From dd895f98b51f9a027e42e401da52086572617c28 Mon Sep 17 00:00:00 2001 From: liuyebo <1515783401@qq.com> Date: Wed, 17 Jul 2024 14:15:02 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BC=98=E5=8C=96=E7=85=A7=E7=89=87=E8=AF=86?= =?UTF-8?q?=E5=88=AB=E5=8A=9F=E8=83=BD=E6=9E=B6=E6=9E=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- photo_review.py | 4 +- photo_review/__init__.py | 8 +- photo_review/photo_review.py | 187 ++++------------------------------- 3 files changed, 24 insertions(+), 175 deletions(-) diff --git a/photo_review.py b/photo_review.py index 399d0b4..f0354b5 100644 --- a/photo_review.py +++ b/photo_review.py @@ -3,7 +3,7 @@ import traceback from auto_email.error_email import send_error_email from log import LOGGING_CONFIG -from photo_review import photo_review, SEND_ERROR_EMAIL +from photo_review import photo_review, SEND_ERROR_EMAIL, RETRY_TIME # 项目必须从此处启动，否则代码中的相对路径可能导致错误的发生 if __name__ == '__main__': @@ -11,7 +11,7 @@ if __name__ == '__main__': logging.config.dictConfig(LOGGING_CONFIG) # 崩溃后的重试次数 - for _ in range(2): + for _ in range(RETRY_TIME + 1): try: logging.info(f"【{program_name}】开始运行") photo_review.main() diff --git a/photo_review/__init__.py b/photo_review/__init__.py index 2c60379..e9dba46 100644 --- a/photo_review/__init__.py +++ b/photo_review/__init__.py @@ -60,10 +60,12 @@ COST_LIST_SCHEMA = PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPE 模型配置 """ SETTLEMENT_IE = Taskflow("information_extraction", schema=SETTLEMENT_LIST_SCHEMA, model="uie-x-base", - task_path="config/model/settlement_list_model", layout_analysis=False, precision='fp16') + task_path="config/model/settlement_list_model", layout_analysis=LAYOUT_ANALYSIS, + precision='fp16') DISCHARGE_IE = Taskflow("information_extraction", schema=DISCHARGE_RECORD_SCHEMA, model="uie-x-base", - task_path="config/model/discharge_record_model", layout_analysis=False, precision='fp16') + task_path="config/model/discharge_record_model", layout_analysis=LAYOUT_ANALYSIS, + precision='fp16') COST_IE = Taskflow("information_extraction", schema=COST_LIST_SCHEMA, model="uie-x-base", device_id=1, - task_path="config/model/cost_list_model", layout_analysis=False, precision='fp16') + task_path="config/model/cost_list_model", layout_analysis=LAYOUT_ANALYSIS, precision='fp16') OCR = PaddleOCR(use_angle_cls=False, lang="ch", show_log=False, gpu_id=1) diff --git a/photo_review/photo_review.py b/photo_review/photo_review.py index d715502..724d3b5 100644 --- a/photo_review/photo_review.py +++ b/photo_review/photo_review.py @@ -1,89 +1,26 @@ import json import logging -import math import os -import sys import tempfile import time -import urllib.request from time import sleep import cv2 -import numpy as np -import paddleclas import requests - -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from sqlalchemy import update from db import MysqlSession from db.mysql import BdYljg, BdYlks, ZxOcr, ZxIeCost, ZxIeDischarge, ZxIeSettlement, ZxPhhd, ZxPhrec from photo_review import PATIENT_NAME, ADMISSION_DATE, DISCHARGE_DATE, MEDICAL_EXPENSES, PERSONAL_CASH_PAYMENT, \ PERSONAL_ACCOUNT_PAYMENT, PERSONAL_FUNDED_AMOUNT, MEDICAL_INSURANCE_TYPE, HOSPITAL, DEPARTMENT, DOCTOR, \ ADMISSION_ID, SETTLEMENT_ID, AGE, OCR, SETTLEMENT_IE, DISCHARGE_IE, COST_IE, PHHD_BATCH_SIZE, SLEEP_MINUTES -from sqlalchemy import update from ucloud import ucloud +from util import image_util, util from util.data_util import handle_date, handle_decimal, parse_department, handle_name, \ handle_insurance_type, handle_original_data, handle_hospital, handle_department, handle_id, handle_age from util.util import get_default_datetime -# 获取图片 -def open_image(img_path): - if img_path.startswith("http"): - # 发送HTTP请求并获取图像数据 - resp = urllib.request.urlopen(img_path) - # 将数据读取为字节流 - image_data = resp.read() - # 将字节流转换为NumPy数组 - image_np = np.frombuffer(image_data, np.uint8) - # 解码NumPy数组为OpenCV图像格式 - image = cv2.imdecode(image_np, cv2.IMREAD_COLOR) - else: - image = cv2.imread(img_path) - return image - - -# 分割大图片 -def split_image(img_path, max_ratio=1.41, best_ration=1.41, overlap=0.05): - split_result = [] - # 打开图片 - img = open_image(img_path) - # 获取图片的宽度和高度 - height, width = img.shape[:2] - # 计算宽高比 - ratio = max(width, height) / min(width, height) - # 检查是否需要裁剪 - if ratio > max_ratio: - # 确定裁剪的尺寸，保持长宽比，以较短边为基准 - new_ratio = best_ration - overlap - if width < height: - # 高度是较长边 - cropped_width = width * best_ration - for i in range(math.ceil(height / (width * new_ratio))): - offset = round(width * new_ratio * i) - # 参数形式为[y1:y2, x1:x2] - cropped_img = img[offset:round(offset + cropped_width), 0:width] - split_result.append({"img": cropped_img, "x_offset": 0, "y_offset": offset}) - # 最后一次裁剪时不足的部分填充黑色 - last_img = split_result[-1]["img"] - split_result[-1]["img"] = cv2.copyMakeBorder(last_img, 0, round(cropped_width - last_img.shape[0]), 0, 0, - cv2.BORDER_CONSTANT, value=(0, 0, 0)) - else: - # 宽度是较长边 - cropped_height = height * best_ration - for i in range(math.ceil(width / (height * new_ratio))): - offset = round(height * new_ratio * i) - cropped_img = img[0:height, offset:round(offset + cropped_height)] - split_result.append({"img": cropped_img, "x_offset": offset, "y_offset": 0}) - # 最后一次裁剪时不足的部分填充黑色 - last_img = split_result[-1]["img"] - split_result[-1]["img"] = cv2.copyMakeBorder(last_img, 0, 0, 0, round(cropped_height - last_img.shape[1]), - cv2.BORDER_CONSTANT, value=(0, 0, 0)) - else: - split_result.append({"img": img, "x_offset": 0, "y_offset": 0}) - return split_result - - # 合并信息抽取结果 def merge_result(result1, result2): for key in result2: @@ -91,91 +28,13 @@ def merge_result(result1, result2): return result1 -# 获取图片旋转角度 -def get_image_rotation_angle(img): - angle = 0 - model = paddleclas.PaddleClas(model_name="text_image_orientation") - result = model.predict(input_data=img) - try: - angle = int(next(result)[0]["label_names"][0]) - except Exception as e: - logging.error("获取图片旋转角度失败", exc_info=e) - return angle - - -# 获取图片旋转角度 -def get_image_rotation_angles(img): - angles = ['0', '90'] - model = paddleclas.PaddleClas(model_name="text_image_orientation") - result = model.predict(input_data=img) - try: - angles = next(result)[0]["label_names"] - except Exception as e: - logging.error("获取图片旋转角度失败", exc_info=e) - return angles - - -# 旋转图片 -def rotate_image(img, angle): - if angle == 0: - return img - height, width, _ = img.shape - if angle == 180: - new_width = width - new_height = height - else: - new_width = height - new_height = width - # 绕图像的中心旋转 - # 参数：旋转中心 旋转度数 scale - matrix = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1) - # 旋转后平移 - matrix[0, 2] += (new_width - width) / 2 - matrix[1, 2] += (new_height - height) / 2 - # 参数：原始图像 旋转参数 元素图像宽高 - rotated = cv2.warpAffine(img, matrix, (new_width, new_height)) - return rotated - - -# 获取图片OCR，并将其box转为两点矩形框 -def get_ocr_layout(ocr, img_path): - def _get_box(old_box): - new_box = [ - min(old_box[0][0], old_box[3][0]), # x1 - min(old_box[0][1], old_box[1][1]), # y1 - max(old_box[1][0], old_box[2][0]), # x2 - max(old_box[2][1], old_box[3][1]), # y2 - ] - return new_box - - def _normal_box(box_data): - # Ensure the height and width of bbox are greater than zero - if box_data[3] - box_data[1] < 0 or box_data[2] - box_data[0] < 0: - return False - return True - - layout = [] - ocr_result = ocr.ocr(img_path, cls=False) - ocr_result = ocr_result[0] - if not ocr_result: - return layout - for segment in ocr_result: - box = segment[0] - box = _get_box(box) - if not _normal_box(box): - continue - text = segment[1][0] - layout.append((box, text)) - return layout - - def ie_temp_image(ie, ocr, image): with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file: cv2.imwrite(temp_file.name, image) ie_result = [] try: - layout = get_ocr_layout(ocr, temp_file.name) + layout = util.get_ocr_layout(ocr, temp_file.name) if not layout: # 无识别结果 ie_result = [] @@ -213,17 +72,17 @@ def information_extraction(ie, phrecs): # 同一批图的标识 identity = int(time.time()) for phrec in phrecs: - pic_path = ucloud.get_private_url(phrec.cfjaddress) - if not pic_path: + img_path = ucloud.get_private_url(phrec.cfjaddress) + if not img_path: continue - split_result = split_image(pic_path) + split_results = image_util.split(img_path) - for img in split_result: - angles = get_image_rotation_angles(img["img"]) - rotated_img = rotate_image(img["img"], int(angles[0])) + for split_result in split_results: + angles = image_util.parse_rotation_angles(split_result["img"]) + rotated_img = image_util.rotate(split_result["img"], int(angles[0])) ie_results = [{"result": ie_temp_image(ie, OCR, rotated_img), "angle": angles[0]}] if not ie_results[0] or len(ie_results[0]) < len(ie.kwargs.get("schema")): - rotated_img = rotate_image(img["img"], int(angles[1])) + rotated_img = image_util.rotate(split_result["img"], int(angles[1])) ie_results.append({"result": ie_temp_image(ie, OCR, rotated_img), "angle": angles[1]}) now = get_default_datetime() @@ -233,8 +92,9 @@ def information_extraction(ie, phrecs): result_json = result_json[:5000] session = MysqlSession() zx_ocr = ZxOcr(pk_phhd=phrec.pk_phhd, pk_phrec=phrec.pk_phrec, id=identity, cfjaddress=phrec.cfjaddress, - content=result_json, rotation_angle=ie_result["angle"], x_offset=img["x_offset"], - y_offset=img["y_offset"], create_time=now, update_time=now) + content=result_json, rotation_angle=ie_result["angle"], + x_offset=split_result["x_offset"], y_offset=split_result["y_offset"], create_time=now, + update_time=now) session.add(zx_ocr) session.commit() session.close() @@ -383,20 +243,15 @@ def cost_task(pk_phhd, cost_list): save_or_update_ie(ZxIeCost, pk_phhd, cost_data) -def settlement_and_discharge_task(pk_phhd, settlement_list, discharge_record): - settlement_task(pk_phhd, settlement_list) - discharge_task(pk_phhd, discharge_record) - - def photo_review(pk_phhd): settlement_list = [] discharge_record = [] cost_list = [] session = MysqlSession() - phrecs = session.query(ZxPhrec.pk_phrec, ZxPhrec.pk_phhd, ZxPhrec.cRectype, ZxPhrec.cfjaddress) \ - .filter(ZxPhrec.pk_phhd == pk_phhd) \ - .all() + phrecs = session.query(ZxPhrec.pk_phrec, ZxPhrec.pk_phhd, ZxPhrec.cRectype, ZxPhrec.cfjaddress).filter( + ZxPhrec.pk_phhd == pk_phhd + ).all() session.close() for phrec in phrecs: if phrec.cRectype == "1": @@ -406,21 +261,14 @@ def photo_review(pk_phhd): elif phrec.cRectype == "4": cost_list.append(phrec) - # with concurrent.futures.ThreadPoolExecutor() as executor: - # executor.submit(settlement_task, pk_phhd, settlement_list) - # executor.submit(discharge_task, pk_phhd, discharge_record) - # # executor.submit(settlement_and_discharge_task, pk_phhd, settlement_list, discharge_record) - # executor.submit(cost_task, pk_phhd, cost_list) settlement_task(pk_phhd, settlement_list) discharge_task(pk_phhd, discharge_record) cost_task(pk_phhd, cost_list) def main(): - # 持续检测新案子 while 1: session = MysqlSession() - # 查询需要识别的案子 phhds = session.query(ZxPhhd.pk_phhd).filter(ZxPhhd.exsuccess_flag == '1').limit(PHHD_BATCH_SIZE).all() # 将状态改为正在识别中 pk_phhd_values = [phhd.pk_phhd for phhd in phhds] @@ -442,6 +290,5 @@ def main(): 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...") + logging.info(f"暂未查询到需要识别的案子，等待{SLEEP_MINUTES}分钟...") sleep(SLEEP_MINUTES * 60)