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| import os | |
| os.system('pip install git+https://github.com/huggingface/transformers.git --upgrade') | |
| 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('sudo apt install tesseract-ocr') | |
| os.system('pip install -q pytesseract') | |
| import gradio as gr | |
| import numpy as np | |
| from transformers import LayoutLMv2FeatureExtractor, LayoutLMv2TokenizerFast, LayoutLMv2ForTokenClassification | |
| from datasets import load_dataset | |
| from PIL import Image, ImageDraw, ImageFont | |
| feature_extractor = LayoutLMv2FeatureExtractor.from_pretrained("microsoft/layoutlmv2-base-uncased") | |
| tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased") | |
| model = LayoutLMv2ForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd") | |
| # load image example | |
| dataset = load_dataset("nielsr/funsd", split="test") | |
| image = Image.open(dataset[0]["image_path"]).convert("RGB") | |
| image.save("document.png") | |
| # define id2label, label2color | |
| 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 | |
| # get words, boxes | |
| encoding_feature_extractor = feature_extractor(image, return_tensors="pt") | |
| words, boxes = encoding_feature_extractor.words, encoding_feature_extractor.boxes | |
| # encode | |
| encoding = tokenizer(words, boxes=boxes, return_offsets_mapping=True, return_tensors="pt") | |
| offset_mapping = encoding.pop('offset_mapping') | |
| encoding["image"] = encoding_feature_extractor.pixel_values | |
| # 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: LayoutLMv2" | |
| description = "Demo for Microsoft's LayoutLMv2, a Transformer for state-of-the-art document image understanding tasks. To use it, simply upload an image or use the example image below. Results will show up in a few seconds." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2012.14740'>LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm'>Github Repo</a></p>" | |
| examples =[['document.png']] | |
| 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) | |
| iface.launch(debug=True) |