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Neural artistic styler
'''
Neural artistic styler
Based on: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, "A Neural Algorithm of Artistic Style", arXiv:1508.06576
Examples: https://www.bonaccorso.eu
See also: https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py
Giuseppe Bonaccorso (https://www.bonaccorso.eu)
'''
from __future__ import print_function
import numpy as np
import math
import keras.backend as K
from keras.applications import vgg16, vgg19
from keras.applications.imagenet_utils import preprocess_input
from scipy.optimize import minimize
from scipy.misc import imread, imsave, imresize
# Set random seed (for reproducibility)
np.random.seed(1000)
class NeuralStyler(object):
def __init__(self, picture_image_filepath, style_image_filepath, destination_folder,
width=512, height=512, weights_filepath=None,
alpha_picture=1.0, alpha_style=0.00001, save_every_n_steps=20,
verbose=False,
convnet='VGG16',
picture_layer='block5_conv1',
style_layers=('block1_conv1',
'block2_conv1',
'block3_conv1',
'block4_conv1',
'block5_conv1')):
'''
Artistic neural styler based on VGG16/19 convolutional network
Based on: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge,
"A Neural Algorithm of Artistic Style", arXiv:1508.06576
Parameters
----------
:param picture_image_filepath: Content file path
:param style_image_filepath: Style file path
:param destination_folder: Result destination folder
:param width: Image width (Default: 512px)
:param height: Image height (Default: 512px)
:param weights_filepath: Optional VGG19 weights filepath (HDF5)
:param alpha_picture: Content loss function weight
:param alpha_style: Style loss function weight
:param save_every_n_steps: Save a picture every n optimization steps
:param verbose: Print loss function values
:param convnet: One of: VGG16 or VGG19
:param picture_layer: Convnet layer used in the content loss function
:param style_layers: Convnet layers used in the style loss function
Usage examples
----------
Picture and style over random:
canvas='random_from_style'
alpha_style=1.0
alpha_picture=0.25
picture_layer='block4_conv1' (both VGG16 and VGG19)
Style over picture:
canvas='picture'
alpha_style=0.0025
alpha_picture=1.0
picture_layer='block4_conv1' (both VGG16 and VGG19)
Picture over style:
canvas='style'
alpha_style=0.001
alpha_picture=1.0
picture_layer='block5_conv1' (both VGG16 and VGG19)
'''
if picture_image_filepath is None or style_image_filepath is None or destination_folder is None:
raise ValueError('Picture, style image or destination filepath is/are missing')
if convnet not in ('VGG16', 'VGG19'):
raise ValueError('Convnet must be one of: VGG16 or VGG19')
self.picture_image_filepath = picture_image_filepath
self.style_image_filepath = style_image_filepath
self.destination_folder = destination_folder
self.width = width
self.height = height
self.image_shape = (self.height, self.width, 3)
self.e_image_shape = (1,) + self.image_shape
self.alpha_picture = alpha_picture
self.alpha_style = alpha_style
self.save_every_n_steps = save_every_n_steps
self.verbose = verbose
self.layers = style_layers if picture_layer in style_layers else style_layers + (picture_layer,)
self.iteration = 0
self.step = 0
self.styled_image = None
# Create convnet
print('Creating convolutional network')
if convnet == 'VGG16':
convnet = vgg16.VGG16(include_top=False, weights='imagenet' if weights_filepath is None else None)
else:
convnet = vgg19.VGG19(include_top=False, weights='imagenet' if weights_filepath is None else None)
if weights_filepath is not None:
print('Loading model weights from: %s' % weights_filepath)
convnet.load_weights(filepath=weights_filepath)
# Convnet output function
self.get_convnet_output = K.function(inputs=[convnet.layers[0].input],
outputs=[convnet.get_layer(t).output for t in self.layers])
# Load picture image
self.picture_image = self.pre_process_image(imread(picture_image_filepath).
reshape(self.e_image_shape).astype(K.floatx()))
print('Loading picture: %s (%dx%d)' % (self.picture_image_filepath,
self.picture_image.shape[2],
self.picture_image.shape[1]))
picture_tensor = K.variable(value=self.get_convnet_output([self.picture_image])[self.layers.index(picture_layer)])
# Load style image
original_style_image = imread(self.style_image_filepath)
print('Loading style image: %s (%dx%d)' % (self.style_image_filepath,
original_style_image.shape[1],
original_style_image.shape[0]))
# Check for style image size
if (original_style_image.shape[0] != self.picture_image.shape[1]) or \
(original_style_image.shape[1] != self.picture_image.shape[2]):
# Resize image
print('Resizing style image to match picture size: (%dx%d)' %
(self.picture_image.shape[2], self.picture_image.shape[1]))
original_style_image = imresize(original_style_image,
size=(self.picture_image.shape[1], self.picture_image.shape[2]),
interp='lanczos')
self.style_image = self.pre_process_image(original_style_image.reshape(self.e_image_shape).astype(K.floatx()))
# Create style tensors
style_outputs = self.get_convnet_output([self.style_image])
style_tensors = [self.gramian(o) for o in style_outputs]
# Compute loss function(s)
print('Compiling loss and gradient functions')
# Picture loss function
picture_loss_function = 0.5 * K.sum(K.square(picture_tensor - convnet.get_layer(picture_layer).output))
# Style loss function
style_loss_function = 0.0
style_loss_function_weight = 1.0 / float(len(style_layers))
for i, style_layer in enumerate(style_layers):
style_loss_function += \
(style_loss_function_weight *
(1.0 / (4.0 * (style_outputs[i].shape[1] ** 2.0) * (style_outputs[i].shape[3] ** 2.0))) *
K.sum(K.square(style_tensors[i] - self.gramian(convnet.get_layer(style_layer).output))))
# Composite loss function
composite_loss_function = (self.alpha_picture * picture_loss_function) + \
(self.alpha_style * style_loss_function)
loss_function_inputs = [convnet.get_layer(l).output for l in self.layers]
loss_function_inputs.append(convnet.layers[0].input)
self.loss_function = K.function(inputs=loss_function_inputs,
outputs=[composite_loss_function])
# Composite loss function gradient
loss_gradient = K.gradients(loss=composite_loss_function, variables=[convnet.layers[0].input])
self.loss_function_gradient = K.function(inputs=[convnet.layers[0].input],
outputs=loss_gradient)
def fit(self, iterations=100, canvas='random', canvas_image_filepath=None, optimization_method='CG'):
'''
Create styled image
:param iterations: Number of optimization iterations
:param canvas: One of:
'random': RGB random image
'random_from_style': random image generated from style image pixels
'random_from_picture': random image generated from picture pixels
'style': Style image
'picture': Picture
'custom': Custom image specified by canvas_image paramater
:param canvas_image_filepath: Canvas initial image file path
:param optimization_method: Optimization method passed to SciPy optimize
(See https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.optimize.minimize.html
for further information)
Allowed options are:
- Nelder-Mead
- Powell
- CG (Default)
- BFGS
- Newton-CG
- L-BFGS-B
- TNC
- COBYLA
- SLSQP
- dogleg
- trust-ncg
'''
if canvas not in ('random', 'random_from_style', 'random_from_picture', 'style', 'picture', 'custom'):
raise ValueError('Canvas must be one of: random, random_from_style, '
'random_from_picture, style, picture, custom')
# Generate random image
if canvas == 'random':
self.styled_image = self.pre_process_image(np.random.uniform(0, 256,
size=self.e_image_shape).astype(K.floatx()))
elif canvas == 'style':
self.styled_image = self.style_image.copy()
elif canvas == 'picture':
self.styled_image = self.picture_image.copy()
elif canvas == 'custom':
self.styled_image = self.pre_process_image(imread(canvas_image_filepath).
reshape(self.e_image_shape).astype(K.floatx()))
else:
self.styled_image = np.ndarray(shape=self.e_image_shape)
for x in range(self.width):
for y in range(self.height):
x_p = np.random.randint(0, self.width - 1)
y_p = np.random.randint(0, self.height - 1)
self.styled_image[0, y, x, :] = \
self.style_image[0, y_p, x_p, :] if canvas == 'random_from_style' \
else self.picture_image[0, y_p, x_p, :]
bounds = None
# Set bounds if the optimization method supports them
if optimization_method in ('L-BFGS-B', 'TNC', 'SLSQP'):
bounds = np.ndarray(shape=(self.styled_image.flatten().shape[0], 2))
bounds[:, 0] = -128.0
bounds[:, 1] = 128.0
print('Starting optimization with method: %r' % optimization_method)
for _ in xrange(iterations):
self.iteration += 1
if self.verbose:
print('Starting iteration: %d' % self.iteration)
minimize(fun=self.loss, x0=self.styled_image.flatten(), jac=self.loss_gradient,
callback=self.callback, bounds=bounds, method=optimization_method)
self.save_image(self.styled_image)
def loss(self, image):
outputs = self.get_convnet_output([image.reshape(self.e_image_shape).astype(K.floatx())])
outputs.append(image.reshape(self.e_image_shape).astype(K.floatx()))
v_loss = self.loss_function(outputs)[0]
if self.verbose:
print('\tLoss: %.2f' % v_loss)
# Check whether loss has become NaN
if math.isnan(v_loss):
print('NaN Loss function value')
return v_loss
def loss_gradient(self, image):
return np.array(self.loss_function_gradient([image.reshape(self.e_image_shape).astype(K.floatx())])).\
astype('float64').flatten()
def callback(self, image):
self.step += 1
self.styled_image = image.copy()
if self.verbose:
print('Optimization step: %d/%d' % (self.step, self.iteration))
if self.step == 1 or self.step % self.save_every_n_steps == 0:
self.save_image(image)
def save_image(self, image):
imsave(self.destination_folder + 'img_' + str(self.step) + '_' + str(self.iteration) + '.jpg',
self.post_process_image(image.reshape(self.e_image_shape).copy()))
@staticmethod
def gramian(filters):
c_filters = K.batch_flatten(K.permute_dimensions(K.squeeze(filters, axis=0), pattern=(2, 0, 1)))
return K.dot(c_filters, K.transpose(c_filters))
@staticmethod
def pre_process_image(image):
return preprocess_input(image)
@staticmethod
def post_process_image(image):
image[:, :, :, 0] += 103.939
image[:, :, :, 1] += 116.779
image[:, :, :, 2] += 123.68
return np.clip(image[:, :, :, ::-1], 0, 255).astype('uint8')[0]
if __name__ == '__main__':
print('Neural artistic styler')
neural_styler = NeuralStyler(picture_image_filepath='picture.jpg',
style_image_filepath='painting.jpg',
destination_folder='\destination\',
width=512,
height=512,
alpha_picture=1.0,
alpha_style=0.0001,
picture_layer='block4_conv1',
style_layers=('block1_conv1',
'block2_conv1',
'block3_conv1',
'block4_conv1',
'block5_conv1'))
# Create styled image
neural_styler.fit(canvas='style')

jveres commented Nov 13, 2016

Hi! I'm trying to run your script but getting this error:

Traceback (most recent call last):
File "neural_styler.py", line 271, in
'block5_conv1'))
File "neural_styler.py", line 121, in init
reshape(self.e_image_shape).astype(K.floatx()))
ValueError: total size of new array must be unchanged

Owner

giuseppebonaccorso commented Nov 14, 2016 edited

The size of style and picture images must be the same and it has to be declared using width and height parameters.

jveres commented Nov 14, 2016

Thanks, it works that way.

Now the limitation of equal dimensions has been removed. When the style image has a different shape, it will be automatically resized.

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