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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