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[50]["image_path"]).convert("RGB").save("example1.png") Image.open(dataset[14]["image_path"]).convert("RGB").save("example2.png") Image.open(dataset[20]["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="References
[1] Y. Xu et al., “LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding.” 2022. Paper Link
[2] LayoutLMv2 training and inference" 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, share=True, debug=False)