def OnTrack(self, event):
pipeline = self.dsviewer.pipeline
if self.tracker == None:
featNames = [s.strip() for s in self.features.split(',')]
def _calcWeights(s):
fw = s.split('*')
if len(fw) == 2:
return float(fw[0]), fw[1]
else:
return 1.0, s
weightedFeats = [_calcWeights(s) for s in featNames]
feats = np.vstack([w * pipeline[fn] for w, fn in weightedFeats])
self.tracker = tracking.Tracker(pipeline['t'], feats)
self.tracker.pNew = self.pNew
self.tracker.r0 = self.r0
self.tracker.linkageCuttoffProb = self.pLinkCutoff
for i in range(1, self.dsviewer.image.data.shape[2]):
L = self.tracker.calcLinkages(i, i - 1)
self.tracker.updateTrack(i, L)
pipeline.selectedDataSource.clumps = self.tracker.clumpIndex
pipeline.selectedDataSource.setMapping('clumpIndex', 'clumps')
clumpSizes = np.zeros_like(self.tracker.clumpIndex)
for i in set(self.tracker.clumpIndex):
ind = (self.tracker.clumpIndex == i)
clumpSizes[ind] = ind.sum()
pipeline.selectedDataSource.clumpSizes = clumpSizes
pipeline.selectedDataSource.setMapping('clumpSize', 'clumpSizes')
def Track(self, objects, newTracker=False):
"""Track objects based on a given set of feature vectors"""
from PYME.Analysis.Tracking import trackUtils
if (self._tracker is None) or not (len(self._tracker.t) == len(
objects['t'])) or newTracker:
featNames = [s.strip() for s in self.features.split(',')]
def _calcWeights(s):
fw = s.split('*')
if len(fw) == 2:
return float(fw[0]), fw[1]
else:
return 1.0, s
weightedFeats = [_calcWeights(s) for s in featNames]
feats = np.vstack(
[w * np.array(objects[fn]) for w, fn in weightedFeats])
self._tracker = tracking.Tracker(
np.array(objects['t']).astype('i'), feats)
self._tracker.pNew = self.pNew
self._tracker.r0 = self.r0
self._tracker.linkageCuttoffProb = self.pLinkCutoff
for i in range(1, (int(objects['t'].max()) + 1)):
L = self._tracker.calcLinkages(i, i - 1)
self._tracker.updateTrack(i, L)
clumpSizes = np.zeros_like(self._tracker.clumpIndex)
for i in set(self._tracker.clumpIndex):
ind = (self._tracker.clumpIndex == i)
clumpSizes[ind] = ind.sum()
trackVelocities = trackUtils.calcTrackVelocity(
objects['x'], objects['y'], self._tracker.clumpIndex, objects['t'])
clumpInfo = {
'clumpIndex': self._tracker.clumpIndex,
'clumpSize': clumpSizes,
'trackVelocity': trackVelocities
}
pipe = {}
pipe.update(clumpInfo)
pipe.update(objects)
# pipe = pd.DataFrame(pipe)
from PYME.IO.tabular import DictSource
pipe = DictSource(pipe)
if 'mdh' in dir(objects):
#propagate metadata
pipe.mdh = objects.mdh
clumps = trackUtils.ClumpManager(pipe)
if self.maxParticleSize > 0 and 'area' in clumps[0].keys():
clumps = [
c for c in clumps.all
if (c.nEvents > self.minTrackLength) and (
c.featuremean['area'] < self.maxParticleSize)
]
else:
clumps = [
c for c in clumps.all if (c.nEvents > self.minTrackLength)
]
# clumpInfo = pd.DataFrame(clumpInfo)
clumpInfo = DictSource(clumpInfo)
if 'mdh' in dir(objects):
#propagate metadata
clumpInfo.mdh = objects.mdh
return clumpInfo, clumps