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style_transfer.py
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style_transfer.py
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import argparse
import json
import os
from datetime import datetime
import cv2
import numpy as np
import tensorflow as tf
from PIL import Image
import components.NIMA.model as nima
import components.VGG19.model as vgg
from components.matting import compute_matting_laplacian
from components.segmentation import compute_segmentation
from components.semantic_merge import merge_segments, reduce_dict, mask_for_tf, extract_segmentation_masks
def style_transfer(content_image, style_image, content_masks, style_masks, init_image, result_dir, timestamp, args):
print("Style transfer started")
content_image = vgg.preprocess(content_image)
style_image = vgg.preprocess(style_image)
weight_restorer = vgg.load_weights()
image_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
vgg19 = vgg.VGG19ConvSub(image_placeholder)
with tf.Session() as sess:
transfer_image = tf.Variable(init_image)
transfer_image_vgg = vgg.preprocess(transfer_image)
transfer_image_nima = nima.preprocess(transfer_image)
sess.run(tf.global_variables_initializer())
weight_restorer.init(sess)
content_conv4_2 = sess.run(fetches=vgg19.conv4_2, feed_dict={image_placeholder: content_image})
style_conv1_1, style_conv2_1, style_conv3_1, style_conv4_1, style_conv5_1 = sess.run(
fetches=[vgg19.conv1_1, vgg19.conv2_1, vgg19.conv3_1, vgg19.conv4_1, vgg19.conv5_1],
feed_dict={image_placeholder: style_image})
with tf.variable_scope("", reuse=True):
vgg19 = vgg.VGG19ConvSub(transfer_image_vgg)
content_loss = calculate_layer_content_loss(content_conv4_2, vgg19.conv4_2)
style_loss = (1. / 5.) * calculate_layer_style_loss(style_conv1_1, vgg19.conv1_1, content_masks, style_masks)
style_loss += (1. / 5.) * calculate_layer_style_loss(style_conv2_1, vgg19.conv2_1, content_masks, style_masks)
style_loss += (1. / 5.) * calculate_layer_style_loss(style_conv3_1, vgg19.conv3_1, content_masks, style_masks)
style_loss += (1. / 5.) * calculate_layer_style_loss(style_conv4_1, vgg19.conv4_1, content_masks, style_masks)
style_loss += (1. / 5.) * calculate_layer_style_loss(style_conv5_1, vgg19.conv5_1, content_masks, style_masks)
photorealism_regularization = calculate_photorealism_regularization(transfer_image_vgg, content_image)
nima_loss = compute_nima_loss(transfer_image_nima)
content_loss = args.content_weight * content_loss
style_loss = args.style_weight * style_loss
photorealism_regularization = args.regularization_weight * photorealism_regularization
nima_loss = args.nima_weight * nima_loss
total_loss = content_loss + style_loss + photorealism_regularization + nima_loss
tf.summary.scalar('Content loss', content_loss)
tf.summary.scalar('Style loss', style_loss)
tf.summary.scalar('Photorealism Regularization', photorealism_regularization)
tf.summary.scalar('NIMA loss', nima_loss)
tf.summary.scalar('Total loss', total_loss)
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(os.path.join(os.path.dirname(__file__), 'logs/{}'.format(timestamp)),
sess.graph)
iterations_dir = os.path.join(result_dir, "iterations")
os.mkdir(iterations_dir)
optimizer = tf.train.AdamOptimizer(learning_rate=args.adam_learning_rate, beta1=args.adam_beta1,
beta2=args.adam_beta2, epsilon=args.adam_epsilon)
train_op = optimizer.minimize(total_loss, var_list=[transfer_image])
sess.run(adam_variables_initializer(optimizer, [transfer_image]))
min_loss, best_image = float("inf"), None
for i in range(args.iterations + 1):
_, result_image, loss, c_loss, s_loss, p_loss, n_loss, summary = sess.run(
fetches=[train_op, transfer_image, total_loss, content_loss, style_loss, photorealism_regularization,
nima_loss, summary_op])
summary_writer.add_summary(summary, i)
if i % args.print_loss_interval == 0:
print(
"Iteration: {0:5} \t "
"Total loss: {1:15.2f} \t "
"Content loss: {2:15.2f} \t "
"Style loss: {3:15.2f} \t "
"Photorealism Regularization: {4:15.2f} \t "
"NIMA loss: {5:15.2f} \t".format(i, loss, c_loss, s_loss, p_loss, n_loss))
if loss < min_loss:
min_loss, best_image = loss, result_image
if i % args.intermediate_result_interval == 0:
save_image(best_image, os.path.join(iterations_dir, "iter_{}.png".format(i)))
return best_image
def adam_variables_initializer(adam_opt, var_list):
adam_vars = [adam_opt.get_slot(var, name)
for name in adam_opt.get_slot_names()
for var in var_list if var is not None]
adam_vars.extend(list(adam_opt._get_beta_accumulators()))
return tf.variables_initializer(adam_vars)
def compute_nima_loss(image):
model = nima.get_nima_model(image)
def mean_score(scores):
scores = tf.squeeze(scores)
si = tf.range(1, 11, dtype=tf.float32)
return tf.reduce_sum(tf.multiply(si, scores), name='nima_score')
nima_score = mean_score(model.output)
nima_loss = tf.identity(10.0 - nima_score, name='nima_loss')
return nima_loss
def calculate_layer_content_loss(content_layer, transfer_layer):
return tf.reduce_mean(tf.squared_difference(content_layer, transfer_layer))
def calculate_layer_style_loss(style_layer, transfer_layer, content_masks, style_masks):
# scale masks to current layer
content_size = tf.TensorShape(transfer_layer.shape[1:3])
style_size = tf.TensorShape(style_layer.shape[1:3])
def resize_masks(masks, size):
return [tf.image.resize_bilinear(mask, size) for mask in masks]
style_masks = resize_masks(style_masks, style_size)
content_masks = resize_masks(content_masks, content_size)
feature_map_count = np.float32(transfer_layer.shape[3].value)
feature_map_size = np.float32(transfer_layer.shape[1].value) * np.float32(transfer_layer.shape[2].value)
means_per_channel = []
for content_mask, style_mask in zip(content_masks, style_masks):
transfer_gram_matrix = calculate_gram_matrix(transfer_layer, content_mask)
style_gram_matrix = calculate_gram_matrix(style_layer, style_mask)
mean = tf.reduce_mean(tf.squared_difference(style_gram_matrix, transfer_gram_matrix))
means_per_channel.append(mean / (2 * tf.square(feature_map_count) * tf.square(feature_map_size)))
style_loss = tf.reduce_sum(means_per_channel)
return style_loss
def calculate_photorealism_regularization(output, content_image):
# normalize content image and out for matting and regularization computation
content_image = content_image / 255.0
output = output / 255.0
# compute matting laplacian
matting = compute_matting_laplacian(content_image[0, ...])
# compute photorealism regularization loss
regularization_channels = []
for output_channel in tf.unstack(output, axis=-1):
channel_vector = tf.reshape(tf.transpose(output_channel), shape=[-1])
matmul_right = tf.sparse_tensor_dense_matmul(matting, tf.expand_dims(channel_vector, -1))
matmul_left = tf.matmul(tf.expand_dims(channel_vector, 0), matmul_right)
regularization_channels.append(matmul_left)
regularization = tf.reduce_sum(regularization_channels)
return regularization
def calculate_gram_matrix(convolution_layer, mask):
matrix = tf.reshape(convolution_layer, shape=[-1, convolution_layer.shape[3]])
mask_reshaped = tf.reshape(mask, shape=[matrix.shape[0], 1])
matrix_masked = matrix * mask_reshaped
return tf.matmul(matrix_masked, matrix_masked, transpose_a=True)
def load_image(filename):
image = np.array(Image.open(filename).convert("RGB"), dtype=np.float32)
image = np.expand_dims(image, axis=0)
return image
def save_image(image, filename):
image = image[0, :, :, :]
image = np.clip(image, 0, 255.0)
image = np.uint8(image)
result = Image.fromarray(image)
result.save(filename)
def change_filename(dir_name, filename, suffix, extension=None):
path, ext = os.path.splitext(filename)
if extension is None:
extension = ext
return os.path.join(dir_name, path + suffix + extension)
def write_metadata(dir, args, load_segmentation):
# collect metadata and write to transfer dir
meta = {
"init": args.init,
"iterations": args.iterations,
"content": args.content_image,
"style": args.style_image,
"content_weight": args.content_weight,
"style_weight": args.style_weight,
"regularization_weight": args.regularization_weight,
"nima_weight": args.nima_weight,
"semantic_thresh": args.semantic_thresh,
"similarity_metric": args.similarity_metric,
"load_segmentation": load_segmentation,
"adam": {
"learning_rate": args.adam_learning_rate,
"beta1": args.adam_beta1,
"beta2": args.adam_beta2,
"epsilon": args.adam_epsilon
}
}
filename = os.path.join(dir, "meta.json")
with open(filename, "w+") as file:
file.write(json.dumps(meta, indent=4))
if __name__ == "__main__":
"""Parse program arguments"""
parser = argparse.ArgumentParser()
parser.add_argument("--content_image", type=str, help="content image path", default="content.png")
parser.add_argument("--style_image", type=str, help="style image path", default="style.png")
parser.add_argument("--output_image", type=str, help="Output image path, default: result.jpg",
default="result.jpg")
parser.add_argument("--iterations", type=int, help="Number of iterations, default: 4000",
default=4000)
parser.add_argument("--intermediate_result_interval", type=int,
help="Interval of iterations until a intermediate result is saved., default: 100",
default=100)
parser.add_argument("--print_loss_interval", type=int,
help="Interval of iterations until the current loss is printed to console., default: 1",
default=1)
parser.add_argument("--content_weight", type=float,
help="Weight of the content loss., default: 1",
default=1)
parser.add_argument("--style_weight", type=float,
help="Weight of the style loss., default: 100",
default=100)
parser.add_argument("--regularization_weight", type=float,
help="Weight of the photorealism regularization.",
default=10 ** 4)
parser.add_argument("--nima_weight", type=float,
help="Weight for nima loss.",
default=10 ** 5)
parser.add_argument("--adam_learning_rate", type=float,
help="Learning rate for the adam optimizer., default: 1.0",
default=1.0)
parser.add_argument("--adam_beta1", type=float,
help="Beta1 for the adam optimizer., default: 0.9",
default=0.9)
parser.add_argument("--adam_beta2", type=float,
help="Beta2 for the adam optimizer., default: 0.999",
default=0.999)
parser.add_argument("--adam_epsilon", type=float,
help="Epsilon for the adam optimizer., default: 1e-08",
default=1e-08)
parser.add_argument("--semantic_thresh", type=float, help="Semantic threshold for label grouping., default: 0.8",
default=0.8)
parser.add_argument("--similarity_metric", type=str, help="Semantic similarity metric for label grouping., default: li",
default="li")
init_image_options = ["noise", "content", "style"]
similarity_metric_options = ["li", "wpath", "jcn", "lin", "wup", "res"]
parser.add_argument("--init", type=str, help="Initialization image (%s).", default="content")
parser.add_argument("--gpu", help="comma separated list of GPU(s) to use.", default="0")
args = parser.parse_args()
assert (args.init in init_image_options)
# For more information on the similarity metrics: http://gsi-upm.github.io/sematch/similarity/#word-similarity
assert (args.similarity_metric in similarity_metric_options)
if args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
timestamp = datetime.now().strftime('%Y_%m_%d_%H_%M')
result_dir = 'result_' + timestamp
os.mkdir(result_dir)
# check if manual segmentation masks are available
content_segmentation_filename = change_filename('', args.content_image, '_seg', '.png')
style_segmentation_filename = change_filename('', args.style_image, '_seg', '.png')
load_segmentation = os.path.exists(content_segmentation_filename) and os.path.exists(style_segmentation_filename)
write_metadata(result_dir, args, load_segmentation)
"""Check if image files exist"""
for path in [args.content_image, args.style_image]:
if path is None or not os.path.isfile(path):
print("Image file {} does not exist.".format(path))
exit(0)
content_image = load_image(args.content_image)
style_image = load_image(args.style_image)
# use existing if available
if (load_segmentation):
print("Load segmentation from files.")
content_segmentation_image = cv2.imread(content_segmentation_filename)
style_segmentation_image = cv2.imread(style_segmentation_filename)
content_segmentation_masks = extract_segmentation_masks(content_segmentation_image)
style_segmentation_masks = extract_segmentation_masks(style_segmentation_image)
else:
print("Create segmentation.")
content_segmentation, style_segmentation = compute_segmentation(args.content_image, args.style_image)
cv2.imwrite(change_filename(result_dir, args.content_image, '_seg_raw', '.png'), content_segmentation)
cv2.imwrite(change_filename(result_dir, args.style_image, '_seg_raw', '.png'), style_segmentation)
content_segmentation_masks, style_segmentation_masks = merge_segments(content_segmentation, style_segmentation,
args.semantic_thresh, args.similarity_metric)
cv2.imwrite(change_filename(result_dir, args.content_image, '_seg', '.png'),
reduce_dict(content_segmentation_masks, content_image))
cv2.imwrite(change_filename(result_dir, args.style_image, '_seg', '.png'),
reduce_dict(style_segmentation_masks, style_image))
if args.init == "noise":
random_noise_scaling_factor = 0.0001
random_noise = np.random.randn(*content_image.shape).astype(np.float32)
init_image = vgg.postprocess(random_noise * random_noise_scaling_factor).astype(np.float32)
elif args.init == "content":
init_image = content_image
elif args.init == "style":
init_image = style_image
else:
print("Init image parameter {} unknown.".format(args.init))
exit(0)
result = style_transfer(content_image, style_image, mask_for_tf(content_segmentation_masks),
mask_for_tf(style_segmentation_masks), init_image, result_dir, timestamp, args)
save_image(result, os.path.join(result_dir, "final_transfer_image.png"))