app.py

# -*- 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 PIL import Image
from matplotlib import gridspec
import matplotlib.pylab as plt
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import gradio as gr

# @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, 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(image)
  img = tf.image.resize(image, 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://live.staticflickr.com/65535/52032998695_f57c61746c_c.jpg'  # @param {type:"string"}
#style_image_url = 'https://live.staticflickr.com/65535/52032731604_a815a0b19f_c.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.



# Load images from app
content_image_input = gr.inputs.Image(label="Content Image")
style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image")



#content_image = load_image(content_image_input, content_img_size)
#style_image = load_image(style_image_input, 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.
'''
def modify(imageinput,style_input):
    content_image = load_image(imageinput, content_img_size)
    style_image = load_image(style_input, 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'])
    outputs = hub_module(tf.constant(imageinput), tf.constant(style_input))
    return outputs[0]
'''
def perform_style_transfer(content_image, style_image):
    
    content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255.
    style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255.
    
    output = hub_module(content_image, style_image)
    stylized_image = output[0]
    
    return Image.fromarray(np.uint8(stylized_image[0] * 255))
#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'])

# Gradio app

#label = gr.outputs.Image(modify(content_image_input, style_image_input))
app_interface = gr.Interface(perform_style_transfer,
                             inputs=[content_image_input, style_image_input],
                             outputs = gr.outputs.Image(),
                             title="Fast Neural Style Transfer",
                             description="Gradio demo for Fast Neural Style Transfer using a pretrained Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image. To learn more about the project, please find the references listed below.",
                             )
app_interface.launch(debug= True)