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examples/colab/tf_hub_fast_style_transfer_for_arbitrary_styles.py

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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 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>
+  <td>
+    <a href="https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2"><img src="https://www.tensorflow.org/images/hub_logo_32px.png" />See TF Hub model</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 TF2 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
+
+print("TF Version: ", tf.__version__)
+print("TF Hub version: ", hub.__version__)
+print("Eager mode enabled: ", tf.executing_eagerly())
+print("GPU available: ", tf.config.list_physical_devices('GPU'))
+
+# @title Define image loading and visualization functions  { display-mode: "form" }
+
+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 = tf.io.decode_image(
+      tf.io.read_file(image_path),
+      channels=3, dtype=tf.float32)[tf.newaxis, ...]
+  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/0/0a/The_Great_Wave_off_Kanagawa.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'])
+
+"""## 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.constant(content_image), tf.constant(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.constant(content_images[content_name]),
+                            tf.constant(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'])