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
|
import click
from app.utils import click_utils
from app.settings import app_cfg
from os.path import join
import time
import numpy as np
from PIL import Image
def image_to_uint8(x):
"""Converts [-1, 1] float array to [0, 255] uint8."""
x = np.asarray(x)
x = (256. / 2.) * (x + 1.)
x = np.clip(x, 0, 255)
x = x.astype(np.uint8)
return x
@click.command('')
# @click.option('-i', '--input', 'opt_dir_in', required=True,
# help='Path to input image glob directory')
# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
@click.pass_context
def cli(ctx):
"""
"""
import tensorflow as tf
import tensorflow_hub as hub
print("Loading module...")
module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')
z = tf.random.normal([8, 120]) # latent samples
outputs = module(z, signature='generate', as_dict=True)
with tf.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
results = sess.run(outputs)
for sample in results['default']:
sample = image_to_uint8(sample)
img = Image.fromarray(sample, "RGB")
fp_img_out = "{}.png".format(int(time.time() * 1000))
img.save(join(app_cfg.DIR_OUTPUTS, fp_img_out))
|
