def main(doTubeFormat, inFileName, outTrajIDfileName, featPath, nrTrajThresh4Tube=0):
tube2trajIDs.check()
outPath = os.path.dirname(outTrajIDfileName)
if not os.path.exists( outPath ):
os.makedirs(outPath)
# read trajectory positions
print '\tGet trajectory positions;',
geoFeat = denseTraj.getFeatFromFileByName(featPath, 'geo')
# if proposals are stored as Tubes already, read them
if doTubeFormat:
tubeProposals = TubeList()
tubeProposals.readHDF5(inFileName)
# initialize with a single name
inHDF5fileClusts = ['tubes']
else:
print '\tGet video dimensions;',
vidInfo = denseTraj.getFeatFromFileByName(featPath, 'vidinfo')
xmax = vidInfo[1]
ymax = vidInfo[2]
# input cluster file
inHDF5fileClusts = h5py.File( inFileName, 'r')
# go over all features
totNrProposals = 0
for featName in inHDF5fileClusts:
if not doTubeFormat:
# get the clustered proposals for this feature type in SLINK pointer-representation
dset = inHDF5fileClusts[featName]
mergedTracks = dset[()]
# convert pointer-representation to tube proposals
if len(mergedTracks.shape) > 1 and mergedTracks.shape[0] > 1:
tubeProposals = denseTraj.createProposals(mergedTracks, geoFeat, nrTrajThresh4Tube, xmax, ymax)
nrProposals = len(tubeProposals)
print '\tFeature: "%s", number of proposals: %d;' % (featName,nrProposals)
# if ok
if nrProposals > 0:
# write as traj IDs
if totNrProposals==0:
outHDF5file = h5py.File( outTrajIDfileName, 'w', compression="gzip", compression_opts=9)
for j in range(nrProposals):
trajIDs = tubeProposals[j].tube2trajIDs(geoFeat)
outHDF5file.create_dataset(str(totNrProposals), data=trajIDs)
totNrProposals += 1
print '\tWrite trajectory IDs to', outTrajIDfileName
outHDF5file.close()
def main(doTubeFormat, inFileName, outIoUfile, gtPath, nrTrajThresh4Tube=-1, featPath=''):
# check if cython is used
tubeIoU.check()
# read ground truth tubes
print '\tRead ground truth;',
gtTubes = TubeList()
gtTubes.readHDF5(gtPath)
outPath = os.path.dirname(outIoUfile)
if not os.path.exists( outPath ):
os.makedirs(outPath)
# if proposals are stored as Tubes already, read them
if doTubeFormat:
tubeProposals = TubeList()
tubeProposals.readHDF5(inFileName)
# initialize with a single name
inHDF5fileClusts = ['tubes']
else:
# otherwise, read trajectory positions and vidinfo to generate proposals later
print '\tGet trajectory positions;',
geoFeat = denseTraj.getFeatFromFileByName(featPath, 'geo')
print '\tGet video dimensions;',
vidInfo = denseTraj.getFeatFromFileByName(featPath, 'vidinfo')
xmax = vidInfo[1]
ymax = vidInfo[2]
# input cluster file
inHDF5fileClusts = h5py.File( inFileName, 'r')
aboMax = []
# go over all features
for featName in inHDF5fileClusts:
if not doTubeFormat:
# get the clustered proposals for this feature type in SLINK pointer-representation
dset = inHDF5fileClusts[featName]
mergedTracks = dset[()]
# convert pointer-representation to tube proposals
if len(mergedTracks.shape) > 1 and mergedTracks.shape[0] > 1:
tubeProposals = denseTraj.createProposals(mergedTracks, geoFeat, nrTrajThresh4Tube, xmax, ymax)
nrProposals = len(tubeProposals)
print '\tFeature: "%s", number of proposals: %d;' % (featName,nrProposals),
# if ok
if nrProposals > 0:
# get Intersection over Union scores
aboMatCur = tubeProposals.computeTubeOverlap(gtTubes)
# if first feature, write to new file otherwise append to existing file
if aboMax == []:
fileOut = open(outIoUfile, 'w')
else:
fileOut = open(outIoUfile, 'a')
# write scores
for prop in range(aboMatCur.shape[0]):
for score in aboMatCur[prop,:]:
fileOut.write('%f ' % score)
fileOut.write('\n')
fileOut.close()
# keep track of the maximum score
curMax = np.max(aboMatCur, axis=0)
if aboMax == []:
aboMax = curMax
else:
aboMax = np.maximum( aboMax, curMax)
print 'Best IoUs:', curMax, '; Best so far:', aboMax
print '\tWriting IoU scores to', outIoUfile
if not doTubeFormat:
inHDF5fileClusts.close()
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 main(doTubeFormat,
inFileName,
outIoUfile,
gtPath,
nrTrajThresh4Tube=-1,
featPath=''):
# check if cython is used
tubeIoU.check()
# read ground truth tubes
print '\tRead ground truth;',
gtTubes = TubeList()
gtTubes.readHDF5(gtPath)
outPath = os.path.dirname(outIoUfile)
if not os.path.exists(outPath):
os.makedirs(outPath)
# if proposals are stored as Tubes already, read them
if doTubeFormat:
tubeProposals = TubeList()
tubeProposals.readHDF5(inFileName)
# initialize with a single name
inHDF5fileClusts = ['tubes']
else:
# otherwise, read trajectory positions and vidinfo to generate proposals later
print '\tGet trajectory positions;',
geoFeat = denseTraj.getFeatFromFileByName(featPath, 'geo')
print '\tGet video dimensions;',
vidInfo = denseTraj.getFeatFromFileByName(featPath, 'vidinfo')
xmax = vidInfo[1]
ymax = vidInfo[2]
# input cluster file
inHDF5fileClusts = h5py.File(inFileName, 'r')
aboMax = []
# go over all features
for featName in inHDF5fileClusts:
if not doTubeFormat:
# get the clustered proposals for this feature type in SLINK pointer-representation
dset = inHDF5fileClusts[featName]
mergedTracks = dset[()]
# convert pointer-representation to tube proposals
if len(mergedTracks.shape) > 1 and mergedTracks.shape[0] > 1:
tubeProposals = denseTraj.createProposals(
mergedTracks, geoFeat, nrTrajThresh4Tube, xmax, ymax)
nrProposals = len(tubeProposals)
print '\tFeature: "%s", number of proposals: %d;' % (featName,
nrProposals),
# if ok
if nrProposals > 0:
# get Intersection over Union scores
aboMatCur = tubeProposals.computeTubeOverlap(gtTubes)
# if first feature, write to new file otherwise append to existing file
if aboMax == []:
fileOut = open(outIoUfile, 'w')
else:
fileOut = open(outIoUfile, 'a')
# write scores
for prop in range(aboMatCur.shape[0]):
for score in aboMatCur[prop, :]:
fileOut.write('%f ' % score)
fileOut.write('\n')
fileOut.close()
# keep track of the maximum score
curMax = np.max(aboMatCur, axis=0)
if aboMax == []:
aboMax = curMax
else:
aboMax = np.maximum(aboMax, curMax)
print 'Best IoUs:', curMax, '; Best so far:', aboMax
print '\tWriting IoU scores to', outIoUfile
if not doTubeFormat:
inHDF5fileClusts.close()
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 main(doTubeFormat,
inFileName,
outTrajIDfileName,
featPath,
nrTrajThresh4Tube=0):
tube2trajIDs.check()
outPath = os.path.dirname(outTrajIDfileName)
if not os.path.exists(outPath):
os.makedirs(outPath)
# read trajectory positions
print '\tGet trajectory positions;',
geoFeat = denseTraj.getFeatFromFileByName(featPath, 'geo')
# if proposals are stored as Tubes already, read them
if doTubeFormat:
tubeProposals = TubeList()
tubeProposals.readHDF5(inFileName)
# initialize with a single name
inHDF5fileClusts = ['tubes']
else:
print '\tGet video dimensions;',
vidInfo = denseTraj.getFeatFromFileByName(featPath, 'vidinfo')
xmax = vidInfo[1]
ymax = vidInfo[2]
# input cluster file
inHDF5fileClusts = h5py.File(inFileName, 'r')
# go over all features
totNrProposals = 0
for featName in inHDF5fileClusts:
if not doTubeFormat:
# get the clustered proposals for this feature type in SLINK pointer-representation
dset = inHDF5fileClusts[featName]
mergedTracks = dset[()]
# convert pointer-representation to tube proposals
if len(mergedTracks.shape) > 1 and mergedTracks.shape[0] > 1:
tubeProposals = denseTraj.createProposals(
mergedTracks, geoFeat, nrTrajThresh4Tube, xmax, ymax)
nrProposals = len(tubeProposals)
print '\tFeature: "%s", number of proposals: %d;' % (featName,
nrProposals)
# if ok
if nrProposals > 0:
# write as traj IDs
if totNrProposals == 0:
outHDF5file = h5py.File(outTrajIDfileName,
'w',
compression="gzip",
compression_opts=9)
for j in range(nrProposals):
trajIDs = tubeProposals[j].tube2trajIDs(geoFeat)
outHDF5file.create_dataset(str(totNrProposals), data=trajIDs)
totNrProposals += 1
print '\tWrite trajectory IDs to', outTrajIDfileName
outHDF5file.close()