def _resolveMergers(self, hypothesesGraph, model):
'''
run merger resolution on the hypotheses graph which contains the current solution
'''
logger.info("Resolving mergers.")
parameters = self.Parameters.value
withTracklets = parameters['withTracklets']
originalGraph = hypothesesGraph.referenceTraxelGraph if withTracklets else hypothesesGraph
resolvedMergersDict = {}
# Enable full graph computation for animal tracking workflow
withFullGraph = False
if 'withAnimalTracking' in parameters and parameters['withAnimalTracking']: # TODO: Setting this parameter outside of the track() function (on AnimalConservationTrackingWorkflow) is not desirable
withFullGraph = True
logger.info("Computing full graph on merger resolver (Only enabled on animal tracking workflow)")
mergerResolver = IlastikMergerResolver(originalGraph, pluginPaths=self.pluginPaths, withFullGraph=withFullGraph)
# Check if graph contains mergers, otherwise skip merger resolving
if not mergerResolver.mergerNum:
logger.info("Graph contains no mergers. Skipping merger resolving.")
else:
# Fit and refine merger nodes using a GMM
# It has to be done per time-step in order to aviod loading the whole video on RAM
traxelIdPerTimestepToUniqueIdMap, uuidToTraxelMap = getMappingsBetweenUUIDsAndTraxels(model)
timesteps = [int(t) for t in traxelIdPerTimestepToUniqueIdMap.keys()]
timesteps.sort()
timeIndex = self.LabelImage.meta.axistags.index('t')
for timestep in timesteps:
roi = [slice(None) for i in range(len(self.LabelImage.meta.shape))]
roi[timeIndex] = slice(timestep, timestep+1)
roi = tuple(roi)
labelImage = self.LabelImage[roi].wait()
# Get coordinates for object IDs in label image. Used by GMM merger fit.
objectIds = vigra.analysis.unique(labelImage[0,...,0])
maxObjectId = max(objectIds)
coordinatesForIds = {}
pool = RequestPool()
for objectId in objectIds:
pool.add(Request(partial(mergerResolver.getCoordinatesForObjectId, coordinatesForIds, labelImage[0, ..., 0], timestep, objectId)))
# Run requests to get object ID coordinates
pool.wait()
# Fit mergers and store fit info in nodes
if coordinatesForIds:
mergerResolver.fitAndRefineNodesForTimestep(coordinatesForIds, maxObjectId, timestep)
# Compute object features, re-run flow solver, update model and result, and get merger dictionary
resolvedMergersDict = mergerResolver.run()
return resolvedMergersDict
def _resolveMergers(self, hypothesesGraph, model):
'''
run merger resolution on the hypotheses graph which contains the current solution
'''
logger.info("Resolving mergers.")
parameters = self.Parameters.value
withTracklets = parameters['withTracklets']
originalGraph = hypothesesGraph.referenceTraxelGraph if withTracklets else hypothesesGraph
resolvedMergersDict = {}
# Enable full graph computation for animal tracking workflow
withFullGraph = False
if 'withAnimalTracking' in parameters and parameters['withAnimalTracking']: # TODO: Setting this parameter outside of the track() function (on AnimalConservationTrackingWorkflow) is not desirable
withFullGraph = True
logger.info("Computing full graph on merger resolver (Only enabled on animal tracking workflow)")
mergerResolver = IlastikMergerResolver(originalGraph, pluginPaths=self.pluginPaths, withFullGraph=withFullGraph)
# Check if graph contains mergers, otherwise skip merger resolving
if not mergerResolver.mergerNum:
logger.info("Graph contains no mergers. Skipping merger resolving.")
else:
# Fit and refine merger nodes using a GMM
# It has to be done per time-step in order to aviod loading the whole video on RAM
traxelIdPerTimestepToUniqueIdMap, uuidToTraxelMap = getMappingsBetweenUUIDsAndTraxels(model)
timesteps = [int(t) for t in traxelIdPerTimestepToUniqueIdMap.keys()]
timesteps.sort()
timeIndex = self.LabelImage.meta.axistags.index('t')
for timestep in timesteps:
roi = [slice(None) for i in range(len(self.LabelImage.meta.shape))]
roi[timeIndex] = slice(timestep, timestep+1)
roi = tuple(roi)
labelImage = self.LabelImage[roi].wait()
# Get coordinates for object IDs in label image. Used by GMM merger fit.
objectIds = vigra.analysis.unique(labelImage[0,...,0])
maxObjectId = max(objectIds)
coordinatesForIds = {}
pool = RequestPool()
for objectId in objectIds:
pool.add(Request(partial(mergerResolver.getCoordinatesForObjectId, coordinatesForIds, labelImage[0, ..., 0], timestep, objectId)))
# Run requests to get object ID coordinates
pool.wait()
# Fit mergers and store fit info in nodes
if coordinatesForIds:
mergerResolver.fitAndRefineNodesForTimestep(coordinatesForIds, maxObjectId, timestep)
# Compute object features, re-run flow solver, update model and result, and get merger dictionary
resolvedMergersDict = mergerResolver.run()
return resolvedMergersDict
def _getEventsVector(self, result, model):
traxelIdPerTimestepToUniqueIdMap, uuidToTraxelMap = getMappingsBetweenUUIDsAndTraxels(model)
timesteps = [t for t in traxelIdPerTimestepToUniqueIdMap.keys()]
mergers, detections, links, divisions = getMergersDetectionsLinksDivisions(result, uuidToTraxelMap)
# Group by timestep for event creation
mergersPerTimestep = getMergersPerTimestep(mergers, timesteps)
linksPerTimestep = getLinksPerTimestep(links, timesteps)
detectionsPerTimestep = getDetectionsPerTimestep(detections, timesteps)
divisionsPerTimestep = getDivisionsPerTimestep(divisions, linksPerTimestep, timesteps)
# Populate events dictionary
events = {}
# Save mergers, links, detections, and divisions
for timestep in traxelIdPerTimestepToUniqueIdMap.keys():
# We need to add an extra column with zeros in order to be backward compatible with the older version
def stackExtraColumnWithZeros(array):
return np.hstack((array, np.zeros((array.shape[0], 1), dtype=array.dtype)))
dis = []
app = []
div = []
mov = []
mer = []
mul = []
dis = np.asarray(dis)
app = np.asarray(app)
div = np.asarray([[k, v[0], v[1]] for k,v in divisionsPerTimestep[timestep].iteritems()])
mov = np.asarray(linksPerTimestep[timestep])
mer = np.asarray([[k,v] for k,v in mergersPerTimestep[timestep].iteritems()])
mul = np.asarray(mul)
events[timestep] = {}
if len(dis) > 0:
events[timestep]['dis'] = dis
if len(app) > 0:
events[timestep]['app'] = app
if len(div) > 0:
events[timestep]['div'] = div
if len(mov) > 0:
events[timestep]['mov'] = mov
if len(mer) > 0:
events[timestep]['mer'] = mer
if len(mul) > 0:
events[timestep]['mul'] = mul
# Write merger results dictionary
resolvedMergersDict = self.ResolvedMergers.value
if resolvedMergersDict:
mergerRes = {}
for idx in mergersPerTimestep[timestep]:
mergerRes[idx] = resolvedMergersDict[int(timestep)][idx]['newIds']
events[timestep]['res'] = mergerRes
else:
logger.info("Resolved Merger Dictionary not available. Please click on the Track button.")
return events
def _getEventsVector(self, result, model):
traxelIdPerTimestepToUniqueIdMap, uuidToTraxelMap = getMappingsBetweenUUIDsAndTraxels(model)
timesteps = [t for t in traxelIdPerTimestepToUniqueIdMap.keys()]
mergers, detections, links, divisions = getMergersDetectionsLinksDivisions(result, uuidToTraxelMap)
# Group by timestep for event creation
mergersPerTimestep = getMergersPerTimestep(mergers, timesteps)
linksPerTimestep = getLinksPerTimestep(links, timesteps)
detectionsPerTimestep = getDetectionsPerTimestep(detections, timesteps)
divisionsPerTimestep = getDivisionsPerTimestep(divisions, linksPerTimestep, timesteps)
# Populate events dictionary
events = {}
# Save mergers, links, detections, and divisions
for timestep in traxelIdPerTimestepToUniqueIdMap.keys():
# We need to add an extra column with zeros in order to be backward compatible with the older version
def stackExtraColumnWithZeros(array):
return np.hstack((array, np.zeros((array.shape[0], 1), dtype=array.dtype)))
dis = []
app = []
div = []
mov = []
mer = []
mul = []
dis = np.asarray(dis)
app = np.asarray(app)
div = np.asarray([[k, v[0], v[1]] for k,v in divisionsPerTimestep[timestep].iteritems()])
mov = np.asarray(linksPerTimestep[timestep])
mer = np.asarray([[k,v] for k,v in mergersPerTimestep[timestep].iteritems()])
mul = np.asarray(mul)
events[timestep] = {}
if len(dis) > 0:
events[timestep]['dis'] = dis
if len(app) > 0:
events[timestep]['app'] = app
if len(div) > 0:
events[timestep]['div'] = div
if len(mov) > 0:
events[timestep]['mov'] = mov
if len(mer) > 0:
events[timestep]['mer'] = mer
if len(mul) > 0:
events[timestep]['mul'] = mul
# Write merger results dictionary
resolvedMergersDict = self.ResolvedMergers.value
if resolvedMergersDict:
mergerRes = {}
for idx in mergersPerTimestep[timestep]:
mergerRes[idx] = resolvedMergersDict[int(timestep)][idx]['newIds']
events[timestep]['res'] = mergerRes
else:
logger.info("Resolved Merger Dictionary not available. Please click on the Track button.")
return events
def test_loading_no_divisions():
model = return_example_model()
result = return_example_result()
# traxel <=> uuid mappings
traxelIdPerTimestepToUniqueIdMap, uuidToTraxelMap = jg.getMappingsBetweenUUIDsAndTraxels(
model)
assert (traxelIdPerTimestepToUniqueIdMap == {
'0': {
'1': 0,
'2': 5
},
'1': {
'1': 4
},
'2': {
'1': 3
},
'3': {
'1': 2,
'2': 1
}
})
assert (uuidToTraxelMap == {
0: [(0, 1)],
1: [(3, 2)],
2: [(3, 1)],
3: [(2, 1)],
4: [(1, 1)],
5: [(0, 2)]
})
# get lists
mergers, detections, links, divisions = jg.getMergersDetectionsLinksDivisions(
result, uuidToTraxelMap)
assert (divisions is None)
assert (mergers == [(2, 1, 2), (1, 1, 2)])
assert (detections == [(0, 1), (3, 2), (3, 1), (2, 1), (1, 1), (0, 2)])
assert (links == [((0, 1), (1, 1)), ((2, 1), (3, 2)), ((2, 1), (3, 1)),
((1, 1), (2, 1)), ((0, 2), (1, 1))])
# events per timestep
timesteps = traxelIdPerTimestepToUniqueIdMap.keys()
mergersPerTimestep = jg.getMergersPerTimestep(mergers, timesteps)
assert (mergersPerTimestep == {'0': {}, '1': {1: 2}, '2': {1: 2}, '3': {}})
detectionsPerTimestep = jg.getDetectionsPerTimestep(detections, timesteps)
assert (detectionsPerTimestep == {
'0': [1, 2],
'1': [1],
'2': [1],
'3': [2, 1]
})
linksPerTimestep = jg.getLinksPerTimestep(links, timesteps)
assert (linksPerTimestep == {
'0': [],
'1': [(1, 1), (2, 1)],
'2': [(1, 1)],
'3': [(1, 2), (1, 1)]
})
# merger links as triplets [("timestep", (sourceId, destId)), (), ...]
mergerLinks = jg.getMergerLinks(linksPerTimestep, mergersPerTimestep,
timesteps)
assert (mergerLinks == [('1', (1, 1)), ('1', (2, 1)), ('3', (1, 2)),
('3', (1, 1)), ('2', (1, 1))])
