def getOmrScore(self):
'''
Returns a ScoreCorrector object of the OMR score. does NOT store it anywhere...
>>> omrPath = omr.correctors.K525omrShortPath
>>> ground = omr.correctors.K525groundTruthShortPath
>>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground)
>>> ssOMR = omrGTP.getOmrScore()
>>> ssOMR
<music21.omr.correctors.ScoreCorrector object at 0x...>
'''
if (self.debug is True):
print('parsing OMR score')
if (self.omrM21Score is None):
self.omrM21Score = converter.parse(self.omrPath)
return correctors.ScoreCorrector(self.omrM21Score)
def getGroundScore(self):
'''
Returns a ScoreCorrector object of the Ground truth score
>>> omrPath = omr.correctors.K525omrShortPath
>>> ground = omr.correctors.K525groundTruthShortPath
>>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground)
>>> ssGT = omrGTP.getGroundScore()
>>> ssGT
<music21.omr.correctors.ScoreCorrector object at 0x...>
'''
if self.debug is True:
print('parsing Ground Truth score')
if (self.groundM21Score is None):
self.groundM21Score = converter.parse(self.groundPath)
return correctors.ScoreCorrector(self.groundM21Score)
def autoCorrelationBestMeasure(inputScore):
'''
Essentially it's the ratio of amount of rhythmic similarity within a piece, which
gives an upper bound on what the omr.corrector.prior measure should be able to
achieve for the flagged measures. If a piece has low rhythmic similarity in general, then
there's no way for a correct match to be found within the unflagged measures in the piece.
Returns a tuple of the total number of NON-flagged measures and the total number
of those measures that have a rhythmic match.
Takes in a stream.Score.
>>> c = converter.parse(omr.correctors.K525omrShortPath) # first 21 measures
>>> totalUnflagged, totalUnflaggedWithMatches = omr.evaluators.autoCorrelationBestMeasure(c)
>>> (totalUnflagged, totalUnflaggedWithMatches)
(71, 64)
>>> print( float(totalUnflaggedWithMatches) / totalUnflagged )
0.901...
Schoenberg has low autoCorrelation.
>>> c = corpus.parse('schoenberg/opus19/movement6')
>>> totalUnflagged, totalUnflaggedWithMatches = omr.evaluators.autoCorrelationBestMeasure(c)
>>> (totalUnflagged, totalUnflaggedWithMatches)
(18, 6)
>>> print( float(totalUnflaggedWithMatches) / totalUnflagged )
0.333...
'''
ss = correctors.ScoreCorrector(inputScore)
allHashes = ss.getAllHashes()
totalMeasures = 0
totalMatches = 0
singleParts = ss.singleParts
for pNum, pHashArray in enumerate(allHashes):
incorrectIndices = singleParts[pNum].getIncorrectMeasureIndices()
for i, mHash in enumerate(pHashArray):
if i in incorrectIndices:
continue
totalMeasures += 1
match = False
## horizontal search...
for j, nHash in enumerate(pHashArray):
if i == j:
continue
if mHash == nHash:
match = True
break
## vertical search...
if match is False:
for otherPNum in range(len(singleParts)):
if otherPNum == pNum:
continue
otherHash = allHashes[otherPNum][i]
if otherHash == mHash:
match = True
break
if match is True:
totalMatches += 1
return (totalMeasures, totalMatches)
