from dataclasses import dataclass from typing import Any 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 # 检测 def predict(self, image: np.ndarray, image_type): image_type = int(image_type) image, angle, result = self._pre_process(image, image_type) print(f'---------- detect angle: {angle} --------') if image_type == 0: if angle != 0: # 角度不为0需要重新识别,字面 _, _, result = self._ocr(image) else: _, _, result = self._ocr(image) return self._post_process(result, angle, image_type) def _pre_process(self, image, image_type) -> (np.ndarray, int, Any): angle, result = self.angle_detector.detect_angle(image, image_type) # angle = detect_angle(image) if angle == 1: image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE) print(angle) # 逆时针 if angle == 2: image = cv2.rotate(image, cv2.ROTATE_180) if angle == 3: image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) return image, angle, result 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, result, angle: int, image_type): line_parser = LineParser(result) line_result = line_parser.parse() print('-------------') print(line_result) print('-------------') 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') ocr_res = parser.parse() res = { "confidence": conf, "card_type": str(image_type), "orientation": angle, # 原angle是逆时针,转成顺时针 **ocr_res } print(res) return res