| 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['data'])[:,:,::-1]).convert('RGB') | |
| title = "OpenPose" | |
| description = "Gradio demo for OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation. 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/1812.08008' target='_blank'>OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields</a> | <a href='https://github.com/CMU-Perceptual-Computing-Lab/openpose' target='_blank'>Github Repo</a></p>" | |
| examples=[['people.jpeg']] | |
| 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,examples=examples) | |
| iface.launch() |