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'''NEURAL STYLE TRANSFER '''
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import gradio as gr
from PIL import Image
np.set_printoptions(suppress=True)
model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
def tensor_to_image(tensor):
tensor *= 255
tensor = np.array(tensor, dtype=np.uint8)
if tensor.ndim > 3:
tensor = tensor[0]
return Image.fromarray(tensor)
def transform_my_model(content_image, style_image):
content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.0
style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.0
stylized_image = model(tf.constant(content_image), tf.constant(style_image))[0]
return tensor_to_image(stylized_image)
demo = gr.Interface(
fn=transform_my_model,
inputs=[gr.Image(label="Content Image"), gr.Image(label="Style Image")],
outputs=gr.Image(label="Result"),
title="Style Transfer",
examples=[
["Content_Images/contnt12.jpg", "VG516.jpg"],
["Content_Images/contnt2.jpg", "Content_Images/styl9.jpg"],
["Content_Images/contnt.jpg", "Content_Images/styl22.jpg"]
],
article="References-\n\nExploring the structure of a real-time, arbitrary neural artistic stylization network. Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin."
)
demo.launch(share=True)
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