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- import cv2
- import numpy as np
- from loguru import logger
- from rapid_table import RapidTable
- from rapidocr_paddle import RapidOCR
- class RapidTableModel(object):
- def __init__(self, ocr_engine):
- self.table_model = RapidTable()
- if ocr_engine is None:
- self.ocr_model_name = "RapidOCR"
- self.ocr_engine = RapidOCR(det_use_cuda=True, cls_use_cuda=True, rec_use_cuda=True)
- else:
- self.ocr_model_name = "PaddleOCR"
- self.ocr_engine = ocr_engine
- def predict(self, image):
- if self.ocr_model_name == "RapidOCR":
- ocr_result, _ = self.ocr_engine(np.asarray(image))
- elif self.ocr_model_name == "PaddleOCR":
- bgr_image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
- ocr_result = self.ocr_engine.ocr(bgr_image)[0]
- ocr_result = [[item[0], item[1][0], item[1][1]] for item in ocr_result if
- len(item) == 2 and isinstance(item[1], tuple)]
- else:
- logger.error("OCR model not supported")
- ocr_result = None
- if ocr_result:
- html_code, table_cell_bboxes, elapse = self.table_model(np.asarray(image), ocr_result)
- return html_code, table_cell_bboxes, elapse
- else:
- return None, None, None
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