Duplicate from luca-martial/neural-style-transfer

Co-authored-by: Luca Martial <[email protected]>

.gitattributes

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README.md

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+---
+title: Neural Style Transfer
+emoji: 💩
+colorFrom: pink
+colorTo: red
+sdk: gradio
+app_file: app.py
+pinned: false
+duplicated_from: luca-martial/neural-style-transfer
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_  
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+import gradio as gr
+import tensorflow as tf
+import tensorflow_hub as hub
+import matplotlib.pyplot as plt
+import numpy as np
+import PIL.Image 
+
+# Load model from TF-Hub
+hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2') 
+
+# Function to convert tensor to image
+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)
+
+# Stylize function
+def stylize(content_image, style_image):
+    # Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
+    content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
+    style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
+    # Stylize image
+    stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
+    return tensor_to_image(stylized_image)
+
+# Add image examples for users    
+joker = ["example_joker.jpeg", "example_polasticot1.jpeg"]
+paris = ["example_paris.jpeg", "example_vangogh.jpeg"]
+einstein = ["example_einstein.jpeg", "example_polasticot2.jpeg"]
+
+# Customize interface
+title = "Fast Neural Style Transfer using TF-Hub"
+description = "Demo for neural style transfer using the pretrained Arbitrary Image Stylization model from TensorFlow Hub."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1705.06830'>Exploring the structure of a real-time, arbitrary neural artistic stylization network</a></p>"
+content_input = gr.inputs.Image(label="Content Image", source="upload")
+style_input = gr.inputs.Image(label="Style Image", source="upload")
+
+# Build and launch
+iface = gr.Interface(fn=stylize, 
+                     inputs=[content_input, style_input], 
+                     outputs="image",
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=[joker, paris, einstein])
+iface.launch()

requirements.txt

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+numpy
+matplotlib
+tensorflow
+tensorflow_hub