Merge pull request #199 from AK391/master

gradio web demo
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Xuebin Qin 2021-05-06 17:25:23 +04:00 committed by GitHub
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## Updates !!!
**(2021-May-5)** [**Gradio Web Demo**](https://gradio.app/hub/AK391/U-2-Net)
**(2021-Apr-29)** Thanks [**Jonathan Benavides Vallejo**](https://www.linkedin.com/in/jonathanbv/) for releasing his App [**LensOCR: Extract Text & Image**](https://apps.apple.com/ch/app/lensocr-extract-text-image/id1549961729?l=en&mt=12), which uses U^2-Net for extracting the image foreground.
![LensOCR APP](figures/LensOCR.jpg)

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gradio/demo.py Normal file
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import cv2
import paddlehub as hub
import gradio as gr
import torch
# Images
torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2018/08/12/16/59/ara-3601194_1280.jpg', 'parrot.jpg')
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(img):
result = model.Segmentation(
images=[cv2.imread(img.name)],
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(type='file', label="Original Image")
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'],
['parrot.jpg']
]
gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()

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numpy==1.15.2
scikit-image==0.14.0
torch==0.4.1.post2
torchvision==0.2.1
torch
torchvision
pillow==7.2.0
opencv-python
paddlepaddle
paddlehub
gradio