def get_gt_tracks_for_roi(gt_db_name, mongo_url, roi):
graph_provider = linajea.CandidateDatabase(gt_db_name, mongo_url)
subgraph = graph_provider[roi]
track_graph = linajea.tracking.TrackGraph(subgraph)
tracks = track_graph.get_tracks()
end_frame = roi.get_offset()[0] + roi.get_shape()[0] - 1
one_d_tracks = []
for track in tracks:
for end_cell in track.get_cells_in_frame(end_frame):
cell_positions = []
current_cell = end_cell
while current_cell is not None:
current_data = track.nodes[current_cell]
cell_positions.append([current_data[dim]
for dim in ['t', 'z', 'y', 'x']])
parent_edges = track.prev_edges(current_cell)
if len(parent_edges) == 1:
current_cell = parent_edges[0][1]
elif len(parent_edges) == 0:
current_cell = None
else:
print("Error: Cell has two parents! Exiting")
return None
one_d_tracks.append(cell_positions)
print("Found %d tracks in roi %s" % (len(one_d_tracks), roi))
return one_d_tracks
def create_graph(self, cells, edges, roi):
db = linajea.CandidateDatabase('test_eval', 'localhost')
graph = db[roi]
graph.add_nodes_from(cells)
graph.add_edges_from(edges)
tg = linajea.tracking.TrackGraph(graph_data=graph, frame_key='t')
return tg
def get_track_from_node(
node_id,
node_frame,
gt_db_name,
mongo_url,
num_frames_before,
num_frames_after=0):
graph_provider = linajea.CandidateDatabase(gt_db_name, mongo_url)
roi = daisy.Roi((node_frame - num_frames_before + 1, 0, 0, 0),
(num_frames_before + num_frames_after, 10e6, 10e6, 10e6))
subgraph = graph_provider[roi]
track_graph = linajea.tracking.TrackGraph(subgraph, frame_key='t')
tracks = track_graph.get_tracks()
for track in tracks:
if track.has_node(node_id):
cell_positions = []
current_cell = node_id
while True:
current_data = track.nodes[current_cell]
if 't' not in current_data:
break
cell_positions.append([current_data[dim]
for dim in ['t', 'z', 'y', 'x']])
parent_edges = list(track.prev_edges(current_cell))
if len(parent_edges) == 1:
current_cell = parent_edges[0][1]
elif len(parent_edges) == 0:
break
else:
print("Error: Cell has two parents! Exiting")
return None
return cell_positions
print("Did not find track with node %d in roi %s"
% (node_id, roi))
return None
def test_solver_node_close_to_edge(self):
# x
# 3| /-4
# 2| /--3
# 1| 0---1
# 0| \--2
# ------------------------------------ t
# 0 1 2
cells = [
{'id': 0, 't': 0, 'z': 1, 'y': 1, 'x': 1, 'score': 2.0},
{'id': 1, 't': 1, 'z': 1, 'y': 1, 'x': 1, 'score': 2.0},
{'id': 2, 't': 2, 'z': 1, 'y': 1, 'x': 0, 'score': 2.0},
{'id': 3, 't': 2, 'z': 1, 'y': 1, 'x': 2, 'score': 2.0},
{'id': 4, 't': 2, 'z': 1, 'y': 1, 'x': 4, 'score': 2.0}
]
edges = [
{'source': 1, 'target': 0, 'score': 1.0, 'prediction_distance': 0.0},
{'source': 2, 'target': 1, 'score': 1.0, 'prediction_distance': 1.0},
{'source': 3, 'target': 1, 'score': 1.0, 'prediction_distance': 1.0},
{'source': 4, 'target': 1, 'score': 1.0, 'prediction_distance': 2.0},
]
db_name = 'linajea_test_solver'
db_host = 'localhost'
graph_provider = linajea.CandidateDatabase(
db_name,
db_host)
roi = daisy.Roi((0, 0, 0, 0), (5, 5, 5, 5))
graph = graph_provider[roi]
ps = {
"cost_appear": 1.0,
"cost_disappear": 1.0,
"cost_split": 0,
"weight_prediction_distance_cost": 0.1,
"weight_node_score": 1.0,
"threshold_node_score": 0.0,
"threshold_edge_score": 0.0,
"max_cell_move": 1.0,
"block_size": [5, 100, 100, 100],
"context": [2, 100, 100, 100],
}
parameters = linajea.tracking.NMTrackingParameters(**ps)
graph.add_nodes_from([(cell['id'], cell) for cell in cells])
graph.add_edges_from([(edge['source'], edge['target'], edge)
for edge in edges])
track_graph = linajea.tracking.TrackGraph(
graph, frame_key='t', roi=graph.roi)
solver = linajea.tracking.NMSolver(track_graph, parameters, 'selected')
for node, data in track_graph.nodes(data=True):
close = solver._check_node_close_to_roi_edge(node, data, 1)
if node in [2, 4]:
close = not close
self.assertFalse(close)
self.delete_db(db_name, db_host)
def test_greedy_node_threshold(self):
# x
# 3| /-4 \
# 2| /--3---5
# 1| 0---1
# 0| \--2
# ------------------------------------ t
# 0 1 2 3
cells = [
{'id': 0, 't': 0, 'z': 1, 'y': 1, 'x': 1, 'score': 2.0},
{'id': 1, 't': 1, 'z': 1, 'y': 1, 'x': 1, 'score': 2.0},
{'id': 2, 't': 2, 'z': 1, 'y': 1, 'x': 0, 'score': 2.0},
{'id': 3, 't': 2, 'z': 1, 'y': 1, 'x': 2, 'score': 1.0},
{'id': 4, 't': 2, 'z': 1, 'y': 1, 'x': 3, 'score': 2.0},
{'id': 5, 't': 3, 'z': 1, 'y': 1, 'x': 2, 'score': 2.0}
]
edges = [
{'source': 1, 'target': 0, 'score': 1.0, 'distance': 0.0},
{'source': 2, 'target': 1, 'score': 1.0, 'distance': 1.0},
{'source': 3, 'target': 1, 'score': 1.0, 'distance': 1.0},
{'source': 4, 'target': 1, 'score': 1.0, 'distance': 2.0},
{'source': 5, 'target': 3, 'score': 1.0, 'distance': 0.0},
{'source': 5, 'target': 4, 'score': 1.0, 'distance': 1.0},
]
db_name = 'linajea_test_solver'
db_host = 'localhost'
graph_provider = linajea.CandidateDatabase(
db_name,
db_host)
roi = daisy.Roi((0, 0, 0, 0), (4, 5, 5, 5))
graph = graph_provider[roi]
graph.add_nodes_from([(cell['id'], cell) for cell in cells])
graph.add_edges_from([(edge['source'], edge['target'], edge)
for edge in edges])
linajea.tracking.greedy_track(
graph,
selected_key='selected',
metric='distance',
frame_key='t',
node_threshold=1.5)
selected_edges = []
for u, v, data in graph.edges(data=True):
if data['selected']:
selected_edges.append((u, v))
expected_result = [
(1, 0),
(2, 1),
(4, 1),
(5, 4)
]
self.assertCountEqual(selected_edges, expected_result)
self.delete_db(db_name, db_host)
def read_nodes_and_edges(self,
db_name,
frames=None,
nodes_key=None,
edges_key=None,
key=None,
filter_unattached=True):
db = linajea.CandidateDatabase(db_name, self.mongo_url)
if frames is None:
frames = [0, 1e10]
roi = daisy.Roi((frames[0], 0, 0, 0),
(frames[1] - frames[0], 1e10, 1e10, 1e10))
if nodes_key is None:
nodes = db.read_nodes(roi)
else:
nodes = db.read_nodes(roi, attr_filter={nodes_key: True})
node_ids = [node['id'] for node in nodes]
logger.debug("Found %d nodes" % len(node_ids))
if edges_key is None and key is not None:
edges_key = key
if edges_key is None:
edges = db.read_edges(roi, nodes=nodes)
else:
edges = db.read_edges(roi,
nodes=nodes,
attr_filter={edges_key: True})
if filter_unattached:
logger.debug("Filtering cells")
filtered_cell_ids = set([edge['source'] for edge in edges] +
[edge['target'] for edge in edges])
filtered_cells = [
cell for cell in nodes if cell['id'] in filtered_cell_ids
]
nodes = filtered_cells
node_ids = filtered_cell_ids
logger.debug("Done filtering cells")
logger.debug("Adjusting ids")
target_min_id = 0
actual_min_id = min(node_ids)
diff = actual_min_id - target_min_id
logger.debug("Subtracting {} from all cell ids".format(diff))
for node in nodes:
node['name'] = node['id']
node['id'] -= diff
for edge in edges:
edge['source'] -= diff
edge['target'] -= diff
return nodes, edges
def test_solver_cell_cycle2(self):
'''x
3| /-4
2| /--3---5
1| 0---1
0| \\--2
------------------------------------ t
0 1 2 3
Should select 0, 1, 3, 5 due to vgg predicting continuation
'''
cells = [{
'id': 0,
't': 0,
'z': 1,
'y': 1,
'x': 1,
'score': 2.0,
'vgg_score': [0, 0, 1]
}, {
'id': 1,
't': 1,
'z': 1,
'y': 1,
'x': 1,
'score': 2.0,
'vgg_score': [0, 0, 1]
}, {
'id': 2,
't': 2,
'z': 1,
'y': 1,
'x': 0,
'score': 2.0,
'vgg_score': [0, 0, 1]
}, {
'id': 3,
't': 2,
'z': 1,
'y': 1,
'x': 2,
'score': 2.0,
'vgg_score': [0, 0, 1]
}, {
'id': 4,
't': 2,
'z': 1,
'y': 1,
'x': 3,
'score': 2.0,
'vgg_score': [0, 0, 1]
}, {
'id': 5,
't': 3,
'z': 1,
'y': 1,
'x': 2,
'score': 2.0,
'vgg_score': [0, 0, 1]
}]
edges = [
{
'source': 1,
'target': 0,
'score': 1.0,
'prediction_distance': 0.0
},
{
'source': 2,
'target': 1,
'score': 1.0,
'prediction_distance': 1.0
},
{
'source': 3,
'target': 1,
'score': 1.0,
'prediction_distance': 1.0
},
{
'source': 4,
'target': 1,
'score': 1.0,
'prediction_distance': 2.0
},
{
'source': 5,
'target': 3,
'score': 1.0,
'prediction_distance': 0.0
},
]
db_name = 'linajea_test_solver'
db_host = 'localhost'
graph_provider = linajea.CandidateDatabase(db_name, db_host)
roi = daisy.Roi((0, 0, 0, 0), (4, 5, 5, 5))
graph = graph_provider[roi]
ps = {
"track_cost": 4.0,
"weight_edge_score": 0.1,
"weight_node_score": -0.1,
"selection_constant": 0.0,
"weight_division": -0.1,
"weight_child": -0.1,
"weight_continuation": -0.1,
"division_constant": 1,
"max_cell_move": 0.0,
"block_size": [5, 100, 100, 100],
"context": [2, 100, 100, 100],
}
parameters = linajea.tracking.TrackingParameters(**ps)
graph.add_nodes_from([(cell['id'], cell) for cell in cells])
graph.add_edges_from([(edge['source'], edge['target'], edge)
for edge in edges])
linajea.tracking.track(graph,
parameters,
frame_key='t',
selected_key='selected',
cell_cycle_key="vgg_score")
selected_edges = []
for u, v, data in graph.edges(data=True):
if data['selected']:
selected_edges.append((u, v))
expected_result = [(1, 0), (3, 1), (5, 3)]
self.assertCountEqual(selected_edges, expected_result)
self.delete_db(db_name, db_host)
def test_solver_multiple_configs(self):
# x
# 3| /-4
# 2| /--3---5
# 1| 0---1
# 0| \--2
# ------------------------------------ t
# 0 1 2 3
cells = [{
'id': 0,
't': 0,
'z': 1,
'y': 1,
'x': 1,
'score': 2.0
}, {
'id': 1,
't': 1,
'z': 1,
'y': 1,
'x': 1,
'score': 2.0
}, {
'id': 2,
't': 2,
'z': 1,
'y': 1,
'x': 0,
'score': 2.0
}, {
'id': 3,
't': 2,
'z': 1,
'y': 1,
'x': 2,
'score': 2.0
}, {
'id': 4,
't': 2,
'z': 1,
'y': 1,
'x': 3,
'score': 2.0
}, {
'id': 5,
't': 3,
'z': 1,
'y': 1,
'x': 2,
'score': 2.0
}]
edges = [
{
'source': 1,
'target': 0,
'score': 1.0,
'prediction_distance': 0.0
},
{
'source': 2,
'target': 1,
'score': 1.0,
'prediction_distance': 1.0
},
{
'source': 3,
'target': 1,
'score': 1.0,
'prediction_distance': 1.0
},
{
'source': 4,
'target': 1,
'score': 1.0,
'prediction_distance': 2.0
},
{
'source': 5,
'target': 3,
'score': 1.0,
'prediction_distance': 0.0
},
]
db_name = 'linajea_test_solver'
db_host = 'localhost'
graph_provider = linajea.CandidateDatabase(db_name, db_host)
roi = daisy.Roi((0, 0, 0, 0), (4, 5, 5, 5))
graph = graph_provider[roi]
ps1 = {
"track_cost": 4.0,
"weight_edge_score": 0.1,
"weight_node_score": -0.1,
"selection_constant": -1.0,
"max_cell_move": 0.0,
"block_size": [5, 100, 100, 100],
"context": [2, 100, 100, 100],
}
ps2 = {
# Making all the values smaller increases the
# relative cost of division
"track_cost": 1.0,
"weight_edge_score": 0.01,
"weight_node_score": -0.01,
"selection_constant": -0.1,
"max_cell_move": 0.0,
"block_size": [5, 100, 100, 100],
"context": [2, 100, 100, 100],
}
parameters = [
linajea.tracking.TrackingParameters(**ps1),
linajea.tracking.TrackingParameters(**ps2)
]
keys = ['selected_1', 'selected_2']
graph.add_nodes_from([(cell['id'], cell) for cell in cells])
graph.add_edges_from([(edge['source'], edge['target'], edge)
for edge in edges])
linajea.tracking.track(graph,
parameters,
frame_key='t',
selected_key=keys)
selected_edges_1 = []
selected_edges_2 = []
for u, v, data in graph.edges(data=True):
if data['selected_1']:
selected_edges_1.append((u, v))
if data['selected_2']:
selected_edges_2.append((u, v))
expected_result_1 = [(1, 0), (2, 1), (3, 1), (5, 3)]
expected_result_2 = [(1, 0), (3, 1), (5, 3)]
self.assertCountEqual(selected_edges_1, expected_result_1)
self.assertCountEqual(selected_edges_2, expected_result_2)
self.delete_db(db_name, db_host)
def extract_edges_in_block(db_name,
db_host,
edge_move_threshold,
block,
use_pv_distance=False):
logger.info("Finding edges in %s, reading from %s", block.write_roi,
block.read_roi)
start = time.time()
graph_provider = linajea.CandidateDatabase(db_name, db_host, mode='r+')
graph = graph_provider[block.read_roi]
if graph.number_of_nodes() == 0:
logger.info("No cells in roi %s. Skipping", block.read_roi)
write_done(block, 'extract_edges', db_name, db_host)
return 0
logger.info("Read %d cells in %.3fs", graph.number_of_nodes(),
time.time() - start)
start = time.time()
t_begin = block.write_roi.get_begin()[0]
t_end = block.write_roi.get_end()[0]
cells_by_t = {
t: [(cell, np.array([data[d] for d in ['z', 'y', 'x']]),
np.array(data['parent_vector']))
for cell, data in graph.nodes(data=True)
if 't' in data and data['t'] == t]
for t in range(t_begin - 1, t_end)
}
for t in range(t_begin, t_end):
pre = t - 1
nex = t
logger.debug(
"Finding edges between cells in frames %d and %d "
"(%d and %d cells)", pre, nex, len(cells_by_t[pre]),
len(cells_by_t[nex]))
if len(cells_by_t[pre]) == 0 or len(cells_by_t[nex]) == 0:
logger.debug("There are no edges between these frames, skipping")
continue
# prepare KD tree for fast lookup of 'pre' cells
logger.debug("Preparing KD tree...")
all_pre_cells = cells_by_t[pre]
kd_data = [cell[1] for cell in all_pre_cells]
pre_kd_tree = cKDTree(kd_data)
for i, nex_cell in enumerate(cells_by_t[nex]):
nex_cell_id = nex_cell[0]
nex_cell_center = nex_cell[1]
nex_parent_center = nex_cell_center + nex_cell[2]
if use_pv_distance:
pre_cells_indices = pre_kd_tree.query_ball_point(
nex_parent_center, edge_move_threshold)
else:
pre_cells_indices = pre_kd_tree.query_ball_point(
nex_cell_center, edge_move_threshold)
pre_cells = [all_pre_cells[i] for i in pre_cells_indices]
logger.debug("Linking to %d cells in previous frame",
len(pre_cells))
if len(pre_cells) == 0:
continue
for pre_cell in pre_cells:
pre_cell_id = pre_cell[0]
pre_cell_center = pre_cell[1]
moved = (pre_cell_center - nex_cell_center)
distance = np.linalg.norm(moved)
prediction_offset = (pre_cell_center - nex_parent_center)
prediction_distance = np.linalg.norm(prediction_offset)
graph.add_edge(nex_cell_id,
pre_cell_id,
distance=distance,
prediction_distance=prediction_distance)
logger.info("Found %d edges", graph.number_of_edges())
logger.info("Extracted edges in %.3fs", time.time() - start)
start = time.time()
graph.write_edges(block.write_roi)
logger.info("Wrote edges in %.3fs", time.time() - start)
write_done(block, 'extract_edges', db_name, db_host)
return 0
def test_solver_basic(self):
# x
# 3| /-4
# 2| /--3---5
# 1| 0---1
# 0| \--2
# ------------------------------------ t
# 0 1 2 3
cells = [
{'id': 0, 't': 0, 'z': 1, 'y': 1, 'x': 1, 'score': 2.0},
{'id': 1, 't': 1, 'z': 1, 'y': 1, 'x': 1, 'score': 2.0},
{'id': 2, 't': 2, 'z': 1, 'y': 1, 'x': 0, 'score': 2.0},
{'id': 3, 't': 2, 'z': 1, 'y': 1, 'x': 2, 'score': 2.0},
{'id': 4, 't': 2, 'z': 1, 'y': 1, 'x': 3, 'score': 2.0},
{'id': 5, 't': 3, 'z': 1, 'y': 1, 'x': 2, 'score': 2.0}
]
edges = [
{'source': 1, 'target': 0, 'score': 1.0, 'prediction_distance': 0.0},
{'source': 2, 'target': 1, 'score': 1.0, 'prediction_distance': 1.0},
{'source': 3, 'target': 1, 'score': 1.0, 'prediction_distance': 1.0},
{'source': 4, 'target': 1, 'score': 1.0, 'prediction_distance': 2.0},
{'source': 5, 'target': 3, 'score': 1.0, 'prediction_distance': 0.0},
]
db_name = 'linajea_test_solver'
db_host = 'localhost'
graph_provider = linajea.CandidateDatabase(
db_name,
db_host)
roi = daisy.Roi((0, 0, 0, 0), (4, 5, 5, 5))
graph = graph_provider[roi]
ps = {
"cost_appear": 2.0,
"cost_disappear": 2.0,
"cost_split": 0,
"weight_prediction_distance_cost": 0.1,
"weight_node_score": 1.0,
"threshold_node_score": 0.0,
"threshold_edge_score": 2.0,
"max_cell_move": 0.0,
"block_size": [5, 100, 100, 100],
"context": [2, 100, 100, 100],
}
parameters = linajea.tracking.NMTrackingParameters(**ps)
graph.add_nodes_from([(cell['id'], cell) for cell in cells])
graph.add_edges_from([(edge['source'], edge['target'], edge)
for edge in edges])
linajea.tracking.nm_track(
graph,
parameters,
frame_key='t',
selected_key='selected')
selected_edges = []
for u, v, data in graph.edges(data=True):
if data['selected']:
selected_edges.append((u, v))
expected_result = [
(1, 0),
(2, 1),
(3, 1),
(5, 3)
]
self.assertCountEqual(selected_edges, expected_result)
self.delete_db(db_name, db_host)
def read_data(self, data):
candidate_db_name = data['db_name']
start_frame, end_frame = data['frames']
matching_threshold = data.get('matching_threshold', 20)
gt_db_name = data['gt_db_name']
assert end_frame > start_frame
roi = Roi((start_frame, 0, 0, 0),
(end_frame - start_frame, 1e10, 1e10, 1e10))
if 'parameters_id' in data:
try:
int(data['parameters_id'])
selected_key = 'selected_' + str(data['parameters_id'])
except:
selected_key = data['parameters_id']
else:
selected_key = None
db = linajea.CandidateDatabase(
candidate_db_name, self.db_host)
db.selected_key = selected_key
gt_db = linajea.CandidateDatabase(gt_db_name, self.db_host)
print("Reading GT cells and edges in %s" % roi)
gt_subgraph = gt_db[roi]
gt_graph = linajea.tracking.TrackGraph(gt_subgraph, frame_key='t')
gt_tracks = list(gt_graph.get_tracks())
print("Found %d GT tracks" % len(gt_tracks))
# tracks_to_xml(gt_cells, gt_tracks, 'linajea_gt.xml')
print("Reading cells and edges in %s" % roi)
subgraph = db.get_selected_graph(roi)
graph = linajea.tracking.TrackGraph(subgraph, frame_key='t')
tracks = list(graph.get_tracks())
print("Found %d tracks" % len(tracks))
if len(graph.nodes) == 0 or len(gt_graph.nodes) == 0:
logger.info("Didn't find gt or reconstruction - returning")
return [], []
m = linajea.evaluation.match_edges(
gt_graph, graph,
matching_threshold=matching_threshold)
(edges_x, edges_y, edge_matches, edge_fps) = m
matched_rec_tracks = []
for track in tracks:
for _, edge_index in edge_matches:
edge = edges_y[edge_index]
if track.has_edge(edge[0], edge[1]):
matched_rec_tracks.append(track)
break
logger.debug("found %d matched rec tracks" % len(matched_rec_tracks))
logger.info("Adding %d gt tracks" % len(gt_tracks))
track_id = 0
cells = []
tracks = []
for track in gt_tracks:
result = self.add_track(track, track_id, group=0)
print(result[0])
if result is None or len(result[0]) == 0:
continue
track_cells, track = result
cells += track_cells
tracks.append(track)
track_id += 1
logger.info("Adding %d matched rec tracks" % len(matched_rec_tracks))
for track in matched_rec_tracks:
result = self.add_track(track, track_id, group=1)
if result is None:
continue
track_cells, track = result
cells += track_cells
tracks.append(track)
track_id += 1
return cells, tracks
def get_node_ids_in_frame(gt_db_name, mongo_url, frame):
graph_provider = linajea.CandidateDatabase(gt_db_name, mongo_url)
roi = daisy.Roi((frame, 0, 0, 0), (1, 10e6, 10e6, 10e6))
nodes = graph_provider.read_nodes(roi)
node_ids = [node['id'] for node in nodes]
return node_ids