.commit_info()
mesh_path = os.path.join(output_path, info['mesh'])
self.voxel_size = vol.resolution[::-1]
self.mesher = Mesher(vol.resolution)
else:
self.mesher = Mesher(voxel_size[::-1])
self.storage = Storage(mesh_path)
def merge_graph(seg_map, merge_output, global_merge_dict, gid=None):
resolution = list(seg_map.values())[0]['resolution']
chunk_size = list(seg_map.values())[0]['chunk_size']
bbox_list = [v['bbox'] for v in seg_map.values()]
minpt = np.min(np.stack([np.array(b.minpt) for b in bbox_list], 0), axis=0)
maxpt = np.max(np.stack([np.array(b.maxpt) for b in bbox_list], 0), axis=0)
union_offset = minpt
union_size = maxpt - minpt
union_bbox = Bbox(minpt, maxpt)
#print(union_bbox)
# create new canvas
cv_merge_path = '%s/precomputed-%d_%d_%d_%d_%d_%d/' % (
merge_output, union_offset[0], union_offset[1], union_offset[2],
union_size[0], union_size[1], union_size[2])
#print(cv_merge_path)
cv_merge = prepare_precomputed(cv_merge_path,
offset=union_offset,
size=union_size,
resolution=resolution,
chunk_size=chunk_size)
#print(cv_merge.shape)
# Pre paint the cv with 0
cv_merge[union_bbox] = np.zeros((union_size), dtype=np.uint32)
cv_args = dict(bounded=True,
fill_missing=True,
autocrop=False,
cache=False,
compress_cache=None,
cdn_cache=False,
progress=False,
provenance=None,
compress=True,
non_aligned_writes=True,
parallel=False)
#val_dict = dict()
#print('>>>>rank: %d, map_keys %s' % (mpi_rank, str(seg_map.keys())))
pbar = tqdm(seg_map.items(), desc='merging')
for seg_key, seg in pbar:
bb = seg['bbox']
cv = CloudVolume('file://' + seg['output'], mip=0, **cv_args)
# val = cv.download_to_shared_memory(np.s_[:], str(i))
val = cv[...]
# print('keys: %s <-> %s' % (seg_key, global_merge_dict.keys()))
if seg_key in global_merge_dict:
val = perform_remap(val, global_merge_dict[seg_key])
#logging.error('rank %d val_shape: %s, bbox %s', mpi_rank, val.shape, bb)
#val_dict[bb] = val
curr_val = cv_merge[bb][:]
non_zeros = curr_val != 0
val[non_zeros] = curr_val[non_zeros]
cv_merge[bb] = val
return {
gid:
dict(
bbox=union_bbox,
output=cv_merge_path,
resolution=resolution,
chunk_size=chunk_size,
)
}
