def extFeat(path, train):
mel_roll, chord_roll = roll(train)
del chord_roll, train
#extract chords
midi_fname = glob.glob(path)
hand_left = []
target = np.zeros((96, len(midi_fname), 4), dtype=np.float64)
for fname, i in zip(midi_fname, range(0, len(midi_fname))):
# vectorize midi input
orig = midi_to_vector(fname)
# extract the beginning of each channel
indx = rm_extra_zeros(orig)
# extract left hand
hand_left = orig[:, indx[0]:indx[1]]
# shape it to a vertical matrix
z2 = hand_left.shape
target[:z2[1], i, :] = np.transpose(hand_left)
target = pitches2chords(target)
# #create inputs
# images = []
# for i in range(0, mel_roll.shape[0]):
# for j in range(0,mel_roll.shape[1],16):
# images.append(mel_roll[i,j:j+16])
# images = np.dstack(images)
# images = images.transpose((2, 0, 1))
# X = images.reshape(images.shape[0], 1, 12, 16)
#
# #create targets
# chords = []
# for i in range(0, target.shape[1]):
# for j in range(0,target.shape[0],2):
# chords.append(target[j:j+1,i,2])
# chords = np.asarray(chords)
# Y = chords.reshape(len(chords),)
# Y = np_utils.to_categorical(Y, 30)
# return X, Y
return target
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 15 03:08:41 2017
@author: Antonia
"""
from featExtractUtils import midi_to_vector, rm_extra_zeros, pitches2chords
import numpy as np
from keras.utils import np_utils
from feature_extraction_clean import roll
import glob
train = True
path = "data/train/*.mid"
mel_roll, chord_roll = roll(train)
del chord_roll, train
#extract chords
midi_fname = glob.glob(path)
hand_left = []
x = []
target = np.zeros((96, len(midi_fname), 4), dtype=np.float64)
for fname, i in zip(midi_fname, range(0, len(midi_fname))):
# vectorize midi input
orig = midi_to_vector(fname)
# extract the beginning of each channel
indx = rm_extra_zeros(orig)
# extract left hand
hand_left = orig[:, indx[0]:indx[1]]
x.append(hand_left.shape[1])
composition_dir = 'data/split/test_left/'
# remove old test input
old_test = glob.glob("%s*.mid" % (composition_dir))
if len(old_test) > 0:
for i in old_test:
os.remove(i)
mel_files = glob.glob("%s*.mid" % (mel_dir))
composition_files = []
for i in range(len(mel_files)):
composition_files.append('%d' % (i + 1))
mel_lowest_note = 60
mel_roll = roll(train)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
test_data = data_utils_compose.createNetInputs(double_mel_roll, 256)
batch_size = 128
thresh = float(input('Threshold (recommended ~ 0.1):'))
print("Loading Model and Weights...")
#Load model file
model_dir = 'models/models json/'
model_files = listdir(model_dir)
print("Choose a file for the json model file:")
print("---------------------------------------")
for i, file in enumerate(model_files):
print(str(i) + " : " + file)
print("---------------------------------------")
