3j4dZddlmZddlmZddlmZdZGddZ d d Z d Z e d k(rddlZejyy) z This module takes two XML files and displays the number of measures that differ between the two before and after running the combined correction models ) annotations) correctors) converterFceZdZdZddZedZejdZdZdZ dZ d Z d Z d Z d Zd ZdZy)OmrGroundTruthPairz Object for making comparisons between an OMR score and the GroundTruth Takes in a path to the OMR and a path to the groundTruth (or a pair of music21.stream.Score objects). See below for examples. Nc@d|_d|_t|dr|j|_||_n||_d|_|j |_t|dr|j|_||_ n||_d|_ |j|_ y)NfilePath) _overriddenDebugnumberOfDifferenceshasattrr omrPath omrM21Score getOmrScoreomrScore groundPathgroundM21ScoregetGroundScore groundScore)selfomrgrounds C/DATA/.local/lib/python3.12/site-packages/music21/omr/evaluators.py__init__zOmrGroundTruthPair.__init__"s $#' 3 #<|jtS|jS)z_ Returns either the debug value set for this evaluator, or globalDebug )r globalDebugrs rdebugzOmrGroundTruthPair.debug7s"  ( (( (rc||_y)N)r )rnewDebugs rrzOmrGroundTruthPair.debugBs (rcX|j|_|j|_y)z$ Parse both scores. N)rrrrrs rparseAllzOmrGroundTruthPair.parseAllFs%((* ..0rcl|jj|jjy)z3 store the Hashes for both scores. N)r getAllHashesrrs rhashAllzOmrGroundTruthPair.hashAllMs& ""$ %%'rc|jr td|js$tj|j |_t j|jS)a Returns a ScoreCorrector object of the OMR score. does not store it anywhere >>> omrPath = omr.correctors.K525omrShortPath >>> ground = omr.correctors.K525groundTruthShortPath >>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground) >>> ssOMR = omrGTP.getOmrScore() >>> ssOMR zparsing OMR score)rprintrrparser rScoreCorrectorrs rrzOmrGroundTruthPair.getOmrScoreTsI :: % &(t||>> omrPath = omr.correctors.K525omrShortPath >>> ground = omr.correctors.K525groundTruthShortPath >>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground) >>> ssGT = omrGTP.getGroundScore() >>> ssGT zparsing Ground Truth score)rr'rrr(rrr)rs rrz!OmrGroundTruthPair.getGroundScoregsI :: . /"""+//$//"BD (()<)<==rc||k(ryy)z\ define the substitution cost for x and y (2 if x and y are unequal else 0) r)rxys r substCostzOmrGroundTruthPair.substCosts 6rcy)z; define the insertion cost for x and y (1) r-rr.s r insertCostzOmrGroundTruthPair.insertCostrcy)z: define the deletion cost for x and y (1) r2r-r3s r deleteCostzOmrGroundTruthPair.deleteCostr5rc t|}t|}t|dzDcgc]}t|dzDcgc]}dc} }}}td|dzD]+}||dz d|j||dz z||d<-td|dzD]+}|d|dz |j||dz z|d|<-td|dzD]l}td|dzD]X}t ||dz |dz|||dz dz||dz |dz |j ||dz ||dz z|||<Zn|||Scc}wcc}}w)zS Computes the min edit distance from target to source. Figure 3.25 r2r)lenranger4r7minr0)rtargetsourcenmjidistances r minEditDistzOmrGroundTruthPair.minEditDists K K7>> omrPath = omr.correctors.K525omrShortPath >>> ground = omr.correctors.K525groundTruthShortPath >>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground) >>> differences = omrGTP.getDifferences() >>> differences 32 r)r rr$rr:r9rC)romrListgtListpartNumomrPartgtPart measureErrorss rgetDifferencesz!OmrGroundTruthPair.getDifferencess$% --,,.!!..0S\*Gg&GG_F ,,Wf=M  $ $ 5 $ + '''r)NN)__name__ __module__ __qualname____doc__rpropertyrsetterr"r%rrr0r4r7rCrKr-rrrrsf1*)) \\))1(;&>J  .(rrNc|t}d}t||}|r td||j}n|}|r td||j}|} |r tdg} d} d} |dura| j |} | j }|r/td|td | j |j| jntt| j D]u}| j |} | j }| t|z } | j}| j|| t| j |jz } w|r8td td td | td td| j}|r8td td td|td td| j| |}|rtdt||j}|r>td|td| td| | jj||| | d}|S)a Get a dictionary showing the efficacy of the omr.correctors.ScoreCorrector on an OMR Score by comparing it to the GroundTruth. Set debug to True to see a lot of intermediary steps. >>> omrFilePath = omr.correctors.K525omrShortPath >>> groundTruthFilePath = omr.correctors.K525groundTruthShortPath >>> returnDict = omr.evaluators.evaluateCorrectingModel(omrFilePath, groundTruthFilePath) >>> for name in sorted(list(returnDict.keys())): ... (name, returnDict[name]) ('newEditDistance', 20) ('numberOfFlaggedMeasures', 13) ('originalEditDistance', 32) ('totalNumberOfMeasures', 84) r2)rrzgetting differenceszOriginal edit distancez2Running Horizontal Model (Prior-based-on-distance)rTzIncorrect measure indices:z Hashed notes:z+for each entry in the array below, we have zt[flagged measure part, flagged measure index, source measure part, source measure index, source measure probability]zHORIZONTAL CORRECTING ARRAYz"**********************************z-Running Vertical Model (Prior-based-on-Parts)zVERTICAL CORRECTING MEASURESz]Finding best from Horizontal and Vertical and replacing flagged measures with source measuresz4done replacing flagged measures with source measuresznew edit distancez%number of flagged measures originallyztotal number of measures)originalEditDistancenewEditDistancenumberOfFlaggedMeasurestotalNumberOfMeasures)rrr'rKr singlePartsgetIncorrectMeasureIndices hashedNotesrunHorizontalCorrectionModelr:r9appendrunVerticalCorrectionModelgenerateCorrectedScorescoreshow)r groundTruthPathroriginalDifferences runOnePartpnomrGTPr myOmrScorescorrectingArrayHorAllPartnumberOfIncorrectMeasuresnumberOfTotalMeasures scorePartincorrectMeasureIndicestempPNcorrectingArrayHorOnePartcorrectingArrayVertAllPart priorScorenewNumberOfDifferences returnDicts revaluateCorrectingModelrrsm& } B GO DF  #$"$3351  &(;<JA  BC " !TMM"% "+"F"F"H  .0G H /1==#4#@#@ A..0C ./F f-I&/&J&J&L # %-D)E E %(1(N(N(P % % , ,-F G !Sv)>)J)J%K K ! 0  ;< D E +-FG 23 =>!"!=!=!?  ;< E F ,.HI 23 0 1))*CE_`J  DE j#224  !#9: 57PQ (*?@  *=%;-F+@BJ rctj|}|j}d}d}|j}t |D]\}}||j }t |D]q\} } | |vr |dz }d} t |D]\} } | | k(r | | k(sd} n| dur0t t|D]}||k(r ||| }|| k(sd} n| dusm|dz }s||fS)a Essentially it's the ratio of amount of rhythmic similarity within a piece, which gives an upper bound on what the omr.corrector.prior measure should be able to achieve for the flagged measures. If a piece has low rhythmic similarity in general, then there's no way for a correct match to be found within the unflagged measures in the piece. Returns a tuple of the total number of NON-flagged measures and the total number of those measures that have a rhythmic match. Takes in a stream.Score. >>> c = converter.parse(omr.correctors.K525omrShortPath) # first 21 measures >>> totalUnflagged, totalUnflaggedWithMatches = omr.evaluators.autoCorrelationBestMeasure(c) >>> (totalUnflagged, totalUnflaggedWithMatches) (71, 64) >>> print( float(totalUnflaggedWithMatches) / totalUnflagged ) 0.901... Schoenberg has low autoCorrelation. >>> c = corpus.parse('schoenberg/opus19/movement6') >>> totalUnflagged, totalUnflaggedWithMatches = omr.evaluators.autoCorrelationBestMeasure(c) >>> (totalUnflagged, totalUnflaggedWithMatches) (18, 6) >>> print( float(totalUnflaggedWithMatches) / totalUnflagged ) 0.333... rr2FT)rr)r$rW enumeraterXr:r9) inputScoress allHashes totalMeasures totalMatchesrWpNum pHashArrayincorrectIndicesrAmHashmatchr@nHash otherPNum otherHashs rautoCorrelationBestMeasurer7s:  " ": .B!IML..K%i0j&t,GGI!*-HAu$$ Q ME&j156E> E 2~!&s;'7!8I D( )) 4Q 7I E) $ "9}! 5.1: < ((r__main__)NNF) rO __future__r music21.omrrmusic21rrrrrrrLmainTestr-rrrs^#" w(w(t=AAFcLB)J zGr