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Create app.py
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app.py
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import os
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os.system('pip install pyyaml==5.1')
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# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158)
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os.system('pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html')
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# install detectron2 that matches pytorch 1.8
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# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
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os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html')
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## install PyTesseract
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os.system('pip install -q pytesseract')
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import gradio as gr
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import numpy as np
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from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
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from datasets import load_dataset
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from PIL import Image, ImageDraw, ImageFont
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processor = LayoutLMv2Processor.from_pretrained("jinhybr/OCR-LM-v1")
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model = LayoutLMv2ForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd")
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# load image example
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dataset = load_dataset("nielsr/funsd", split="test")
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image = Image.open(dataset[0]["image_path"]).convert("RGB")
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image = Image.open("./demo.jpg")
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image.save("document.jpg")
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# define id2label, label2color
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labels = dataset.features['ner_tags'].feature.names
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id2label = {v: k for v, k in enumerate(labels)}
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label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
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def unnormalize_box(bbox, width, height):
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return [
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width * (bbox[0] / 1000),
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height * (bbox[1] / 1000),
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width * (bbox[2] / 1000),
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height * (bbox[3] / 1000),
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]
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def iob_to_label(label):
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label = label[2:]
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if not label:
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return 'other'
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return label
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def process_image(image):
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width, height = image.size
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# encode
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encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
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offset_mapping = encoding.pop('offset_mapping')
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# forward pass
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outputs = model(**encoding)
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# get predictions
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predictions = outputs.logits.argmax(-1).squeeze().tolist()
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token_boxes = encoding.bbox.squeeze().tolist()
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# only keep non-subword predictions
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is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
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true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
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true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
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# draw predictions over the image
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draw = ImageDraw.Draw(image)
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font = ImageFont.load_default()
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for prediction, box in zip(true_predictions, true_boxes):
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predicted_label = iob_to_label(prediction).lower()
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draw.rectangle(box, outline=label2color[predicted_label])
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draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
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return image
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title = "Interactive demo: OCR Document Parser"
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description = "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'."
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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>"
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examples =[['document.png']]
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css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
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#css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
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# css = ".output_image, .input_image {height: 600px !important}"
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css = ".image-preview {height: auto !important;}"
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iface = gr.Interface(fn=process_image,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Image(type="pil", label="annotated image"),
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title=title,
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description=description,
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article=article,
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examples=examples,
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css=css,
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enable_queue=True)
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iface.launch(debug=True)
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