Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,496 Bytes
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import torch
import gradio as gr
import spaces
from inference_gradio import inference_one_image, model_init
MODEL_PATH = "./checkpoints/docres.pkl"
HEADER = """
<div align="center">
<p>
<span style="font-size: 30px; vertical-align: bottom;"> DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks </span>
</p>
<p style="margin-top: -15px;">
<a href="https://arxiv.org/abs/2405.04408" target="_blank" style="color: grey;">ArXiv Paper</a>
<a href="https://github.com/ZZZHANG-jx/DocRes" target="_blank" style="color: grey;">GitHub Repository</a>
</p>
</div>
🖼️ Upload an image of a document (or choose one from examples below).
✔️ Choose the tasks you want to perform on the document.
🚀 Click "Run" and the model will enhance the document according to the selected tasks!
"""
possible_tasks = [
"dewarping",
"deshadowing",
"appearance",
"deblurring",
"binarization",
]
@spaces.GPU(duration=60)
def run_tasks(image, tasks):
device = "cuda" if torch.cuda.is_available() else "cpu"
# load model
model = model_init(MODEL_PATH, device)
# run inference
bgr_image = image[..., ::-1].copy()
bgr_restored_image = inference_one_image(model, bgr_image, tasks, device)
if bgr_restored_image.ndim == 3:
rgb_image = bgr_restored_image[..., ::-1]
else:
rgb_image = bgr_restored_image
return rgb_image
with gr.Blocks() as demo:
gr.Markdown(HEADER)
task = gr.CheckboxGroup(choices=possible_tasks, label="Tasks", value=["appearance"])
with gr.Row():
input_image = gr.Image(label="Raw Image", type="numpy")
output_image = gr.Image(label="Enhanced Image", type="numpy")
button = gr.Button()
button.click(run_tasks, inputs=[input_image, task], outputs=[output_image])
gr.Examples(
examples=[
["input/218_in.png", ["dewarping", "deshadowing", "appearance"]],
["input/151_in.png", ["dewarping", "deshadowing", "appearance"]],
["input/for_debluring.png", ["deblurring"]],
["input/for_appearance.png", ["appearance"]],
["input/for_deshadowing.jpg", ["deshadowing"]],
["input/for_dewarping.png", ["dewarping"]],
["input/for_binarization.png", ["binarization"]],
],
inputs=[input_image, task],
outputs=[output_image],
fn=run_tasks,
cache_examples="lazy",
)
demo.launch()
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