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#!/usr/bin/env python
import pathlib
import gradio as gr
import numpy as np
import PIL.Image as Image
from model import Model, random_color, vis_mask
model = Model()
def run(image_path, threshold, max_num_mask):
image = np.asarray(Image.open(image_path).convert('RGB'))
masks = model(image_path, threshold, max_num_mask)
for mask in masks:
image = vis_mask(image, mask, random_color(rgb=True))
return image
TITLE = 'MaskCut'
DESCRIPTION = 'This is an unofficial demo for https://github.com/facebookresearch/CutLER.'
paths = sorted(pathlib.Path('CutLER/maskcut/imgs').glob('*.jpg'))
demo = gr.Interface(
fn=run,
inputs=[
gr.Image(label='Input image', type='filepath'),
gr.Slider(label='Threshold used for producing binary graph',
minimum=0,
maximum=1,
value=0.15,
step=0.01),
gr.Slider(label='The maximum number of pseudo-masks per image',
minimum=1,
maximum=20,
value=3,
step=1),
],
outputs=gr.Image(label='Result', type='numpy'),
examples=[[path.as_posix(), 0.15, 3] for path in paths],
title=TITLE,
description=DESCRIPTION)
demo.queue().launch()
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