File size: 1,475 Bytes
f612944 8aa2bcc f612944 8aa2bcc f612944 dc3edb7 f612944 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 |
import os
import gradio as gr
from pathlib import Path
os.system("git clone https://github.com/YangtaoWANG95/TokenCut.git")
os.chdir("TokenCut")
os.system("wget https://raw.githubusercontent.com/YangtaoWANG95/TokenCut/master/examples/VOC07_000064.jpg -O parrot.jpg")
def inference(img):
os.system("python main_tokencut.py --image_path "+img+" --visualize all --resize 320")
filename = Path(img).stem
return "./outputs/TokenCut-vit_small16_k/"+filename+"_TokenCut_attn.jpg","./outputs/TokenCut-vit_small16_k/"+filename+"_TokenCut_pred.jpg"
title="TokenCut"
description="Gradio demo for TokenCut: Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut. To use it, simply upload your image or click on one of the examples to load them. We resize the smaller edge of the image to 320 to accelerate inference time. Read more at the links below"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2202.11539' target='_blank'>Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut</a> | <a href='https://github.com/YangtaoWANG95/TokenCut' target='_blank'>Github Repo</a></p>"
examples=[['parrot.jpg']]
gr.Interface(inference,gr.inputs.Image(type="filepath"),[gr.outputs.Image(type="filepath",label="TokenCut_attn"),gr.outputs.Image(type="filepath",label="TokenCut_predication")],title=title,description=description,article=article,examples=examples).launch(enable_queue=True)
|