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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
from PIL import Image
@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):
"""
"""
module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')
# Sample a batch of 8 random latent vectors (z) from the Gaussian prior. Then
# call the generator on the latent samples to generate a batch of images with
# shape [8, 128, 128, 3] and range [-1, 1].
z = tf.random.normal([8, 120]) # latent samples
gen_samples = module(z, signature='generate', as_dict=True)['upsampled']
for sample in gen_samples:
img = Image.fromarray(sample, "RGB")
fp_img_out = int(time.time() * 1000) + '.png'
img.save(join(app_cfg.DIR_OUTPUTS, fp_img_out))
# # Given a batch of 256x256 RGB images in range [-1, 1], call the encoder to
# # compute predicted latents z and other features (e.g. for use in downstream
# # recognition tasks).
# images = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])
# features = module(images, signature='encode', as_dict=True)
# # Get the predicted latent sample `z_sample` from the dict of features.
# # Other available features include `avepool_feat` and `bn_crelu_feat`, used in
# # the representation learning results.
# z_sample = features['z_sample'] # shape [?, 120]
# # Compute reconstructions of the input `images` by passing the encoder's output
# # `z_sample` back through the generator. Note that raw generator outputs are
# # half the resolution of encoder inputs (128x128). To get upsampled generator
# # outputs matching the encoder input resolution (256x256), instead use:
# # recons = module(z_sample, signature='generate', as_dict=True)['upsampled']
# recons = module(z_sample, signature='generate') # shape [?, 128, 128, 3]
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