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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| style_transfer_model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2") | |
| 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 = style_transfer_model(content_image, style_image) | |
| stylized_image = output[0] | |
| return Image.fromarray(np.uint8(stylized_image[0] * 255)) | |
| content_image_input = gr.inputs.Image(label="Content Image") | |
| style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image") | |
| # Examples | |
| golden_gate = ["examples/golden_gate_bridge.jpeg", "examples/the_great_wave.jpeg"] | |
| joshua_tree = ["examples/joshua_tree.jpeg", "examples/starry_night.jpeg"] | |
| glacier = ["examples/glacier_national_park.jpeg", "examples/the_scream.jpeg"] | |
| app_interface = gr.Interface(fn=perform_style_transfer, | |
| inputs=[content_image_input, style_image_input], | |
| 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, or click one of the examples to load them. To learn more about the project, please find the references listed below.", | |
| examples=[golden_gate, joshua_tree, glacier]) | |
| app_interface.launch() |