mirror of
https://git.mirrors.martin98.com/https://github.com/xuebinqin/U-2-Net
synced 2025-04-20 12:49:49 +08:00
37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
import cv2
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import paddlehub as hub
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import gradio as gr
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import torch
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# Images
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torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2018/08/12/16/59/ara-3601194_1280.jpg', 'parrot.jpg')
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torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/10/21/14/46/fox-1758183_1280.jpg', 'fox.jpg')
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model = hub.Module(name='U2Net')
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def infer(img):
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result = model.Segmentation(
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images=[cv2.imread(img.name)],
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paths=None,
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batch_size=1,
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input_size=320,
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output_dir='output',
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visualization=True)
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return result[0]['front'][:,:,::-1], result[0]['mask']
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inputs = gr.inputs.Image(type='file', label="Original Image")
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outputs = [
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gr.outputs.Image(type="numpy",label="Front"),
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gr.outputs.Image(type="numpy",label="Mask")
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]
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title = "U^2-Net"
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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."
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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>"
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examples = [
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['fox.jpg'],
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['parrot.jpg']
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]
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gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |