layoutlm (without fine-tune)
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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