def computeMOT(self, i):
# create sqlite and cfg file with id i
cfg_name = config_files +str(i)+'.cfg'
sql_name = sqlite_files +str(i)+'.sqlite'
open(cfg_name,'w').close()
config = ConfigObj(cfg_name)
cfg_list.write_config(i ,config)
command = ['cp', 'tracking_only.sqlite', sql_name]
process = subprocess.Popen(command)
process.wait()
command = ['trajextract.py', args.inputVideo, '-o', args.homography, '-t', cfg_name, '-d', sql_name, '--gf']
# suppress output of grouping extraction
devnull = open(os.devnull, 'wb')
process = subprocess.Popen(command, stdout = devnull)
process.wait()
obj = trajstorage.CVsqlite(sql_name)
print "loading", i
obj.loadObjects()
motp, mota, mt, mme, fpt, gt = moving.computeClearMOT(cdb.annotations, obj.objects, args.matchDistance, firstFrame, lastFrame)
if motp is None:
motp = 0
self.lock.acquire()
self.IDlist.put(i)
self.motplist.put(motp)
self.motalist.put(mota)
obj.close()
if args.PrintMOTA:
print("ID: mota:{} motp:{}".format(mota, motp))
self.lock.release()
return mota
def computeMOT(self, i):
obj = trajstorage.CVsqlite(sqlite_files + str(i) + ".sqlite")
obj.loadObjects()
motp, mota, mt, mme, fpt, gt = moving.computeClearMOT(
cdb.annotations, obj.objects, args.matchDistance, firstFrame,
lastFrame)
self.lock.acquire()
self.IDlist.put(i)
self.motalist.put(mota)
obj.close()
if args.PrintMOTA:
print("ID", i, " : ", mota)
self.lock.release()
return mota
def computeMOT(i, lock, printlock, motalist, IDlist):
obj = trajstorage.CVsqlite(sqlite_files + str(i) + ".sqlite")
obj.loadObjects()
motp, mota, mt, mme, fpt, gt = moving.computeClearMOT(
cdb.annotations, obj.objects, args.matchDistance, firstFrame,
lastFrame)
lock.acquire()
IDlist.put(i)
motalist.put(mota)
obj.close()
lock.release()
printlock.acquire()
if args.PrintMOTA:
print("Done ID ----- ", i, "With MOTA:", mota)
else:
print("Done ID ----- ", i)
printlock.release()
if args.maskFilename is not None:
maskObjects = []
from matplotlib.pyplot import imread
mask = imread(args.maskFilename)
if len(mask) > 1:
mask = mask[:,:,0]
for obj in objects:
maskObjects += obj.getObjectsInMask(mask, inv(homography), 2) # TODO add option to keep object if at least one feature in mask
objects = maskObjects
annotations = storage.loadGroundTruthFromSqlite(args.groundTruthDatabaseFilename)
for a in annotations:
a.computeCentroidTrajectory(homography)
if args.display:
motp, mota, mt, mme, fpt, gt, gtMatches, toMatches = moving.computeClearMOT(annotations, objects, args.matchingDistance, args.firstInstant, args.lastInstant, True)
else:
motp, mota, mt, mme, fpt, gt = moving.computeClearMOT(annotations, objects, args.matchingDistance, args.firstInstant, args.lastInstant)
print 'MOTP: {}'.format(motp)
print 'MOTA: {}'.format(mota)
print 'Number of missed objects.frames: {}'.format(mt)
print 'Number of mismatches: {}'.format(mme)
print 'Number of false alarms.frames: {}'.format(fpt)
if args.display:
cvutils.displayTrajectories(args.videoFilename, objects, {}, inv(homography), args.firstInstant, args.lastInstant, annotations = annotations, gtMatches = gtMatches, toMatches = toMatches)#, rescale = args.rescale, nFramesStep = args.nFramesStep, saveAllImages = args.saveAllImages, undistort = (undistort or args.undistort), intrinsicCameraMatrix = intrinsicCameraMatrix, distortionCoefficients = distortionCoefficients, undistortedImageMultiplication = undistortedImageMultiplication)
#print('Ground truth matches')
#print(gtMatches)
#print('Object matches')