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import os
import cv2
import paddlehub as hub
import gradio as gr
import torch
torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/10/21/14/46/fox-1758183_1280.jpg', 'fox.jpg')
model = hub.Module(name='U2Net')
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']
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='https://arxiv.org/abs/2005.09007'>U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection</a> | <a href='https://github.com/xuebinqin/U-2-Net'>Github Repo</a></p>"
examples = [
['fox.jpg','fox.jpg','Image'],
]
gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |