Spaces:
Runtime error
Runtime error
| import os | |
| os.system('pip install pip --upgrade') | |
| os.system('pip install -q git+https://github.com/huggingface/transformers.git') | |
| 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 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("jinhybr/OCR-LayoutLMv3-Invoice", apply_ocr=True) | |
| model = AutoModelForTokenClassification.from_pretrained("jinhybr/OCR-LayoutLMv3-Invoice") | |
| # load image example | |
| dataset = load_dataset("jinhybr/WildReceipt", split="test") | |
| Image.open(dataset[1]["image_path"]).convert("RGB").save("example1.png") | |
| Image.open(dataset[3]["image_path"]).convert("RGB").save("example2.png") | |
| Image.open(dataset[25]["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 = { | |
| "Date_key": 'red', | |
| "Date_value": 'green', | |
| "Ignore": 'orange', | |
| "Others": 'orange', | |
| "Prod_item_key": 'red', | |
| "Prod_item_value": 'green', | |
| "Prod_price_key": 'red', | |
| "Prod_price_value": 'green', | |
| "Prod_quantity_key": 'red', | |
| "Prod_quantity_value": 'green', | |
| "Store_addr_key": 'red', | |
| "Store_addr_value": 'green', | |
| "Store_name_key": 'red', | |
| "Store_name_value": 'green', | |
| "Subtotal_key": 'red', | |
| "Subtotal_value": 'green', | |
| "Tax_key": 'red', | |
| "Tax_value": 'green', | |
| "Tel_key": 'red', | |
| "Tel_value": 'green', | |
| "Time_key": 'red', | |
| "Time_value": 'green', | |
| "Tips_key": 'red', | |
| "Tips_value": 'green', | |
| "Total_key": 'red', | |
| "Total_value": 'blue' | |
| } | |
| 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) | |
| # Print label and value | |
| label_text = f"Label: {predicted_label}" | |
| value_text = f"Value: {prediction}" | |
| print(label_text) | |
| print(value_text) | |
| print("------") | |
| return image | |
| title = "OCR Invoice - Information Extraction - LayoutLMv3" | |
| description = "Fine-tuned Microsoft's LayoutLMv3 on WildReceipt Dataset to parse Invoice OCR document. 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><br>[3] Hongbin Sun, Zhanghui Kuang, Xiaoyu Yue, Chenhao Lin, and Wayne Zhang. 2021. Spatial Dual-Modality Graph Reasoning for Key Information Extraction. arXiv. DOI:https://doi.org/10.48550/ARXIV.2103.14470 <a href='https://doi.org/10.48550/ARXIV.2103.14470'>Paper Link</a>" | |
| examples =[['example1.png'],['example2.png'],['example3.png'],['inv2.jpg']] | |
| 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=True) | |