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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 scipy.stats import truncnorm
from subprocess import call
import cv2 as cv
from PIL import Image
from glob import glob
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
def truncated_z_sample(batch_size, z_dim, truncation):
values = truncnorm.rvs(-2, 2, size=(batch_size, z_dim))
return truncation * values
def truncated_z_single(z_dim, truncation):
values = truncnorm.rvs(-2, 2, size=(1, z_dim))
return truncation * values
def create_labels(batch_size, vocab_size, num_classes):
label = np.zeros((batch_size, vocab_size))
for i in range(batch_size):
for _ in range(random.randint(1, num_classes)):
j = random.randint(0, vocab_size-1)
label[i, j] = random.random()
label[i] /= label[i].sum()
return label
def imconvert_uint8(im):
im = np.clip(((im + 1) / 2.0) * 256, 0, 255)
im = np.uint8(im)
return im
def imconvert_float32(im):
im = np.float32(im)
im = (im / 256) * 2.0 - 1
return im
def imread(filename):
img = cv.imread(filename, cv.IMREAD_UNCHANGED)
if img is not None:
if len(img.shape) > 2:
img = img[...,::-1]
return img
def imwrite(filename, img):
if img is not None:
if len(img.shape) > 2:
img = img[...,::-1]
return cv.imwrite(filename, img)
def imgrid(imarray, cols=5, pad=1):
if imarray.dtype != np.uint8:
raise ValueError('imgrid input imarray must be uint8')
pad = int(pad)
assert pad >= 0
cols = int(cols)
assert cols >= 1
N, H, W, C = imarray.shape
rows = int(np.ceil(N / float(cols)))
batch_pad = rows * cols - N
assert batch_pad >= 0
post_pad = [batch_pad, pad, pad, 0]
pad_arg = [[0, p] for p in post_pad]
imarray = np.pad(imarray, pad_arg, 'constant', constant_values=255)
H += pad
W += pad
grid = (imarray
.reshape(rows, cols, H, W, C)
.transpose(0, 2, 1, 3, 4)
.reshape(rows*H, cols*W, C))
if pad:
grid = grid[:-pad, :-pad]
return grid
@click.command('')
@click.option('-i', '--input', 'opt_fp_in', required=True,
help='Path to input image')
@click.option('-s', '--dims', 'opt_dims', default=128, type=int,
help='Dimensions of BigGAN network (128, 256, 512)')
# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
@click.pass_context
def cli(ctx, opt_fp_in, opt_dims):
"""
Search for an image in BigGAN using gradient descent
"""
import tensorflow as tf
import tensorflow_hub as hub
generator = hub.Module('https://tfhub.dev/deepmind/biggan-' + str(opt_dims) + '/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()}
input_z = inputs['z']
input_y = inputs['y']
input_trunc = inputs['truncation']
output = generator(inputs)
sess = tf.compat.v1.Session()
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
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'))
for path in paths:
find_nearest_vector(generator, sess, input_z, input_y, input_trunc, output, path, opt_dims)
else:
find_nearest_vector(generator, sess, input_z, input_y, input_trunc, output, opt_fp_in, opt_dims)
def find_nearest_vector(generator, sess, input_z, input_y, input_trunc, output, opt_fp_in, opt_dims):
z_dim = input_z.shape.as_list()[1]
vocab_size = input_y.shape.as_list()[1]
# scalar truncation value in [0.02, 1.0]
batch_size = 25
truncation = 1.0
z = truncated_z_sample(batch_size, z_dim, truncation/2)
num_classes = 1
y = create_labels(batch_size, vocab_size, num_classes)
if 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))
os.makedirs(join(app_cfg.DIR_OUTPUTS, fp_frames), exist_ok=True)
target_im = imread(opt_fp_in)
w = target_im.shape[1]
h = target_im.shape[0]
if w <= h:
scale = opt_dims / w
else:
scale = opt_dims / h
#print("{} {}".format(w, h))
target_im = cv.resize(target_im,(0,0), fx=scale, fy=scale)
phi_target = imconvert_float32(target_im)
phi_target = phi_target[:opt_dims,:opt_dims]
if phi_target.shape[2] == 4:
phi_target = phi_target[:,:,1:4]
phi_target = np.expand_dims(phi_target, 0)
phi_target = np.repeat(phi_target, batch_size, axis=0)
else:
fp_frames = "frames_{}".format(int(time.time() * 1000))
os.makedirs(join(app_cfg.DIR_OUTPUTS, fp_frames), exist_ok=True)
z_target = np.repeat(truncated_z_single(z_dim, truncation), batch_size, axis=0)
y_target = np.repeat(create_labels(1, vocab_size, 1), batch_size, axis=0)
feed_dict = {input_z: z_target, input_y: y_target, input_trunc: truncation}
phi_target = sess.run(output, feed_dict=feed_dict)
target_im = imgrid(imconvert_uint8(phi_target), cols=5)
imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_0000_target.png'), target_im)
#dy_dx = g.gradient(y, x)
cost = tf.reduce_sum(tf.pow(output - phi_target, 2))
dc_dz, dc_dy, = tf.gradients(cost, [input_z, input_y])
#dc_dy, = tf.gradients(cost, [input_y])
lr_z = 0.0001
lr_y = 0.000001
#z = truncated_z_sample(batch_size, z_dim, truncation/2)
feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
phi_start = sess.run(output, feed_dict=feed_dict)
start_im = imgrid(imconvert_uint8(phi_start), cols=5)
imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_0000_start.png'), start_im)
try:
for i in range(1000):
feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
grad_z, grad_y = sess.run([dc_dz, dc_dy], feed_dict=feed_dict)
z -= grad_z * lr_z
y -= grad_y * lr_y
lr_z *= 0.997
lr_y *= 0.999
if i % 30 == 0:
lr_y *= 1.002
y = np.clip(y, 0, 1)
for j in range(batch_size):
y[j] /= y[j].sum()
if i > 200 and i % 100 == 0:
mean = np.mean(y, axis=0)
y = y * 3 / 4 + mean / 4
indices = np.logical_or(z <= -2*truncation, z >= +2*truncation)
z[indices] = np.random.randn(np.count_nonzero(indices))
feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
phi_guess = sess.run(output, feed_dict=feed_dict)
guess_im = imgrid(imconvert_uint8(phi_guess), cols=5)
imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_{:04d}.png'.format(i)), guess_im)
if i % 20 == 0:
print('lr: {}, iter: {}, grad_z: {}, grad_y: {}'.format(lr_z, i, np.std(grad_z), np.std(grad_y)))
#print('lr: {}, iter: {}, grad_z: {}'.format(lr, i, np.std(grad_z)))
#print('lr: {}, iter: {}, grad_y: {}'.format(lr, i, np.std(grad_y)))
except KeyboardInterrupt:
pass
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)
print("Done")
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