import os os.system("pip install pyyaml==5.1") # workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158) os.system( "pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html" ) # install detectron2 that matches pytorch 1.8 # See https://detectron2.readthedocs.io/tutorials/install.html for instructions os.system( "pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html" ) ## install PyTesseract os.system("pip install -q pytesseract") import gradio as gr import numpy as np from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification from datasets import load_dataset from PIL import Image, ImageDraw, ImageFont processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base") model = LayoutLMv3ForTokenClassification.from_pretrained( "nielsr/layoutlmv3-finetuned-funsd" ) # load image example dataset = load_dataset("nielsr/funsd", split="test") image = Image.open(dataset[0]["image_path"]).convert("RGB") image = Image.open("./invoice.png") image.save("document.png") labels = dataset.features["ner_tags"].feature.names id2label = {v: k for v, k in enumerate(labels)} label2color = { "question": "blue", "answer": "green", "header": "orange", "other": "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): label = label[2:] if not label: return "other" 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 = "Interactive demo: LayoutLMv3" description = "Demo for Microsoft's LayoutLMv3, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on FUNSD, a dataset of manually annotated forms. It annotates the words appearing in the image as QUESTION/ANSWER/HEADER/OTHER. To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'." article = "

LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking | Github Repo

" examples = [["document.png"]] css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}" # css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }" # css = ".output_image, .input_image {height: 600px !important}" css = ".image-preview {height: auto !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, enable_queue=True, ) iface.launch(debug=True)