def savejob(id, data):
job = session.query(Job).get(id)
for path in job.paths:
session.delete(path)
for pi in job.predicate_instances:
for pa in pi.predicate_annotations:
session.delete(pa)
session.delete(pi)
for s in job.sentences:
for sa in s.annotations:
session.delete(sa)
session.delete(s)
session.commit()
paths = readpaths(data["tracks"])
for path in paths:
job.paths.append(path)
for pi in readpredicates(data["predicates"], paths):
job.predicate_instances.append(pi)
for s in readsentences(data['sentences']):
job.sentences.append(s)
session.add(job)
session.commit()
def markcomplete(hitid, assignmentid, workerid):
"""
Marks a job as complete. Usually this is called right before the
MTurk form is submitted.
"""
hit = session.query(models.HIT).filter(models.HIT.hitid == hitid).one()
hit.markcompleted(workerid, assignmentid)
session.add(hit)
session.commit()
def savedonationstatus(hitid, donation):
"""
Saves the donation statistics
"""
hit = session.query(models.HIT).filter(models.HIT.hitid == hitid).one()
hit.opt2donate = float(donation)
hit.opt2donate = min(max(hit.opt2donate, 0), 1)
session.add(hit)
session.commit()
def makehomographydir(video):
logger.debug("cwd: {0}".format(os.getcwd()))
savedir = os.path.join(HOMOGRAPHY_DIR, video.slug)
absdir = os.path.abspath(savedir)
if not os.path.isdir(absdir):
os.makedirs(absdir)
video.homographylocation = absdir
session.add(video)
session.commit()
return absdir
def saveeventlog(hitid, events):
"""
Records the event log to database.
"""
hit = session.query(models.HIT).filter(models.HIT.hitid == hitid).one()
for timestamp, domain, message in events:
timestamp = datetime.fromtimestamp(int(timestamp) / 1000)
event = EventLog(hit=hit, domain=domain, message=message, timestamp=timestamp)
session.add(event)
session.commit()
def savejob(id, tracks):
job = session.query(Job).get(id)
for path in job.paths:
session.delete(path)
session.commit()
for path in readpaths(tracks):
job.paths.append(path)
session.add(job)
session.commit()
def savehomography(slug, homography):
query = session.query(Video).filter(Video.slug == slug)
if query.count() != 1:
raise ValueError("Invalid video slug")
video = query[0]
savedir = video.homographylocation
if savedir is None:
savedir = makehomographydir(video)
savelocation = os.path.join(savedir, "homography.npy")
np.save(savelocation, np.array(homography))
session.add(video)
session.commit()
def __call__(self, args):
query = session.query(HIT)
query = query.filter(HIT.useful == True)
if args.hit:
query = query.filter(HIT.hitid == args.id)
else:
worker = session.query(Worker).get(args.id)
if not worker:
print "Worker \"{0}\" not found".format(args.id)
return
if not args.no_block:
worker.block("HIT was invalid.")
print "Blocked worker \"{0}\"".format(args.id)
session.add(worker)
query = query.filter(HIT.workerid == args.id)
for hit in query:
replacement = hit.invalidate()
session.add(hit)
print "Invalidated {0}".format(hit.hitid)
if replacement:
session.add(replacement)
if not args.no_publish:
session.commit()
replacement.publish()
session.add(replacement)
print "Respawned with {0}".format(replacement.hitid)
session.commit()
def savejobstats(hitid, timeaccepted, timecompleted, environ):
"""
Saves statistics for a job.
"""
hit = session.query(models.HIT).filter(models.HIT.hitid == hitid).one()
hit.timeaccepted = datetime.fromtimestamp(int(timeaccepted) / 1000)
hit.timecompleted = datetime.fromtimestamp(int(timecompleted) / 1000)
hit.timeonserver = datetime.now()
hit.ipaddress = environ.get("HTTP_X_FORWARDED_FOR", None)
hit.ipaddress = environ.get("REMOTE_ADDR", hit.ipaddress)
session.add(hit)
session.commit()
def __call__(self, args):
session = database.connect()
acceptkeys = []
rejectkeys = []
warnkeys = []
for f in args.accept:
acceptkeys.extend(line.strip() for line in open(f))
for f in args.reject:
rejectkeys.extend(line.strip() for line in open(f))
for f in args.warn:
warnkeys.extend(line.strip() for line in open(f))
try:
query = session.query(HIT)
query = query.filter(HIT.completed == True)
query = query.filter(HIT.compensated == False)
query = query.join(HITGroup)
query = query.filter(HITGroup.offline == False)
if args.limit:
query = query.limit(args.limit)
for hit in query:
if not hit.check():
print "WARNING: {0} failed payment check, ignoring".format(hit.hitid)
continue
try:
self.process(hit, acceptkeys, rejectkeys, warnkeys,
args.validated, args.default)
if hit.compensated:
if hit.accepted:
print "Accepted HIT {0}".format(hit.hitid)
else:
print "Rejected HIT {0}".format(hit.hitid)
session.add(hit)
except CommunicationError as e:
hit.compensated = True
session.add(hit)
print "Error with HIT {0}: {1}".format(hit.hitid, e)
finally:
session.commit()
session.close()
def __call__(self, args, group):
if args.number < 1:
print "error: number must be >= 1"
return
for _ in range(args.number):
job = Job(group = group)
session.add(job)
for activity in args.activities:
activity = Activity(text = activity, job = job)
session.add(activity)
session.commit()
if args.number == 1:
print "Created 1 job."
else:
print "Created {0} jobs.".format(args.number)
def __call__(self, args):
session = database.connect()
try:
query = session.query(HIT)
query = query.join(HITGroup)
query = query.filter(HITGroup.offline == args.offline)
query = query.filter(HIT.ready == True)
if args.disable:
if args.offline:
print "Cannot disable offline HITs."
return
query = query.filter(HIT.published == True)
query = query.filter(HIT.completed == False)
if args.limit > 0:
query = query.limit(args.limit)
for hit in query:
try:
hitid = hit.disable()
print "Disabled {0}".format(hitid)
except Exception as e:
print "Unable to disable HIT {0}!".format(hit.hitid)
print e
session.add(hit)
else:
query = query.filter(HIT.published == False)
if args.limit > 0:
query = query.limit(args.limit)
for hit in query:
if args.offline:
print hit.offlineurl(config.localhost)
else:
hit.publish()
print "Published {0}".format(hit.hitid)
session.add(hit)
session.commit()
finally:
session.commit()
session.close()
def savejob1(id, data):
# data contain comment, orientation, tracks
job = session.query(Job).get(id)
# seperate three parts
#job.actionstart = data[0][0]
#job.actionstop = data[1][0]
#job.orientation = data[2][0]
job.comment = data[3][0]
if job.comment == "null":
job.comment = "NULL"
tracks = data[4]
# delete old path in the database
for path in job.paths:
session.delete(path)
session.commit()
for path in readpaths(tracks):
job.paths.append(path)
session.add(job)
session.commit()
def respawnjob(id):
job = session.query(Job).get(id)
replacement = job.markastraining()
job.worker.verified = True
session.add(job)
session.add(replacement)
session.commit()
replacement.publish()
session.add(replacement)
session.commit()
def __call__(self, args, group):
print "Checking integrity..."
# read first frame to get sizes
path = Video.getframepath(0, args.location)
try:
im = Image.open(path)
except IOError:
print "Cannot read {0}".format(path)
return
width, height = im.size
print "Searching for last frame..."
# search for last frame
toplevel = max(int(x)
for x in os.listdir(args.location))
secondlevel = max(int(x)
for x in os.listdir("{0}/{1}".format(args.location, toplevel)))
maxframes = max(int(os.path.splitext(x)[0])
for x in os.listdir("{0}/{1}/{2}"
.format(args.location, toplevel, secondlevel))) + 1
print "Found {0} frames.".format(maxframes)
# can we read the last frame?
path = Video.getframepath(maxframes - 1, args.location)
try:
im = Image.open(path)
except IOError:
print "Cannot read {0}".format(path)
return
# check last frame sizes
if im.size[0] != width and im.size[1] != height:
print "First frame dimensions differs from last frame"
return
if session.query(Video).filter(Video.slug == args.slug).count():
print "Video {0} already exists!".format(args.slug)
return
if args.train_with:
if args.for_training:
print "A training video cannot require training"
return
print "Looking for training video..."
trainer = session.query(Video)
trainer = trainer.filter(Video.slug == args.train_with)
if not trainer.count():
print ("Training video {0} does not exist!"
.format(args.train_with))
return
trainer = trainer.one()
else:
trainer = None
# create video
video = Video(slug = args.slug,
location = os.path.realpath(args.location),
width = width,
height = height,
totalframes = maxframes,
skip = args.skip,
perobjectbonus = args.per_object_bonus,
completionbonus = args.completion_bonus,
trainwith = trainer,
isfortraining = args.for_training,
blowradius = args.blow_radius)
if args.for_training:
video.trainvalidator = qa.tolerable(args.for_training_overlap,
args.for_training_tolerance,
args.for_training_mistakes)
print "Training validator is {0}".format(video.trainvalidator)
session.add(video)
print "Binding labels and attributes..."
# create labels and attributes
labelcache = {}
attributecache = {}
lastlabel = None
for labeltext in args.labels:
if labeltext[0] == "~":
if lastlabel is None:
print "Cannot assign an attribute without a label!"
return
labeltext = labeltext[1:]
attribute = Attribute(text = labeltext)
session.add(attribute)
lastlabel.attributes.append(attribute)
attributecache[labeltext] = attribute
else:
label = Label(text = labeltext)
session.add(label)
video.labels.append(label)
labelcache[labeltext] = label
lastlabel = label
print "Creating symbolic link..."
symlink = "public/frames/{0}".format(video.slug)
try:
os.remove(symlink)
except:
pass
os.symlink(video.location, symlink)
print "Creating segments..."
# create shots and jobs
if args.for_training:
segment = Segment(video = video)
if args.for_training_start:
segment.start = args.for_training_start
if segment.start < 0:
segment.start = 0
else:
segment.start = 0
if args.for_training_stop:
segment.stop = args.for_training_stop
if segment.stop > video.totalframes - 1:
segment.stop = video.totalframes - 1
else:
segment.stop = video.totalframes - 1
job = Job(segment = segment, group = group, ready = False)
session.add(segment)
session.add(job)
elif args.use_frames:
with open(args.use_frames) as useframes:
for line in useframes:
ustart, ustop = line.split()
ustart, ustop = int(ustart), int(ustop)
validlength = float(ustop - ustart)
numsegments = math.ceil(validlength / args.length)
segmentlength = math.ceil(validlength / numsegments)
for start in range(ustart, ustop, int(segmentlength)):
stop = min(start + segmentlength + args.overlap + 1,
ustop)
segment = Segment(start = start,
stop = stop,
video = video)
job = Job(segment = segment, group = group)
session.add(segment)
session.add(job)
else:
startframe = args.start_frame
stopframe = args.stop_frame
if not stopframe:
stopframe = video.totalframes - 1
for start in range(startframe, stopframe, args.length):
stop = min(start + args.length + args.overlap + 1,
stopframe)
segment = Segment(start = start,
stop = stop,
video = video)
job = Job(segment = segment, group = group)
session.add(segment)
session.add(job)
if args.per_object_bonus:
group.schedules.append(
PerObjectBonus(amount = args.per_object_bonus))
if args.completion_bonus:
group.schedules.append(
CompletionBonus(amount = args.completion_bonus))
session.add(group)
if args.for_training and args.for_training_data:
print ("Loading training ground truth annotations from {0}"
.format(args.for_training_data))
with open(args.for_training_data, "r") as file:
pathcache = {}
for line in file:
(id, xtl, ytl, xbr, ybr,
frame, outside, occluded, generated,
label) = line.split(" ")
if int(generated):
continue
if id not in pathcache:
print "Imported new path {0}".format(id)
label = labelcache[label.strip()[1:-1]]
pathcache[id] = Path(job = job, label = label)
box = Box(path = pathcache[id])
box.xtl = int(xtl)
box.ytl = int(ytl)
box.xbr = int(xbr)
box.ybr = int(ybr)
box.frame = int(frame)
box.outside = int(outside)
box.occluded = int(occluded)
pathcache[id].boxes.append(box)
session.commit()
if args.for_training:
if args.for_training and args.for_training_data:
print "Video and ground truth loaded."
else:
print "Video loaded and ready for ground truth:"
print ""
print "\t{0}".format(job.offlineurl(config.localhost))
print ""
print "Visit this URL to provide training with ground truth."
else:
print "Video loaded and ready for publication."
def savejob(id, tracks):
job = session.query(Job).get(id)
# Update current job
for path in job.paths:
session.delete(path)
session.commit()
for path in readpaths(tracks):
logger.info(path)
job.paths.append(path)
session.add(job)
session.commit()
# Update neigboring segments
video = job.segment.video
prevseg, nextseg = video.getsegmentneighbors(job.segment)
mergesegments = [s for s in [prevseg, job.segment, nextseg] if s is not None]
updatesegments = [s for s in [prevseg, nextseg] if s is not None]
# Create list of merged boxes with given label and userid
labeledboxes = []
for boxes, paths in merge.merge(mergesegments, threshold=0.8):
path = paths[0]
for p in paths:
if p.job.segmentid == job.segmentid:
path = p
break
labeledboxes.append((path.label, path.userid, boxes))
# Remove paths in neighboring segments
for segment in updatesegments:
for path in segment.paths:
session.delete(path)
session.commit()
# Add merged paths to neighboring segments
for label, userid, boxes in labeledboxes:
frames = sorted([box.frame for box in boxes])
for segment in updatesegments:
for job in segment.jobs:
path = Path()
path.label = label
path.userid = userid
addedbox = False
for box in boxes:
if segment.start <= box.frame <= segment.stop:
newbox = Box(path=path)
newbox.frombox(box)
if not box.lost:
addedbox = True
# Some segments and paths might not overlap
if addedbox:
# Add in first frame if it's missing
if (frames[0] < segment.start < frames[-1]
and segment.start not in frames):
newbox = Box(path=path)
newbox.generated = False
newbox.frombox(
[box for box in LinearFill(boxes)
if box.frame == segment.start][0]
)
job.paths.append(path)
session.add(job)
session.commit()
def __call__(self, args):
if args.load:
for data in pickle.load(open(args.load)):
worker = Worker.lookup(data[0])
worker.numsubmitted = data[1]
worker.numacceptances = data[2]
worker.numrejections = data[3]
worker.blocked = data[4]
worker.donatedamount = data[5]
worker.bonusamount = data[6]
worker.verified = data[7]
print "Loaded {0}".format(worker.id)
session.add(worker)
session.commit()
elif args.dump:
data = []
for worker in session.query(Worker):
data.append((worker.id,
worker.numsubmitted,
worker.numacceptances,
worker.numrejections,
worker.blocked,
worker.donatedamount,
worker.bonusamount,
worker.verified))
print "Dumped {0}".format(worker.id)
pickle.dump(data, open(args.dump, "w"))
elif args.block:
worker = Worker.lookup(args.block)
worker.block("Poor quality work.")
session.add(worker)
session.commit()
print "Blocked {0}".format(args.block)
elif args.unblock:
worker = Worker.lookup(args.unblock)
worker.unblock("Continue working.")
session.add(worker)
session.commit()
print "Unblocked {0}".format(args.unblock)
elif args.search:
query = session.query(Worker)
query = query.filter(Worker.id.like(args.search + "%"))
if query.count():
print "Matches:"
for worker in query:
print worker.id
else:
print "No matches."
elif args.summary:
query = session.query(Worker)
query = query.filter(Worker.id == args.summary)
if query.count():
worker = query.one()
print "Submitted: {0}".format(worker.numsubmitted)
print "Accepted: {0}".format(worker.numacceptances)
print "Rejected: {0}".format(worker.numrejections)
print "Bonuses: {0}".format(worker.bonusamount)
print "Donated: {0}".format(worker.donatedamount)
print "Verified: {0}".format(worker.verified)
print "Blocked: {0}".format(worker.blocked)
if args.location:
print "Locations: {0}".format(", ".join(set(x.country for x in worker.locations)))
else:
print "No matches."
else:
workers = session.query(Worker)
workers = workers.order_by(Worker.numacceptances)
for worker in workers:
extra = ""
if worker.blocked:
extra = "BLOCKED"
if args.location:
locs = set(x.country for x in worker.locations)
if locs:
locs = ", ".join(locs)
extra += " " + locs
extra = extra.strip()
data = (worker.id,
worker.numsubmitted,
worker.numacceptances,
worker.numrejections,
extra)
print "{0:<15} {1:>5} jobs {2:>5} acc {3:>5} rej {4}".format(*data)
def __call__(self, args):
video = session.query(Video).filter(Video.slug == args.slug)
if video.count() == 0:
print "Video {0} does not exist!".format(args.slug)
raise SystemExit()
video = video.one()
print "Parsing text data"
data = {}
if args.json:
data = self.getdatajson(args.labelfile)
else:
data = self.getdatatext(args.labelfile, video.totalframes)
scale = args.scale
if args.dimensions or args.original_video or args.original_frame:
print "Computing scale"
if args.original_video:
w, h = ffmpeg.extract(args.original_video).next().size
elif args.original_frame:
w, h = Image.open(args.original_frame).size
else:
w, h = args.dimensions.split("x")
w = float(w)
h = float(h)
s = float(video.width) / w
if s * h > video.height:
s = float(video.height) / h
scale = s
print "Scale = {0}".format(scale)
segmentcount = 1
for segment in video.segments:
print "Segment {0} of {1}".format(segmentcount, len(video.segments))
segmentcount += 1
for job in segment.jobs:
for boxid in data:
label = data[boxid]['label']
boxes = data[boxid]['boxes']
query = session.query(Label).filter(Label.videoid == video.id).filter(Label.text == label)
if query.count() == 0:
continue
label = query.one()
newpath = Path(label=label)
visible = False
for frame, boxdata in boxes.iteritems():
frame = int(frame)
if frame < segment.start or segment.stop <= frame or (frame % video.blowradius != 0):
continue
newbox = Box(path=newpath)
#newbox.xtl = max(boxdata['xtl'], 0)
#newbox.ytl = max(boxdata['ytl'], 0)
#newbox.xbr = max(boxdata['xbr'], 0)
#newbox.ybr = max(boxdata['ybr'], 0)
newbox.xtl = boxdata['xtl']
newbox.ytl = boxdata['ytl']
newbox.xbr = boxdata['xbr']
newbox.ybr = boxdata['ybr']
newbox.occluded = boxdata['occluded']
newbox.outside = boxdata['outside']
newbox.generated = boxdata['generated']
newbox.frame = frame
scalebox = newbox.getbox()
scalebox = scalebox.transform(scale)
newbox.xtl = scalebox.xtl
newbox.ytl = scalebox.ytl
newbox.xbr = scalebox.xbr
newbox.ybr = scalebox.ybr
if not newbox.outside:
visible = True
if visible:
job.paths.append(newpath)
session.add(job)
session.commit()
def __call__(self, args, group):
print "Checking integrity..."
# read first frame to get sizes
path = Video.getframepath(0, args.location)
try:
im = Image.open(path)
except IOError:
print "Cannot read {0}".format(path)
return
width, height = im.size
print "Searching for last frame..."
# search for last frame
toplevel = max(int(x)
for x in os.listdir(args.location))
secondlevel = max(int(x)
for x in os.listdir("{0}/{1}".format(args.location, toplevel)))
maxframes = max(int(os.path.splitext(x)[0])
for x in os.listdir("{0}/{1}/{2}"
.format(args.location, toplevel, secondlevel))) + 1
print "Found {0} frames.".format(maxframes)
# can we read the last frame?
path = Video.getframepath(maxframes - 1, args.location)
try:
im = Image.open(path)
except IOError:
print "Cannot read {0}".format(path)
return
# check last frame sizes
if im.size[0] != width and im.size[1] != height:
print "First frame dimensions differs from last frame"
return
if session.query(Video).filter(Video.slug == args.slug).count():
print "Video {0} already exists!".format(args.slug)
return
if args.train_with:
if args.for_training:
print "A training video cannot require training"
return
print "Looking for training video..."
trainer = session.query(Video)
trainer = trainer.filter(Video.slug == args.train_with)
if not trainer.count():
print ("Training video {0} does not exist!"
.format(args.train_with))
return
trainer = trainer.one()
else:
trainer = None
# create video
video = Video(slug = args.slug,
location = os.path.realpath(args.location),
width = width,
height = height,
totalframes = maxframes,
skip = args.skip,
perobjectbonus = args.per_object_bonus,
completionbonus = args.completion_bonus,
trainwith = trainer,
isfortraining = args.for_training,
blowradius = args.blow_radius,
action = args.action,
pose = args.pose)
if args.for_training:
video.trainvalidator = qa.tolerable(args.for_training_overlap,
args.for_training_tolerance,
args.for_training_mistakes)
print "Training validator is {0}".format(video.trainvalidator)
session.add(video)
print "Binding labels and attributes..."
# create labels and attributes
labelcache = {}
attributecache = {}
lastlabel = None
for labeltext in args.labels:
if labeltext[0] == "~":
if lastlabel is None:
print "Cannot assign an attribute without a label!"
return
labeltext = labeltext[1:]
attribute = Attribute(text = labeltext)
session.add(attribute)
lastlabel.attributes.append(attribute)
attributecache[labeltext] = attribute
else:
label = Label(text = labeltext)
session.add(label)
video.labels.append(label)
labelcache[labeltext] = label
lastlabel = label
print "Creating symbolic link..."
symlink = "public/frames/{0}".format(video.slug)
try:
os.remove(symlink)
except:
pass
os.symlink(video.location, symlink)
print "Creating segments..."
# create shots and jobs
if args.for_training:
segment = Segment(video = video)
if args.for_training_start:
segment.start = args.for_training_start
if segment.start < 0:
segment.start = 0
else:
segment.start = 0
if args.for_training_stop:
segment.stop = args.for_training_stop
if segment.stop > video.totalframes - 1:
segment.stop = video.totalframes - 1
else:
segment.stop = video.totalframes - 1
job = Job(segment = segment, group = group, ready = False)
session.add(segment)
session.add(job)
elif args.use_frames:
with open(args.use_frames) as useframes:
for line in useframes:
ustart, ustop = line.split()
ustart, ustop = int(ustart), int(ustop)
validlength = float(ustop - ustart)
numsegments = math.ceil(validlength / args.length)
segmentlength = math.ceil(validlength / numsegments)
for start in range(ustart, ustop, int(segmentlength)):
stop = min(start + segmentlength + args.overlap + 1,
ustop)
segment = Segment(start = start,
stop = stop,
video = video)
job = Job(segment = segment, group = group)
session.add(segment)
session.add(job)
else:
startframe = args.start_frame
stopframe = args.stop_frame
if not stopframe:
stopframe = video.totalframes - 1
for start in range(startframe, stopframe, args.length):
stop = min(start + args.length + args.overlap + 1,
stopframe)
segment = Segment(start = start,
stop = stop,
video = video)
job = Job(segment = segment, group = group)
session.add(segment)
session.add(job)
if args.per_object_bonus:
group.schedules.append(
PerObjectBonus(amount = args.per_object_bonus))
if args.completion_bonus:
group.schedules.append(
CompletionBonus(amount = args.completion_bonus))
session.add(group)
if args.for_training and args.for_training_data:
print ("Loading training ground truth annotations from {0}"
.format(args.for_training_data))
with open(args.for_training_data, "r") as file:
pathcache = {}
for line in file:
(id, xtl, ytl, xbr, ybr,
frame, outside, occluded, generated,
label) = line.split(" ")
if int(generated):
continue
if id not in pathcache:
print "Imported new path {0}".format(id)
label = labelcache[label.strip()[1:-1]]
pathcache[id] = Path(job = job, label = label)
box = Box(path = pathcache[id])
box.xtl = int(xtl)
box.ytl = int(ytl)
box.xbr = int(xbr)
box.ybr = int(ybr)
box.frame = int(frame)
box.outside = int(outside)
box.occluded = int(occluded)
pathcache[id].boxes.append(box)
session.commit()
if args.for_training:
if args.for_training and args.for_training_data:
print "Video and ground truth loaded."
else:
print "Video loaded and ready for ground truth:"
print ""
print "\t{0}".format(job.offlineurl(config.localhost))
print ""
print "Visit this URL to provide training with ground truth."
else:
print "Video loaded and ready for publication."
def __call__(self, args, group):
print "Checking integrity..."
# read first frame to get sizes
path = Video.getframepath(0, args.location)
try:
im = Image.open(path)
except IOError:
print "Cannot read {0}".format(path)
return
width, height = im.size
print "Searching for last frame..."
# search for last frame
toplevel = max(int(x) for x in os.listdir(args.location))
secondlevel = max(
int(x)
for x in os.listdir("{0}/{1}".format(args.location, toplevel)))
maxframes = max(
int(os.path.splitext(x)[0])
for x in os.listdir("{0}/{1}/{2}".format(args.location, toplevel,
secondlevel))) + 1
print "Found {0} frames.".format(maxframes)
# can we read the last frame?
path = Video.getframepath(maxframes - 1, args.location)
try:
im = Image.open(path)
except IOError:
print "Cannot read {0}".format(path)
return
# check last frame sizes
if im.size[0] != width and im.size[1] != height:
print "First frame dimensions differs from last frame"
return
if session.query(Video).filter(Video.slug == args.slug).count():
print "Video {0} already exists!".format(args.slug)
return
if args.train_with:
if args.for_training:
print "A training video cannot require training"
return
print "Looking for training video..."
trainer = session.query(Video)
trainer = trainer.filter(Video.slug == args.train_with)
if not trainer.count():
print("Training video {0} does not exist!".format(
args.train_with))
return
trainer = trainer.one()
else:
trainer = None
# create video
video = Video(slug=args.slug,
location=os.path.realpath(args.location),
width=width,
height=height,
totalframes=maxframes,
skip=args.skip,
perobjectbonus=args.per_object_bonus,
completionbonus=args.completion_bonus,
trainwith=trainer,
isfortraining=args.for_training,
blowradius=args.blow_radius)
if args.for_training:
video.trainvalidator = qa.tolerable(args.for_training_overlap,
args.for_training_tolerance,
args.for_training_mistakes)
print "Training validator is {0}".format(video.trainvalidator)
session.add(video)
print "Binding labels and attributes..."
# create labels and attributes
labelcache = {}
attributecache = {}
lastlabel = None
for labeltext in args.labels:
if labeltext[0] == "~":
if lastlabel is None:
print "Cannot assign an attribute without a label!"
return
labeltext = labeltext[1:]
attribute = Attribute(text=labeltext)
session.add(attribute)
lastlabel.attributes.append(attribute)
attributecache[labeltext] = attribute
else:
label = Label(text=labeltext)
session.add(label)
video.labels.append(label)
labelcache[labeltext] = label
lastlabel = label
print "Creating symbolic link..."
symlink = "public/frames/{0}".format(video.slug)
try:
os.remove(symlink)
except:
pass
os.symlink(video.location, symlink)
print "Creating segments..."
# create shots and jobs
job_list = []
segment_list = []
if args.for_training:
segment = Segment(video=video)
if args.for_training_start:
segment.start = args.for_training_start
if segment.start < 0:
segment.start = 0
else:
segment.start = 0
if args.for_training_stop:
segment.stop = args.for_training_stop
if segment.stop > video.totalframes - 1:
segment.stop = video.totalframes - 1
else:
segment.stop = video.totalframes - 1
job = Job(segment=segment, group=group, ready=False)
job_list.append(job)
segment_list.append(segment)
session.add(segment)
session.add(job)
elif args.use_frames:
with open(args.use_frames) as useframes:
for line in useframes:
ustart, ustop = line.split()
ustart, ustop = int(ustart), int(ustop)
validlength = float(ustop - ustart)
numsegments = math.ceil(validlength / args.length)
segmentlength = math.ceil(validlength / numsegments)
for start in range(ustart, ustop, int(segmentlength)):
stop = min(start + segmentlength + args.overlap + 1,
ustop)
segment = Segment(start=start, stop=stop, video=video)
job = Job(segment=segment, group=group)
job_list.append(job)
segment_list.append(segment)
session.add(segment)
session.add(job)
else:
startframe = args.start_frame
stopframe = args.stop_frame
if not stopframe:
stopframe = video.totalframes - 1
for start in range(startframe, stopframe, args.length):
stop = min(start + args.length + args.overlap + 1, stopframe)
segment = Segment(start=start, stop=stop, video=video)
job = Job(segment=segment, group=group)
job_list.append(job)
segment_list.append(segment)
session.add(segment)
session.add(job)
if args.per_object_bonus:
group.schedules.append(
PerObjectBonus(amount=args.per_object_bonus))
if args.completion_bonus:
group.schedules.append(
CompletionBonus(amount=args.completion_bonus))
session.add(group)
if args.for_training and args.for_training_data:
print("Loading training ground truth annotations from {0}".format(
args.for_training_data))
with open(args.for_training_data, "r") as file:
pathcache = {}
for line in file:
(id, xtl, ytl, xbr, ybr, frame, outside, occluded,
generated, label) = line.split(" ")
if int(generated):
continue
if id not in pathcache:
print "Imported new path {0}".format(id)
label = labelcache[label.strip()[1:-1]]
pathcache[id] = Path(job=job, label=label)
box = Box(path=pathcache[id])
box.xtl = int(xtl)
box.ytl = int(ytl)
box.xbr = int(xbr)
box.ybr = int(ybr)
box.frame = int(frame)
box.outside = int(outside)
box.occluded = int(occluded)
pathcache[id].boxes.append(box)
session.commit()
# Save the annotated file in the database
if args.annot is not None:
with open(args.annot, 'r') as annot_file:
annotated_tracks = json.load(annot_file)
# Scale annotations if annot-video-height is given as argument
annot_scalar = 1.0
if args.annot_video_height is not None:
annot_scalar = video.height / float(
args.annot_video_height) * 1.5
print('Scale factor: {}'.format(annot_scalar))
# Scale bboxes and convert labels
converted_tracks = []
for a_labels, a_tracks, a_attribs in annotated_tracks:
scaled_a_tracks = {}
keep_tracks = a_tracks.keys()
# Blow-radius for annotations
if args.annot_blow_radius > 0:
keep_tracks = sorted(map(int, keep_tracks))
prev_frame_id = keep_tracks[0]
blown_tracks = [keep_tracks[0]]
for frame_id in keep_tracks:
if frame_id > prev_frame_id + args.annot_blow_radius:
prev_frame_id = frame_id
blown_tracks.append(frame_id)
keep_tracks = map(unicode, blown_tracks)
# Conversion
for track_id, track_data in a_tracks.iteritems():
if track_id in keep_tracks:
scaled_track = [
x * annot_scalar for x in track_data[:4]
]
scaled_track.extend(track_data[4:])
scaled_a_tracks[track_id] = scaled_track
converted_tracks.append(
[labelcache[a_labels].id, scaled_a_tracks, a_attribs])
for j, s in zip(job_list, segment_list):
job_data = []
for a_labels, a_tracks, a_attribs in converted_tracks:
# Sort by track number
sorted_a_tracks = natural_sort(a_tracks.keys())
sorted_a_attribs = natural_sort(a_attribs.keys())
job_tracks = {}
job_attribs = {k.id: {} for k in attributecache.values()}
for track_id in sorted_a_tracks:
track_data = a_tracks[track_id]
track_id = int(track_id)
if s.start <= track_id <= s.stop:
job_tracks[track_id] = track_data
if track_id > s.stop:
break
for track_id in sorted_a_attribs:
attrib_data = a_attribs[track_id]
track_id = int(track_id)
if s.start <= track_id <= s.stop:
for k in job_attribs.keys():
job_attribs[k][track_id] = 0
job_attribs[
attributecache[attrib_data].id][track_id] = 1
if track_id > s.stop:
break
job_data.append([a_labels, job_tracks, job_attribs])
savejob(j.id, job_data)
if args.for_training:
if args.for_training and args.for_training_data:
print "Video and ground truth loaded."
else:
print "Video loaded and ready for ground truth:"
print ""
print "\t{0}".format(job.offlineurl(config.localhost))
print ""
print "Visit this URL to provide training with ground truth."
else:
print "Video loaded and ready for publication."