418 lines
17 KiB
Python
418 lines
17 KiB
Python
import json
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import logging
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import math
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import os
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import sys
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import tempfile
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import time
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import urllib.request
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import cv2
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import numpy as np
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import paddleclas
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from paddlenlp import Taskflow
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from paddleocr import PaddleOCR
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from time import sleep
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from sqlalchemy import update
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from config.keys import PATIENT_NAME, ADMISSION_DATE, DISCHARGE_DATE, MEDICAL_EXPENSES, PERSONAL_CASH_PAYMENT, \
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PERSONAL_ACCOUNT_PAYMENT, PERSONAL_FUNDED_AMOUNT, MEDICAL_INSURANCE_TYPE, HOSPITAL, DEPARTMENT, DOCTOR, \
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SETTLEMENT_LIST_SCHEMA, DISCHARGE_RECORD_SCHEMA, COST_LIST_SCHEMA
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from config.mysql import MysqlSession
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from config.photo_review import PHHD_BATCH_SIZE, SLEEP_MINUTES, LAYOUT_ANALYSIS
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from photo_review.entity.bd_yljg import BdYljg
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from photo_review.entity.bd_ylks import BdYlks
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from photo_review.entity.zx_ie_cost import ZxIeCost
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from photo_review.entity.zx_ie_discharge import ZxIeDischarge
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from photo_review.entity.zx_ie_settlement import ZxIeSettlement
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from photo_review.entity.zx_ocr import ZxOcr
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from photo_review.entity.zx_phhd import ZxPhhd
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from photo_review.entity.zx_phrec import ZxPhrec
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from photo_review.util.data_util import handle_date, handle_decimal, parse_department, handle_name, \
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handle_insurance_type, handle_original_data, handle_hospital, handle_department
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from photo_review.util.util import get_default_datetime
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from ucloud import ucloud
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# 获取图片
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def open_image(img_path):
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if img_path.startswith("http"):
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# 发送HTTP请求并获取图像数据
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resp = urllib.request.urlopen(img_path)
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# 将数据读取为字节流
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image_data = resp.read()
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# 将字节流转换为NumPy数组
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image_np = np.frombuffer(image_data, np.uint8)
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# 解码NumPy数组为OpenCV图像格式
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image = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
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else:
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image = cv2.imread(img_path)
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return image
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# 分割大图片
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def split_image(img_path, max_ratio=2.82, best_ration=1.41, overlap=0.05):
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split_result = []
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# 打开图片
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img = open_image(img_path)
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# 获取图片的宽度和高度
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height, width = img.shape[:2]
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# 计算宽高比
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ratio = max(width, height) / min(width, height)
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# 检查是否需要裁剪
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if ratio > max_ratio:
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# 确定裁剪的尺寸，保持长宽比，以较短边为基准
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new_ratio = best_ration - overlap
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if width < height:
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# 高度是较长边
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cropped_width = width * best_ration
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for i in range(math.ceil(height / (width * new_ratio))):
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offset = round(width * new_ratio * i)
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# 参数形式为[y1:y2, x1:x2]
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cropped_img = img[offset:round(offset + cropped_width), 0:width]
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split_result.append({"img": cropped_img, "x_offset": 0, "y_offset": offset})
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# 最后一次裁剪时不足的部分填充黑色
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last_img = split_result[-1]["img"]
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split_result[-1]["img"] = cv2.copyMakeBorder(last_img, 0, round(cropped_width - last_img.shape[0]), 0, 0,
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cv2.BORDER_CONSTANT, value=(0, 0, 0))
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else:
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# 宽度是较长边
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cropped_height = height * best_ration
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for i in range(math.ceil(width / (height * new_ratio))):
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offset = round(height * new_ratio * i)
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cropped_img = img[0:height, offset:round(offset + cropped_height)]
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split_result.append({"img": cropped_img, "x_offset": offset, "y_offset": 0})
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# 最后一次裁剪时不足的部分填充黑色
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last_img = split_result[-1]["img"]
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split_result[-1]["img"] = cv2.copyMakeBorder(last_img, 0, 0, 0, round(cropped_height - last_img.shape[1]),
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cv2.BORDER_CONSTANT, value=(0, 0, 0))
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else:
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split_result.append({"img": img, "x_offset": 0, "y_offset": 0})
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return split_result
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# 合并信息抽取结果
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def merge_result(result1, result2):
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for key in result2:
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result1[key] = result1.get(key, []) + result2[key]
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return result1
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# 获取图片旋转角度
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def get_image_rotation_angle(img):
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angle = 0
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model = paddleclas.PaddleClas(model_name="text_image_orientation")
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result = model.predict(input_data=img)
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try:
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angle = int(next(result)[0]["label_names"][0])
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except Exception as e:
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logging.error("获取图片旋转角度失败", exc_info=e)
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return angle
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# 获取图片旋转角度
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def get_image_rotation_angles(img):
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angles = ['0', '90']
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model = paddleclas.PaddleClas(model_name="text_image_orientation")
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result = model.predict(input_data=img)
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try:
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angles = next(result)[0]["label_names"]
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except Exception as e:
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logging.error("获取图片旋转角度失败", exc_info=e)
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return angles
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# 旋转图片
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def rotate_image(img, angle):
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if angle == 0:
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return img
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height, width, _ = img.shape
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if angle == 180:
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new_width = width
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new_height = height
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else:
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new_width = height
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new_height = width
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# 绕图像的中心旋转
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# 参数：旋转中心 旋转度数 scale
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matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
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# 旋转后平移
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matrix[0, 2] += (new_width - width) / 2
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matrix[1, 2] += (new_height - height) / 2
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# 参数：原始图像 旋转参数 元素图像宽高
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rotated = cv2.warpAffine(img, matrix, (new_width, new_height))
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return rotated
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# 获取图片OCR，并将其box转为两点矩形框
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def get_ocr_layout(ocr, img_path):
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def _get_box(old_box):
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new_box = [
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min(old_box[0][0], old_box[3][0]), # x1
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min(old_box[0][1], old_box[1][1]), # y1
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max(old_box[1][0], old_box[2][0]), # x2
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max(old_box[2][1], old_box[3][1]), # y2
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]
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return new_box
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def _normal_box(box_data):
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# Ensure the height and width of bbox are greater than zero
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if box_data[3] - box_data[1] < 0 or box_data[2] - box_data[0] < 0:
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return False
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return True
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layout = []
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ocr_result = ocr.ocr(img_path, cls=False)
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ocr_result = ocr_result[0]
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if not ocr_result:
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return layout
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for segment in ocr_result:
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box = segment[0]
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box = _get_box(box)
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if not _normal_box(box):
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continue
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text = segment[1][0]
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layout.append((box, text))
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return layout
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def ie_temp_image(ie, ocr, image):
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with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
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cv2.imwrite(temp_file.name, image)
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ie_result = []
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try:
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layout = get_ocr_layout(ocr, temp_file.name)
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if not layout:
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# 无识别结果
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ie_result = []
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else:
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ie_result = ie({"doc": temp_file.name, "layout": layout})[0]
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except Exception as e:
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logging.error("信息抽取时出错", exc_info=e)
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finally:
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try:
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os.remove(temp_file.name)
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except Exception as e:
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logging.info(f"删除临时文件 {temp_file.name} 时出错", exc_info=e)
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return ie_result
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# 关键信息提取
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def information_extraction(ie, phrecs):
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result = {}
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# 同一批图的标识
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identity = int(time.time())
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ocr = PaddleOCR(use_angle_cls=False, lang="ch", show_log=False)
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for phrec in phrecs:
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pic_path = ucloud.get_private_url(phrec.cfjaddress)
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if not pic_path:
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continue
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split_result = split_image(pic_path)
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for img in split_result:
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angles = get_image_rotation_angles(img["img"])
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rotated_img = rotate_image(img["img"], int(angles[0]))
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ie_results = [{"result": ie_temp_image(ie, ocr, rotated_img), "angle": angles[0]}]
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if not ie_results[0] or len(ie_results[0]) < len(ie.kwargs.get("schema")):
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rotated_img = rotate_image(img["img"], int(angles[1]))
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ie_results.append({"result": ie_temp_image(ie, ocr, rotated_img), "angle": angles[1]})
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now = get_default_datetime()
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for ie_result in ie_results:
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result_json = json.dumps(ie_result["result"], ensure_ascii=False)
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if len(result_json) > 5000:
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result_json = result_json[:5000]
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session = MysqlSession()
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zx_ocr = ZxOcr(pk_phhd=phrec.pk_phhd, pk_phrec=phrec.pk_phrec, id=identity, cfjaddress=phrec.cfjaddress,
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content=result_json, rotation_angle=ie_result["angle"], x_offset=img["x_offset"],
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y_offset=img["y_offset"], create_time=now, update_time=now)
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session.add(zx_ocr)
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session.commit()
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session.close()
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result = merge_result(result, ie_result["result"])
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return result
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# 从keys中获取准确率最高的value
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def get_best_value_in_keys(source, keys):
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# 最终结果
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result = None
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# 最大可能性
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best_probability = 0
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for key in keys:
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values = source.get(key)
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if values:
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for value in values:
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text = value.get("text")
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probability = value.get("probability")
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if text and probability > best_probability:
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result = text
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best_probability = probability
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return result
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# 从keys中获取所有value组成list
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def get_values_of_keys(source, keys):
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result = []
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for key in keys:
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value = source.get(key)
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if value:
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for v in value:
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v = v.get("text")
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if v:
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result.append(v)
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# 去重
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return list(set(result))
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def save_or_update_ie(table, pk_phhd, data):
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data = {k: v for k, v in data.items() if v is not None and v != ""}
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obj = table(**data)
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session = MysqlSession()
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db_data = session.query(table).filter_by(pk_phhd=pk_phhd).one_or_none()
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now = get_default_datetime()
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if db_data:
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# 更新
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db_data.update_time = now
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for k, v in data.items():
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setattr(db_data, k, v)
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else:
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# 新增
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obj.create_time = now
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obj.update_time = now
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session.add(obj)
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session.commit()
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session.close()
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def photo_review(pk_phhd):
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settlement_list = []
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discharge_record = []
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cost_list = []
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session = MysqlSession()
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phrecs = session.query(ZxPhrec.pk_phrec, ZxPhrec.pk_phhd, ZxPhrec.cRectype, ZxPhrec.cfjaddress) \
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.filter(ZxPhrec.pk_phhd == pk_phhd) \
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.all()
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session.close()
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for phrec in phrecs:
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if phrec.cRectype == "1":
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settlement_list.append(phrec)
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elif phrec.cRectype == "3":
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discharge_record.append(phrec)
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elif phrec.cRectype == "4":
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cost_list.append(phrec)
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settlement_list_ie_result = information_extraction(
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Taskflow("information_extraction", schema=SETTLEMENT_LIST_SCHEMA, model="uie-x-base",
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task_path="config/model/settlement_list_model", layout_analysis=LAYOUT_ANALYSIS), settlement_list)
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settlement_data = {
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"pk_phhd": pk_phhd,
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"name": handle_name(get_best_value_in_keys(settlement_list_ie_result, PATIENT_NAME)),
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"admission_date_str": handle_original_data(get_best_value_in_keys(settlement_list_ie_result, ADMISSION_DATE)),
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"discharge_date_str": handle_original_data(get_best_value_in_keys(settlement_list_ie_result, DISCHARGE_DATE)),
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"medical_expenses_str": handle_original_data(
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get_best_value_in_keys(settlement_list_ie_result, MEDICAL_EXPENSES)),
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"personal_cash_payment_str": handle_original_data(
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get_best_value_in_keys(settlement_list_ie_result, PERSONAL_CASH_PAYMENT)),
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"personal_account_payment_str": handle_original_data(
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get_best_value_in_keys(settlement_list_ie_result, PERSONAL_ACCOUNT_PAYMENT)),
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"personal_funded_amount_str": handle_original_data(
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get_best_value_in_keys(settlement_list_ie_result, PERSONAL_FUNDED_AMOUNT)),
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"medical_insurance_type": handle_insurance_type(
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get_best_value_in_keys(settlement_list_ie_result, MEDICAL_INSURANCE_TYPE))
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}
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settlement_data["admission_date"] = handle_date(settlement_data["admission_date_str"])
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settlement_data["admission_date"] = handle_date(settlement_data["admission_date_str"])
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settlement_data["discharge_date"] = handle_date(settlement_data["discharge_date_str"])
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settlement_data["medical_expenses"] = handle_decimal(settlement_data["medical_expenses_str"])
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settlement_data["personal_cash_payment"] = handle_decimal(settlement_data["personal_cash_payment_str"])
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settlement_data["personal_account_payment"] = handle_decimal(settlement_data["personal_account_payment_str"])
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settlement_data["personal_funded_amount"] = handle_decimal(settlement_data["personal_funded_amount_str"])
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save_or_update_ie(ZxIeSettlement, pk_phhd, settlement_data)
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discharge_record_ie_result = information_extraction(
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Taskflow("information_extraction", schema=DISCHARGE_RECORD_SCHEMA, model="uie-x-base",
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task_path="config/model/discharge_record_model", layout_analysis=LAYOUT_ANALYSIS), discharge_record)
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discharge_data = {
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"pk_phhd": pk_phhd,
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"hospital": handle_hospital(get_best_value_in_keys(discharge_record_ie_result, HOSPITAL)),
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"department": handle_department(get_best_value_in_keys(discharge_record_ie_result, DEPARTMENT)),
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"name": handle_name(get_best_value_in_keys(discharge_record_ie_result, PATIENT_NAME)),
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"admission_date_str": handle_original_data(get_best_value_in_keys(discharge_record_ie_result, ADMISSION_DATE)),
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"discharge_date_str": handle_original_data(get_best_value_in_keys(discharge_record_ie_result, DISCHARGE_DATE)),
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"doctor": handle_name(get_best_value_in_keys(discharge_record_ie_result, DOCTOR))
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}
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discharge_data["admission_date"] = handle_date(discharge_data["admission_date_str"])
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discharge_data["discharge_date"] = handle_date(discharge_data["discharge_date_str"])
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hospital_value = get_values_of_keys(discharge_record_ie_result, HOSPITAL)
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if hospital_value:
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session = MysqlSession()
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yljg = session.query(BdYljg.pk_yljg, BdYljg.name) \
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.filter(BdYljg.name.in_(hospital_value)).limit(1).one_or_none()
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session.close()
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if yljg:
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discharge_data["pk_yljg"] = yljg.pk_yljg
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discharge_data["hospital"] = yljg.name
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department_value = get_values_of_keys(discharge_record_ie_result, DEPARTMENT)
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if department_value:
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department_values = []
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for dept in department_value:
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department_values += parse_department(dept)
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department_values = list(set(department_values))
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if department_values:
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session = MysqlSession()
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ylks = session.query(BdYlks.pk_ylks, BdYlks.name) \
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.filter(BdYlks.name.in_(department_values)).limit(1).one_or_none()
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session.close()
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if ylks:
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discharge_data["pk_ylks"] = ylks.pk_ylks
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discharge_data["department"] = ylks.name
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save_or_update_ie(ZxIeDischarge, pk_phhd, discharge_data)
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cost_list_ie_result = information_extraction(
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Taskflow("information_extraction", schema=COST_LIST_SCHEMA, model="uie-x-base",
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task_path="config/model/cost_list_model", layout_analysis=LAYOUT_ANALYSIS), cost_list)
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cost_data = {
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"pk_phhd": pk_phhd,
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"name": handle_name(get_best_value_in_keys(cost_list_ie_result, PATIENT_NAME)),
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"admission_date_str": handle_original_data(get_best_value_in_keys(cost_list_ie_result, ADMISSION_DATE)),
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"discharge_date_str": handle_original_data(get_best_value_in_keys(cost_list_ie_result, DISCHARGE_DATE)),
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"medical_expenses_str": handle_original_data(get_best_value_in_keys(cost_list_ie_result, MEDICAL_EXPENSES))
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}
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cost_data["admission_date"] = handle_date(cost_data["admission_date_str"])
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cost_data["discharge_date"] = handle_date(cost_data["discharge_date_str"])
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cost_data["medical_expenses"] = handle_decimal(cost_data["medical_expenses_str"])
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save_or_update_ie(ZxIeCost, pk_phhd, cost_data)
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def main():
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# 持续检测新案子
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while 1:
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session = MysqlSession()
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# 查询需要识别的案子
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phhds = session.query(ZxPhhd.pk_phhd).filter(ZxPhhd.exsuccess_flag == '1').limit(PHHD_BATCH_SIZE).all()
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session.close()
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if phhds:
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for phhd in phhds:
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pk_phhd = phhd.pk_phhd
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logging.info(f"开始识别：{pk_phhd}")
|
||
photo_review(pk_phhd)
|
||
|
||
# 识别完成更新标识
|
||
session = MysqlSession()
|
||
update_flag = (update(ZxPhhd).where(ZxPhhd.pk_phhd == pk_phhd).values(exsuccess_flag=8))
|
||
session.execute(update_flag)
|
||
session.commit()
|
||
session.close()
|
||
else:
|
||
# 没有查询到新案子，等待一段时间后再查
|
||
log = logging.getLogger()
|
||
log.info(f"暂未查询到新案子，等待{SLEEP_MINUTES}分钟...")
|
||
sleep(SLEEP_MINUTES * 60)
|