def runTest(self):
print "ManyToMany"
mg = MatchingGraph(self.groundtruth_skeletons,
self.reconstructed_skeletons,
self.distance_threshold,
self.voxel_size,
verbose=True,
distance_cost=True,
initialize_all=True)
nodes_gt, nodes_rec, edges_gt_rec, labels_gt, labels_rec, edge_costs, edge_conflicts, edge_pairs = mg.export_to_comatch()
try:
# Quadmatch
label_matches, node_matches, num_splits, num_merges, num_fps, num_fns = match_components(nodes_gt, nodes_rec, edges_gt_rec, labels_gt,
labels_rec, edge_conflicts=edge_conflicts, max_edges=10, edge_costs=edge_costs)
except TypeError:
# Comatch
label_matches, node_matches, num_splits, num_merges, num_fps, num_fns = match_components(nodes_gt, nodes_rec, edges_gt_rec, labels_gt,
labels_rec, allow_many_to_many=True, edge_costs=edge_costs, no_match_costs=1000.)
print "label matches:", label_matches
print "node_matches:", node_matches
comatch_errors = {"splits": num_splits, "num_merges": num_merges, "num_fps": num_fps, "num_fns": num_fns}
print comatch_errors
mg.import_matches(node_matches)
output_dir = test_data_dir + "/MatchingManytoMany"
mg.export_all(output_dir)
with open(output_dir + "/macro_errors.json", "w+") as f:
json.dump(comatch_errors, f)
def test_match():
nodes_x = list(range(1, 8))
nodes_y = list(range(101, 111))
edges_xy = [
(1, 101),
(2, 102),
(2, 103),
(2, 104),
(2, 109),
(3, 103),
(3, 108),
(4, 104),
(4, 109),
(5, 105),
(5, 110),
(6, 106),
(7, 107),
]
node_labels_x = {n: 1 for n in nodes_x}
node_labels_y = {n: 2 for n in nodes_y}
node_labels_y[108] = 3
node_labels_y[109] = 3
node_labels_y[110] = 3
edge_conflicts = [[(3, 103), (3, 108)], [(4, 104), (4, 109)],
[(5, 105), (5, 110)], [(2, 109), (2, 104)],
[(2, 109), (2, 103)], [(2, 102), (2, 109)]]
label_matches, node_matches, splits, merges, fps, fns = comatch.match_components(
nodes_x,
nodes_y,
edges_xy,
node_labels_x,
node_labels_y,
edge_conflicts=edge_conflicts)
print(node_matches)
print("splits: %d" % splits)
print("merges: %d" % merges)
print("fps : %d" % fps)
print("fns : %d" % fns)
# the other way around
label_matches, node_matches, splits, merges, fps, fns = comatch.match_components(
nodes_y,
nodes_x, [(v, u) for (u, v) in edges_xy],
node_labels_y,
node_labels_x,
edge_conflicts=[[tuple([c[0][1], c[0][0]]),
tuple([c[1][1], c[1][0]])] for c in edge_conflicts])
print(node_matches)
print("splits: %d" % splits)
print("merges: %d" % merges)
print("fps : %d" % fps)
print("fns : %d" % fns)
def runTest(self):
print "Import matches"
mg = MatchingGraph(self.groundtruth_skeletons,
self.reconstructed_skeletons,
self.distance_threshold,
self.voxel_size,
verbose=True,
initialize_all=True)
nodes_gt, nodes_rec, edges_gt_rec, labels_gt, labels_rec, edge_costs, edge_conflicts, edge_pairs = mg.export_to_comatch()
label_matches, node_matches, num_splits, num_merges, num_fps, num_fns = match_components(nodes_gt, nodes_rec, edges_gt_rec, labels_gt, labels_rec)
matches = node_matches
# Everything is matched
self.assertTrue(len(matches) == mg.get_number_of_vertices()/2)
mg.import_matches(matches)
for v in mg.get_vertex_iterator():
self.assertTrue(mg.is_tp(v))
self.assertFalse(mg.is_fp(v))
self.assertFalse(mg.is_fn(v))
for e in mg.get_edge_iterator():
self.assertFalse(mg.is_split(e))
self.assertFalse(mg.is_merge(e))
def evaluate_matching_graph(matching_graph,
max_edges=1,
export_to=None,
optimality_gap=0.0,
time_limit=None,
n_gts=-1,
n_recs=-1):
if max_edges > 1 or max_edges == None:
edge_conflicts = True
else:
edge_conflicts = False
nodes_gt, nodes_rec, edges_gt_rec, labels_gt, labels_rec, edge_costs, edge_conflicts, edge_pairs = matching_graph.export_to_comatch(
edge_conflicts=edge_conflicts, edge_pairs=False)
logger.info("Match using hungarian match...")
label_matches, node_matches, num_splits, num_merges, num_fps, num_fns = match_components(
nodes_gt,
nodes_rec,
edges_gt_rec,
labels_gt,
labels_rec,
edge_conflicts=edge_conflicts,
max_edges=max_edges,
optimality_gap=optimality_gap,
time_limit=time_limit)
matching_graph.import_matches(node_matches)
topological_errors = {
"n_gt": n_gts,
"n_rec": n_recs,
"splits": num_splits,
"merges": num_merges,
"fps": num_fps,
"fns": num_fns
}
node_errors = matching_graph.evaluate()
if export_to is not None:
matching_graph.export_all(export_to)
with open(export_to + "/object_stats.txt", "w+") as f:
json.dump(topological_errors, f)
return matching_graph, topological_errors, node_errors
def score_graph(
predicted_tracings: nx.Graph,
reference_tracings: nx.Graph,
match_threshold: float,
location_attr: str,
metric: Metric,
**metric_kwargs,
):
# Match the graphs:
edges_xy = get_edges_xy(predicted_tracings, reference_tracings,
location_attr, match_threshold)
nodes_x = list(predicted_tracings.nodes)
nodes_y = list(reference_tracings.nodes)
node_labels_x = {
node: cc
for cc, cc_nodes in enumerate(
nx.connected_components(predicted_tracings)) for node in cc_nodes
}
node_labels_y = {
node: cc
for cc, cc_nodes in enumerate(
nx.connected_components(reference_tracings)) for node in cc_nodes
}
label_matches, node_matches, splits, merges, fps, fns = comatch.match_components(
nodes_x, nodes_y, edges_xy, node_labels_x, node_labels_y)
# evaluate the matching
return evaluate_matching(
metric,
node_matches,
node_labels_x,
node_labels_y,
predicted_tracings,
reference_tracings,
location_attr,
**metric_kwargs,
)
def add_mst_snapshot_with_stats(
context,
matching_data,
mst_snapshot,
gt,
threshold_inds=None,
graph_node_attrs=None,
graph_edge_attrs=None,
false_pos_threshold=None,
):
if false_pos_threshold is None:
false_pos_threshold = 0
mst = tree_from_snapshot(
mst_snapshot,
"mst",
graph_node_attrs=graph_node_attrs,
graph_edge_attrs=graph_edge_attrs,
)
label_matchings, node_matchings, node_labels_mst, node_labels_gt, thresholds = (
matching_data
)
edges_to_add = list(mst.edges.items())
thresholded_graph = nx.Graph()
for i, threshold in enumerate(thresholds):
for j, ((u, v), attrs) in reversed(list(enumerate(edges_to_add))):
if attrs["distance"] < threshold:
thresholded_graph.add_node(u, **mst.nodes[u])
thresholded_graph.add_node(v, **mst.nodes[v])
thresholded_graph.add_edge(u, v, **attrs)
del edges_to_add[j]
if i in threshold_inds:
temp = copy.deepcopy(thresholded_graph)
false_pos_nodes = []
for cc in nx.connected_components(temp):
cc_graph = temp.subgraph(cc)
min_loc = None
max_loc = None
for node, attrs in cc_graph.nodes.items():
node_loc = attrs["location"]
if min_loc is None:
min_loc = node_loc
else:
min_loc = np.min(np.array([node_loc, min_loc]), axis=0)
if max_loc is None:
max_loc = node_loc
else:
max_loc = np.max(np.array([node_loc, max_loc]), axis=0)
if np.linalg.norm(min_loc - max_loc) < false_pos_threshold:
false_pos_nodes += list(cc)
for node in false_pos_nodes:
temp.remove_node(node)
nodes_x = list(temp.nodes)
nodes_y = list(gt.nodes)
node_labels_x = {
node: component
for component, nodes in enumerate(nx.connected_components(temp))
for node in nodes
}
node_labels_y = {
node: component
for component, nodes in enumerate(nx.connected_components(gt))
for node in nodes
}
edges_yx = get_edges_xy(
gt, temp, location_attr="location", node_match_threshold=4000
)
edges_xy = [(v, u) for u, v in edges_yx]
(label_matches, node_matches, splits, merges, fps, fns) = match_components(
nodes_x, nodes_y, edges_xy, node_labels_x, node_labels_y
)
erl, details = psudo_graph_edit_distance(
node_matches,
node_labels_x,
node_labels_y,
temp,
gt,
"location",
node_spacing=5000,
details=True,
)
add_match_layers(
context,
temp,
gt,
label_matchings[i],
node_matchings[i],
node_labels_mst[i],
node_labels_gt[i],
name=f"Matched-{threshold:.3f}",
)
(3, 108),
(4, 104),
(4, 109),
(5, 105),
(5, 110),
(6, 106),
(7, 107),
]
node_labels_x = { n: 1 for n in nodes_x }
node_labels_y = { n: 2 for n in nodes_y }
node_labels_y[108] = 3
node_labels_y[109] = 3
node_labels_y[110] = 3
label_matches, node_matches, splits, merges, fps, fns = comatch.match_components(
nodes_x, nodes_y,
edges_xy,
node_labels_x, node_labels_y)
print(node_matches)
print("splits: %d"%splits)
print("merges: %d"%merges)
print("fps : %d"%fps)
print("fns : %d"%fns)
# the other way around
label_matches, node_matches, splits, merges, fps, fns = comatch.match_components(
nodes_y, nodes_x,
[ (v, u) for (u, v) in edges_xy ],
node_labels_y, node_labels_x)
print(node_matches)
def process(self, batch, request):
num_thresholds = self.num_thresholds
threshold_range = self.threshold_range
outputs = gp.Batch()
gt_graph = batch[self.gt].to_nx_graph().to_undirected()
mst_graph = batch[self.mst].to_nx_graph().to_undirected()
if self.connectivity is not None:
connectivity_graph = batch[
self.connectivity].to_nx_graph().to_undirected()
# assert mst_graph.number_of_nodes() > 0, f"mst_graph is empty!"
if self.details is not None:
matching_details_graph = nx.Graph()
if mst_graph.number_of_nodes() == 0:
node_offset = max([node
for node in mst_graph.nodes] + [-1]) + 1
label_offset = len(list(
nx.connected_components(mst_graph))) + 1
for node, attrs in mst_graph.nodes.items():
matching_details_graph.add_node(node,
**copy.deepcopy(attrs))
for edge, attrs in mst_graph.edges.items():
matching_details_graph.add_edge(edge[0], edge[1],
**copy.deepcopy(attrs))
for node, attrs in gt_graph.nodes.items():
matching_details_graph.add_node(node + node_offset,
**copy.deepcopy(attrs))
matching_details_graph.nodes[node + node_offset]["id"] = (
node + node_offset)
for edge, attrs in gt_graph.edges.items():
matching_details_graph.add_edge(edge[0] + node_offset,
edge[1] + node_offset,
**copy.deepcopy(attrs))
edges = [(edge, attrs[self.edge_threshold_attr])
for edge, attrs in mst_graph.edges.items()]
edges = list(sorted(edges, key=lambda x: x[1]))
edge_lens = [e[1] for e in edges]
# min_threshold = edges[0][1]
if len(edge_lens) > 0:
min_threshold = edge_lens[int(len(edge_lens) * threshold_range[0])]
max_threshold = edge_lens[int(len(edge_lens) * threshold_range[1])
- 1]
else:
min_threshold = 0
max_threshold = 1
thresholds = np.linspace(min_threshold,
max_threshold,
num=num_thresholds)
current_threshold_mst = nx.Graph()
edge_deque = deque(edges)
edit_distances = []
split_costs = []
merge_costs = []
false_pos_costs = []
false_neg_costs = []
num_nodes = []
num_edges = []
best_score = None
best_graph = None
for threshold_index, threshold in enumerate(thresholds):
logger.warning(f"Using threshold: {threshold}")
while len(edge_deque) > 0 and edge_deque[0][1] <= threshold:
(u, v), _ = edge_deque.popleft()
attrs = mst_graph.edges[(u, v)]
current_threshold_mst.add_edge(u, v)
current_threshold_mst.add_node(u, **mst_graph.nodes[u])
current_threshold_mst.add_node(v, **mst_graph.nodes[v])
if self.connectivity is not None:
temp = nx.Graph()
next_node = max([node
for node in connectivity_graph.nodes]) + 1
for i, cc in enumerate(
nx.connected_components(current_threshold_mst)):
component_subgraph = current_threshold_mst.subgraph(cc)
for node in component_subgraph.nodes:
temp.add_node(node,
**dict(connectivity_graph.nodes[node]))
temp.nodes[node]["component"] = i
for edge in connectivity_graph.edges:
if (edge[0] in temp.nodes and edge[1] in temp.nodes
and temp.nodes[edge[0]]["component"]
== temp.nodes[edge[1]]["component"]):
temp.add_edge(
edge[0], edge[1],
**dict(connectivity_graph.edges[edge]))
elif False:
path = nx.shortest_path(connectivity_graph,
edge[0], edge[1])
cloned_path = []
for node in path:
if node in temp.nodes:
cloned_path.append(node)
else:
cloned_path.append(next_node)
next_node += 1
path_len = len(cloned_path) - 1
for i, j in zip(range(path_len),
range(1, path_len + 1)):
u = cloned_path[i]
if u not in temp.nodes:
temp.add_node(
u,
**dict(
connectivity_graph.nodes[path[i]]))
v = cloned_path[j]
if v not in temp.nodes:
temp.add_node(
v,
**dict(
connectivity_graph.nodes[path[j]]))
temp.add_edge(
u,
v,
**dict(connectivity_graph.edges[path[i],
path[j]]),
)
else:
temp = copy.deepcopy(current_threshold_mst)
for i, cc in enumerate(nx.connected_components(temp)):
for node in cc:
attrs = temp.nodes[node]
attrs["component"] = i
# remove small connected_components
false_pos_nodes = []
for cc in nx.connected_components(temp):
cc_graph = temp.subgraph(cc)
min_loc = None
max_loc = None
for node, attrs in cc_graph.nodes.items():
node_loc = attrs[self.location_attr]
if min_loc is None:
min_loc = node_loc
else:
min_loc = np.min(np.array([node_loc, min_loc]), axis=0)
if max_loc is None:
max_loc = node_loc
else:
max_loc = np.max(np.array([node_loc, max_loc]), axis=0)
if np.linalg.norm(min_loc -
max_loc) < self.small_component_threshold:
false_pos_nodes += list(cc)
for node in false_pos_nodes:
temp.remove_node(node)
nodes_x = list(temp.nodes)
nodes_y = list(gt_graph.nodes)
node_labels_x = {
node: attrs["component"]
for node, attrs in temp.nodes.items()
}
node_labels_y = {
node: component
for component, nodes in enumerate(
nx.connected_components(gt_graph)) for node in nodes
}
edges_yx = get_edges_xy(
gt_graph,
temp,
location_attr=self.location_attr,
node_match_threshold=self.comatch_threshold,
)
edges_xy = [(v, u) for u, v in edges_yx]
result = match_components(
nodes_x,
nodes_y,
edges_xy,
copy.deepcopy(node_labels_x),
copy.deepcopy(node_labels_y),
)
if self.details is not None:
# add a match details graph to the batch
# details is a graph containing nodes from both mst and gt
# to access details of a node, use `details.nodes[node]["details"]`
# where the details returned are a numpy array of shape (num_thresholds, _).
# the _ values stored per threshold are success, fp, fn, merge, split, selected, mst, gt
# NODES
# success, mst: n matches to only nodes of 1 label, which matches its own label
# success, gt: n matches to only nodes of 1 label, which matches its own label
# fp: n in mst matches to nothing
# fn: n in gt matches to nothing
# merge: n in mst matches to a node with label not matching its own
# split: n in gt matches to a node with label not matching its own
# selected: n in mst in thresholded graph
# EDGES
# success, mst: both endpoints successful
# success, gt: both endpoints successful
# fp: both endpoints fp
# fn: both endpoints fn
# merge: e in mst: only one endpoint successful
# split: e in gt: only one endpoint successful
# selected: e in mst in thresholded graph
(label_matches, node_matches, splits, merges, fps,
fns) = result
# create lookup tables:
x_label_match_lut = {}
y_label_match_lut = {}
for a, b in label_matches:
x_matches = x_label_match_lut.setdefault(a, set())
x_matches.add(b)
y_matches = y_label_match_lut.setdefault(b, set())
y_matches.add(a)
x_node_match_lut = {}
y_node_match_lut = {}
for a, b in node_matches:
x_matches = x_node_match_lut.setdefault(a, set())
x_matches.add(b)
y_matches = y_node_match_lut.setdefault(b, set())
y_matches.add(a)
for node, attrs in matching_details_graph.nodes.items():
gt = int(node >= node_offset)
mst = 1 - gt
if gt == 1:
node = node - node_offset
selected = gt or (node in temp.nodes())
if selected:
success, fp, fn, merge, split, label_pair = self.node_matching_result(
node,
gt,
x_label_match_lut,
y_label_match_lut,
x_node_match_lut,
y_node_match_lut,
node_labels_x,
node_labels_y,
)
else:
success, fp, fn, merge, split, label_pair = (
0,
0,
0,
0,
0,
(-1, -1),
)
data = attrs.setdefault(
"details", np.zeros((len(thresholds), 7), dtype=bool))
data[threshold_index] = [
selected,
success,
fp,
fn,
merge,
split,
gt,
]
label_pairs = attrs.setdefault("label_pair", [])
label_pairs.append(label_pair)
assert len(label_pairs) == threshold_index + 1
for (u, v), attrs in matching_details_graph.edges.items():
(
u_selected,
u_success,
u_fp,
u_fn,
u_merge,
u_split,
u_gt,
) = matching_details_graph.nodes[u]["details"][
threshold_index]
(
v_selected,
v_success,
v_fp,
v_fn,
v_merge,
v_split,
v_gt,
) = matching_details_graph.nodes[v]["details"][
threshold_index]
assert u_gt == v_gt
e_gt = u_gt
u_label_pair = matching_details_graph.nodes[u][
"label_pair"][threshold_index]
v_label_pair = matching_details_graph.nodes[v][
"label_pair"][threshold_index]
e_selected = u_selected and v_selected
e_success = (e_selected and u_success and v_success
and (u_label_pair == v_label_pair))
e_fp = u_fp and v_fp
e_fn = u_fn and v_fn
e_merge = e_selected and (not e_success) and (
not e_fp) and not e_gt
e_split = e_selected and (not e_success) and (
not e_fn) and e_gt
assert not (e_success and e_merge)
assert not (e_success and e_split)
data = attrs.setdefault(
"details", np.zeros((len(thresholds), 7), dtype=bool))
if e_success:
label_pairs = attrs.setdefault("label_pair", [])
label_pairs.append(u_label_pair)
assert len(label_pairs) == threshold_index + 1
else:
label_pairs = attrs.setdefault("label_pair", [])
label_pairs.append((-1, -1))
assert len(label_pairs) == threshold_index + 1
data[threshold_index] = [
e_selected,
e_success,
e_fp,
e_fn,
e_merge,
e_split,
e_gt,
]
edit_distance, (
split_cost,
merge_cost,
false_pos_cost,
false_neg_cost,
) = psudo_graph_edit_distance(
result[1],
node_labels_x,
node_labels_y,
temp,
gt_graph,
self.location_attr,
node_spacing=self.edit_distance_node_spacing,
details=True,
)
edit_distances.append(edit_distance)
split_costs.append(split_cost)
merge_costs.append(merge_cost)
false_pos_costs.append(false_pos_cost)
false_neg_costs.append(false_neg_cost)
num_nodes.append(len(temp.nodes))
num_edges.append(len(temp.edges))
# save the best version:
if best_score is None:
best_score = edit_distance
best_graph = copy.deepcopy(temp)
elif edit_distance < best_score:
best_score = edit_distance
best_graph = copy.deepcopy(temp)
outputs[self.output] = gp.Array(
np.array([
edit_distances,
thresholds,
num_nodes,
num_edges,
split_costs,
merge_costs,
false_pos_costs,
false_neg_costs,
]),
gp.ArraySpec(nonspatial=True),
)
if self.output_graph is not None:
outputs[self.output_graph] = gp.Graph.from_nx_graph(
best_graph,
gp.GraphSpec(roi=batch[self.gt].spec.roi, directed=False))
if self.details is not None:
outputs[self.details] = gp.Graph.from_nx_graph(
matching_details_graph,
gp.GraphSpec(roi=batch[self.gt].spec.roi, directed=False),
)
return outputs
def evaluate(matching_graph,
max_edges=1,
optimality_gap=0.0,
time_limit=None,
n_gts=-1,
n_recs=-1):
nodes_gt, nodes_rec, labels_gt, labels_rec, edges_gt_rec, edge_conflicts = matching_graph.export(
)
if nodes_gt and nodes_rec:
label_matches, node_matches, num_splits, num_merges, num_fps, num_fns = match_components(
nodes_gt,
nodes_rec,
edges_gt_rec,
labels_gt,
labels_rec,
edge_conflicts=edge_conflicts,
max_edges=max_edges,
optimality_gap=optimality_gap,
time_limit=time_limit)
topological_errors = {
"n_gt": len(set(v for v in labels_gt.values())),
"n_rec": len(set([v for v in labels_rec.values()])),
"splits": num_splits,
"merges": num_merges,
"fps": num_fps,
"fns": num_fns
}
matching_graph.import_node_matches(node_matches)
node_errors = matching_graph.get_stats()
else:
if not nodes_gt:
fps = matching_graph.rec_ccs
fns = 0
nodes_fps = len(nodes_rec)
nodes_fns = 0
if not nodes_rec:
fps = 0
fns = matching_graph.gt_ccs
nodes_fps = 0
nodes_fns = len(nodes_gt)
topological_errors = {
"n_gt": matching_graph.gt_ccs,
"n_rec": matching_graph.rec_ccs,
"splits": 0,
"merges": 0,
"fps": fps,
"fns": fns
}
node_errors = {
"vertices": 0,
"edges": 0,
"tps_rec": 0,
"tps_gt": 0,
"fps": nodes_fps,
"fns": nodes_fns,
"merges": 0,
"splits": 0
}
return node_errors, topological_errors