251205更新

utils/client.py

@@ -90,7 +90,7 @@ class MySQLClient:
 class CacheClient:
     """缓存客户端"""
 
-    def __init__(self, cache_ttl: int = 360, database: str = "Caches.db"):
+    def __init__(self, cache_ttl: int = 360, database: str = "SQLite.db"):
         """
         :param cache_ttl: 缓存生存时间，单位为天
         :param database: 缓存数据库名称
@@ -154,20 +154,31 @@ class CacheClient:
         :return: 缓存
         """
         with threading.Lock():  # 线程锁，保证并发安全
-            with self.connection.cursor() as cursor:
             # noinspection PyBroadException
             try:
+                # 创建游标
+                cursor = self.connection.cursor()
                 # 根据缓存唯一标识查询有效缓存
                 cursor.execute(
                     "SELECT cache FROM caches WHERE guid = ? AND timestamp >= ?",
                     (guid, time.time() - self.cache_ttl * 86400),
                 )
                 if result := cursor.fetchone():
+                    print()
                     return json.loads(result[0])
                 return None
+            # 若发生异常则回滚事务并返回None
             except Exception:
                 self.connection.rollback()
                 return None
+            finally:
+                # 确保游标关闭（关键：释放资源）
+                if cursor:
+                    # noinspection PyBroadException
+                    try:
+                        cursor.close()
+                    except Exception:
+                        pass
 
     def update(self, guid: str, cache: Dict) -> bool:
         """
@@ -177,9 +188,10 @@ class CacheClient:
         :return: 成功返回True，失败返回False
         """
         with threading.Lock():  # 线程锁，保证并发安全
-            with self.connection.cursor() as cursor:
             # noinspection PyBroadException
             try:
+                # 创建游标
+                cursor = self.connection.cursor()
                 # 新增或覆盖缓存
                 cursor.execute(
                     "INSERT OR REPLACE INTO caches (guid, cache, timestamp) VALUES (?, ?, ?)",
@@ -192,9 +204,18 @@ class CacheClient:
                 # 提交事务
                 self.connection.commit()
                 return True
+            # 若发生异常则回滚事务并返回None
             except Exception:
-                    self.connection.rollback()  # 异常回滚事务
+                self.connection.rollback()
                 return False
+            finally:
+                # 确保游标关闭（关键：释放资源）
+                if cursor:
+                    # noinspection PyBroadException
+                    try:
+                        cursor.close()
+                    except Exception:
+                        pass
 
 
 """

票据理赔自动化/main.py

@@ -5,24 +5,23 @@
 功能清单
 https://liubiren.feishu.cn/docx/WFjTdBpzroUjQvxxrNIcKvGnneh?from=from_copylink
 """
-import hashlib
 import json
 import re
 import uuid
 from base64 import b64encode
 from datetime import datetime
 from decimal import Decimal, ROUND_HALF_UP
+from hashlib import md5
 from pathlib import Path
+from typing import Optional, Tuple
 
 import cv2
 import numpy
-import pandas
 from dateutil.parser import parse
 from jinja2 import Environment, FileSystemLoader
-from jionlp import parse_location
 from zen import ZenDecision, ZenEngine
 
-from utils.client import Authenticator, HTTPClient
+from utils.client import Authenticator, HTTPClient, CacheClient
 
 
 # from utils.ocr import fuzzy_match
@@ -33,111 +32,120 @@ from utils.client import Authenticator, HTTPClient
 # -------------------------
 
 
-def image_read():
-    """本地读取影像件"""
-    # 影像件读取（默认转为单通道灰度图，实际需下载影像件）
-    image_ndarray = cv2.imread(globals()["image_path"].as_posix(), cv2.IMREAD_GRAYSCALE)
-    if image_ndarray is None:
-        return None
-
-    # 编码为图像字节流
 # noinspection PyShadowingNames
-    success, image_bytes = cv2.imencode(f".{globals()["image_format"]}", image_ndarray)
+def image_read(
-    if not success or image_bytes is None:
+    image_path: Path,
-        return None
+) -> Tuple[numpy.ndarray | None, str | None, bytes | None]:
-
+    """
-    return image_bytes
+    本地打开并读取影像件
-
+    :param image_path: 影像件路径对象
-
+    :return: 影像件数组、影像件格式和影像件字节流
-def images_compression(**kwargs) -> tuple[str | None, str | None]:
+    """
-    """影像件压缩并BASE64编码"""
+    # noinspection PyBroadException
+    try:
+        # 影像件打开并读取（默认转为单通道灰度图）
+        image_ndarray = cv2.imread(image_path.as_posix(), cv2.IMREAD_GRAYSCALE)
+        if image_ndarray is None:
+            raise RuntimeError("影像件打开并读取发生异常")
 
         # 影像件格式
-    image_format = kwargs.get("image_format", globals()["image_format"])
+        image_format = image_path.suffix.lower()
-    if image_format is None:
-        return None, None
 
-    # 影像件
+        # 按照影像件格式将影像件数组编码
-    image = kwargs.get("image", globals()["image"])
+        success, image_ndarray_encoded = cv2.imencode(image_format, image_ndarray)
-    if image is None:
+        if not success or image_ndarray_encoded is None:
-        return None, None
+            raise RuntimeError("编码为图像字节数组发生异常")
+        # 将编码后图像数组转为字节流
+        image_bytes = image_ndarray_encoded.tobytes()
 
-    # 编码为图像字节流
+        return image_ndarray, image_format, image_bytes
-    success, image_bytes = cv2.imencode(ext=f".{image_format}", img=image)
+    except Exception:
-    # 若发生异常则返回NONE
+        return None, None, None
-    if not success or image_bytes is None:
-        return None, None
 
-    # 生成影像件唯一标识
-    image_guid = hashlib.md5(image_bytes.tobytes()).hexdigest().upper()
 
-    # BASE64编码
+# noinspection PyShadowingNames
-    image_base64 = b64encode(image_bytes.tobytes()).decode("utf-8")
+def images_compress(
+    image_ndarray, image_format, image_bytes, image_size_specified=2
+) -> Optional[str]:
+    """
+    影像件压缩
+    :param image_ndarray: 影像件数组
+    :param image_format: 影像件格式
+    :param image_bytes: 影像件字节流
+    :param image_size_specified: 指定影像件大小，单位为兆字节（MB）
+    :return: 压缩后影像件BASE64编码
+    """
+    # 将指定影像件大小单位由兆字节转为字节
+    image_size_specified = image_size_specified * 1024 * 1024
 
-    # 将指定影像件大小单位由MB转为B
+    # 影像件BASE64编码
-    image_size_specified = kwargs.get("image_size_specified", 2) * 1048576
+    image_base64 = b64encode(image_bytes).decode("utf-8")
-
-    # 若影像件BASE64编码后大小小于指定影像件大小则返回
     if len(image_base64) <= image_size_specified:
-        return image_guid, image_base64
+        return image_base64
 
-    # 双循环压缩影像件
+    # 通过调整影像件质量和尺寸达到压缩影像件目的
-    # 外循环压缩：通过降低影像件质量实现压缩影像件大小
+    # 外循环压缩：通过调整影像件质量实现压缩影像件大小
-    for quality in range(90, 0, -10):
+    for quality in range(90, 50, -10):
-
+        image_ndarray_copy = image_ndarray.copy()
-        image_copy = image.copy()
+        # 内循环压缩：通过调整影像件尺寸实现压缩影像件大小
-
-        # 内循环压缩：通过等比例调整影像件尺寸实现压缩影像件大小
         for i in range(25):
-
+            # 按照影像件格式和影像件质量将影像件数组编码
-            success, image_bytes = cv2.imencode(
+            success, image_ndarray_encoded = cv2.imencode(
-                ext=f".{image_format}",
+                image_format,
-                img=image_copy,
+                image_ndarray_copy,
                 params=(
                     [cv2.IMWRITE_PNG_COMPRESSION, 10 - quality // 10]
                     if image_format == "png"
                     else [cv2.IMWRITE_JPEG_QUALITY, quality]
                 ),
             )
-            if not success or image_bytes is None:
+            if not success or image_ndarray_encoded is None:
                 break
 
-            image_base64 = b64encode(image_bytes.tobytes()).decode("utf-8")
-            if len(image_base64) <= image_size_specified:
-                return image_guid, image_base64
-
-            # 调整影像件尺寸
-            image_copy = cv2.resize(
-                src=image_copy,
-                dsize=(int(image_copy.shape[0] * 0.9), int(image_copy.shape[1] * 0.9)),
-                interpolation=cv2.INTER_AREA,
-            )
-
-            # 若调整后影像件尺寸中长或宽小于200像素则停止调整影像件尺寸
-            if min(image_copy.shape[:2]) < 200:
-                break
-
-    # 若仍未压缩至指定影像件大小则返回NONE
-    return None, None
-
-
-def images_classification(**kwargs) -> tuple[str | None, str | None]:
-    """影像件分类"""
-
-    # 影像件全局唯一标识：优先使用关键词变量，其次使用全局变量，再次使用随机唯一标识
-    image_guid = kwargs.get(
-        "image_guid", globals().get("image_guid", uuid.uuid4().hex.upper())
-    )
-
-    # 影像件格式
-    image_format = kwargs.get("image_format", globals()["image_format"])
-    if image_format is None:
-        return None, None
-
             # 影像件BASE64编码
-    image_base64 = kwargs.get("image_base64", globals()["image_base64"])
+            image_base64 = b64encode(image_ndarray_encoded.tobytes()).decode("utf-8")
-    if image_base64 is None:
+            if len(image_base64) <= image_size_specified:
-        return None, None
+                return image_base64
+
+            # 调整影像件尺寸
+            image_ndarray_copy = cv2.resize(
+                image_ndarray_copy,
+                (
+                    int(image_ndarray_copy.shape[0] * 0.9),
+                    int(image_ndarray_copy.shape[1] * 0.9),
+                ),
+                interpolation=cv2.INTER_AREA,
+            )
+            # 若调整后影像件尺寸中长或宽小于350像素则停止调整影像件尺寸
+            if min(image_ndarray_copy.shape[:2]) < 350:
+                break
+
+    return None
+
+
+def images_classify(
+    image, image_ndarray, image_format, image_bytes
+) -> tuple[str | None, str | None]:
+    """
+    影像件分类并旋正
+    :param image: 影像件数据
+    :param image_ndarray: 影像件数组
+    :param image_format: 影像件格式
+    :param image_bytes: 影像件字节流
+    :return: 压缩后影像件BASE64编码
+    """
+    # 影像件唯一标识
+    image_uuid = image["影像件唯一标识"]
+    with CacheClient() as client:
+        # 根据作业环节和影像件唯一标识生成缓存唯一标识
+        cache_guid = md5(("初审" + image_uuid).encode("utf-8")).hexdigest().upper()
+        cache = client.query(cache_guid)
+
+    if cache is not None:
+        # 影像件类型
+        image_type = cache["image_type"]
+        # 影像件方向
+        image_o
 
     # 请求深圳快瞳影像件分类接口
     response = globals()["http_client"].post(
@@ -1180,39 +1188,38 @@ if __name__ == "__main__":
         for image_index, image_path in enumerate(
             sorted(
                 [
-                    image_path
+                    x
-                    for image_path in case_path.glob(pattern="*")
+                    for x in case_path.glob(pattern="*")
-                    if image_path.is_file()
+                    if x.is_file() and x.suffix.lower() in [".jpg", ".jpeg", ".png"]
-                    and image_path.suffix.lower() in [".jpg", ".jpeg", ".png"]
                 ],  # 实际作业亦仅支持JPG、JPEG或PNG
-                key=lambda x: x.name,
+                key=lambda x: x.stat().st_ctime,  # 根据影像件创建时间顺序排序
             ),
             1,
         ):
             # 初始化影像件数据
             image = {
                 "原始影像件": {
-                    "影像件地址": image_path.as_posix(),
+                    "影像件地址": image_path.as_posix(),  # 将影像件路径对象转为字符串
                     "影像件名称": (image_name := image_path.stem),
-                    "影像件格式": (image_format := image_path.suffix.lower()),
                 },
             }
 
-            # 本地读取影像件（实际从影像件服务器下载）
+            # 本地打开并读取影像件（实际作业为从作业系统的影像件服务器下载并读取影像件，因赔案签收时会转存影像件至影像件服务器，若下载并读取影像件发生异常则技术支持排查）
-            image_bytes = image_read()
+            image_ndarray, image_format, image_bytes = image_read(image_path)
-            if image_bytes is None:
+            # 若本地打开并读取影像件发生异常则跳过该影像件
-                # noinspection PyTypeChecker
+            if image_format is None or image_bytes is None:
-                image["影像件唯一标识"] = None
+                raise RuntimeError("本地打开并读取影像件发生异常")
-                continue
 
+            image["原始影像件"]["影像件格式"] = image_format
             # 生成影像件唯一标识
             # noinspection PyTypeChecker
-            image["影像件唯一标识"] = (
+            image["影像件唯一标识"] = md5(image_bytes).hexdigest().upper()
-                image_guid := hashlib.md5(image_bytes.tobytes()).hexdigest().upper()
+
-            )
+            # 影像件分类
 
             print(image)
             exit()
+"""
 
             # 影像件压缩（输出BASE64编码）
             image_guid, image_base64 = images_compression()
@@ -1586,3 +1593,4 @@ if __name__ == "__main__":
                     }
                 )
             )
+"""