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| import os | |
| # build detectron2 from source | |
| # we can't build detectron2 in requirements.txt because it needs PyTorch installed first, | |
| # but requirements.txt will try to build wheels before installing any packages. | |
| os.system("pip install git+https://github.com/facebookresearch/detectron2.git") | |
| import gradio as gr | |
| import numpy as np | |
| from datasets import load_dataset | |
| from PIL import Image, ImageDraw, ImageFont | |
| from transformers import LayoutLMv2ForTokenClassification, LayoutLMv2Processor | |
| processor = LayoutLMv2Processor.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 = Image.open("./invoice.png") | |
| 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 | |
| # 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: LayoutLMv2" | |
| description = "Demo for Microsoft's LayoutLMv2, 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 = "<p style='text-align: center'><a href='https://arxiv.org/abs/2012.14740' target='_blank'>LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>" | |
| 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;}" | |
| gr.Interface( | |
| fn=process_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Image(type="pil", label="annotated image"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| css=css, | |
| ).launch() | |