Duplicate from FrankAst/image_mixer

Co-authored-by: Franco Astegiano <FrankAst@users.noreply.huggingface.co>

.github/workflows/main.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN}}
+        run: git push --force https://FrankAst:$HF_TOKEN@huggingface.co/spaces/FrankAst/image_mixer

README.md

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+---
+title: Image_mixer
+emoji: ⚡
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 2.9.4
+app_file: app.py
+pinned: false
+duplicated_from: FrankAst/image_mixer
+---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.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 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)
+

requirements.txt

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+numpy==1.21.2
+tensorflow==2.2.0
+tensorflow_hub==0.12.0