def generate(self):
cadence_generator = CadencesUtil()
# cadences = cadence_generator.generate()
cadences = cadence_generator.getLikesCadences()
len_cadences = len(cadences)
num_sequencias = 8
score = stream.Score()
part_chords = stream.Part()
part_melody = stream.Part()
for i in range(0, num_sequencias):
cadencia = cadences[random.randint(0, len_cadences - 1)]
num_notas_array = [8, 3, 5]
for i in range(0, len(cadencia)):
chor = copy.deepcopy(self.chords[cadencia[i]])
num_notas = num_notas_array[i]
chor.quarterLength = num_notas * .5
part_melody.append(chor)
# Obtemos as notas do acorde
pitches = chor.pitches
velocidade_notas = random.randint(1, 2)
# Iteramos as notas do acorde pra a melodia
for i_notas in range(0, num_notas * velocidade_notas):
idx_pitch = random.randint(0, len(pitches) - 1)
note = note_mus21.Note(pitches[idx_pitch])
note.quarterLength = 0.5 / velocidade_notas
part_chords.append(note)
score.insert(0, part_melody)
score.insert(0, part_chords)
# sc.show()
mf = midi.translate.streamToMidiFile(score)
import datetime
mf.open(('%s/musics/%s.midi' % (Configs.get_project_home(), datetime.datetime.now().strftime("%Y%m%d%H%M%S"))),
"wb")
mf.write()
mf.close
def gen_midi_of_cadences(self):
# TODO
cadence_generator = CadencesUtil()
cadences = cadence_generator.generate()
for cadence in cadences:
score = stream.Score()
part = stream.Part()
for i in range(0, len(cadence)):
chor = copy.deepcopy(self.chords[cadence[i]])
chor.quarterLength = 2
part.append(chor)
score.insert(0, part)
mf = midi.translate.streamToMidiFile(score)
mf.open(('%s/cadences/cad%s.midi' % Configs.get_project_home(), '-'.join(str(e) for e in cadence)), "wb")
mf.write()
mf.close
def index():
from os import listdir
from os.path import isfile, join
items = []
cadenceUtil = CadencesUtil()
cadenceUtil.learnFromDataset()
for f in listdir('cadences'):
if isfile(join('cadences', f)) and f.endswith('.midi'):
cadence_str, intervals, distances = CadencesUtil.extractDistancesFromCadence(f)
dist_array = np.ndarray(buffer=np.array(distances, dtype=int), shape=(1, 2), dtype=int)
predict = cadenceUtil.classify(dist_array)[0]
midi_item = MIDIItem(f, url_for('static', filename=f), ('/like/%s' % f), ('/dislike/%s' % f),
predict=predict)
items.append(midi_item)
shuffle(items)
return render_template('home.html', midi_items=items)