Create README.md

README.md

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+# LayoutXLM finetuned on XFUN.ja
+
+```python
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from pathlib import Path
+from itertools import chain
+from tqdm.notebook import tqdm
+from pdf2image import convert_from_path
+from transformers import LayoutXLMProcessor, LayoutLMv2ForTokenClassification
+
+import os
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+labels = [
+    'O',
+    'B-QUESTION',
+    'B-ANSWER',
+    'B-HEADER',
+    'I-ANSWER',
+    'I-QUESTION',
+    'I-HEADER'
+]
+id2label = {v: k for v, k in enumerate(labels)}
+label2id = {k: v for v, k in enumerate(labels)}
+
+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):
+    label = label[2:]
+    if not label:
+        return 'other'
+    return label
+
+label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
+
+def infer(image, processor, model, label2color):
+    # Use this if you're loading images
+    # image = Image.open(img_path).convert("RGB")
+    
+    image = image.convert("RGB") # loading PDFs
+    encoding = processor(image, return_offsets_mapping=True, return_tensors="pt", truncation=True, max_length=514)
+    offset_mapping = encoding.pop('offset_mapping')
+    outputs = model(**encoding)
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+    token_boxes = encoding.bbox.squeeze().tolist()
+
+    width, height = image.size
+    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 = ImageDraw.Draw(image)
+
+    font = ImageFont.load_default()
+
+    for prediction, box in zip(true_predictions, true_boxes):
+        predicted_label = iob_to_label(prediction).lower()
+        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
+
+processor = LayoutXLMProcessor.from_pretrained('beomus/layoutxlm')
+model = LayoutLMv2ForTokenClassification.from_pretrained("beomus/layoutxlm")
+
+
+# imgs = [img_path for img_path in Path('/your/path/imgs/').glob('*.jpg')]
+
+imgs = [convert_from_path(img_path) for img_path in Path('/your/path/pdfs/').glob('*.pdf')]
+imgs = list(chain.from_iterable(imgs))
+
+
+outputs = [infer(img_path, processor, model, label2color) for img_path in tqdm(imgs)]
+
+# type(outputs[0]) -> PIL.Image.Image
+```