| import os | |
| os.system("hub install openpose_body_estimation==1.0.0") | |
| import gradio as gr | |
| import paddlehub as hub | |
| import numpy as np | |
| from PIL import Image | |
| model = hub.Module(name='openpose_body_estimation') | |
| def inference(image): | |
| result = model.predict(image.name) | |
| return Image.fromarray(np.uint8(result[0])[:,:,::-1]).convert('RGB') | |
| title = "OpenPose" | |
| description = "Gradio demo for UGATIT-selfie2anime. 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/1907.10830' target='_blank'>U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation</a> | <a href='https://github.com/taki0112/UGATIT' target='_blank'>Github Repo</a></p>" | |
| iface = gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),enable_queue=True,title=title,article=article,description=description) | |
| iface.launch() |