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import os |
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os.system("gdown https://drive.google.com/uc?id=1-95IOJ-2y9BtmABiffIwndPqNZD_gLnV") |
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os.system("unzip big-lama.zip") |
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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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from PIL import Image |
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import numpy as np |
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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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img.save("./data/data.png") |
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result = model.Segmentation( |
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images=[cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)], |
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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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im = Image.fromarray(result[0]['mask']) |
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im.save("./data/data_mask.png") |
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os.system('python predict.py model.path=./big-lama indir=./data outdir=./dataout device=cpu') |
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return "./dataout/data_mask.png" |
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inputs = gr.inputs.Image(type='file', label="Original Image") |
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outputs = gr.outputs.Image(type="numpy",label="output") |
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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() |
