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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 warnings |
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warnings.filterwarnings("ignore") |
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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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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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import pytesseract |
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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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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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def main(img): |
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image = process_image(img) |
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return image |