def predict(filepath, classifier):
"""
Input: filepath to the image file (string)
Output: MusicXML (string) and midi (base 64 string)
"""
basename = osp.basename(filepath)
filename, extension = osp.splitext(basename)
cached_file = Path(osp.join("cached_stream", filename + '.pkl'))
if cached_file.is_file():
song_stream = converter.thaw(cached_file)
else:
symbols, seg = Segmenter.symbols_from_File(filepath, use_cache=True)
y = classifier.predict_symbols(symbols, use_class_numbers=True)
for i in range(len(y)):
symbols[i].work_out_type(y[i])
song = Song()
song.add_symbols(symbols)
song.parse_symbols()
converter.freeze(song.stream,
fmt='pickle',
fp=osp.join(os.getcwd(), "cached_stream",
filename + ".pkl"))
song_stream = song.stream
# get musicxml as a string
out = musicxml.m21ToXml.GeneralObjectExporter(song_stream).parse()
outStr = out.decode('utf-8')
# save midi file, then convert into b64 format
song_stream.write('midi', osp.join('resources', 'mid', filename + '.mid'))
file = open(osp.join('resources', 'mid', filename + '.mid'), 'rb').read()
b64 = base64.b64encode(file)
return outStr.replace('\n', ''), b64
