def overlay_segmentation(db_host,
db_name,
roi_offset,
roi_size,
selected_attr,
solved_attr,
edge_collection,
segmentation_container,
segmentation_dataset,
segmentation_number,
voxel_size=(40, 4, 4)):
graph_provider = MongoDbGraphProvider(db_name,
db_host,
directed=False,
position_attribute=['z', 'y', 'x'],
edges_collection=edge_collection)
graph_roi = daisy.Roi(roi_offset, roi_size)
segmentation = daisy.open_ds(segmentation_container, segmentation_dataset)
intersection_roi = segmentation.roi.intersect(graph_roi).snap_to_grid(
voxel_size)
nx_graph = graph_provider.get_graph(intersection_roi,
nodes_filter={selected_attr: True},
edges_filter={selected_attr: True})
for node_id, data in nx_graph.nodes(data=True):
node_position = daisy.Coordinate((data["z"], data["y"]), data["x"])
nx_graph.nodes[node_id]["segmentation_{}".format(
segmentation_number)] = segmentation[node_position]
graph_provider.write_nodes(intersection_roi)
def setup(self):
self.updates(self.graph, self.spec[self.graph].copy())
if self.client is None:
self.client = MongoDbGraphProvider(
self.db_name,
self.db_host,
mode=self.mode,
directed=False,
nodes_collection=self.component_collection,
edges_collection=self.component_edge_collection,
)
def __init__(
self,
dbname: str,
url: str,
points: List[GraphKey],
graph_specs: Optional[Union[GraphSpec, List[GraphSpec]]] = None,
directed: bool = False,
total_roi: Roi = None,
nodes_collection: str = "nodes",
edges_collection: str = "edges",
meta_collection: str = "meta",
endpoint_names: Tuple[str, str] = ("u", "v"),
position_attribute: str = "position",
node_attrs: Optional[List[str]] = None,
edge_attrs: Optional[List[str]] = None,
nodes_filter: Optional[Dict[str, Any]] = None,
edges_filter: Optional[Dict[str, Any]] = None,
num_nodes=100000,
dist_attribute=None,
min_dist=29000,
):
self.points = points
graph_specs = (graph_specs if graph_specs is not None else GraphSpec(
Roi(Coordinate([None] * 3), Coordinate([None] * 3)),
directed=False))
specs = (graph_specs if isinstance(graph_specs, list)
and len(graph_specs) == len(points) else [graph_specs] *
len(points))
self.specs = {key: spec for key, spec in zip(points, specs)}
self.position_attribute = position_attribute
self.node_attrs = node_attrs
self.edge_attrs = edge_attrs
self.nodes_filter = nodes_filter
self.edges_filter = edges_filter
self.dist_attribute = dist_attribute
self.min_dist = min_dist
self.num_nodes = num_nodes
self.graph_provider = MongoDbGraphProvider(
dbname,
url,
mode="r+",
directed=directed,
total_roi=None,
nodes_collection=nodes_collection,
edges_collection=edges_collection,
meta_collection=meta_collection,
endpoint_names=endpoint_names,
position_attribute=position_attribute,
)
def setup(self):
# Initialize client. Doesn't the daisy mongodb graph provider handle this?
if self.client is None:
self.client = MongoDbGraphProvider(
self.db_name,
self.db_host,
mode=self.mode,
directed=self.directed,
nodes_collection=self.nodes_collection,
edges_collection=self.edges_collection,
)
self.updates(self.mst, self.spec[self.mst].copy())
def setUp(self):
# y: 1| 0(s)------1(s)------2(s)
# 2|
# 3| 3(s) 6(s)
# 4| | |
# 5| 4(s)-(ns)-9(s)-(ns)-7(s)
# 6| | |
# 7| 5(s) 8(s)
# |-------------------------------->
# x: 1 2 3
#
# s = selected
# ns = not selected
self.nodes = [
{'id': 0, 'z': 1, 'y': 1, 'x': 1, 'selected': True, 'solved': True},
{'id': 1, 'z': 1, 'y': 1, 'x': 2, 'selected': True, 'solved': True},
{'id': 2, 'z': 1, 'y': 1, 'x': 3, 'selected': True, 'solved': True},
{'id': 3, 'z': 1, 'y': 3, 'x': 1, 'selected': True, 'solved': True},
{'id': 4, 'z': 1, 'y': 5, 'x': 1, 'selected': True, 'solved': True},
{'id': 5, 'z': 1, 'y': 7, 'x': 1, 'selected': True, 'solved': True},
{'id': 6, 'z': 1, 'y': 3, 'x': 3, 'selected': True, 'solved': True},
{'id': 7, 'z': 1, 'y': 5, 'x': 3, 'selected': True, 'solved': True},
{'id': 8, 'z': 1, 'y': 7, 'x': 3, 'selected': True, 'solved': True},
{'id': 9, 'z': 1, 'y': 5, 'x': 2, 'selected': True, 'solved': True}
]
self.edges = [{'u': 0, 'v': 1, 'evidence': 0.5, 'selected': True, 'solved': True},
{'u': 1, 'v': 2, 'evidence': 0.5, 'selected': True, 'solved': True},
{'u': 3, 'v': 4, 'evidence': 0.5, 'selected': True, 'solved': True},
{'u': 4, 'v': 5, 'evidence': 0.5, 'selected': True, 'solved': True},
{'u': 6, 'v': 7, 'evidence': 0.5, 'selected': True, 'solved': True},
{'u': 7, 'v': 8, 'evidence': 0.5, 'selected': True, 'solved': True},
{'u': 4, 'v': 9, 'evidence': 0.5, 'selected': False, 'solved': False},
{'u': 7, 'v': 9, 'evidence': 0.5, 'selected': False, 'solved': False}
]
#self.nodes = self.nodes[:3]
self.db_name = 'micron_test_solver'
config = configparser.ConfigParser()
config.read(os.path.expanduser("../mongo.ini"))
self.db_host = "mongodb://{}:{}@{}:{}".format(config.get("Credentials", "user"),
config.get("Credentials", "password"),
config.get("Credentials", "host"),
config.get("Credentials", "port"))
self.graph_provider = MongoDbGraphProvider(self.db_name,
self.db_host,
mode='w',
position_attribute=['z', 'y', 'x'])
self.roi = daisy.Roi((0,0,0), (4,4,4))
self.graph = self.graph_provider[self.roi]
self.graph.add_nodes_from([(node['id'], node) for node in self.nodes])
self.graph.add_edges_from([(edge['u'], edge['v'], edge) for edge in self.edges])
self.solve_params = {"graph": self.graph,
"evidence_factor": 12,
"comb_angle_factor": 14,
"start_edge_prior": 180,
"selection_cost": -80}
def setup(self):
for key, spec in self.specs.items():
self.provides(key, spec)
if self.graph_provider is None:
self.graph_provider = MongoDbGraphProvider(
self.dbname,
self.url,
mode="r+",
directed=self.directed,
total_roi=None,
nodes_collection=self.nodes_collection,
edges_collection=self.edges_collection,
meta_collection=self.meta_collection,
endpoint_names=self.endpoint_names,
position_attribute=self.position_attribute,
)
def get_graph(db_host, db_name, roi_offset, roi_size, solve_number,
edge_collection):
"""
Get selected subgraph containing no isolated
selected edges. (All edges with not
2 selected vertices are filtered out)
"""
selected_attr = "selected_{}".format(solve_number)
solved_attr = "solved_{}".format(solve_number)
graph_provider = MongoDbGraphProvider(db_name,
db_host,
directed=False,
position_attribute=['z', 'y', 'x'],
edges_collection=edge_collection)
roi = daisy.Roi(roi_offset, roi_size)
nx_graph = graph_provider.get_graph(
roi,
nodes_filter={selected_attr: True},
edges_filter={selected_attr: True},
node_attrs=[selected_attr, solved_attr, "x", "y", "z"],
edge_attrs=[selected_attr, solved_attr, "x", "y", "z"])
# NOTE: We filter edges that do not have
# two selected vertices.
edges_to_remove = set()
nodes_to_remove = set()
for e in nx_graph.edges():
u = nx_graph.nodes()[e[0]]
v = nx_graph.nodes()[e[1]]
if not u:
edges_to_remove.add((e[0], e[1]))
nodes_to_remove.add(e[0])
if not v:
edges_to_remove.add((e[0], e[1]))
nodes_to_remove.add(e[1])
nx_graph.remove_edges_from(edges_to_remove)
nx_graph.remove_nodes_from(nodes_to_remove)
return nx_graph
def test_solve(self):
# y: 1| 0--1--2
# x: 1 2 3
# Test all combinations of
# s/ns
combinations = [(False, False), (False, True), (True, True)]
for v0 in combinations:
for v1 in combinations:
for v2 in combinations:
for e0 in combinations:
for e1 in combinations:
self.nodes = [
{'id': 0, 'z': 1, 'y': 1, 'x': 1, 'selected': v0[0], 'solved': v0[1]},
{'id': 1, 'z': 1, 'y': 1, 'x': 2, 'selected': v1[0], 'solved': v1[1]},
{'id': 2, 'z': 1, 'y': 1, 'x': 3, 'selected': v2[0], 'solved': v2[1]},
]
self.edges = [{'u': 0, 'v': 1, 'evidence': 0.5, 'selected': e0[0], 'solved': e0[1]},
{'u': 1, 'v': 2, 'evidence': 0.5, 'selected': e1[0], 'solved': e1[1]}]
self.db_name = 'micron_test_solver'
config = configparser.ConfigParser()
config.read(os.path.expanduser("../mongo.ini"))
self.db_host = "mongodb://{}:{}@{}:{}".format(config.get("Credentials", "user"),
config.get("Credentials", "password"),
config.get("Credentials", "host"),
config.get("Credentials", "port"))
self.graph_provider = MongoDbGraphProvider(self.db_name,
self.db_host,
mode='w',
position_attribute=['z', 'y', 'x'])
self.roi = daisy.Roi((0,0,0), (4,4,4))
self.graph = self.graph_provider[self.roi]
self.graph.add_nodes_from([(node['id'], node) for node in self.nodes])
self.graph.add_edges_from([(edge['u'], edge['v'], edge) for edge in self.edges])
self.solve_params = {"graph": self.graph,
"evidence_factor": 12,
"comb_angle_factor": 14,
"start_edge_prior": 180,
"selection_cost": -80}
solver = Solver(**self.solve_params)
solver.initialize()
solver.solve()
graph = self.solve_params["graph"]
selected_edges = [(e[0], e[1]) for e in graph.edges(data=True) if e[2]["selected"]]
selected_nodes = [v[0] for v in graph.nodes(data=True) if v[1]["selected"]]
client = pymongo.MongoClient(self.db_host)
client.drop_database(self.db_name)
def label_connected_components(db_host,
db_name,
roi,
selected_attr,
solved_attr,
edge_collection,
label_attribute="label"):
graph_provider = MongoDbGraphProvider(db_name,
db_host,
directed=False,
position_attribute=['z', 'y', 'x'],
edges_collection=edge_collection)
graph = graph_provider.get_graph(roi,
nodes_filter={selected_attr: True},
edges_filter={selected_attr: True})
lut = find_connected_components(graph,
node_component_attribute=label_attribute,
return_lut=True)
return graph, lut
def get_graph(db_host,
db_name,
roi_offset=(158000, 121800, 448616),
roi_size=(7600, 5200, 2200),
selected_only=False,
selected_attr="selected",
solved_attr="solved",
edge_collection="edges"):
graph = MongoDbGraphProvider(db_name,
db_host,
directed=False,
position_attribute=['z', 'y', 'x'],
edges_collection=edge_collection)
roi = daisy.Roi(roi_offset, roi_size)
nodes, edges = graph.read_blockwise(roi,
block_size=daisy.Coordinate(
(10000, 10000, 10000)),
num_workers=40)
if len(edges["u"]) != 0:
if selected_only:
edges = [(u, v, selected, solved) for u, v, selected, solved in
zip(edges["u"], edges["v"], edges[selected_attr],
edges[solved_attr]) if selected]
nodes = {node_id: (z,y,x) for z,y,x,node_id, selected in\
zip(nodes["z"], nodes["y"], nodes["x"], nodes["id"], nodes[selected_attr]) if selected}
else:
edges = [(u, v, selected, solved) for u, v, selected, solved in
zip(edges["u"], edges["v"], edges[selected_attr],
edges[solved_attr])]
else:
edges = []
return nodes, edges
def solve_in_block(
db_host,
skeletonization_db,
subsampled_skeletonization_db,
time_limit,
solve_number,
graph_number,
location_attr,
u_name,
v_name,
**kwargs,
):
logger.info("Solve in block")
subsampled_provider = MongoDbGraphProvider(subsampled_skeletonization_db,
db_host,
mode="r+",
directed=True)
skeletonization_provider = MongoDbGraphProvider(skeletonization_db,
db_host,
mode="r+")
client = daisy.Client()
while True:
logger.info("Acquire block")
block = client.acquire_block()
if not block:
return 0
logger.debug("Solving in block %s", block)
if check_function(
block,
"solve_s{}".format(solve_number),
subsampled_skeletonization_db,
db_host,
):
client.release_block(block, 0)
continue
start_time = time.time()
skeletonization = skeletonization_provider.get_graph(
block.read_roi, node_inclusion="dangling", edge_inclusion="both")
# anything in matched was solved previously and must be maintained.
pre_solved = subsampled_provider.get_graph(block.read_roi,
node_inclusion="dangling",
edge_inclusion="both")
# if len(skeletonization.nodes()) > 10_000:
# to_remove = set(skeletonization.nodes()) - set(pre_solved.nodes())
# skeletonization.remove_nodes_from(to_remove)
# logger.info(f"Solving for {len(skeletonization.nodes())} would take too long")
# logger.info(f"Ignoring {len(to_remove)} nodes and skipping this block!")
logger.info(
f"Reading skeletonization with {len(skeletonization.nodes)} nodes and "
+
f"{len(skeletonization.edges)} edges took {time.time() - start_time} seconds"
)
if len(skeletonization.nodes) == 0:
logger.info(
f"No consensus nodes in roi {block.read_roi}. Skipping")
write_done(block, f"solve_s{solve_number}",
subsampled_skeletonization_db, db_host)
client.release_block(block, 0)
continue
logger.info("PreProcess...")
start_time = time.time()
logger.info(
f"Skeletoniation has {len(skeletonization.nodes)} nodes "
f"and {len(skeletonization.edges)} edges before subsampling")
num_removed = remove_line_nodes(skeletonization, location_attr)
logger.info(f"Removed {num_removed} nodes from skeletonization!")
num_nodes, num_edges = write_matched(
db_host,
subsampled_skeletonization_db,
block,
skeletonization,
pre_solved,
location_attr,
u_name,
v_name,
)
logger.info(
f"Writing matched graph with {num_nodes} nodes and {num_edges} edges "
f"took {time.time()-start_time} seconds")
logger.info("Write done")
write_done(
block,
"solve_s{}".format(solve_number),
subsampled_skeletonization_db,
db_host,
)
logger.info("Release block")
client.release_block(block, 0)
return 0
class GlobalConnectedComponents(BatchFilter):
def __init__(
self,
db_name,
db_host,
collection,
graph,
size_attr,
component_attr,
read_size,
write_size,
mode="r+",
):
self.db_name = db_name
self.db_host = db_host
self.collection = collection
self.component_collection = f"{self.collection}_components"
self.component_edge_collection = f"{self.collection}_component_edges"
self.client = None
self.mode = mode
self.graph = graph
self.size_attr = size_attr
self.component_attr = component_attr
self.read_size = read_size
self.write_size = write_size
self.context = (read_size - write_size) / 2
assert self.read_size == self.write_size + self.context * 2
def setup(self):
self.updates(self.graph, self.spec[self.graph].copy())
if self.client is None:
self.client = MongoDbGraphProvider(
self.db_name,
self.db_host,
mode=self.mode,
directed=False,
nodes_collection=self.component_collection,
edges_collection=self.component_edge_collection,
)
def prepare(self, request):
deps = BatchRequest()
deps[self.graph] = request[self.graph].copy()
assert (request[self.graph].roi.get_shape() == self.read_size
), f"Got wrong size graph in request"
return deps
def process(self, batch, request):
g = batch[self.graph].to_nx_graph()
logger.debug(
f"{self.name()} got graph with {g.number_of_nodes()} nodes, and "
f"{g.number_of_edges()} edges!")
write_roi = batch[self.graph].spec.roi.grow(-self.context,
-self.context)
contained_nodes = [
node for node, attr in g.nodes.items()
if write_roi.contains(attr["location"])
]
contained_components = set(g.nodes[n][self.component_attr]
for n in contained_nodes)
logger.debug(f"Graph contains {len(contained_nodes)} nodes with "
f"{len(contained_components)} components in write_roi")
component_graph = self.client.get_graph(roi=write_roi,
node_inclusion="dangling",
edge_inclusion="either")
for node in contained_nodes:
attrs = g.nodes[node]
block_component_id = attrs[self.component_attr]
global_component_id = component_graph.nodes[block_component_id][
self.component_attr]
attrs[self.component_attr] = global_component_id
attrs[self.size_attr] = component_graph.nodes[block_component_id][
self.size_attr]
logger.debug(f"Graph contains {len(contained_nodes)} nodes with "
f"{len(contained_components)} components in write_roi")
outputs = Batch()
outputs[self.graph] = Graph.from_nx_graph(
g, batch[self.graph].spec.copy())
return outputs
def extract_edges_in_block(db_name, db_host, soft_mask_container,
soft_mask_dataset, distance_threshold,
evidence_threshold, graph_number, block):
graph_provider = MongoDbGraphProvider(
db_name,
db_host,
mode='r+',
position_attribute=['z', 'y', 'x'],
directed=False,
edges_collection='edges_g{}'.format(graph_number))
if check_function(graph_provider.database, block,
"edges_g{}".format(graph_number)):
return 0
logger.debug("Finding edges in %s, reading from %s", block.write_roi,
block.read_roi)
start = time.time()
soft_mask_array = daisy.open_ds(soft_mask_container, soft_mask_dataset)
graph = graph_provider[block.read_roi.intersect(soft_mask_array.roi)]
if graph.number_of_nodes() == 0:
logger.info("No nodes in roi %s. Skipping", block.read_roi)
write_done(graph_provider.database, block,
'edges_g{}'.format(graph_number))
return 0
logger.debug("Read %d candidates in %.3fs", graph.number_of_nodes(),
time.time() - start)
start = time.time()
"""
candidates = [(candidate_id,
np.array([data[d] for d in ['z', 'y', 'x']]))
for candidate_id, data in graph.nodes(data=True) if 'z' in data]
"""
candidates = np.array(
[[candidate_id] + [data[d] for d in ['z', 'y', 'x']]
for candidate_id, data in graph.nodes(data=True) if 'z' in data],
dtype=np.uint64)
kdtree_start = time.time()
kdtree = KDTree([[candidate[1], candidate[2], candidate[3]]
for candidate in candidates])
#kdtree = KDTree(candidates[])
pairs = kdtree.query_pairs(distance_threshold, p=2.0, eps=0)
logger.debug("Query pairs in %.3fs", time.time() - kdtree_start)
soft_mask_array = daisy.open_ds(soft_mask_container, soft_mask_dataset)
voxel_size = np.array(soft_mask_array.voxel_size, dtype=np.uint32)
soft_mask_roi = block.read_roi.snap_to_grid(
voxel_size=voxel_size).intersect(soft_mask_array.roi)
soft_mask_array_data = soft_mask_array.to_ndarray(roi=soft_mask_roi)
sm_dtype = soft_mask_array_data.dtype
if sm_dtype == np.uint8: # standard pipeline pm 0-255
pass
elif sm_dtype == np.float32 or sm_dtype == np.float64:
if not (soft_mask_array_data.min() >= 0
and soft_mask_array_data.max() <= 1):
raise ValueError(
"Provided soft_mask has dtype float but not in range [0,1], abort"
)
else:
soft_mask_array_data *= 255
else:
raise ValueError("Soft mask dtype {} not understood".format(sm_dtype))
soft_mask_array_data = soft_mask_array_data.astype(np.float64)
if evidence_threshold is not None:
soft_mask_array_data = (soft_mask_array_data >= evidence_threshold *
255).astype(np.float64) * 255
offset = np.array(np.array(soft_mask_roi.get_offset()) / voxel_size,
dtype=np.uint64)
evidence_start = time.time()
if pairs:
pairs = np.array(list(pairs), dtype=np.uint64)
evidence_array = cpp_get_evidence(candidates, pairs,
soft_mask_array_data, offset,
voxel_size)
graph.add_weighted_edges_from(evidence_array, weight='evidence')
logger.debug("Accumulate evidence in %.3fs",
time.time() - evidence_start)
logger.debug("Found %d edges", graph.number_of_edges())
logger.debug("Extracted edges in %.3fs", time.time() - start)
start = time.time()
graph.write_edges(block.write_roi)
logger.debug("Wrote edges in %.3fs", time.time() - start)
else:
logger.debug("No pairs in block, skip")
write_done(graph_provider.database, block,
'edges_g{}'.format(graph_number))
return 0
import itertools
from pathlib import Path
import logging
from tqdm import tqdm
from config_parser import read_data_config
logging.basicConfig(level=logging.INFO)
config = read_data_config(Path("config.ini"))
print(config["sample"])
consensus_provider = MongoDbGraphProvider(config["consensus_db"],
config["db_host"],
mode="r+",
directed=True)
subdivided_provider = MongoDbGraphProvider(config["subdivided_db"],
config["db_host"],
mode="w",
directed=True)
consensus = consensus_provider.get_graph(config["roi"],
node_inclusion="dangling",
edge_inclusion="either")
print(f"Read consensus with {len(consensus.nodes)} nodes")
subdivided = subdivided_provider.get_graph(config["roi"],
node_inclusion="dangling",
class DaisyGraphProvider(BatchProvider):
"""
See documentation for mongo graph provider at
https://github.com/funkelab/daisy/blob/0.3-dev/daisy/persistence/mongodb_graph_provider.py#L17
"""
def __init__(
self,
dbname: str,
url: str,
points: List[GraphKey],
graph_specs: Optional[Union[GraphSpec, List[GraphSpec]]] = None,
directed: bool = False,
total_roi: Roi = None,
nodes_collection: str = "nodes",
edges_collection: str = "edges",
meta_collection: str = "meta",
endpoint_names: Tuple[str, str] = ("u", "v"),
position_attribute: str = "position",
node_attrs: Optional[List[str]] = None,
edge_attrs: Optional[List[str]] = None,
nodes_filter: Optional[Dict[str, Any]] = None,
edges_filter: Optional[Dict[str, Any]] = None,
edge_inclusion: str = "either",
node_inclusion: str = "dangling",
fail_on_inconsistent_node: bool = False,
):
self.points = points
graph_specs = (graph_specs if graph_specs is not None else GraphSpec(
Roi(Coordinate([None] * 3), Coordinate([None] * 3)),
directed=False))
specs = (graph_specs if isinstance(graph_specs, list)
and len(graph_specs) == len(points) else [graph_specs] *
len(points))
self.specs = {key: spec for key, spec in zip(points, specs)}
self.directed = directed
self.nodes_collection = nodes_collection
self.edges_collection = edges_collection
self.meta_collection = meta_collection
self.endpoint_names = endpoint_names
self.position_attribute = position_attribute
self.position_attribute = position_attribute
self.node_attrs = node_attrs
self.edge_attrs = edge_attrs
self.nodes_filter = nodes_filter
self.edges_filter = edges_filter
self.edge_inclusion = edge_inclusion
self.node_inclusion = node_inclusion
self.dbname = dbname
self.url = url
self.nodes_collection = nodes_collection
self.fail_on_inconsistent_node = fail_on_inconsistent_node
self.graph_provider = None
def setup(self):
for key, spec in self.specs.items():
self.provides(key, spec)
if self.graph_provider is None:
self.graph_provider = MongoDbGraphProvider(
self.dbname,
self.url,
mode="r+",
directed=self.directed,
total_roi=None,
nodes_collection=self.nodes_collection,
edges_collection=self.edges_collection,
meta_collection=self.meta_collection,
endpoint_names=self.endpoint_names,
position_attribute=self.position_attribute,
)
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
for key, spec in request.items():
logger.debug(f"fetching {key} in roi {spec.roi}")
requested_graph = self.graph_provider.get_graph(
spec.roi,
edge_inclusion=self.edge_inclusion,
node_inclusion=self.node_inclusion,
node_attrs=self.node_attrs,
edge_attrs=self.edge_attrs,
nodes_filter=self.nodes_filter,
edges_filter=self.edges_filter,
)
logger.debug(
f"got {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges"
)
failed_nodes = []
for node, attrs in requested_graph.nodes.items():
try:
attrs["location"] = np.array(
attrs[self.position_attribute], dtype=np.float32)
except KeyError:
logger.warning(
f"node: {node} was written (probably part of an edge), but never given coordinates!"
)
failed_nodes.append(node)
attrs["id"] = node
for node in failed_nodes:
if self.fail_on_inconsistent_node:
raise ValueError(
f"Mongodb contains node {node} without location! "
f"It was probably written as part of an edge")
requested_graph.remove_node(node)
if spec.directed:
requested_graph = requested_graph.to_directed()
else:
requested_graph = requested_graph.to_undirected()
points = Graph.from_nx_graph(requested_graph, spec)
points.relabel_connected_components()
points.crop(spec.roi)
batch[key] = points
logger.debug(f"{key} with {len(list(points.nodes))} nodes")
timing.stop()
batch.profiling_stats.add(timing)
return batch
def solve_in_block(db_host,
db_name,
evidence_factor,
comb_angle_factor,
start_edge_prior,
selection_cost,
time_limit,
solve_number,
graph_number,
selected_attr="selected",
solved_attr="solved",
**kwargs):
print("Solve in block")
graph_provider = MongoDbGraphProvider(
db_name,
db_host,
mode='r+',
position_attribute=['z', 'y', 'x'],
edges_collection="edges_g{}".format(graph_number))
client = daisy.Client()
while True:
print("Acquire block")
block = client.acquire_block()
if not block:
return
logger.debug("Solving in block %s", block)
if check_function(block, 'solve_s{}'.format(solve_number), db_name,
db_host):
client.release_block(block, 0)
continue
start_time = time.time()
graph = graph_provider.get_graph(block.read_roi)
num_nodes = graph.number_of_nodes()
num_edges = graph.number_of_edges()
logger.info(
"Reading graph with %d nodes and %d edges took %s seconds" %
(num_nodes, num_edges, time.time() - start_time))
if num_edges == 0:
logger.info("No edges in roi %s. Skipping" % block.read_roi)
write_done(block, 'solve_s{}'.format(solve_number), db_name,
db_host)
client.release_block(block, 0)
continue
print("solve")
solver = Solver(graph, evidence_factor, comb_angle_factor,
start_edge_prior, selection_cost, time_limit,
selected_attr, solved_attr)
solver.initialize()
solver.solve()
start_time = time.time()
graph.update_edge_attrs(block.write_roi,
attributes=[selected_attr, solved_attr])
graph.update_node_attrs(block.write_roi,
attributes=[selected_attr, solved_attr])
logger.info(
"Updating attributes %s & %s for %d edges took %s seconds" %
(selected_attr, solved_attr, num_edges, time.time() - start_time))
print("Write done")
write_done(block, 'solve_s{}'.format(solve_number), db_name, db_host)
print("Release block")
client.release_block(block, 0)
return 0
class FilteredDaisyGraphProvider(BatchProvider):
"""
See documentation for mongo graph provider at
https://github.com/funkelab/daisy/blob/0.3-dev/daisy/persistence/mongodb_graph_provider.py#L17
"""
def __init__(
self,
dbname: str,
url: str,
points: List[GraphKey],
graph_specs: Optional[Union[GraphSpec, List[GraphSpec]]] = None,
directed: bool = False,
total_roi: Roi = None,
nodes_collection: str = "nodes",
edges_collection: str = "edges",
meta_collection: str = "meta",
endpoint_names: Tuple[str, str] = ("u", "v"),
position_attribute: str = "position",
node_attrs: Optional[List[str]] = None,
edge_attrs: Optional[List[str]] = None,
nodes_filter: Optional[Dict[str, Any]] = None,
edges_filter: Optional[Dict[str, Any]] = None,
num_nodes=100000,
dist_attribute=None,
min_dist=29000,
):
self.points = points
graph_specs = (graph_specs if graph_specs is not None else GraphSpec(
Roi(Coordinate([None] * 3), Coordinate([None] * 3)),
directed=False))
specs = (graph_specs if isinstance(graph_specs, list)
and len(graph_specs) == len(points) else [graph_specs] *
len(points))
self.specs = {key: spec for key, spec in zip(points, specs)}
self.position_attribute = position_attribute
self.node_attrs = node_attrs
self.edge_attrs = edge_attrs
self.nodes_filter = nodes_filter
self.edges_filter = edges_filter
self.dist_attribute = dist_attribute
self.min_dist = min_dist
self.num_nodes = num_nodes
self.graph_provider = MongoDbGraphProvider(
dbname,
url,
mode="r+",
directed=directed,
total_roi=None,
nodes_collection=nodes_collection,
edges_collection=edges_collection,
meta_collection=meta_collection,
endpoint_names=endpoint_names,
position_attribute=position_attribute,
)
def setup(self):
for key, spec in self.specs.items():
self.provides(key, spec)
def provide(self, request):
timing = Timing(self)
timing.start()
batch = Batch()
for key, spec in request.items():
logger.debug(f"fetching {key} in roi {spec.roi}")
requested_graph = self.graph_provider.get_graph(
spec.roi,
edge_inclusion="either",
node_inclusion="dangling",
node_attrs=self.node_attrs,
edge_attrs=self.edge_attrs,
nodes_filter=self.nodes_filter,
edges_filter=self.edges_filter,
)
logger.debug(
f"got {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges"
)
for node, attrs in list(requested_graph.nodes.items()):
if self.dist_attribute in attrs:
if attrs[self.dist_attribute] < self.min_dist:
requested_graph.remove_node(node)
logger.debug(
f"{len(requested_graph.nodes)} nodes remaining after filtering by distance"
)
if len(requested_graph.nodes) > self.num_nodes:
nodes = list(requested_graph.nodes)
nodes_to_keep = set(random.sample(nodes, self.num_nodes))
for node in list(requested_graph.nodes()):
if node not in nodes_to_keep:
requested_graph.remove_node(node)
for node, attrs in requested_graph.nodes.items():
attrs["location"] = np.array(attrs[self.position_attribute],
dtype=np.float32)
attrs["id"] = node
if spec.directed:
requested_graph = requested_graph.to_directed()
else:
requested_graph = requested_graph.to_undirected()
logger.debug(
f"providing {key} with {len(requested_graph.nodes)} nodes and {len(requested_graph.edges)} edges"
)
points = Graph.from_nx_graph(requested_graph, spec)
points.crop(spec.roi)
batch[key] = points
timing.stop()
batch.profiling_stats.add(timing)
return batch
class MongoWriteGraph(gp.BatchFilter):
def __init__(
self,
mst,
db_host,
db_name,
read_size,
write_size,
voxel_size,
directed,
mode="r+",
collection="",
position_attr="position",
node_attrs: List[str] = None,
edge_attrs: List[str] = None,
):
self.mst = mst
self.db_host = db_host
self.db_name = db_name
self.client = None
self.voxel_size = voxel_size
self.read_size = read_size
self.write_size = write_size
self.context = (read_size - write_size) / 2
assert self.write_size + (self.context * 2) == self.read_size
self.directed = directed
self.mode = mode
self.position_attr = position_attr
self.nodes_collection = f"{collection}_nodes"
self.edges_collection = f"{collection}_edges"
self.node_attrs = node_attrs if node_attrs is not None else []
self.node_attrs += [position_attr]
self.edge_attrs = edge_attrs if edge_attrs is not None else []
def setup(self):
# Initialize client. Doesn't the daisy mongodb graph provider handle this?
if self.client is None:
self.client = MongoDbGraphProvider(
self.db_name,
self.db_host,
mode=self.mode,
directed=self.directed,
nodes_collection=self.nodes_collection,
edges_collection=self.edges_collection,
)
self.updates(self.mst, self.spec[self.mst].copy())
def prepare(self, request):
deps = gp.BatchRequest()
deps[self.mst] = request[self.mst].copy()
assert (request[self.mst].roi.get_shape() == self.read_size
), f"Got wrong size graph in request"
return deps
def process(self, batch, request):
voxel_size = self.voxel_size
read_roi = batch[self.mst].spec.roi
write_roi = read_roi.grow(-self.context, -self.context)
mst = batch[self.mst].to_nx_graph()
# get saved graph with 'dangling' edges. Some nodes may not be in write_roi
# Edges crossing boundary should only be saved if lower id is contained in write_roi
mongo_graph = self.client.get_graph(
write_roi,
node_attrs=self.node_attrs,
edge_attrs=self.edge_attrs,
node_inclusion="dangling",
edge_inclusion="either",
)
for node in list(mongo_graph.nodes()):
if self.position_attr not in mongo_graph.nodes[node]:
mongo_graph.remove_node(node)
num_created_nodes = 0
num_updated_nodes = 0
for node, attrs in mst.nodes.items():
loc = attrs["location"]
pos = np.floor(loc / voxel_size)
node_id = int(math.cantor_number(pos))
# only update attributes of nodes in the write_roi
if write_roi.contains(loc):
attrs_to_save = {
self.position_attr: tuple(float(v) for v in loc)
}
for attr in self.node_attrs:
if attr in attrs:
value = attrs[attr]
if isinstance(value, Iterable):
value = tuple(float(v) for v in value)
if isinstance(value, np.float32) or (isinstance(
value, Iterable) and any(
[isinstance(v, np.float32)
for v in value])):
raise ValueError(
f"value of attr {attr} is a np.float32")
attrs_to_save[attr] = value
if node_id in mongo_graph:
num_updated_nodes += 1
mongo_attrs = mongo_graph.nodes[node_id]
mongo_attrs.update(attrs_to_save)
else:
num_created_nodes += 1
mongo_graph.add_node(node_id, **attrs_to_save)
num_created_edges = 0
num_updated_edges = 0
for (u, v), attrs in mst.edges.items():
u_loc = mst.nodes[u]["location"]
u_pos = np.floor(u_loc / voxel_size)
u_id = int(math.cantor_number(u_pos))
v_loc = mst.nodes[v]["location"]
v_pos = np.floor(v_loc / voxel_size)
v_id = int(math.cantor_number(v_pos))
# node a is the node with the lower id out of u, v
a_loc, a_id, b_loc, b_id = ((u_loc, u_id, v_loc,
v_id) if u_id < v_id else
(v_loc, v_id, u_loc, u_id))
# only write edge if a is contained
# may create a node without any attributes if node creation is
# dependent on your field of view and the neighboring block
# fails to create the same node.
if write_roi.contains(a_loc):
attrs_to_save = {}
for attr in self.edge_attrs:
if attr in attrs:
value = attrs[attr]
if isinstance(value, Iterable):
value = tuple(float(v) for v in value)
if isinstance(value, np.float32) or (isinstance(
value, Iterable) and any(
[isinstance(v, np.float32)
for v in value])):
raise ValueError(
f"value of attr {attr} is a np.float32")
attrs_to_save[attr] = value
if (u_id, v_id) in mongo_graph.edges:
num_updated_edges += 1
mongo_attrs = mongo_graph.edges[(u_id, v_id)]
mongo_attrs.update(attrs_to_save)
else:
num_created_edges += 1
mongo_graph.add_edge(u_id, v_id, **attrs_to_save)
for node in mst.nodes:
if node in mongo_graph.nodes and write_roi.contains(
mst.nodes[node]["location"]):
assert all(
np.isclose(
mongo_graph.nodes[node][self.position_attr],
mst.nodes[node]["location"],
))
if write_roi.contains(
mongo_graph.nodes[node][self.position_attr]):
assert (mongo_graph.nodes[node]["component_id"] ==
mst.nodes[node]["component_id"])
if len(mongo_graph.nodes) > 0:
mongo_graph.write_nodes(roi=write_roi, attributes=self.node_attrs)
if len(mongo_graph.edges) > 0:
for edge, attrs in mongo_graph.edges.items():
for attr in self.edge_attrs:
assert attr in attrs
mongo_graph.write_edges(roi=write_roi, attributes=self.edge_attrs)
class MongoWriteComponents(BatchFilter):
def __init__(
self,
graph,
db_host,
db_name,
read_size,
write_size,
component_attr,
node_attrs=None,
collection="",
mode="r+",
):
self.graph = graph
self.db_host = db_host
self.db_name = db_name
self.read_size = read_size
self.write_size = write_size
self.context = (read_size - write_size) / 2
self.mode = mode
self.component_attr = component_attr
assert self.write_size + (self.context * 2) == self.read_size
self.collection = collection
self.component_collection = f"{self.collection}_components"
self.component_edge_collection = f"{self.collection}_component_edges"
if node_attrs is None:
self.node_attrs = []
else:
self.node_attrs = node_attrs
self.client = None
def setup(self):
self.updates(self.graph, self.spec[self.graph].copy())
if self.client is None:
self.client = MongoDbGraphProvider(
self.db_name,
self.db_host,
mode=self.mode,
directed=False,
nodes_collection=self.component_collection,
edges_collection=self.component_edge_collection,
)
def prepare(self, request):
deps = BatchRequest()
deps[self.graph] = request[self.graph].copy()
assert (request[self.graph].roi.get_shape() == self.read_size
), f"Got wrong size graph in request"
return deps
def process(self, batch, request):
graph = batch[self.graph].to_nx_graph()
logger.debug(
f"{self.name()} got graph with {graph.number_of_nodes()} nodes and "
f"{graph.number_of_edges()} edges!")
write_roi = batch[self.graph].spec.roi.grow(-self.context,
-self.context)
contained_nodes = [
node for node, attr in graph.nodes.items()
if write_roi.contains(attr["location"])
]
contained_components = set(graph.nodes[n][self.component_attr]
for n in contained_nodes)
logger.debug(f"Graph contains {len(contained_nodes)} nodes with "
f"{len(contained_components)} components in write_roi")
mongo_graph = self.client.get_graph(
write_roi,
node_attrs=[],
edge_attrs=[],
node_inclusion="dangling",
edge_inclusion="either",
)
num_new_components = 0
num_new_component_edges = 0
for node, attrs in graph.nodes.items():
if write_roi.contains(attrs["location"]):
cc_id = attrs[self.component_attr]
node_attrs = {}
for attr in self.node_attrs:
node_attrs[attr] = attrs[attr]
if cc_id not in mongo_graph.nodes:
num_new_components += 1
mongo_graph.add_node(cc_id,
position=write_roi.get_center(),
**node_attrs)
else:
for k, v in node_attrs.items():
assert mongo_graph.nodes[cc_id][k] == v
# Always write crossing edges if neither component id is None. Other end
# point wont have a component id unless it has already been processed,
# thus it is the duty of the second pass to write the edge, regardless
# of whether the lower or upper end point is contained.
for u, v in graph.edges:
u_loc = graph.nodes[u]["location"]
v_loc = graph.nodes[v]["location"]
if write_roi.contains(u_loc) or write_roi.contains(v_loc):
u_cc_id = graph.nodes[u].get(self.component_attr)
v_cc_id = graph.nodes[v].get(self.component_attr)
if u_cc_id is None or v_cc_id is None:
continue
elif u_cc_id == v_cc_id:
continue
elif (u_cc_id, v_cc_id) not in mongo_graph.edges:
num_new_component_edges += 1
mongo_graph.add_edge(u_cc_id, v_cc_id)
logger.debug(
f"{self.name()} writing {num_new_components} new components and "
f"{num_new_component_edges} new component edges!")
mongo_graph.write_nodes()
mongo_graph.write_edges(ignore_v=False)
