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| import torch | |
| import numpy as np | |
| from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Processor, LayoutLMv3ForTokenClassification | |
| from PIL import Image, ImageDraw, ImageFont | |
| from utils import OCR, unnormalize_box | |
| tokenizer = LayoutLMv3TokenizerFast.from_pretrained("mp-02/layoutlmv3-finetuned-cord-sroie", apply_ocr=False) | |
| processor = LayoutLMv3Processor.from_pretrained("mp-02/layoutlmv3-finetuned-cord-sroie", apply_ocr=False) | |
| model = LayoutLMv3ForTokenClassification.from_pretrained("mp-02/layoutlmv3-finetuned-cord-sroie") | |
| id2label = model.config.id2label | |
| label2id = model.config.label2id | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model.to(device) | |
| import json | |
| def token2json(words, labels): | |
| result = {} | |
| current_entity = None | |
| current_text = [] | |
| for word, label in zip(words, labels): | |
| if label.startswith("B-"): | |
| if current_entity: | |
| result[current_entity] = " ".join(current_text).strip() | |
| current_text = [] | |
| current_entity = label[2:].lower() | |
| current_text = [word] | |
| elif label.startswith("I-"): | |
| if current_entity == label[2:].lower(): | |
| current_text.append(word) | |
| else: | |
| # Gestione di sequenze I- non precedute da B- | |
| if current_entity: | |
| result[current_entity] = " ".join(current_text).strip() | |
| current_entity = label[2:].lower() | |
| current_text = [word] | |
| else: # Label "O" | |
| if current_entity: | |
| result[current_entity] = " ".join(current_text).strip() | |
| current_entity = None | |
| current_text = [] | |
| # Aggiunge l'ultima entità se presente | |
| if current_entity: | |
| result[current_entity] = " ".join(current_text).strip() | |
| return json.dumps(result, ensure_ascii=False, indent=2) | |
| def prediction(image): | |
| boxes, words = OCR(image) | |
| encoding = processor(image, words, boxes=boxes, return_offsets_mapping=True, return_tensors="pt", truncation=True) | |
| offset_mapping = encoding.pop('offset_mapping') | |
| for k, v in encoding.items(): | |
| encoding[k] = v.to(device) | |
| outputs = model(**encoding) | |
| predictions = outputs.logits.argmax(-1).squeeze().tolist() | |
| token_boxes = encoding.bbox.squeeze().tolist() | |
| probabilities = torch.softmax(outputs.logits, dim=-1) | |
| confidence_scores = probabilities.max(-1).values.squeeze().tolist() | |
| inp_ids = encoding.input_ids.squeeze().tolist() | |
| inp_words = [tokenizer.decode(i) for i in inp_ids] | |
| 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]] | |
| true_confidence_scores = [confidence_scores[idx] for idx, conf in enumerate(confidence_scores) if not is_subword[idx]] | |
| true_words = [] | |
| for id, i in enumerate(inp_words): | |
| if not is_subword[id]: | |
| true_words.append(i) | |
| else: | |
| true_words[-1] = true_words[-1]+i | |
| true_predictions = true_predictions[1:-1] | |
| true_boxes = true_boxes[1:-1] | |
| true_words = true_words[1:-1] | |
| true_confidence_scores = true_confidence_scores[1:-1] | |
| for i, conf in enumerate(true_confidence_scores): | |
| if conf < 0.6 : | |
| true_predictions[i] = "O" | |
| d = token2json(true_words, true_predictions) | |
| """for id, i in enumerate(true_predictions): | |
| if i not in d.keys(): | |
| d[i] = true_words[id] | |
| else: | |
| d[i] = d[i] + ", " + true_words[id] | |
| d = {k: v.strip() for (k, v) in d.items()} | |
| d.pop("O")""" | |
| draw = ImageDraw.Draw(image, "RGBA") | |
| font = ImageFont.load_default() | |
| for prediction, box, confidence in zip(true_predictions, true_boxes, true_confidence_scores): | |
| draw.rectangle(box) | |
| draw.text((box[0]+10, box[1]-10), text=prediction+ ", "+ str(confidence), font=font, fill="black", font_size="15") | |
| return image, d | |