app.py

#!/usr/bin/env python
# coding: utf-8

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import os
import cv2
import paddlehub as hub
import gradio as gr
import torch
import urllib.request


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# Fetch image for analysis
img_url = "http://claireye.com.tw/img/230212a.jpg"
urllib.request.urlretrieve(img_url, "pose.jpg")
model = hub.Module(name='U2Net')


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def infer(webcam, img,option):
  if option == "webcam":
      webcam.save('temp.jpg')
      result = model.Segmentation(
          images=[cv2.imread("temp.jpg")],
          paths=None,
          batch_size=1,
          input_size=320,
          output_dir='output',
          visualization=True)
  else:
      img.save('temp.jpg')
      result = model.Segmentation(
          images=[cv2.imread("temp.jpg")],
          paths=None,
          batch_size=1,
          input_size=320,
          output_dir='output',
          visualization=True)
  return result[0]['front'][:,:,::-1], result[0]['mask']


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inputs = [gr.inputs.Image(source="webcam", label="Webcam", type="pil",optional=True),gr.inputs.Image(source="upload", label="Input Image", type="pil",optional=True),gr.inputs.Radio(choices=["webcam","Image"], type="value", default="Image", label="Input Type")]
outputs = [
           gr.outputs.Image(type="numpy",label="Front"),
           gr.outputs.Image(type="numpy",label="Mask")
           ]

title = "U^2-Net"
description = "demo for U^2-Net. 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='http://claireye.com.tw'>Claireye</a> | 2023</p>"
examples = [
  ['pose.jpg','pose.jpg','Image'],
]


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gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()


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