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import os | |
os.system('pip install "git+https://github.com/facebookresearch/[email protected]#egg=detectron2"') | |
import layoutparser as lp | |
import gradio as gr | |
# PrimaLayout | |
model0 = lp.Detectron2LayoutModel('lp://PrimaLayout/mask_rcnn_R_50_FPN_3x/config') | |
# PubLayNet | |
model1 = lp.Detectron2LayoutModel('lp://PubLayNet/faster_rcnn_R_50_FPN_3x/config') | |
model2 = lp.Detectron2LayoutModel('lp://PubLayNet/mask_rcnn_R_50_FPN_3x/config') | |
model3 = lp.Detectron2LayoutModel('lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config') | |
def lpi(img): | |
# You need to load the image somewhere else, e.g., image = cv2.imread(...) | |
layout0 = model0.detect(img) | |
layout1 = model1.detect(img) | |
layout2 = model2.detect(img) | |
layout3 = model3.detect(img) | |
img0 = lp.draw_box(img, layout0) | |
img1 = lp.draw_box(img, layout1) | |
img2 = lp.draw_box(img, layout2) | |
img3 = lp.draw_box(img, layout3) | |
return img0, img1, img2, img3 # With extra configurations | |
inputs = gr.inputs.Image(type='pil', label="Original Image") | |
outputs = [ | |
gr.outputs.Image(type="pil",label="Output Image (PrimaLayout/faster_rcnn_R_50_FPN_3x)"), | |
gr.outputs.Image(type="pil",label="Output Image (PubLayNet/faster_rcnn_R_50_FPN_3x)"), | |
gr.outputs.Image(type="pil",label="Output Image (PubLayNet/mask_rcnn_R_50_FPN_3x)"), | |
gr.outputs.Image(type="pil",label="Output Image (PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x)") | |
] | |
title = "Layout Parser (PrimaLayout & PubLayNet models)" | |
description = "demo for Layout Parser with PrimaLayout & PubLayNet models. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2103.15348'>LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis</a> | <a href='https://github.com/Layout-Parser/layout-parser'>Github Repo</a></p>" | |
examples = [ | |
['example-table.jpeg'], | |
['paper-image.jpeg'] | |
] | |
gr.Interface(lpi, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |