KerasでNeural style transferを実行する方法

KerasでNeural style transferを実行する方法をご紹介します。

条件

  • Python 3.7.0
  • Keras 2.2.4
  • tensorflow 1.14.0
  • Pillow 6.1.0
  • Windows 10 64bit

Neural Style Transferとは?

Neural Style Transfer(NST)は、デジタル画像またはビデオを操作して別の画像の外観または視覚的スタイルを採用するソフトウェアアルゴリズムの一種です。

出典:wikipedia

事前準備

必要なライブラリをインストールします。

以下のコマンドを実行します。

Keras

pip install keras

Tensorflow

pip install tensorflow

Pillow

pip install pillow

その他

GPUで実行するとより高速になります。
GPUでKerasを実行する方法は以下の記事をご参照ください。

Windows 10でtensorflow-gpuを使う方法

Neural style transfer 実行

ソース

以下のサイト(Keras Documentation)からソースをコピーし、neural_style_transfer.pyとして保存します。

https://keras.io/examples/neural_style_transfer/

# neural_style_transfer.py

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

from keras.applications import vgg19
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.')
parser.add_argument('--iter', type=int, default=10, required=False,
                    help='Number of iterations to run.')
parser.add_argument('--content_weight', type=float, default=0.025, required=False,
                    help='Content weight.')
parser.add_argument('--style_weight', type=float, default=1.0, required=False,
                    help='Style weight.')
parser.add_argument('--tv_weight', type=float, default=1.0, required=False,
                    help='Total Variation weight.')

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
iterations = args.iter

# these are the weights of the different loss components
total_variation_weight = args.tv_weight
style_weight = args.style_weight
content_weight = args.content_weight

# dimensions of the generated picture.
width, height = load_img(base_image_path).size
img_nrows = 400
img_ncols = int(width * img_nrows / height)

# 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 = vgg19.preprocess_input(img)
    return img

# util function to convert a tensor into a valid image


def deprocess_image(x):
    if K.image_data_format() == 'channels_first':
        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_data_format() == 'channels_first':
    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 VGG19 network with our 3 images as input
# the model will be loaded with pre-trained ImageNet weights
model = vgg19.VGG19(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_data_format() == 'channels_first':
        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)) / (4.0 * (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_data_format() == 'channels_first':
        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(0.0)
layer_features = outputs_dict['block5_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 isinstance(grads, (list, tuple)):
    outputs += grads
else:
    outputs.append(grads)

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


def eval_loss_and_grads(x):
    if K.image_data_format() == 'channels_first':
        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
x = preprocess_image(base_image_path)

for i in range(iterations):
    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
    save_img(fname, img)
    end_time = time.time()
    print('Image saved as', fname)
    print('Iteration %d completed in %ds' % (i, end_time - start_time))

ディレクトリ構成

以下のようなディレクトリとします。

imgの下に、元の画像(baseとreference)を配置して、resultsの下に生成画像(my_result_at_iteration_*.png)が保存されるようにします。

/
    neural_style_transfer.py
    img/
        base.jpg
        reference.jpg
    results/
        my_result_at_iteration_*.png

コマンド実行

neural_style_transfer.pyのファイルが存在するディレクトリで、以下のコマンドを実行します。

python neural_style_transfer.py img/base.jpg img/reference.jpg results/my_result

以下のようなパラメータを指定することも出来ます。

--iter, To specify the number of iterations     the style transfer takes place (Default is 10)
--content_weight, The weight given to the content loss (Default is 0.025)
--style_weight, The weight given to the style loss (Default is 1.0)
--tv_weight, The weight given to the total variation loss (Default is 1.0)

実行結果

base画像

reference画像

生成画像

resultsの下に生成画像(my_result_at_iteration_*.png)が保存されます。

イテレーションが進むにつれて、スタイルがより強く適用されていくことが分かります。

参考

Keras Documentation:Neural style transfer

https://keras.io/examples/neural_style_transfer/

Image Style Transfer Using Convolutional Neural Networks

http://openaccess.thecvf.com/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf

Wikipedia:DeepDream

https://en.wikipedia.org/wiki/Neural_Style_Transfer

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