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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 5 14:02:31 2024
@author: jacquema
Evaluation of the score of the Fake Real Book dataset
"""
import sys
import logging
sys.path.append('/Users/xavriley/Projects/pse/lib')
import pse
import os
from pathlib import Path, PosixPath
from datetime import datetime
import re
from operator import itemgetter, attrgetter
import pandas
import music21 as m21
import PSeval as ps
########################
## ##
## global variables ##
## ##
########################
# path to ASAP dataset
_dataset_root = '/Users/xavriley/Dropbox/PhD/Datasets/FiloBass ISMIR Publication/musicxml/'
# default score file name
_score_suffix = '.xml'
# root of evaluation dir
_eval_root = '../../PSeval'
Path(_eval_root).mkdir(parents=True, exist_ok=True)
# name of dir for evaluation output
_output_dir = 'augASAP'
timestamp = str(datetime.today().strftime('%Y%m%d-%H%M'))
# MuseScore commandline executable
_mscore = '/Applications/MuseScore 4.app/Contents/MacOS/mscore'
#################################
## ##
## extraction of dataset files ##
## ##
#################################
# corpus can be 'leads' or 'piano'
def FiloBassCorpus(corpus):
"""build a list of scores in a subdirectory of FiloBass"""
global _dataset_root
global _score_suffix
dataset_path = Path(_dataset_root)
assert isinstance(dataset_path, PosixPath)
if not os.path.exists(dataset_path):
print(dataset_path, 'not found')
return
# map: opus_name -> path
dataset = dict()
files = os.listdir(dataset_path)
for file in files:
filepath = dataset_path/file
# skip directories
if os.path.isdir(filepath):
continue
# check the extension in the file name
if (os.path.splitext(file)[1] == _score_suffix):
# map score name to file path
dataset[os.path.splitext(file)[0]] = filepath
# sort the list alphabetically
dataset = dict(sorted(dataset.items()))
return dataset
def accids(ks, notes):
c = 0
for note in notes:
if note.pitch.accidental != ks.accidentalByStep(note.name):
c += 1
return c
def FiloBass_table(corpus='leads'):
assert(corpus == 'leads' or corpus == 'piano')
table = []
dataset = FiloBassCorpus(corpus)
names = sorted(list(dataset)) # list of index in dataset
for name in names:
if (dataset.get(name) == None):
print(name, "not found in dataset", corpus)
continue
file = dataset[name]
score = m21.converter.parse(file.as_posix())
assert(len(score.parts) > 0)
part = score.parts[0]
fpart = part.flatten()
keys = fpart.getElementsByClass([m21.key.Key, m21.key.KeySignature])
notes = fpart.getElementsByClass(m21.note.Note)
row = []
row.append(name)
row.append(keys[0].sharps if len(keys) > 0 else None)
row.append(len(part.getElementsByClass(m21.stream.Measure)))
row.append(len(notes))
row.append(accids(keys[0], notes) if len(keys) > 0 else None)
row.append(len(score.parts))
row.append(len(keys))
table.append(row)
df = pandas.DataFrame(table)
df.columns = ['name', 'KS','# bars', '# notes', '# accids', '# parts', '# keys']
df['KS'] = df['KS'].map('{:n}'.format)
return df
# df.fillna('NaN').to_csv(file, header=True, index=False)
###########################################
## ##
## automatic evaluation of whole dataset ##
## ##
###########################################
# list of opus names with issues
skip = ['All-the-Things-You-Are',
'Alone-Together',
'Apple-Jump',]
def eval_FiloBass(corpus='leads', algo=ps.pse.Algo_PSE,
tons=104, kpre=33, kpost=23,
output_dir='', filename='',
debug=True, mark=True):
global _eval_root
assert(corpus == 'leads' or corpus == 'piano')
timestamp = datetime.today().strftime('%Y%m%d-%H%M')
# default output dir name
if output_dir == '':
output_dir = timestamp
output_path = Path(_eval_root)/'evalFiloBass'/output_dir
if not os.path.isdir(output_path):
if not os.path.isdir(Path(_eval_root)/'evalFiloBass'):
os.mkdir(Path(_eval_root)/'evalFiloBass')
os.mkdir(output_path)
else:
print('WARNING: dir', output_path, 'exists')
stat = ps.Stats()
dataset = FiloBassCorpus(corpus)
names = sorted(list(dataset)) # list of index in dataset
print('\n', 'starting evaluation of FiloBass dataset -', len(names), 'entries\n')
for name in names:
if (name in skip):
print('\n', name, 'SKIP\n')
continue
if (dataset.get(name) == None):
print(name, "not found in dataset", corpus)
continue
file = dataset[name]
print('\n', name, '\n')
s = m21.converter.parse(file.as_posix())
(ls, lld) = ps.eval_score(score=s, stat=stat,
sid=0, title=name, composer='',
algo=algo,
nbtons=tons, # for PSE
kpre=kpre, kpost=kpost, # for PS13
debug=debug, mark=mark)
if mark and not ps.empty_difflist(lld):
write_score(s, output_path, name)
# display and save evaluation table
# default table file name
if filename == '':
filename = 'FRWeval'+'_'+corpus+str(tons)+'_'+timestamp
stat.show()
df = stat.get_dataframe() # create pands dataframe
df.pop('part') # del column part number (always 0)
df.to_csv(output_path/(filename+'.csv') , header=True, index=False)
stat.write_datasum(output_path/(filename+'_sum.csv'))
def eval_FiloBassitem(name, corpus='leads', algo=ps.pse.Algo_PSE,
tons=104, kpre=33, kpost=23, dflag=True, mflag=True):
assert(len(name) > 0)
assert(corpus == 'leads' or corpus == 'piano')
dataset = FiloBassCorpus(corpus)
if (dataset.get(name) == None):
print(name, "not found in dataset", corpus)
return
file = dataset[name]
score = m21.converter.parse(file.as_posix())
stat = ps.Stats()
# ground truth ks, estimated ks, nnb of nontes and list of diff notes
#(k_gt, gt_est, nn, ld) = ps.eval_part(part=part, stat=stat, nbtons=tons,
# debug=dflag, mark=mflag)
(ls, lld) = ps.eval_score(score=score, stat=stat,
sid=0, title=name, composer='',
algo=algo,
nbtons=tons, # for PSE
kpre=kpre, kpost=kpost, # for PS13
debug=dflag, mark=mflag)
stat.show()
assert(len(lld) == 1) # always 1 unique part in LG dataset
if mflag and len(lld[0]) > 0:
score.show()
write_score(score, Path(os.getcwd()), name)
def write_score(score, output_path, outname):
if not os.path.isdir(output_path):
os.mkdir(output_path)
xmlfile = output_path/(outname+'.musicxml')
score.write('musicxml', fp=xmlfile)
def write_score2(score, output_path, outname):
assert(len(outname) > 0)
if not os.path.isdir(output_path):
os.mkdir(output_path)
output_path = output_path/outname
if not os.path.isdir(output_path):
os.mkdir(output_path)
xmlfile = output_path/(outname+'.musicxml')
score.write('musicxml', fp=xmlfile)
# pdffile = dirname+'/'+outname+'.pdf'
# os.system(_mscore + ' -o ' + pdffile + ' ' + xmlfile)
def debug(name, corpus='leads'):
assert(len(name) > 0)
dataset = FiloBassCorpus(corpus)
if (dataset.get(name) == None):
print(name, "not found in dataset", corpus)
return
file = dataset[name]
score = m21.converter.parse(file)
lp = score.getElementsByClass(m21.stream.Part)
ln = ps.extract_part(lp[0]) # first and unique part
for (n, b, s) in ln:
a = 'sp.add('
a += str(n.pitch.midi)
a += ', '
a += str(b)
a += ', '
a += 'true' if s else 'false'
a += ');'
print(a)
#sp = ps.Speller()
#sp.debug(True)
#ps.add_tons(0, sp)
#sp.add_notes(ln1[:61], sp)
#sp.spell()
if __name__=="__main__":
eval_FiloBass()
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