def createProposals(mergedTracks, featSpat, cutOffLevel, xmax=np.inf, ymax=np.inf, regionSize=15):
"""create SingleTube proposals from the 'mergedTracks' clusters in trajectory clusters format"""
# how many proposals
nrProposals = mergedTracks.shape[0]
# how many trajectory tracks
nrTracks = featSpat.shape[0]
zeroFound = True
while zeroFound:
# the list of tubes that will be created
tubeLst = TubeList()
# the list of tubes that will be sub-sampled
selectedTubes = TubeList()
# keep track of the nr of trajectories per merge
nrTraj = np.zeros(nrProposals)
nrTrajT1 = np.zeros(nrProposals)
nrTrajT2 = np.zeros(nrProposals)
for p in xrange(nrProposals):
# clusterformat: a merged ID, and trackIDs t1 and t2
[mergedID, t1, t2] = mergedTracks[p][0:3]
if t1 < nrTracks:
# if a trackID is lower than nrTracks it is a trajectory
tube1 = traj2tube(featSpat[t1,0], featSpat[t1,1:], xmax, ymax, regionSize)
nrT1 = 1
else:
# otherwise it is a previously merged proposal
idx = int(t1) - nrTracks
tube1 = tubeLst[idx]
nrT1 = nrTraj[idx]
if t2 < nrTracks:
# if a trackID is lower than nrTracks it is a trajectory
tube2 = traj2tube(featSpat[t2,0], featSpat[t2,1:], xmax, ymax, regionSize)
nrT2 = 1
else:
# otherwise it is a previously merged proposal
idx = int(t2) - nrTracks
tube2 = tubeLst[idx]
nrT2 = nrTraj[idx]
# the nr of trajectories of this merge
nrTraj[p] = nrT1 + nrT2
nrTrajT1[p] = nrT1
nrTrajT2[p] = nrT2
# make a copy of t1 and merge it with t2
newTube = tube1.copy()
newTube.merge(tube2)
tubeLst.append(newTube)
# if to subsample, add to the selected list
if nrT1 >= cutOffLevel and nrT2 >= cutOffLevel:
selectedTubes.append(newTube)
if len(selectedTubes) == 0:
cutOffLevel = cutOffLevel/2
if cutOffLevel <= 0:
return selectedTubes
else:
return selectedTubes
def createProposals(mergedTracks, featSpat, cutOffLevel, xmax=np.inf, ymax=np.inf, regionSize=15):
"""create SingleTube proposals from the 'mergedTracks' clusters in trajectory clusters format"""
# how many proposals
nrProposals = mergedTracks.shape[0]
# how many trajectory tracks
nrTracks = featSpat.shape[0]
zeroFound = True
while zeroFound:
# the list of tubes that will be created
tubeLst = TubeList()
# the list of tubes that will be sub-sampled
selectedTubes = TubeList()
# keep track of the nr of trajectories per merge
nrTraj = np.zeros(nrProposals)
nrTrajT1 = np.zeros(nrProposals)
nrTrajT2 = np.zeros(nrProposals)
for p in xrange(nrProposals):
# clusterformat: a merged ID, and trackIDs t1 and t2
[mergedID, t1, t2] = mergedTracks[p][0:3]
if t1 < nrTracks:
# if a trackID is lower than nrTracks it is a trajectory
tube1 = traj2tube(featSpat[t1, 0], featSpat[t1, 1:], xmax, ymax, regionSize)
nrT1 = 1
else:
# otherwise it is a previously merged proposal
idx = int(t1) - nrTracks
tube1 = tubeLst[idx]
nrT1 = nrTraj[idx]
if t2 < nrTracks:
# if a trackID is lower than nrTracks it is a trajectory
tube2 = traj2tube(featSpat[t2, 0], featSpat[t2, 1:], xmax, ymax, regionSize)
nrT2 = 1
else:
# otherwise it is a previously merged proposal
idx = int(t2) - nrTracks
tube2 = tubeLst[idx]
nrT2 = nrTraj[idx]
# the nr of trajectories of this merge
nrTraj[p] = nrT1 + nrT2
nrTrajT1[p] = nrT1
nrTrajT2[p] = nrT2
# make a copy of t1 and merge it with t2
newTube = tube1.copy()
newTube.merge(tube2)
tubeLst.append(newTube)
# if to subsample, add to the selected list
if nrT1 >= cutOffLevel and nrT2 >= cutOffLevel:
selectedTubes.append(newTube)
if len(selectedTubes) == 0:
cutOffLevel = cutOffLevel / 2
if cutOffLevel <= 0:
return selectedTubes
else:
return selectedTubes
