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- import re
- from dataclasses import dataclass
- from enum import Enum
- from typing import Tuple, List
- import cv2
- import numpy as np
- from paddleocr import PaddleOCR
- from core.line_parser import LineParser
- class Direction(Enum):
- TOP = 0
- RIGHT = 1
- BOTTOM = 2
- LEFT = 3
- # 父类
- class OcrAnchor(object):
- # 输入识别anchor的名字, 如身份证号
- def __init__(self, name: str, d: List[Direction]):
- self.name = name
- # anchor位置
- self.direction = d
- def t_func(anchor, c, is_horizontal):
- if is_horizontal:
- return 0 if anchor[1] < c[1] else 2
- else:
- return 1 if anchor[0] > c[0] else 3
- def l_func(anchor, c, is_horizontal):
- if is_horizontal:
- return 0 if anchor[0] < c[0] else 2
- else:
- return 1 if anchor[1] < c[1] else 3
- def b_func(anchor, c, is_horizontal):
- if is_horizontal:
- return 0 if anchor[1] > c[1] else 2
- else:
- return 1 if anchor[0] < c[0] else 3
- def r_func(anchor, c, is_horizontal):
- if is_horizontal:
- return 0 if anchor[0] > c[0] else 2
- else:
- return 1 if anchor[1] > c[1] else 3
- self.direction_funcs = {
- Direction.TOP: t_func,
- Direction.BOTTOM: b_func,
- Direction.LEFT: l_func,
- Direction.RIGHT: r_func,
- }
- # 获取中心区域坐标 -> (x, y)
- def get_rec_area(self, res) -> Tuple[float, float]:
- """获得整张身份证的识别区域, 返回识别区域的中心点"""
- boxes = []
- for row in res:
- for r in row:
- boxes.extend(r.box)
- boxes = np.stack(boxes)
- l, t = np.min(boxes, 0)
- r, b = np.max(boxes, 0)
- return (l + r) / 2, (t + b) / 2
- def is_anchor(self, txt, box) -> bool:
- pass
- def find_anchor(self, res) -> Tuple[bool, float, float]:
- """
- 寻找锚点 中心点坐标
- """
- for row in res:
- for r in row:
- txt = r.txt.replace('-', '').replace(' ', '')
- box = r.box
- if self.is_anchor(txt, box):
- l, t = np.min(box, 0)
- r, b = np.max(box, 0)
- return True, (l + r) / 2, (t + b) / 2
- return False, 0., 0.
- # 定位 锚点 -> 角度
- def locate_anchor(self, res, is_horizontal) -> int:
- found, id_cx, id_cy = self.find_anchor(res)
- # 如果识别不到身份证号
- if not found: raise Exception(f'识别不到anchor{self.name}')
- cx, cy = self.get_rec_area(res)
- # print(f'id_cx: {id_cx}, id_cy: {id_cy}')
- # print(f'cx: {cx}, cy: {cy}')
- pre = None
- for d in self.direction:
- f = self.direction_funcs.get(d, None)
- angle = f((id_cx, id_cy), (cx, cy), is_horizontal)
- if pre is None:
- pre = angle
- else:
- if angle != pre:
- raise Exception('angle is not compatiable')
- return pre
- # 子类1 人像面
- class FrontSideAnchor(OcrAnchor):
- def __init__(self, name: str, d: List[Direction]):
- super(FrontSideAnchor, self).__init__(name, d)
- def is_anchor(self, txt, box) -> bool:
- txts = re.findall('\d{10,18}', txt)
- if len(txts) > 0:
- return True
- return False
- def locate_anchor(self, res, is_horizontal) -> int:
- return super(FrontSideAnchor, self).locate_anchor(res, is_horizontal)
- # 子类2 国徽面
- class BackSideAnchor(OcrAnchor):
- def __init__(self, name: str, d: List[Direction]):
- super(BackSideAnchor, self).__init__(name, d)
- def is_anchor(self, txt, box) -> bool:
- txt = txt.replace('.', '')
- txts = re.findall('有效期', txt)
- if len(txts) > 0:
- return True
- return False
- def locate_anchor(self, res, is_horizontal) -> int:
- return super(BackSideAnchor, self).locate_anchor(res, is_horizontal)
- def detect_angle(result, ocr_anchor: OcrAnchor):
- filters = [lambda x: x.is_slope, lambda x: x.txt.replace(' ', '').encode('utf-8').isalpha()]
- lp = LineParser(result, filters)
- res = lp.parse()
- print('------ angle ocr -------')
- print(res)
- print('------ angle ocr -------')
- is_horizontal = lp.is_horizontal
- return ocr_anchor.locate_anchor(res, is_horizontal)
- @dataclass
- class AngleDetector(object):
- """
- 角度检测器
- """
- ocr: PaddleOCR
- def detect_angle(self, img, image_type):
- image_type = int(image_type)
- ocr_anchor = BackSideAnchor('有效期', [Direction.BOTTOM]) if image_type != 0 else FrontSideAnchor('身份证号', [
- Direction.BOTTOM])
- result = self.ocr.ocr(img, cls=True)
- if not result: raise Exception("对不起,未识别到有效区域,请检查后上传,谢谢")
- try:
- angle = detect_angle(result, ocr_anchor)
- return angle, result
- except Exception as e:
- print(e)
- # 如果第一次识别不到,旋转90度再识别
- img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
- result = self.ocr.ocr(img, cls=True)
- angle = detect_angle(result, ocr_anchor)
- # 旋转90度之后要重新计算角度
- return (angle - 1 + 4) % 4, result
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