def modeContrastive(pathToLabel, pathToEvidence, submissionName):
# get list of submission
submissions = getSubmissions()
if submissionName == 'primary':
reportErrorAndExit(
'Please use a different name for your contrastive submission.')
if not submissions.empty:
nContrastive = len(submissions[submissions.type == 'contrastive'])
if nContrastive > 3:
reportErrorAndExit(
'Please delete one of your existing '
'contrastive submissions first.')
nSameName = len(submissions[submissions.name == submissionName])
if nSameName > 0:
reportErrorAndExit(
'Please choose a different name or '
'delete the existing submission with same name.')
initializeForSubmission()
# load evidence/label files and check their validity
label = loadLabel(pathToLabel)
evidence = loadEvidence(pathToEvidence)
try:
checkSubmission(label, evidence)
except ValueError, e:
reportErrorAndExit(e.message)
def modeCheck(pathToLabel, pathToEvidence):
initializeForSubmission()
label = loadLabel(pathToLabel)
evidence = loadEvidence(pathToEvidence)
try:
checkSubmission(label, evidence)
except ValueError, e:
reportErrorAndExit(e.message)
def loadFiles(shot, reference, label):
shot = loadShot(shot)
label = loadLabel(label)
checkSubmission(shot, label)
reference = loadLabelReference(reference)
return shot, reference, label
def loadFiles(shot, reference, evireference, label, evidence, consensus=None):
shot = loadShot(shot)
label = loadLabel(label)
evidence = loadEvidence(evidence)
# check that labels are only provided for selected shots
labelShots = set(
tuple(s) for _, s in label[['videoID', 'shotNumber']].iterrows())
if not labelShots.issubset(set(shot.index)):
msg = ('Labels should only be computed for provided shots.')
raise ValueError(msg)
# check that evidence is provided for every unique label
labelNames = set(label['personName'].unique())
evidenceNames = set(evidence['personName'].unique())
if labelNames != evidenceNames:
msg = ('There must be exactly one evidence '
'per unique name in label submission.')
raise ValueError(msg)
# check that there is no more than one evidence per label
if len(evidenceNames) != len(evidence):
msg = ('There must be exactly one evidence '
'per unique name in label submission.')
raise ValueError(msg)
# check that evidences are chosen among selected shots
evidenceShots = set(tuple(s) for _, s in evidence[['videoID', 'shotNumber']].iterrows())
if not evidenceShots.issubset(set(shot.index)):
msg = ('Evidences should only be chosen among provided shots.')
raise ValueError(msg)
# only keep labels for shot with consensus
if consensus:
consensus = loadShot(consensus)
mask = label.apply(
lambda x: (x['videoID'], x['shotNumber']) in set(consensus.index),
axis=1)
label = label[mask]
reference = loadLabelReference(reference)
evireference = loadEvidenceReference(evireference)
return reference, evireference, label, evidence
def loadFiles(shot, reference, label, consensus=None):
shot = loadShot(shot)
label = loadLabel(label)
# check that labels are only provided for selected shots
labelShots = set(tuple(s) for _, s in label[["videoID", "shotNumber"]].iterrows())
if not labelShots.issubset(set(shot.index)):
msg = "Labels should only be computed for provided shots."
raise ValueError(msg)
# only keep labels for shot with consensus
if consensus:
consensus = loadShot(consensus)
mask = label.apply(lambda x: (x["videoID"], x["shotNumber"]) in set(consensus.index), axis=1)
label = label[mask]
reference = loadLabelReference(reference)
return reference, label
def modePrimary(pathToLabel, pathToEvidence):
# get list of submission
submissions = getSubmissions()
if not submissions.empty:
nPrimary = len(submissions[submissions.type == 'primary'])
if nPrimary > 0:
reportErrorAndExit(
'Please delete your existing '
'primary submission first.')
initializeForSubmission()
# load evidence/label files and check their validity
label = loadLabel(pathToLabel)
evidence = loadEvidence(pathToEvidence)
try:
checkSubmission(label, evidence)
except ValueError, e:
reportErrorAndExit(e.message)
def loadFiles(shot, reference, label, consensus=None):
shot = loadShot(shot)
label = loadLabel(label)
# check that labels are only provided for selected shots
labelShots = set(
tuple(s) for _, s in label[['videoID', 'shotNumber']].iterrows())
if not labelShots.issubset(set(shot.index)):
msg = ('Labels should only be computed for provided shots.')
raise ValueError(msg)
# only keep labels for shot with consensus
if consensus:
consensus = loadShot(consensus)
mask = label.apply(
lambda x: (x['videoID'], x['shotNumber']) in set(consensus.index),
axis=1)
label = label[mask]
reference = loadLabelReference(reference)
return reference, label