Theivaprakasham's picture
updated requirements
b967e15
import os
os.system('pip install torch==1.8.0+cpu torchvision==0.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html')
os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.8/index.html')
import gradio as gr
import numpy as np
from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
from datasets import load_dataset
from PIL import Image, ImageDraw, ImageFont
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
model = LayoutLMv2ForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv2-finetuned-sroie_mod")
# load image example
dataset = load_dataset("darentang/generated", split="test")
Image.open(dataset[2]["image_path"]).convert("RGB").save("example1.png")
Image.open(dataset[1]["image_path"]).convert("RGB").save("example2.png")
Image.open(dataset[0]["image_path"]).convert("RGB").save("example3.png")
# define id2label, label2color
labels = dataset.features['ner_tags'].feature.names
id2label = {v: k for v, k in enumerate(labels)}
label2color = {'b-abn': "blue",
'b-biller': "blue",
'b-biller_address': "black",
'b-biller_post_code': "green",
'b-due_date': "orange",
'b-gst': 'red',
'b-invoice_date': 'red',
'b-invoice_number': 'violet',
'b-subtotal': 'green',
'b-total': 'green',
'i-biller_address': 'blue',
'o': 'violet'}
def unnormalize_box(bbox, width, height):
return [
width * (bbox[0] / 1000),
height * (bbox[1] / 1000),
width * (bbox[2] / 1000),
height * (bbox[3] / 1000),
]
def iob_to_label(label):
return label
def process_image(image):
width, height = image.size
# encode
encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
offset_mapping = encoding.pop('offset_mapping')
# forward pass
outputs = model(**encoding)
# get predictions
predictions = outputs.logits.argmax(-1).squeeze().tolist()
token_boxes = encoding.bbox.squeeze().tolist()
# only keep non-subword predictions
is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
# draw predictions over the image
draw = ImageDraw.Draw(image)
font = ImageFont.load_default()
for prediction, box in zip(true_predictions, true_boxes):
predicted_label = iob_to_label(prediction).lower()
draw.rectangle(box, outline=label2color[predicted_label])
draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
return image
title = "Invoice Information extraction using LayoutLMv2 model"
description = "Invoice Information Extraction - We use Microsoft's LayoutLMv2 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding.” 2022. <a href='https://arxiv.org/abs/2012.14740'>Paper Link</a><br>[2] <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2/FUNSD'>LayoutLMv2 training and inference</a>"
examples =[['example1.png'],['example2.png'],['example3.png']]
css = """.output_image, .input_image {height: 600px !important}"""
iface = gr.Interface(fn=process_image,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Image(type="pil", label="annotated image"),
title=title,
description=description,
article=article,
examples=examples,
css=css,
analytics_enabled = True, enable_queue=True)
iface.launch(inline=False,debug=False)