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import os | |
import glob | |
import gradio as gr | |
import yaml | |
from utils import load_config, get_args, download_model | |
from ocr import OCR | |
import PIL | |
import numpy as np | |
config = load_config() | |
download_model('inference.pdmodel', config['inference.pdmodel']) | |
download_model('inference.pdiparams', config['inference.pdiparams']) | |
model = OCR(get_args(config['svtr_nums']['params'])) | |
def process_image(image): | |
img = image.convert('RGB') | |
img = np.array(img) | |
val, conf = model(img) | |
return val, conf | |
title = "Interactive demo: SVTR-Tiny" | |
description = "Demo for SVTR-Tiny. The model supports only digit recognition. To use it, simply upload a (single-text line) image or use one of the example images below and click 'submit'. Results will show up in a few seconds." | |
article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2205.00159.pdf'>SVTR: Scene Text Recognition with a Single Visual Model</a> | <a href='https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/doc/doc_en/algorithm_rec_svtr_en.md'>Github Repo</a></p>" | |
demo = gr.Interface(fn=process_image, | |
inputs=gr.inputs.Image(type="pil"), | |
outputs=gr.outputs.Textbox(), | |
title=title, | |
description=description, | |
article=article, | |
examples=glob.glob('examples/*.jpeg')) | |
demo.launch(server_name="0.0.0.0", server_port=7860, debug=True) |