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from typing import Tuple
import gradio as gr
import os
from PIL import Image
from datasets import load_dataset
from prepare_samples import prepare_samples
DIR_PATH = os.path.dirname(__file__)
def inference(rgb: Image.Image, depth: Image.Image) -> Tuple[Image.Image]:
return rgb
dataset = load_dataset("RGBD-SOD/test", "v1", split="train", cache_dir="data")
# with gr.Blocks() as demo:
# with gr.Row(elem_id="center"):
# gr.Markdown("# BBS-Net Demo")
TITLE = "BBS-Net Demo"
DESCRIPTION = "Gradio demo for BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network."
examples = prepare_samples()
demo = gr.Interface(
fn=inference,
inputs=[
gr.inputs.Image(label="RGB", type="pil"),
gr.inputs.Image(label="Depth", type="pil"),
],
outputs=[
gr.outputs.Image(label="Prediction", type="pil"),
],
title=TITLE,
examples=examples,
description=DESCRIPTION,
)
if __name__ == "__main__":
demo.launch(enable_queue=True)
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