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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):
"""
Generate a random BigGAN image
"""
import tensorflow as tf
import tensorflow_hub as hub
print("Loading module...")
module = hub.Module('https://tfhub.dev/deepmind/biggan-128/2')
# module = hub.Module('https://tfhub.dev/deepmind/biggan-256/2')
# module = hub.Module('https://tfhub.dev/deepmind/biggan-512/2')
batch_size = 8
truncation = 0.5 # scalar truncation value in [0.02, 1.0]
z = truncation * tf.random.truncated_normal([batch_size, 120]) # noise sample
y_index = tf.random.uniform([batch_size], maxval=1000, dtype=tf.int32)
y = tf.one_hot(y_index, 1000)
outputs = module(dict(y=y, z=z, truncation=truncation))
with tf.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
results = sess.run(outputs)
print(results)
for sample in results:
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))
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