-
Notifications
You must be signed in to change notification settings - Fork 2
/
utils.py
205 lines (160 loc) · 6.87 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
"""
Some codes from https://github.com/Newmu/dcgan_code
"""
from __future__ import division
import math
import os
import datetime
import pprint
import scipy.misc
import numpy as np
import pretty_midi as pm
import copy
import config
import write_midi
# from dataprocessing import select_instrument, piano_roll_to_pretty_midi
import tensorflow as tf
try:
_imread = scipy.misc.imread
except AttributeError:
from imageio import imread as _imread
pp = pprint.PrettyPrinter()
get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])
# -----------------------------
# new added functions for cyclegan
class ImagePool(object):
def __init__(self, maxsize=50):
self.maxsize = maxsize
self.num_img = 0
self.images = []
def __call__(self, image):
if self.maxsize <= 0:
return image
if self.num_img < self.maxsize:
self.images.append(image)
self.num_img += 1
return image
if np.random.rand() > 0.5:
idx = int(np.random.rand()*self.maxsize)
tmp1 = copy.copy(self.images[idx])[0]
self.images[idx][0] = image[0]
idx = int(np.random.rand()*self.maxsize)
tmp2 = copy.copy(self.images[idx])[1]
self.images[idx][1] = image[1]
return [tmp1, tmp2]
else:
return image
def load_test_data(image_path, fine_size=256):
img = imread(image_path)
img = scipy.misc.imresize(img, [fine_size, fine_size])
img = img/127.5 - 1
return img
def load_train_data(image_path, load_size=286, fine_size=256, is_testing=False):
img_A = imread(image_path[0])
img_B = imread(image_path[1])
if not is_testing:
img_A = scipy.misc.imresize(img_A, [load_size, load_size])
img_B = scipy.misc.imresize(img_B, [load_size, load_size])
h1 = int(np.ceil(np.random.uniform(1e-2, load_size-fine_size)))
w1 = int(np.ceil(np.random.uniform(1e-2, load_size-fine_size)))
img_A = img_A[h1:h1+fine_size, w1:w1+fine_size]
img_B = img_B[h1:h1+fine_size, w1:w1+fine_size]
if np.random.random() > 0.5:
img_A = np.fliplr(img_A) # Flip array in the left/right direction
img_B = np.fliplr(img_B)
else:
img_A = scipy.misc.imresize(img_A, [fine_size, fine_size])
img_B = scipy.misc.imresize(img_B, [fine_size, fine_size])
img_A = img_A/127.5 - 1.
img_B = img_B/127.5 - 1.
img_AB = np.concatenate((img_A, img_B), axis=2)
# img_AB shape: (fine_size, fine_size, input_c_dim + output_c_dim)
return img_AB
def load_midi_data(midi_path):
midi_A = pm.PrettyMIDI(midi_path[0])
midi_B = pm.PrettyMIDI(midi_path[1])
piano_roll_A = select_instrument(midi_A)[1]
piano_roll_B = select_instrument(midi_B)[1]
piano_roll_A.reshape(piano_roll_A.shape[0], piano_roll_A.shape[1], 1)
piano_roll_B.reshape(piano_roll_B.shape[0], piano_roll_B.shape[1], 1)
piano_roll_AB = np.concatenate((piano_roll_A, piano_roll_B), axis=2)
return piano_roll_AB
def load_npy_data(npy_data):
npy_A = np.load(npy_data[0]) * 1.
npy_B = np.load(npy_data[1]) * 1.
npy_AB = np.concatenate((npy_A.reshape(npy_A.shape[0], npy_A.shape[1], 3),
npy_B.reshape(npy_B.shape[0], npy_B.shape[1], 3)), axis=2)
return npy_AB
# -----------------------------
def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False):
return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w)
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)
def imread(path, is_grayscale = False):
if (is_grayscale):
return _imread(path, flatten=True).astype(np.float)
else:
return _imread(path, mode='RGB').astype(np.float)
def merge_images(images, size):
return inverse_transform(images)
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j*h:j*h+h, i*w:i*w+w, :] = image
return img
def imsave(images, size, path):
return scipy.misc.imsave(path, merge(images, size))
def center_crop(x, crop_h, crop_w, resize_h=64, resize_w=64):
if crop_w is None:
crop_w = crop_h
h, w = x.shape[:2]
j = int(round((h - crop_h)/2.))
i = int(round((w - crop_w)/2.))
return scipy.misc.imresize(x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])
def transform(image, npx=64, is_crop=True, resize_w=64):
# npx : # of pixels width/height of image
if is_crop:
cropped_image = center_crop(image, npx, resize_w=resize_w)
else:
cropped_image = image
return np.array(cropped_image)/127.5 - 1.
def inverse_transform(images):
return (images+1.)/2.
# def save_midis(bars, file_path):
# pm_out = piano_roll_to_pretty_midi(np.transpose(bars), fs=8)
# pm_out.write(file_path)
def save_midis(bars, file_path, tempo=80.0):
padded_bars = np.concatenate((np.zeros((bars.shape[0], bars.shape[1], 24, bars.shape[3])), bars,
np.zeros((bars.shape[0], bars.shape[1], 20, bars.shape[3]))), axis=2)
pause = np.zeros((bars.shape[0], 64, 128, bars.shape[3]))
images_with_pause = padded_bars
images_with_pause = images_with_pause.reshape(-1, 64, padded_bars.shape[2], padded_bars.shape[3])
images_with_pause_list = []
for ch_idx in range(padded_bars.shape[3]):
images_with_pause_list.append(images_with_pause[:, :, :, ch_idx].reshape(images_with_pause.shape[0],
images_with_pause.shape[1],
images_with_pause.shape[2]))
# write_midi.write_piano_rolls_to_midi(images_with_pause_list, program_nums=[33, 0, 25, 49, 0],
# is_drum=[False, True, False, False, False], filename=file_path, tempo=80.0)
write_midi.write_piano_rolls_to_midi(images_with_pause_list, filename=file_path,
tempo=tempo, beat_resolution=4)
def get_sample_shape(sample_size):
if sample_size >= 64 and sample_size % 8 == 0:
return [8, sample_size//8]
elif sample_size >= 48 and sample_size % 6 == 0:
return [6, sample_size//6]
elif sample_size >= 24 and sample_size % 4 == 0:
return [4, sample_size/4]
elif sample_size >= 15 and sample_size % 3 == 0:
return [3, sample_size//3]
elif sample_size >= 8 and sample_size % 2 == 0:
return [2, sample_size//2]
def get_rand_samples(x, sample_size=64):
random_idx = np.random.choice(x.shape[0], sample_size, replace=False)
return x[random_idx] * 2. - 1.
def get_now_datetime():
now = datetime.datetime.now().strftime('%Y-%m-%d')
return str(now)