'''Neural style transfer with Keras.

Run the script with:
```
python neural_style_transfer.py path_to_your_base_image.jpg path_to_your_reference.jpg prefix_for_results
```
e.g.:
```
python neural_style_transfer.py img/tuebingen.jpg img/starry_night.jpg results/my_result
```

It is preferable to run this script on GPU, for speed.

Example result: https://twitter.com/fchollet/status/686631033085677568

# Details

Style transfer consists in generating an image
with the same "content" as a base image, but with the
"style" of a different picture (typically artistic).

This is achieved through the optimization of a loss function
that has 3 components: "style loss", "content loss",
and "total variation loss":

- The total variation loss imposes local spatial continuity between
the pixels of the combination image, giving it visual coherence.

- The style loss is where the deep learning keeps in --that one is defined
using a deep convolutional neural network. Precisely, it consists in a sum of
L2 distances between the Gram matrices of the representations of
the base image and the style reference image, extracted from
different layers of a convnet (trained on ImageNet). The general idea
is to capture color/texture information at different spatial
scales (fairly large scales --defined by the depth of the layer considered).

 - The content loss is a L2 distance between the features of the base
image (extracted from a deep layer) and the features of the combination image,
keeping the generated image close enough to the original one.

# References
    - [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)
'''

from __future__ import print_function
from keras.preprocessing.image import load_img, img_to_array
from scipy.misc import imsave
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
import time
import argparse

from keras.applications import vgg16
from keras import backend as K

parser = argparse.ArgumentParser(description='Neural style transfer with Keras.')
parser.add_argument('base_image_path', metavar='base', type=str,
                    help='Path to the image to transform.')
parser.add_argument('style_reference_image_path', metavar='ref', type=str,
                    help='Path to the style reference image.')
parser.add_argument('result_prefix', metavar='res_prefix', type=str,
                    help='Prefix for the saved results.')

args = parser.parse_args()
base_image_path = args.base_image_path
style_reference_image_path = args.style_reference_image_path
result_prefix = args.result_prefix

# these are the weights of the different loss components
total_variation_weight = 1.
style_weight = 1.
content_weight = 0.025

# dimensions of the generated picture.
img_nrows = 400
img_ncols = 400
assert img_ncols == img_nrows, 'Due to the use of the Gram matrix, width and height must match.'

# util function to open, resize and format pictures into appropriate tensors
def preprocess_image(image_path):
    img = load_img(image_path, target_size=(img_nrows, img_ncols))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg16.preprocess_input(img)
    return img

# util function to convert a tensor into a valid image
def deprocess_image(x):
    if K.image_dim_ordering() == 'th':
        x = x.reshape((3, img_nrows, img_ncols))
        x = x.transpose((1, 2, 0))
    else:
        x = x.reshape((img_nrows, img_ncols, 3))
    # Remove zero-center by mean pixel
    x[:, :, 0] += 103.939
    x[:, :, 1] += 116.779
    x[:, :, 2] += 123.68
    # 'BGR'->'RGB'
    x = x[:, :, ::-1]
    x = np.clip(x, 0, 255).astype('uint8')
    return x

# get tensor representations of our images
base_image = K.variable(preprocess_image(base_image_path))
style_reference_image = K.variable(preprocess_image(style_reference_image_path))

# this will contain our generated image
if K.image_dim_ordering() == 'th':
    combination_image = K.placeholder((1, 3, img_nrows, img_ncols))
else:
    combination_image = K.placeholder((1, img_nrows, img_ncols, 3))

# combine the 3 images into a single Keras tensor
input_tensor = K.concatenate([base_image,
                              style_reference_image,
                              combination_image], axis=0)

# build the VGG16 network with our 3 images as input
# the model will be loaded with pre-trained ImageNet weights
model = vgg16.VGG16(input_tensor=input_tensor,
                    weights='imagenet', include_top=False)
print('Model loaded.')

# get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])

# compute the neural style loss
# first we need to define 4 util functions

# the gram matrix of an image tensor (feature-wise outer product)
def gram_matrix(x):
    assert K.ndim(x) == 3
    if K.image_dim_ordering() == 'th':
        features = K.batch_flatten(x)
    else:
        features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
    gram = K.dot(features, K.transpose(features))
    return gram

# the "style loss" is designed to maintain
# the style of the reference image in the generated image.
# It is based on the gram matrices (which capture style) of
# feature maps from the style reference image
# and from the generated image
def style_loss(style, combination):
    assert K.ndim(style) == 3
    assert K.ndim(combination) == 3
    S = gram_matrix(style)
    C = gram_matrix(combination)
    channels = 3
    size = img_nrows * img_ncols
    return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))

# an auxiliary loss function
# designed to maintain the "content" of the
# base image in the generated image
def content_loss(base, combination):
    return K.sum(K.square(combination - base))

# the 3rd loss function, total variation loss,
# designed to keep the generated image locally coherent
def total_variation_loss(x):
    assert K.ndim(x) == 4
    if K.image_dim_ordering() == 'th':
        a = K.square(x[:, :, :img_nrows-1, :img_ncols-1] - x[:, :, 1:, :img_ncols-1])
        b = K.square(x[:, :, :img_nrows-1, :img_ncols-1] - x[:, :, :img_nrows-1, 1:])
    else:
        a = K.square(x[:, :img_nrows-1, :img_ncols-1, :] - x[:, 1:, :img_ncols-1, :])
        b = K.square(x[:, :img_nrows-1, :img_ncols-1, :] - x[:, :img_nrows-1, 1:, :])
    return K.sum(K.pow(a + b, 1.25))

# combine these loss functions into a single scalar
loss = K.variable(0.)
layer_features = outputs_dict['block4_conv2']
base_image_features = layer_features[0, :, :, :]
combination_features = layer_features[2, :, :, :]
loss += content_weight * content_loss(base_image_features,
                                      combination_features)

feature_layers = ['block1_conv1', 'block2_conv1',
                  'block3_conv1', 'block4_conv1',
                  'block5_conv1']
for layer_name in feature_layers:
    layer_features = outputs_dict[layer_name]
    style_reference_features = layer_features[1, :, :, :]
    combination_features = layer_features[2, :, :, :]
    sl = style_loss(style_reference_features, combination_features)
    loss += (style_weight / len(feature_layers)) * sl
loss += total_variation_weight * total_variation_loss(combination_image)

# get the gradients of the generated image wrt the loss
grads = K.gradients(loss, combination_image)

outputs = [loss]
if type(grads) in {list, tuple}:
    outputs += grads
else:
    outputs.append(grads)

f_outputs = K.function([combination_image], outputs)

def eval_loss_and_grads(x):
    if K.image_dim_ordering() == 'th':
        x = x.reshape((1, 3, img_nrows, img_ncols))
    else:
        x = x.reshape((1, img_nrows, img_ncols, 3))
    outs = f_outputs([x])
    loss_value = outs[0]
    if len(outs[1:]) == 1:
        grad_values = outs[1].flatten().astype('float64')
    else:
        grad_values = np.array(outs[1:]).flatten().astype('float64')
    return loss_value, grad_values

# this Evaluator class makes it possible
# to compute loss and gradients in one pass
# while retrieving them via two separate functions,
# "loss" and "grads". This is done because scipy.optimize
# requires separate functions for loss and gradients,
# but computing them separately would be inefficient.
class Evaluator(object):
    def __init__(self):
        self.loss_value = None
        self.grads_values = None

    def loss(self, x):
        assert self.loss_value is None
        loss_value, grad_values = 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

evaluator = Evaluator()

# run scipy-based optimization (L-BFGS) over the pixels of the generated image
# so as to minimize the neural style loss
if K.image_dim_ordering() == 'th':
    x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.
else:
    x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.

for i in range(10):
    print('Start of iteration', i)
    start_time = time.time()
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
                                     fprime=evaluator.grads, maxfun=20)
    print('Current loss value:', min_val)
    # save current generated image
    img = deprocess_image(x.copy())
    fname = result_prefix + '_at_iteration_%d.png' % i
    imsave(fname, img)
    end_time = time.time()
    print('Image saved as', fname)
    print('Iteration %d completed in %ds' % (i, end_time - start_time))