import os os.system("gdown https://drive.google.com/uc?id=1-95IOJ-2y9BtmABiffIwndPqNZD_gLnV") os.system("unzip big-lama.zip") import cv2 import paddlehub as hub import gradio as gr import torch from PIL import Image import numpy as np # 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): img.save("./data/data.png") result = model.Segmentation( images=[cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)], paths=None, batch_size=1, input_size=320, output_dir='output', visualization=True) im = Image.fromarray(result[0]['mask']) im.save("./data/data_mask.png") os.system('python predict.py model.path=./big-lama indir=./data outdir=./dataout device=cpu') return "./dataout/data_mask.png" inputs = gr.inputs.Image(type='file', label="Original Image") outputs = gr.outputs.Image(type="numpy",label="output") 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 = "

U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection | Github Repo

" examples = [ ['fox.jpg'], ['parrot.jpg'] ] gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()