rapid_table.py 1.3 KB

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  1. import cv2
  2. import numpy as np
  3. from loguru import logger
  4. from rapid_table import RapidTable
  5. from rapidocr_paddle import RapidOCR
  6. class RapidTableModel(object):
  7. def __init__(self, ocr_engine):
  8. self.table_model = RapidTable()
  9. if ocr_engine is None:
  10. self.ocr_model_name = "RapidOCR"
  11. self.ocr_engine = RapidOCR(det_use_cuda=True, cls_use_cuda=True, rec_use_cuda=True)
  12. else:
  13. self.ocr_model_name = "PaddleOCR"
  14. self.ocr_engine = ocr_engine
  15. def predict(self, image):
  16. if self.ocr_model_name == "RapidOCR":
  17. ocr_result, _ = self.ocr_engine(np.asarray(image))
  18. elif self.ocr_model_name == "PaddleOCR":
  19. bgr_image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
  20. ocr_result = self.ocr_engine.ocr(bgr_image)[0]
  21. ocr_result = [[item[0], item[1][0], item[1][1]] for item in ocr_result if
  22. len(item) == 2 and isinstance(item[1], tuple)]
  23. else:
  24. logger.error("OCR model not supported")
  25. ocr_result = None
  26. if ocr_result:
  27. html_code, table_cell_bboxes, elapse = self.table_model(np.asarray(image), ocr_result)
  28. return html_code, table_cell_bboxes, elapse
  29. else:
  30. return None, None, None