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# -*- coding: utf-8 -*- | |
"""TF-Hub: Fast Style Transfer for Arbitrary Styles.ipynb | |
Automatically generated by Colaboratory. | |
Original file is located at | |
https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb | |
##### Copyright 2019 The TensorFlow Hub Authors. | |
Licensed under the Apache License, Version 2.0 (the "License"); | |
""" | |
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# ============================================================================== | |
"""# Fast Style Transfer for Arbitrary Styles | |
<table class="tfo-notebook-buttons" align="left"> | |
<td> | |
<a target="_blank" href="https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a> | |
</td> | |
<td> | |
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a> | |
</td> | |
<td> | |
<a target="_blank" href="https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a> | |
</td> | |
<td> | |
<a href="https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/tf2_arbitrary_image_stylization.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a> | |
</td> | |
</table> | |
Based on the model code in [magenta](https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization) and the publication: | |
[Exploring the structure of a real-time, arbitrary neural artistic stylization | |
network](https://arxiv.org/abs/1705.06830). | |
*Golnaz Ghiasi, Honglak Lee, | |
Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens*, | |
Proceedings of the British Machine Vision Conference (BMVC), 2017. | |
## Setup | |
Let's start with importing TF-2 and all relevant dependencies. | |
""" | |
import functools | |
import os | |
from matplotlib import gridspec | |
import matplotlib.pylab as plt | |
import numpy as np | |
import tensorflow as tf | |
import tensorflow_hub as hub | |
import PIL.Image | |
print("TF Version: ", tf.__version__) | |
print("TF-Hub version: ", hub.__version__) | |
print("Eager mode enabled: ", tf.executing_eagerly()) | |
print("GPU available: ", tf.test.is_gpu_available()) | |
# @title Define image loading and visualization functions { display-mode: "form" } | |
def tensor_to_image(tensor): | |
tensor = tensor*255 | |
tensor = np.array(tensor, dtype=np.uint8) | |
if np.ndim(tensor)>3: | |
assert tensor.shape[0] == 1 | |
tensor = tensor[0] | |
return PIL.Image.fromarray(tensor) | |
def crop_center(image): | |
"""Returns a cropped square image.""" | |
shape = image.shape | |
new_shape = min(shape[1], shape[2]) | |
offset_y = max(shape[1] - shape[2], 0) // 2 | |
offset_x = max(shape[2] - shape[1], 0) // 2 | |
image = tf.image.crop_to_bounding_box( | |
image, offset_y, offset_x, new_shape, new_shape) | |
return image | |
@functools.lru_cache(maxsize=None) | |
def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True): | |
"""Loads and preprocesses images.""" | |
# Cache image file locally. | |
image_path = tf.keras.utils.get_file(os.path.basename(image_url)[-128:], image_url) | |
# Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. | |
img = plt.imread(image_path).astype(np.float32)[np.newaxis, ...] | |
if img.max() > 1.0: | |
img = img / 255. | |
if len(img.shape) == 3: | |
img = tf.stack([img, img, img], axis=-1) | |
img = crop_center(img) | |
img = tf.image.resize(img, image_size, preserve_aspect_ratio=True) | |
return img | |
def show_n(images, titles=('',)): | |
n = len(images) | |
image_sizes = [image.shape[1] for image in images] | |
w = (image_sizes[0] * 6) // 320 | |
plt.figure(figsize=(w * n, w)) | |
gs = gridspec.GridSpec(1, n, width_ratios=image_sizes) | |
for i in range(n): | |
plt.subplot(gs[i]) | |
plt.imshow(images[i][0], aspect='equal') | |
plt.axis('off') | |
plt.title(titles[i] if len(titles) > i else '') | |
plt.show() | |
"""Let's get as well some images to play with.""" | |
# @title Load example images { display-mode: "form" } | |
content_image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Golden_Gate_Bridge_from_Battery_Spencer.jpg/640px-Golden_Gate_Bridge_from_Battery_Spencer.jpg' # @param {type:"string"} | |
style_image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/Great_Wave_off_Kanagawa2.jpg/800px-Great_Wave_off_Kanagawa2.jpg' # @param {type:"string"} | |
output_image_size = 384 # @param {type:"integer"} | |
# The content image size can be arbitrary. | |
content_img_size = (output_image_size, output_image_size) | |
# The style prediction model was trained with image size 256 and it's the | |
# recommended image size for the style image (though, other sizes work as | |
# well but will lead to different results). | |
style_img_size = (256, 256) # Recommended to keep it at 256. | |
content_image = load_image(content_image_url, content_img_size) | |
style_image = load_image(style_image_url, style_img_size) | |
style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME') | |
#show_n([content_image, style_image], ['Content image', 'Style image']) | |
load_image | |
"""## Import TF-Hub module""" | |
# Load TF-Hub module. | |
hub_handle = 'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2' | |
hub_module = hub.load(hub_handle) | |
"""The signature of this hub module for image stylization is: | |
``` | |
outputs = hub_module(content_image, style_image) | |
stylized_image = outputs[0] | |
``` | |
Where `content_image`, `style_image`, and `stylized_image` are expected to be 4-D Tensors with shapes `[batch_size, image_height, image_width, 3]`. | |
In the current example we provide only single images and therefore the batch dimension is 1, but one can use the same module to process more images at the same time. | |
The input and output values of the images should be in the range [0, 1]. | |
The shapes of content and style image don't have to match. Output image shape | |
is the same as the content image shape. | |
## Demonstrate image stylization | |
""" | |
# Stylize content image with given style image. | |
# This is pretty fast within a few milliseconds on a GPU. | |
outputs = hub_module(tf.convert_to_tensor(content_image), tf.convert_to_tensor(style_image)) | |
stylized_image = outputs[0] | |
# Visualize input images and the generated stylized image. | |
#show_n([content_image, style_image, stylized_image], titles=['Original content image', 'Style image', 'Stylized image']) | |
"""## Let's try it on more images""" | |
# @title To Run: Load more images { display-mode: "form" } | |
content_urls = dict( | |
sea_turtle='https://upload.wikimedia.org/wikipedia/commons/d/d7/Green_Sea_Turtle_grazing_seagrass.jpg', | |
tuebingen='https://upload.wikimedia.org/wikipedia/commons/0/00/Tuebingen_Neckarfront.jpg', | |
grace_hopper='https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg', | |
) | |
style_urls = dict( | |
kanagawa_great_wave='https://upload.wikimedia.org/wikipedia/commons/0/0a/The_Great_Wave_off_Kanagawa.jpg', | |
kandinsky_composition_7='https://upload.wikimedia.org/wikipedia/commons/b/b4/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg', | |
hubble_pillars_of_creation='https://upload.wikimedia.org/wikipedia/commons/6/68/Pillars_of_creation_2014_HST_WFC3-UVIS_full-res_denoised.jpg', | |
van_gogh_starry_night='https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/1024px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg', | |
turner_nantes='https://upload.wikimedia.org/wikipedia/commons/b/b7/JMW_Turner_-_Nantes_from_the_Ile_Feydeau.jpg', | |
munch_scream='https://upload.wikimedia.org/wikipedia/commons/c/c5/Edvard_Munch%2C_1893%2C_The_Scream%2C_oil%2C_tempera_and_pastel_on_cardboard%2C_91_x_73_cm%2C_National_Gallery_of_Norway.jpg', | |
picasso_demoiselles_avignon='https://upload.wikimedia.org/wikipedia/en/4/4c/Les_Demoiselles_d%27Avignon.jpg', | |
picasso_violin='https://upload.wikimedia.org/wikipedia/en/3/3c/Pablo_Picasso%2C_1911-12%2C_Violon_%28Violin%29%2C_oil_on_canvas%2C_Kr%C3%B6ller-M%C3%BCller_Museum%2C_Otterlo%2C_Netherlands.jpg', | |
picasso_bottle_of_rum='https://upload.wikimedia.org/wikipedia/en/7/7f/Pablo_Picasso%2C_1911%2C_Still_Life_with_a_Bottle_of_Rum%2C_oil_on_canvas%2C_61.3_x_50.5_cm%2C_Metropolitan_Museum_of_Art%2C_New_York.jpg', | |
fire='https://upload.wikimedia.org/wikipedia/commons/3/36/Large_bonfire.jpg', | |
derkovits_woman_head='https://upload.wikimedia.org/wikipedia/commons/0/0d/Derkovits_Gyula_Woman_head_1922.jpg', | |
amadeo_style_life='https://upload.wikimedia.org/wikipedia/commons/8/8e/Untitled_%28Still_life%29_%281913%29_-_Amadeo_Souza-Cardoso_%281887-1918%29_%2817385824283%29.jpg', | |
derkovtis_talig='https://upload.wikimedia.org/wikipedia/commons/3/37/Derkovits_Gyula_Talig%C3%A1s_1920.jpg', | |
amadeo_cardoso='https://upload.wikimedia.org/wikipedia/commons/7/7d/Amadeo_de_Souza-Cardoso%2C_1915_-_Landscape_with_black_figure.jpg' | |
) | |
content_image_size = 384 | |
style_image_size = 256 | |
content_images = {k: load_image(v, (content_image_size, content_image_size)) for k, v in content_urls.items()} | |
style_images = {k: load_image(v, (style_image_size, style_image_size)) for k, v in style_urls.items()} | |
style_images = {k: tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME') for k, style_image in style_images.items()} | |
#@title Specify the main content image and the style you want to use. { display-mode: "form" } | |
content_name = 'sea_turtle' # @param ['sea_turtle', 'tuebingen', 'grace_hopper'] | |
style_name = 'munch_scream' # @param ['kanagawa_great_wave', 'kandinsky_composition_7', 'hubble_pillars_of_creation', 'van_gogh_starry_night', 'turner_nantes', 'munch_scream', 'picasso_demoiselles_avignon', 'picasso_violin', 'picasso_bottle_of_rum', 'fire', 'derkovits_woman_head', 'amadeo_style_life', 'derkovtis_talig', 'amadeo_cardoso'] | |
stylized_image = hub_module(tf.convert_to_tensor(content_images[content_name]), | |
tf.convert_to_tensor(style_images[style_name]))[0] | |
#show_n([content_images[content_name], style_images[style_name], stylized_image],titles=['Original content image', 'Style image', 'Stylized image']) | |
file_name = 'stylized-image.png' | |
tensor = tf.Variable(stylized_image, validate_shape=False) | |
tensor_to_image(tensor).save(file_name) |
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