Spaces:
Sleeping
Sleeping
| import torch | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image, ImageDraw, ImageFont | |
| from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Processor, LayoutLMv3ForTokenClassification | |
| from utils import OCR, unnormalize_box | |
| # ["O", "B-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"] | |
| labels = ["O", "COMPANY", "COMPANY", "DATE", "DATE", "ADDRESS", "ADDRESS", "TOTAL", "TOTAL"] | |
| id2label = {v: k for v, k in enumerate(labels)} | |
| label2id = {k: v for v, k in enumerate(labels)} | |
| tokenizer = LayoutLMv3TokenizerFast.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False) | |
| processor = LayoutLMv3Processor.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie", apply_ocr=False) | |
| model = LayoutLMv3ForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv3-finetuned-sroie") | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model.to(device) | |
| def blur(image, boxes): | |
| image = np.array(image) | |
| for box in boxes: | |
| blur_x = int(box[0]) | |
| blur_y = int(box[1]) | |
| blur_width = int(box[2]-box[0]) | |
| blur_height = int(box[3]-box[1]) | |
| roi = image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width] | |
| blur_image = cv2.GaussianBlur(roi, (201, 201), 0) | |
| image[blur_y:blur_y + blur_height, blur_x:blur_x + blur_width] = blur_image | |
| return Image.fromarray(image, 'RGB') | |
| 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, j in enumerate(true_confidence_scores): | |
| if j < 0.8: ##################################### | |
| true_predictions[i] = "O" | |
| d = {} | |
| 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()} | |
| if "O" in d: d.pop("O") | |
| if "TOTAL" in d: d.pop("TOTAL") | |
| blur_boxes = [] | |
| for prediction, box in zip(true_predictions, true_boxes): | |
| if prediction != 'O' and prediction != 'TOTAL': | |
| blur_boxes.append(box) | |
| image = (blur(image, blur_boxes)) | |
| #draw = ImageDraw.Draw(image, "RGBA") | |
| #font = ImageFont.load_default() | |
| #for prediction, box in zip(preds, bboxes): | |
| # draw.rectangle(box) | |
| # draw.text((box[0]+10, box[1]-10), text=prediction, font=font, fill="black", font_size="8") | |
| return d, image | |