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robot_label_in.py
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robot_label_in.py
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#!/usr/bin/env python
# encoding: utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2015 CNRS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# AUTHORS
# Hervé BREDIN -- http://herve.niderb.fr/
# Johann POIGNANT -- http://johannpoignant.github.io/
"""
MediaEval label in robot
Usage:
robot_label_in [options]
Options:
-h --help Show this screen.
--version Show version.
--debug Show debug information.
--url=URL Camomile server URL
[default: http://api.mediaeval.niderb.fr]
--password=P45sw0Rd Password
--refresh=N Refresh annotation status every N sec
[default: 86400].
--period=N Query label queue every N sec [default: 600].
--limit=N Approximate maximum number of items in
label queue [default: 400].
--skip-empty Put into the queue only shot with hypothesis
--videos=PATH List of video to process
--other=N Number of alternative person names [default: 10]
--log=DIR Path to log directory.
--queue=NAME Label incoming queue [default: mediaeval.label.in]
--no-unknown-consensus Stop looking for consensus when unknown
--queries=list Put into the queue only shot with hypothesis in the list of queries
"""
from common import RobotCamomile, create_logger
from docopt import docopt
from datetime import datetime
from random import sample
emptyAtLaunch = True
ANCHORS = set(["david_pujadas",
"beatrice_schonberg",
"laurent_delahousse",
"francoise_laborde"])
UNKNOWN = '?unknown?'
arguments = docopt(__doc__, version='0.1')
# Camomile API
url = arguments['--url']
password = arguments['--password']
noUnknownConsensus = arguments['--no-unknown-consensus']
# debugging and logging
debug = arguments['--debug']
log = arguments['--log']
logger = create_logger('robot_label_in', path=log, debug=debug)
# how often to refresh annotation status
refresh = int(arguments['--refresh'])
# how often to pick queue length
period = int(arguments['--period'])
# approximate maximum number of items in queue
limit = int(arguments['--limit'])
# put into the queue only shot with hypothesis
skipEmpty = arguments['--skip-empty']
# put into the queue only shot with hypothesis in the list of queries
queries = False
if arguments['--queries']:
queries = set([])
for line in open(arguments['--queries']).read().splitlines():
queries.add(line)
# only annotate those videos
videos = arguments['--videos']
other = int(arguments['--other'])
queueName = arguments['--queue']
robot = RobotCamomile(
url, 'robot_label', password=password,
period=period, logger=logger)
# test corpus
test = robot.getCorpusByName('mediaeval.test')
# layer containing submission shots
submissionShotLayer = robot.getLayerByName(
test, 'mediaeval.submission_shot')
# layer containing every label annotations
allLayer = robot.getLayerByName(
test, 'mediaeval.groundtruth.label.all')
# layer containing consensus label annotations
consensusLayer = robot.getLayerByName(
test, 'mediaeval.groundtruth.label.consensus')
# layer containing "unknown" annotations
unknownLayer = robot.getLayerByName(
test, 'mediaeval.groundtruth.label.unknown')
# mugshot layer
mugshotLayer = robot.getLayerByName(
test, 'mediaeval.groundtruth.evidence.mugshot')
# filled by this script and popped by label annotation front-end
labelInQueue = robot.getQueueByName(queueName)
# load list of media in test corpus
# as {name: id} dictionary
media = {medium.name: medium._id for medium in robot.getMedia(test)}
if videos:
with open(videos, 'r') as f:
videos = [v.strip() for v in f]
media = [media[v] for v in videos if v in media]
else:
media = media.values()
logger.info('load mapping of all existing label layers')
# load mapping of all existing label layers
LAYER_MAPPING = {}
for layer in robot.getLayers(test,
data_type='mediaeval.persondiscovery.label'):
# skip original submission layers
if 'copy' not in layer.description:
continue
# skip deleted submission layers
if 'deleted' in layer.description:
continue
# default to empty mapping
LAYER_MAPPING[layer._id] = layer.description.get('mapping', {})
LOADED = {}
SUBMISSION_SHOTS = {}
SORTED_SUBMISSION_SHOTS = {}
SHOTS = {}
ANNOTATION_HYPOTHESES = {}
def loadOnDemand(medium):
# load list of shots in test corpus
# as {id: details} dictionary
logger.info('loading submission shots')
SUBMISSION_SHOTS[medium] = {}
for shot in robot.getAnnotations(submissionShotLayer, medium=medium):
SUBMISSION_SHOTS[medium][shot._id] = {
'id_medium': shot.id_medium,
'start': shot.fragment.segment.start,
'end': shot.fragment.segment.end}
# sort SUBMISSION_SHOTS by medium and chronologically
SORTED_SUBMISSION_SHOTS[medium] = sorted(
SUBMISSION_SHOTS[medium],
key=lambda s: (SUBMISSION_SHOTS[medium][s]['start']))
# subset of submission shots
SHOTS[medium] = set([s for s, d in SUBMISSION_SHOTS[medium].iteritems()
if d['id_medium'] == medium])
logger.info('hypotheses')
# get hypothesis person names
ANNOTATION_HYPOTHESES[medium] = {}
for layer in LAYER_MAPPING:
logger.debug('hypotheses - medium = {medium} / layer = {layer}'.format(
medium=medium, layer=layer))
ANNOTATION_HYPOTHESES[medium][layer] = {}
for shot in SUBMISSION_SHOTS[medium]:
ANNOTATION_HYPOTHESES[medium][layer][shot] = set([])
for a in robot.getAnnotations(layer=layer, medium=medium):
ANNOTATION_HYPOTHESES[medium][layer][a.fragment].add(a.data.person_name)
LOADED[medium] = True
def update(medium):
# load on demand (done only once)
if medium not in LOADED:
loadOnDemand(medium)
logger.info('refresh - loading consensus shots')
# shots for which a consensus has already been reached
shotWithConsensus = {}
for annotation in robot.getAnnotations(consensusLayer, medium=medium):
data = annotation.get('data', {})
# HACK - data might be u'' - I don't know why
if not data:
data = {}
shotWithConsensus[annotation.fragment] = set(data)
# shots for which a unknown has been annotated
shotWithUnknown = set([])
if noUnknownConsensus:
for annotation in robot.getAnnotations(unknownLayer, medium=medium):
shotWithUnknown.add(annotation.fragment)
# shots for which we are still missing annotations
# in order to reach a consensus
remainingShots = SHOTS[medium]
remainingShots -= set(shotWithConsensus.keys())
remainingShots -= shotWithUnknown
# update layer mapping
for layer in LAYER_MAPPING:
LAYER_MAPPING[layer] = robot.getLayer(layer).description.get('mapping', {})
logger.info('refresh - loading person names with mugshot')
# set of person name with a mugshot
personNameWithMugshot = set(
robot.getLayer(mugshotLayer).description.mugshots.keys())
logger.info('refresh - building hypothesis for remaining shots')
hypotheses = {}
annotators = {}
allAnnotations = robot.getAnnotations(layer=allLayer, medium=medium)
for shot in remainingShots:
hypotheses[shot] = set([])
annotators[shot] = set([])
for annotation in [a for a in allAnnotations if a.fragment == shot]:
# get set of all hypothesis already annotated
hypotheses[shot].update(
set(annotation.data.get('known', {}).keys()))
# get set of users who already annotated this shot
annotators[shot] = set([annotation.data.annotator])
skip_shot = False
for layer, mapping in LAYER_MAPPING.iteritems():
for hypothesizedPersonName in ANNOTATION_HYPOTHESES[medium][layer][shot]:
# find how the hypothesized name was mapped
correctedPersonName = mapping.get(hypothesizedPersonName, None)
# in case it has not been checked yet
# skip this shot entirely
if correctedPersonName is None:
logger.info(
'refresh - skipping shot {shot} because evidence '
'for {name} has not been checked yet.'.format(
shot=shot, name=hypothesizedPersonName))
skip_shot = True
break
# in case it has been checked but found to NOT be an evidence
# don't add this hypothesis
if correctedPersonName is False:
continue
# in case the mapped person name does not have a mugshot yet
# skip this shot entirely
if correctedPersonName not in personNameWithMugshot:
logger.info(
'refresh - skipping shot {shot} because no mugshot '
'is available for {name}.'.format(
shot=shot, name=correctedPersonName))
skip_shot = True
break
hypotheses[shot].add(correctedPersonName)
if skip_shot:
break
if skip_shot:
del hypotheses[shot]
del annotators[shot]
logger.info('refresh - gathering alternative hypotheses')
others = {}
for shot in hypotheses:
others[shot] = set([])
i = SORTED_SUBMISSION_SHOTS[medium].index(shot)
n = len(SORTED_SUBMISSION_SHOTS[medium])
nearShots = SORTED_SUBMISSION_SHOTS[medium][max(i - other, 0):
min(i + other, n)]
others[shot].update(ANCHORS)
for nearShot in nearShots:
others[shot].update(
shotWithConsensus.get(nearShot, set([])))
others[shot].update(
hypotheses.get(nearShot, set([])))
# remove person already in hypotheses
others[shot] -= hypotheses[shot]
# remove ?unknown?
others[shot] -= set([UNKNOWN])
return hypotheses, others, annotators
while True:
# randomize media order
for medium in sample(media, len(media)):
# only refresh this medium
now = datetime.now()
hypotheses, others, annotators = update(medium)
t = datetime.now()
logger.info('refresh - medium {medium} finished in {seconds:d} seconds'.format(
medium=medium, seconds=int((t - now).total_seconds())))
for shot in SORTED_SUBMISSION_SHOTS[medium]:
# shot was skipped
if shot not in hypotheses:
continue
# do not annotate a shot if there is no hypothesis
if hypotheses[shot] == set([]) and skipEmpty:
continue
if queries and not hypotheses[shot].intersection(queries):
continue
item = {}
item['id_shot'] = shot
item['id_medium'] = SUBMISSION_SHOTS[medium][shot]['id_medium']
item['start'] = SUBMISSION_SHOTS[medium][shot]['start'] + 0.5
item['end'] = SUBMISSION_SHOTS[medium][shot]['end'] - 0.5
item['hypothesis'] = list(hypotheses[shot])
item['others'] = list(others[shot])
item['annotated_by'] = list(annotators[shot])
logger.debug('new annotation for shot {shot}'.format(
shot=shot))
# empty queue at launch time
if emptyAtLaunch:
robot.updateQueue(labelInQueue, elements=[])
emptyAtLaunch = False
robot.enqueue_fair(labelInQueue, item, limit=limit)