| import os | |
| os.system("hub install stylepro_artistic==1.0.1") | |
| import gradio as gr | |
| import paddlehub as hub | |
| import numpy as np | |
| from PIL import Image | |
| import cv2 | |
| stylepro_artistic = hub.Module(name="stylepro_artistic") | |
| def inference(content,style): | |
| result = stylepro_artistic.style_transfer( | |
| images=[{ | |
| 'content': cv2.imread(content.name), | |
| 'styles': [cv2.imread(style.name)] | |
| }]) | |
| return Image.fromarray(np.uint8(result[0]['data'])[:,:,::-1]).convert('RGB') | |
| title = " StyleProNet" | |
| description = "Gradio demo for Parameter-Free Style Projection for Arbitrary Style Transfer. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2003.07694'target='_blank'>Parameter-Free Style Projection for Arbitrary Style Transfer</a> | <a href='https://github.com/PaddlePaddle/PaddleHub' target='_blank'>Github Repo</a></p>" | |
| examples=[['mona1.jpeg','starry.jpeg']] | |
| iface = gr.Interface(inference, inputs=[gr.inputs.Image(type="file",label='content'),gr.inputs.Image(type="file",label='style')], outputs=gr.outputs.Image(type="pil"),enable_queue=True,title=title,article=article,description=description,examples=examples) | |
| iface.launch() |