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| import os | |
| os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu') | |
| import gradio as gr | |
| import numpy as np | |
| from transformers import AutoModelForTokenClassification | |
| from datasets.features import ClassLabel | |
| from transformers import AutoProcessor | |
| from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D | |
| import torch | |
| from datasets import load_metric | |
| from transformers import LayoutLMv3ForTokenClassification | |
| from transformers.data.data_collator import default_data_collator | |
| from transformers import AutoModelForTokenClassification | |
| from datasets import load_dataset | |
| from PIL import Image, ImageDraw, ImageFont | |
| processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=True) | |
| model = AutoModelForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie") | |
| # load image example | |
| dataset = load_dataset("darentang/sroie", split="test") | |
| Image.open(dataset[2]["image_path"]).convert("RGB").save("example1.png") | |
| Image.open(dataset[1]["image_path"]).convert("RGB").save("example2.png") | |
| Image.open(dataset[0]["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-ADDRESS": 'blue', | |
| "B-COMPANY": 'red', | |
| "B-DATE": 'green', | |
| "B-TOTAL": 'violet', | |
| "I-ADDRESS": 'green', | |
| "I-COMPANY": 'blue', | |
| "I-DATE": 'red', | |
| "I-TOTAL": 'red', | |
| "O": 'orange' | |
| } | |
| 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): | |
| print(type(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) | |
| 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 = "Bill Information Extraction using LayoutLMv3 model" | |
| description = "Bill Information Extraction - We use Microsoft’s LayoutLMv3 trained on SROIE Dataset to predict the Company Name, Address, Date, and Total Amount from Bills. To use it, simply upload an image or use the example image below. Results will show up in a few seconds." | |
| article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. <a href='https://arxiv.org/abs/2204.08387'>Paper Link</a><br>[2] <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3'>LayoutLMv3 training and inference</a>" | |
| 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=False, debug=False) |