layoutlm (without fine-tune)

PHOTO EMBED

Sun Apr 16 2023 10:30:54 GMT+0000 (Coordinated Universal Time)

Saved by @mehla99_shubham #python

def loadFromLayoutlmv2():
  feature_extractor = LayoutLMv2FeatureExtractor.from_pretrained("microsoft/layoutlmv2-base-uncased")# apply_ocr is set to True by default
  tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased") 
  model = LayoutLMv2ForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd")
  return feature_extractor,tokenizer,model

def labelForBoxes():
  dataset = load_dataset("nielsr/funsd", split="test")
  # define id2label, label2color
  labels = dataset.features['ner_tags'].feature.names
  id2label = {v: k for v, k in enumerate(labels)}
  label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
  return id2label, label2color

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

def process_image(image,id2label,label2color,feature_extractor,tokenizer,model):
    
    # Convert the image to RGB format
    image = image.convert('RGB')
    width, height = image.size

    # get words, boxes
    encoding_feature_extractor = feature_extractor(image, return_tensors="pt")
    words, boxes = encoding_feature_extractor.words, encoding_feature_extractor.boxes
    # encode
    encoding = tokenizer(words, boxes=boxes, truncation=True, return_offsets_mapping=True, return_tensors="pt")
    offset_mapping = encoding.pop('offset_mapping')
    encoding["image"] = encoding_feature_extractor.pixel_values
    # 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).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,true_boxes,words,true_predictions,true_boxes,is_subword
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