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import os |
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os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu') |
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import gradio as gr |
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import numpy as np |
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from transformers import AutoModelForTokenClassification |
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from datasets.features import ClassLabel |
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from transformers import AutoProcessor |
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from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D |
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import torch |
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from datasets import load_metric |
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from transformers import LayoutLMv3ForTokenClassification |
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from transformers.data.data_collator import default_data_collator |
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import pytesseract |
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from transformers import AutoModelForTokenClassification |
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from datasets import load_dataset |
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from PIL import Image, ImageDraw, ImageFont |
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pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe' |
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processor = AutoProcessor.from_pretrained("kaydee/layoutlmv3-wildreceipt", apply_ocr=True) |
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model = AutoModelForTokenClassification.from_pretrained("kaydee/layoutlmv3-wildreceipt") |
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dataset = load_dataset("kaydee/wildreceipt", split="test") |
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dataset[20]["image"].convert("RGB").save("example1.png") |
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dataset[13]["image"].convert("RGB").save("example2.png") |
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dataset[15]["image"].convert("RGB").save("example3.png") |
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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 = { |
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"Date_key": 'red', |
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"Date_value": 'green', |
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"Ignore": 'orange', |
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"Others": 'orange', |
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"Prod_item_key": 'red', |
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"Prod_item_value": 'green', |
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"Prod_price_key": 'red', |
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"Prod_price_value": 'green', |
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"Prod_quantity_key": 'red', |
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"Prod_quantity_value": 'green', |
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"Store_addr_key": 'red', |
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"Store_addr_value": 'green', |
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"Store_name_key": 'red', |
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"Store_name_value": 'green', |
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"Subtotal_key": 'red', |
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"Subtotal_value": 'green', |
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"Tax_key": 'red', |
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"Tax_value": 'green', |
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"Tel_key": 'red', |
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"Tel_value": 'green', |
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"Time_key": 'red', |
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"Time_value": 'green', |
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"Tips_key": 'red', |
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"Tips_value": 'green', |
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"Total_key": 'red', |
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"Total_value": 'blue' |
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} |
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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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return label |
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def process_image(image): |
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print(type(image)) |
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width, height = image.size |
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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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outputs = model(**encoding) |
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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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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 = 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) |
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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 = "Receipt information extraction using LayoutLMv3 model" |
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description = "Reciept information extraction - Here I use Microsoft's LayoutLMv3 trained on WildReceipt Dataset to predict the keys and values. To use it, simply upload an image or use the example image below. Results will show up in a few seconds." |
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examples =[['example1.png'],['example2.png'],['example3.png']] |
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css = """.output_image, .input_image {height: 600px !important}""" |
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iface = gr.Interface(fn=process_image, |
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inputs=gr.Image(type="pil"), |
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outputs=gr.Image(type="pil", label="annotated image"), |
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title=title, |
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description=description, |
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examples=examples, |
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css=css, |
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analytics_enabled = True) |
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iface.launch(inline=False, share=False, debug=False) |