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import click
from app.utils import click_utils
from app.settings import app_cfg
import os
from os.path import join
import time
import numpy as np
import random
from subprocess import call
import cv2 as cv
from PIL import Image
from glob import glob
import tensorflow as tf
import tensorflow_hub as hub
import shutil
import h5py
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from app.search.json import save_params_latent, save_params_dense
from app.search.image import image_to_uint8, imconvert_uint8, imconvert_float32, \
imread, imwrite, imgrid, resize_and_crop_image
from app.search.vector import truncated_z_sample, truncated_z_single, create_labels
@click.command('')
@click.option('-i', '--input', 'opt_fp_in', required=True,
help='Path to input image')
@click.option('-d', '--dims', 'opt_dims', default=512, type=int,
help='Dimensions of BigGAN network (128, 256, 512)')
@click.option('-s', '--steps', 'opt_steps', default=500, type=int,
help='Number of optimization iterations')
@click.option('-l', '--limit', 'opt_limit', default=1000, type=int,
help='Limit the number of images to process')
@click.option('-v', '--video', 'opt_video', is_flag=True,
help='Export a video for each dataset')
@click.option('-t', '--tag', 'opt_tag', default='inverse_' + str(int(time.time() * 1000)),
help='Tag this dataset')
# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
@click.pass_context
def cli(ctx, opt_fp_in, opt_dims, opt_steps, opt_limit, opt_video, opt_tag):
"""
Search for an image (class vector) in BigGAN using gradient descent
"""
sess = tf.compat.v1.Session()
if os.path.isdir(opt_fp_in):
paths = glob(os.path.join(opt_fp_in, '*.jpg')) + \
glob(os.path.join(opt_fp_in, '*.jpeg')) + \
glob(os.path.join(opt_fp_in, '*.png'))
else:
paths = [opt_fp_in]
fp_inverses = os.path.join(app_cfg.DIR_INVERSES, opt_tag)
os.makedirs(fp_inverses, exist_ok=True)
save_params_latent(fp_inverses, opt_tag)
save_params_dense(fp_inverses, opt_tag)
out_file = h5py.File(join(fp_inverses, 'dataset.hdf5'), 'w')
out_images = out_file.create_dataset('xtrain', (len(paths), 3, 512, 512,), dtype='float32')
out_labels = out_file.create_dataset('ytrain', (len(paths), 1000,), dtype='float32')
out_latent = out_file.create_dataset('ztrain', (len(paths), 128,), dtype='float32')
out_fns = out_file.create_dataset('fn', (len(paths),), dtype=h5py.string_dtype())
for index, path in enumerate(paths):
if index == opt_limit:
break
out_fns[index] = os.path.basename(path)
fp_frames = find_nearest_vector(sess, path, opt_dims, out_images, out_labels, out_latent, opt_steps, index)
if opt_video:
export_video(fp_frames)
def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_latent, opt_steps, index):
"""
Find the closest latent and class vectors for an image. Store the class vector in an HDF5.
"""
generator = hub.Module('https://tfhub.dev/deepmind/biggan-512/2')
# inputs = {k: tf.compat.v1.placeholder(v.dtype, v.get_shape().as_list(), k)
# for k, v in generator.get_input_info_dict().items()}
batch_size = 1
truncation = 1.0
z_dim = 128
vocab_size = 1000
img_size = 512
num_channels = 3
z_initial = truncated_z_sample(batch_size, z_dim, truncation/2)
y_initial = create_labels(batch_size, vocab_size, 10)
z_lr = 0.001
y_lr = 0.00001
input_z = tf.compat.v1.Variable(z_initial, dtype=np.float32, constraint=lambda t: tf.clip_by_value(t, -2, 2))
input_y = tf.compat.v1.Variable(y_initial, dtype=np.float32, constraint=lambda t: tf.clip_by_value(t, 0, 1))
input_trunc = tf.compat.v1.constant(1.0)
output = generator({
'z': input_z,
'y': input_y,
'truncation': input_trunc,
})
target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels))
# loss = tf.losses.compute_weighted_loss(tf.square(output - target), weights=mask)
loss = tf.compat.v1.losses.mean_squared_error(target, output)
train_step_z = tf.train.AdamOptimizer(z_lr).minimize(loss, var_list=[input_z], name='AdamOpterZ')
train_step_y = tf.train.AdamOptimizer(y_lr).minimize(loss, var_list=[input_y], name='AdamOpterY')
target_im, fp_frames = load_target_image(opt_fp_in)
# crop image and convert to format for next script
phi_target_for_inversion = resize_and_crop_image(target_im, 512)
b = np.dsplit(phi_target_for_inversion, 3)
phi_target_for_inversion = np.stack(b).reshape((3, 512, 512))
out_images[index] = phi_target_for_inversion
# create phi target for the latent / label pass
phi_target = resize_and_crop_image(target_im, opt_dims)
phi_target = np.expand_dims(phi_target, 0)
phi_target = np.repeat(phi_target, batch_size, axis=0)
# IMPORTANT: initialize variables before running the session
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
feed_dict = {
target: phi_target,
}
# feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
phi_start = sess.run(output, feed_dict=feed_dict)
start_im = imconvert_uint8(phi_start)
imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_0000_start.png'), start_im)
try:
print("Iteration start")
for i in range(opt_steps):
curr_loss, _, _ = sess.run([loss, train_step_z, train_step_y], feed_dict=feed_dict)
phi_guess = sess.run(output)
guess_im = imconvert_uint8(phi_guess)
imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_{:04d}.png'.format(i)), guess_im)
if i % 20 == 0:
print('iter: {}, loss: {}'.format(i, curr_loss))
except KeyboardInterrupt:
pass
z_guess = sess.run(input_z)
y_guess = sess.run(input_y)
out_labels[index] = y_guess
out_latent[index] = z_guess
return fp_frames
def export_video(fp_frames):
print("Exporting video...")
cmd = [
'/home/lens/bin/ffmpeg',
'-y', # '-v', 'quiet',
'-r', '30',
'-i', join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_%04d.png'),
'-pix_fmt', 'yuv420p',
join(app_cfg.DIR_OUTPUTS, fp_frames + '.mp4')
]
# print(' '.join(cmd))
call(cmd)
shutil.rmtree(join(app_cfg.DIR_OUTPUTS, fp_frames))
def load_target_image(opt_fp_in):
print("Loading {}".format(opt_fp_in))
fn = os.path.basename(opt_fp_in)
fbase, ext = os.path.splitext(fn)
fp_frames = "frames_{}_{}".format(fbase, int(time.time() * 1000))
fp_frames_fullpath = join(app_cfg.DIR_OUTPUTS, fp_frames)
print("Output to {}".format(fp_frames_fullpath))
os.makedirs(fp_frames_fullpath, exist_ok=True)
target_im = imread(opt_fp_in)
return target_im, fp_frames
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