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- from dataclasses import dataclass
- from core.line_parser import LineParser
- from core.parser import *
- from core.direction import *
- import numpy as np
- from paddleocr import PaddleOCR
- @dataclass
- class IdCardOcr:
- ocr: PaddleOCR
- angle_detector: AngleDetector
- def predict(self, image: np.ndarray, image_type: str = '0'):
- # image, angle = self._pre_process(image)
- txts, confs, result = self._ocr(image)
- angle = self.angle_detector.detect_angle(image, result)
- line_parser = LineParser(result)
- line_result = line_parser.parse()
- conf = line_parser.confidence
- if int(image_type) == 0:
- parser = FrontParser(line_result)
- elif int(image_type) == 1:
- parser = BackParser(line_result)
- else:
- raise Exception('无法识别')
- return self._post_process(conf, angle, parser, image_type)
- def _pre_process(self, image) -> (np.ndarray, int):
- angle = detect_angle(image)
- print(angle) # 逆时针
- if angle == 180:
- image = cv2.rotate(image, cv2.ROTATE_180)
- if angle == 90:
- image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
- if angle == 270:
- image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
- return image, angle
- def _ocr(self, image):
- # 获取模型检测结果
- result = self.ocr.ocr(image, cls=True)
- print("------------------")
- print(result)
- if not result:
- raise Exception('无法识别')
- confs = [line[1][1] for line in result]
- # 将检测到的文字放到一个列表中
- txts = [line[1][0] for line in result]
- # print("......................................")
- # print(txts)
- # print("......................................")
- return txts, confs, result
- def _post_process(self, conf, angle: int, parser: Parser, image_type: str):
- ocr_res = parser.parse()
- res = {
- "confidence": conf,
- "card_type": image_type,
- "orientation": angle // 90, # 原angle是逆时针,转成顺时针
- **ocr_res
- }
- print(res)
- return res
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