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# -*- encoding: utf-8 -*-
# @Author: SWHL
# @Contact: liekkaskono@163.com
import os
os.system('pip install -r requirements.txt')
import cv2
import gradio as gr
from ctrnet_infer import CTRNetInfer
def inference(img_path):
img = cv2.imread(img_path)
pred = ctrnet(img)
pred = cv2.cvtColor(pred, cv2.COLOR_BGR2RGB)
return pred
model_path = 'models/CTRNet_G.onnx'
ctrnet = CTRNetInfer(model_path)
title = 'CTRNet Demo'
description = '''This is the demo for the paper “Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context”. Github Repo: https://github.com/lcy0604/CTRNet'''
css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
examples = [['images/1.jpg'], ['images/2.jpg'], ['images/4.jpg']]
gr.Interface(
inference,
inputs=[
gr.inputs.Image(type='filepath', label='Input'),
],
outputs=[
gr.outputs.Image(type='filepath', label='Output_image'),
],
title=title,
description=description,
examples=examples,
css=css,
allow_flagging='never',
enable_queue=True
).launch(debug=True, enable_queue=True)
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