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style_transfer.py
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style_transfer.py
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import time
from PIL import Image
import numpy as np
from keras import backend
from keras.applications.vgg16 import VGG16
from scipy.optimize import fmin_l_bfgs_b
from logger import logger
class VGG16StyleTransfer:
"""Main class that contains everything you need to perform style transfer"""
def __init__(self):
"""
some default parameters based on paper: https://arxiv.org/abs/1603.08155
"""
self.height = 512
self.width = 512
self.content_weight = 0.025
self.style_weight = 5.0
self.total_variation_weight = 1.0
self.loss = backend.variable(0.)
self.model = None
self.content_image = None
self.style_image = None
self.combination_image = None
self.tensor = None
self.f_outputs = None
self.feature_layers = ['block1_conv2', 'block2_conv2',
'block3_conv3', 'block4_conv3',
'block5_conv3']
def load_pictures(self, base_image, style_image):
"""
:param base_image: string - path to base image that you whant to transform
:param style_image: string - path to image with style that you want to apply to base image
:return:
"""
# open pictures
content_image = Image.open(base_image)
content_image = content_image.resize((self.width, self.height))
style_image = Image.open(style_image)
style_image = style_image.resize((self.width, self.height))
# convert to machine readable format
content_array = np.asarray(content_image, dtype='float32')
content_array = np.expand_dims(content_array, axis=0)
style_array = np.asarray(style_image, dtype='float32')
style_array = np.expand_dims(style_array, axis=0)
content_array[:, :, :, 0] -= 103.939
content_array[:, :, :, 1] -= 116.779
content_array[:, :, :, 2] -= 123.68
content_array = content_array[:, :, :, ::-1]
style_array[:, :, :, 0] -= 103.939
style_array[:, :, :, 1] -= 116.779
style_array[:, :, :, 2] -= 123.68
style_array = style_array[:, :, :, ::-1]
self.content_image = backend.variable(content_array)
self.style_image = backend.variable(style_array)
self.combination_image = backend.placeholder((1, self.height, self.width, 3))
# create tensor that can be read by VGG16 model
self.tensor = backend.concatenate([self.content_image,
self.style_image,
self.combination_image], axis=0)
def _load_vgg16(self):
# download vgg16
self.model = VGG16(input_tensor=self.tensor, weights='imagenet',
include_top=False)
def _content_loss(self, content, combination):
"""The content loss is the (scaled, squared)
Euclidean distance between feature representations
of the content and combination images."""
return backend.sum(backend.square(combination - content))
def _gram_matrix(self, x):
"""
Compute Gram matrix -
The terms of this matrix are proportional to the covariances of corresponding
sets of features, and thus captures information about which
features tend to activate together
"""
features = backend.batch_flatten(backend.permute_dimensions(x, (2, 0, 1)))
gram = backend.dot(features, backend.transpose(features))
return gram
def _style_loss(self, style, combination):
S = self._gram_matrix(style)
C = self._gram_matrix(combination)
channels = 3
size = self.height * self.width
return backend.sum(backend.square(S - C)) / (4. * (channels ** 2) * (size ** 2))
def _total_variation_loss(self, x):
a = backend.square(x[:, :self.height - 1, :self.width - 1, :] - x[:, 1:, :self.width - 1, :])
b = backend.square(x[:, :self.height - 1, :self.width - 1, :] - x[:, :self.height - 1, 1:, :])
return backend.sum(backend.pow(a + b, 1.25))
def _eval_loss_and_grads(self, x):
x = x.reshape((1, self.height, self.width, 3))
outs = self.f_outputs([x])
loss_value = outs[0]
grad_values = outs[1].flatten().astype('float64')
return loss_value, grad_values
def transfer_style(self, iterations=20, output_file='file'):
self._load_vgg16()
layers = dict([(layer.name, layer.output) for layer in self.model.layers])
layer_features = layers['block2_conv2']
content_image_features = layer_features[0, :, :, :]
combination_features = layer_features[2, :, :, :]
self.loss += self.content_weight * self._content_loss(content_image_features, combination_features)
for layer_name in self.feature_layers:
layer_features = layers[layer_name]
style_features = layer_features[1, :, :, :]
combination_features = layer_features[2, :, :, :]
sl = self._style_loss(style_features, combination_features)
self.loss += (self.style_weight / len(self.feature_layers)) * sl
self.loss += self.total_variation_weight * self._total_variation_loss(self.combination_image)
grads = backend.gradients(self.loss, self.combination_image)
outputs = [self.loss]
outputs += grads
self.f_outputs = backend.function([self.combination_image], outputs)
evaluator = Evaluator(self._eval_loss_and_grads)
x = np.random.uniform(0, 255, (1, self.height, self.width, 3)) - 128.
for i in range(iterations):
logger.info('Start of iteration: {}'.format(i))
start_time = time.time()
x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
fprime=evaluator.grads, maxfun=20)
logger.info('Current loss value: {}'.format(min_val))
end_time = time.time()
logger.info('Iteration {} completed in {}s'.format(i, round(end_time - start_time)))
x = x.reshape((self.height, self.width, 3))
x = x[:, :, ::-1]
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
x = np.clip(x, 0, 255).astype('uint8')
img = Image.fromarray(x)
img.save('{}.jpg'.format(output_file))
class Evaluator:
def __init__(self, eval_loss_and_grads):
self.loss_value = None
self.grads_values = None
self.eval_loss_and_grads = eval_loss_and_grads
def loss(self, x):
assert self.loss_value is None
loss_value, grad_values = self.eval_loss_and_grads(x)
self.loss_value = loss_value
self.grad_values = grad_values
return self.loss_value
def grads(self, x):
assert self.loss_value is not None
grad_values = np.copy(self.grad_values)
self.loss_value = None
self.grad_values = None
return grad_values