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#!/usr/bin/env python
import pathlib
import gradio as gr
from model import run_model
DESCRIPTION = '# [CutS3D](https://leonsick.github.io/cuts3d/): Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation \n\n' \
'This is a demo for the CutS3D Zero-Shot model. The model is trained on ImageNet, initially with unsupervised pseudo-masks and then further with one round of self-training. The first prediction will likely be slow as the model is downloaded. Subsequent predictions will be faster. The template for this space was borrowed from the original CutLER space by [hysts](https://huggingface.co/hysts).' \
paths = sorted(pathlib.Path('demo_imgs').glob('*.jpg'))
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
image = gr.Image(label='Input image', type='filepath')
score_threshold = gr.Slider(label='Score threshold',
minimum=0,
maximum=1,
value=0.45,
step=0.05)
run_button = gr.Button('Run')
with gr.Column():
result = gr.Image(label='Result', type='numpy')
with gr.Row():
gr.Examples(examples=[[path.as_posix()] for path in paths],
inputs=image)
run_button.click(fn=run_model,
inputs=[
image,
score_threshold,
],
outputs=result,
api_name='run')
demo.queue(max_size=60).launch(debug=True)
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