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import gradio as gr |
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import numpy as np |
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from PIL import Image |
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import tensorflow as tf |
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import tensorflow_hub as hub |
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style_transfer_model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2") |
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def perform_style_transfer(content_image, style_image): |
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content_image = tf.convert_to_tensor(content_image, np.float32)[tf.newaxis, ...] / 255. |
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style_image = tf.convert_to_tensor(style_image, np.float32)[tf.newaxis, ...] / 255. |
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output = style_transfer_model(content_image, style_image) |
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stylized_image = output[0] |
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return Image.fromarray(np.uint8(stylized_image[0] * 255)) |
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content_image_input = gr.inputs.Image(label="Content Image") |
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style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image") |
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golden_gate = ["golden_gate_bridge.jpeg", "the_great_wave.jpeg"] |
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joshua_tree = ["joshua_tree.jpeg", "starry_night.jpeg"] |
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glacier = ["glacier_national_park.jpeg", "the_scream.jpg"] |
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app_interface = gr.Interface(fn=perform_style_transfer, |
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inputs=[content_image_input, style_image_input], |
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outputs="image", |
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title="Fast Neural Style Transfer", |
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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, or click one of the examples to load them. To learn more about the project, please find the references listed below.", |
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examples=[glacier, golden_gate, joshua_tree], |
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article="**References**\n\n" |
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"<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>1. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub</a> \n" |
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"<a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer' target='_blank'>2. The idea to build a neural style transfer application was inspired from this Hugging Face Space </a>") |
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app_interface.launch() |
