parent
973ca8113c
commit
ae932ab487
Binary file not shown.
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@ -1 +1 @@
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{"szkt": {"token": "a62c56ace614a6546191d5af8ca8b1513cfaeaea7ce67d0a37de994ab6c2aa4e2a0b058e0da575ff376dd51dc19c5ad353ab2761cb6d9db4d521b83adeee2979b78f7ae70765b26985165b6266d084b75f2f918008966e72a116d8bca5ec4c7cecc5223f78fa47b4d40aa9cf5277a11b0b967ad06e84ef7c4acbc53ccdef936c062b2d037ae0dad8c29d50426b668ec349cc8c0099a0270e16f97d31e4f058bc086334468f88d934c7fd1464ed3800833d2f486dc06f0689b99abbb78a8ebf4a3877bd82d0dd765dc09b7a1594fa8849d51f59282a81048c52e82e8320d1ad042a6c307ca831647cba4356564704780f", "expired_timestamp": 1759201579.393386}}
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{"szkt": {"token": "a62c56ace614a6546191d5af8ca8b1513cfaeaea7ce67d0a37de994ab6c2aa4e2a0b058e0da575ff376dd51dc19c5ad353ab2761cb6d9db4d521b83adeee2979b78f7ae70765b26985165b6266d084b75f2f918008966e72a116d8bca5ec4c7cecc5223f78fa47b4d40aa9cf5277a11b0b967ad06e84ef7c4acbc53ccdef936c062b2d037ae0dad8c29d50426b668ec349cc8c0099a0270e16f97d31e4f058bc086334468f88d934c7fd1464ed3800833d2f486dc06f0689b99abbb78a8ebf4a3877bd82d0dd765dc09b7a1594fa8849d51f59282a81048c52e82e8320d1ad042a6c307ca831647cba4356564704780f", "expired_timestamp": 1859201579.393386}}
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311
utils/client.py
311
utils/client.py
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@ -103,7 +103,7 @@ class CacheClient:
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try:
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# 创建缓存数据库连接
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self.connection = sqlite3.connect(
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database=database,
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database=(Path(__file__).parent.resolve() / database), # 当前目录下创建缓存数据库
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check_same_thread=False,
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timeout=30, # 缓存数据库锁超时时间（单位：秒），默认为30秒，避免并发锁死
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)
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@ -164,7 +164,6 @@ class CacheClient:
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(guid, time.time() - self.cache_ttl * 86400),
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)
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if result := cursor.fetchone():
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print()
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return json.loads(result[0])
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return None
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# 若发生异常则回滚事务并返回None
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@ -218,19 +217,10 @@ class CacheClient:
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pass
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"""
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封装urllib.request的相关操作
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使用方法：
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client = HTTPClient()
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response = client.post(url)
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"""
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class TokenBucket:
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def __init__(self, refill_rate, max_tokens):
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"""令牌桶，基于令牌桶算法限制请求频率"""
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# 填充令牌速率（个/秒）
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self.refill_rate = refill_rate
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# 令牌桶最大令牌数
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@ -243,18 +233,15 @@ class TokenBucket:
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# 获取令牌
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# noinspection PyMissingReturnStatement
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def acquire(self) -> tuple[bool, float]:
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with threading.Lock():
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# 本次填充令牌时间戳
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refill_timestamp = time.monotonic()
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# 重新计算令牌桶中令牌数
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self.tokens = min(
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self.max_tokens,
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self.tokens
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+ self.refill_rate * (refill_timestamp - self.refill_timestamp),
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)
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self.refill_timestamp = refill_timestamp
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# 若令牌桶当前令牌数大于等于1则减少令牌
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@ -323,7 +310,6 @@ class Arguments(BaseModel):
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:param stream: 是否启用流式传输
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:param guid: 全局唯一标识
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"""
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# 统一资源定位符
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url: HttpUrl = Field(default=...)
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# 查询参数
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@ -365,43 +351,38 @@ class Arguments(BaseModel):
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return self
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# HTTP客户端
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class HTTPClient:
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"""请求客户端"""
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def __init__(
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self,
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timeout: int = 60,
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default_headers: Optional[Dict[str, str]] = None,
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total: int = 3,
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backoff_factor: float = 0.5,
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timeout: int = 60,
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cache_enabled: bool = False,
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cache_ttl: int = 360,
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):
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"""
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:param timeout: 超时时间，单位为秒
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:param default_headers: 默认请求头
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:param total: 最大重试次数
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:param backoff_factor: 重试间隔退避因子
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:param cache_enabled: 是否使用缓存
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:param timeout: 超时时间，单位为秒
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:param cache_enabled: 使用缓存
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:param cache_ttl: 缓存生存时间，单位为天
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"""
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# 超时时间
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self.timeout = timeout
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# 创建HTTP会话并挂载适配器
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# 创建请求会话并挂载适配器
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self.session = self._create_session(
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default_headers=default_headers, total=total, backoff_factor=backoff_factor
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)
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# 是否使用缓存
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# 初始化超时时间
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self.timeout = timeout
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# 初始化使用缓存
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self.cache_enabled = cache_enabled
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# 缓存生存时间
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self.cache_ttl = cache_ttl
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# 若使用缓存，则初始化缓存数据库
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if self.cache_enabled:
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self._initialize_cache_database()
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# 初始化缓存生存时间，单位为秒
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self.cache_ttl = cache_ttl * 24 * 60 * 60
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# 创建HTTP会话并挂载适配器
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# 创建请求会话并挂载适配器
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@staticmethod
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def _create_session(
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total: int,
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@ -409,11 +390,11 @@ class HTTPClient:
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default_headers: Optional[Dict[str, str]] = None,
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) -> Session:
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"""
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:param default_headers 默认请求头
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:param total 最大重试次数
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:param backoff_factor 重试间隔退避因子
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:param default_headers: 默认请求头
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:param total: 最大重试次数
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:param backoff_factor: 重试间隔退避因子
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:return Session: 会话对象
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"""
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# 创建会话对象
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session = Session()
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@ -429,7 +410,7 @@ class HTTPClient:
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502,
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503,
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504,
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], # 408:请求超时，502:网关错误，503:服务不可用，504:网关超时
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], # 408为请求超时，502为网关错误，503为服务不可用，504为网关超时
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total=total,
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respect_retry_after_header=True,
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backoff_factor=backoff_factor,
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@ -444,94 +425,21 @@ class HTTPClient:
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return session
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def _initialize_cache_database(self):
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"""初始化缓存数据库"""
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# 创建缓存数据库连接（使用SQLite）
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self.cache_connection = sqlite3.connect(
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database="SQLite.db", check_same_thread=False
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)
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self.cache_connection.execute(
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"""CREATE TABLE IF NOT EXISTS caches (guid TEXT PRIMARY KEY, response TEXT, timestamp REAL)"""
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)
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# 创建时间戳索引
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self.cache_connection.execute(
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"""CREATE INDEX IF NOT EXISTS index_timestamp ON caches(timestamp)"""
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)
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# 删除过期缓存
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self.cache_connection.execute(
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"DELETE FROM caches WHERE timestamp < ?",
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(time.time() - self.cache_ttl * 86400,), # 缓存生存时间单位转为秒
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)
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# 提交事物
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self.cache_connection.commit()
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# 在缓存数据库查询响应
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def _query_response(self, guid: str) -> Optional[Dict]:
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with threading.Lock():
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cursor = None
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try:
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# 创建游标
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cursor = self.cache_connection.cursor()
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# 根据请求唯一标识查询响应
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cursor.execute(
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"SELECT response FROM caches WHERE guid = ? AND timestamp >= ?",
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(guid, time.time() - self.cache_ttl * 86400),
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)
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if result := cursor.fetchone():
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return json.loads(result[0])
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return None
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# 若发生异常则返回NONE
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except:
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self.cache_connection.rollback()
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return None
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finally:
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if cursor:
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cursor.close()
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# 将响应保存至缓存数据库
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def _save_response(self, guid: str, response: Dict):
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with threading.Lock():
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cursor = None
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try:
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# 创建游标
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cursor = self.cache_connection.cursor()
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# 新增或覆盖响应
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cursor.execute(
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"INSERT OR REPLACE INTO caches (guid, response, timestamp) VALUES (?, ?, ?)",
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(guid, json.dumps(response, ensure_ascii=False), time.time()),
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)
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# 提交事物
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self.cache_connection.commit()
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# 若发生异常则返回NONE
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except:
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self.cache_connection.rollback()
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finally:
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if cursor:
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cursor.close()
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# GET请求
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def get(self, **kwargs) -> Union[Dict, str]:
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return self._request(method="GET", arguments=Arguments(**kwargs))
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# POST请求
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def post(self, **kwargs) -> Union[Dict, str]:
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return self._request(method="POST", arguments=Arguments(**kwargs))
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# 文件下载
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# DOWNLOAD请求
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def download(
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self, stream=False, chunk_size=1024, **kwargs
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) -> Union[Dict, str, Generator[bytes, None, None]]:
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response = self._request(
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method="GET", arguments=Arguments(**{"stream": stream, **kwargs})
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)
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# 若禁用流式传输，则返回响应
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if not stream:
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return response
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@ -539,100 +447,91 @@ class HTTPClient:
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return self._process_stream_response(response=response, chunk_size=chunk_size)
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def _request(self, method: Literal["GET", "POST"], arguments: Arguments) -> Any:
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"""发送请求"""
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"""请求"""
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# 请求参数模型
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arguments = arguments.model_dump(exclude_none=True, by_alias=True)
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# URL转为字符串
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# URL对象转为字符串
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arguments["url"] = str(arguments["url"])
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# 重构表单数据
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if arguments.get("data") is not None:
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arguments["data"] = {
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key: value
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for key, value in arguments["data"].items()
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if value is not None
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k: v
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for k, v in arguments["data"].items()
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if v is not None
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}
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# 重构JSON格式数据
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# 重构JSON数据
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if arguments.get("json_data") is not None:
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arguments["json_data"] = {
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key: value
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for key, value in arguments["json_data"].items()
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if value is not None
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k: v
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for k, v in arguments["json_data"].items()
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if v is not None
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}
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# 重构文件数据
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if arguments.get("files") is not None:
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files_valid = {}
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# 遍历文件数据键值对
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for key, value in arguments["files"].items():
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if isinstance(value, (tuple, list)):
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match len(value):
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# 若文件数据包括文件名称和文件内容
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files = {}
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for k, v in arguments["files"].items():
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if isinstance(v, (tuple, list)):
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match len(v):
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# 文件数据包括文件名称和文件内容
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case 2:
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files_valid[key] = (value[0], value[1], None, None)
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# 若文件数据包含文件名称、文件内容和内容类型
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files[k] = (v[0], v[1], None, None)
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# 文件数据包含文件名称、文件内容和内容类型
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case 3:
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files_valid[key] = (value[0], value[1], value[2], None)
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# 若文件数据包含文件名称、文件内容、内容类型和请求头
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files[k] = (v[0], v[1], v[2], None)
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# 文件数据包含文件名称、文件内容、内容类型和请求头
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case 4:
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files_valid[key] = (value[0], value[1], value[2], value[3])
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arguments.update({"files": files_valid})
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files[k] = (v[0], v[1], v[2], v[3])
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arguments.update({"files": files})
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# 全局唯一标识
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guid = arguments.pop("guid", None)
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# 若使用缓存且本次请求参数包含全局唯一标识，则优先返回缓存数据库中响应
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# 若使用缓存且全局唯一标识非空则查询缓存
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if self.cache_enabled and guid is not None:
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# 在缓存数据库查询响应
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response = self._query_response(guid=guid)
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# 若缓存响应非空则返回
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if response is not None:
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return response
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with CacheClient(cache_ttl=self.cache_ttl) as cache_client:
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cache = cache_client.query(guid)
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# 若缓存非空则返回
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if cache is not None:
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return cache
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try:
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# 发送请求
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response = self.session.request(
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method=method, timeout=self.timeout, **arguments
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)
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# 若返回错误状态码则抛出异常
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response.raise_for_status()
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# 处理响应
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response = self._process_response(response=response)
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|
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# 若请求全局唯一标识非NONE则响应保存至缓存数据库
|
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# noinspection PyUnboundLocalVariable
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if guid is not None:
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# noinspection PyUnboundLocalVariable
|
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self._save_response(guid=guid, response=response)
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# 若使用缓存且全局唯一标识非空则更新缓存
|
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if self.cache_enabled and guid is not None:
|
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with CacheClient(cache_ttl=self.cache_ttl) as cache_client:
|
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cache_client.update(guid, response)
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|
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return response
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|
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except Exception as exception:
|
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# 尝试根据响应解析响应状态码和错误信息，否则进行构造
|
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# 尝试根据响应解析错误状态码和错误信息，否则进行构造
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# noinspection PyBroadException
|
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try:
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# JSON反序列化
|
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# noinspection PyUnboundLocalVariable
|
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# 响应反序列化
|
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response_decoded = response.json()
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# 响应状态码
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# 错误状态码
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status = response_decoded["status"]
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# 错误信息
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message = response_decoded["message"]
|
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except:
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status = getattr(getattr(exception, "response", None), "status", None)
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url = arguments["url"]
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message = str(exception).split("\n")[0]
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# 重新构建错误信息
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message = f"{method} {url} failed: {message}"
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# 重构错误信息
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message = f"{method} {arguments["url"]} failed: {str(exception).split("\n")[0]}"
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raise RequestException(status=status, message=message)
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|
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# 处理响应
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||||
@staticmethod
|
||||
def _process_response(response: Response) -> Any:
|
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|
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# 响应内容
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content = response.content
|
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# 若响应内容为空则返回NONE
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|
|
@ -643,12 +542,11 @@ class HTTPClient:
|
|||
content_type = (
|
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response.headers.get("Content-Type", "").split(";")[0].strip().lower()
|
||||
)
|
||||
|
||||
# 根据内容类型匹配解析返回内容方法
|
||||
# noinspection PyUnreachableCode
|
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match content_type:
|
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case "application/json" | "text/json":
|
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# JSON反序列化
|
||||
# 响应反序列化
|
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return response.json()
|
||||
case "application/xml" | "text/xml":
|
||||
# 解析为XML（ELEMENT对象）
|
||||
|
|
@ -667,7 +565,6 @@ class HTTPClient:
|
|||
def _process_stream_response(
|
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response: Response, chunk_size: int
|
||||
) -> Generator[bytes, None, None]: # 生成器不接受发SEND发送至、结束时返回NONE
|
||||
|
||||
# 检查数据分块
|
||||
if not isinstance(chunk_size, int) and isinstance(chunk_size, bool):
|
||||
raise ValueError("chunk_size must type=int")
|
||||
|
|
@ -689,14 +586,12 @@ class Authenticator:
|
|||
self,
|
||||
):
|
||||
"""认证器（用于获取访问令牌）"""
|
||||
|
||||
# 初始化
|
||||
self._initialize()
|
||||
|
||||
def _initialize(self):
|
||||
"""初始化访问凭证"""
|
||||
|
||||
# 创建访问凭证地址对象
|
||||
"""初始化"""
|
||||
# 初始化访问凭证地址对象
|
||||
self.certifications_path = (
|
||||
Path(__file__).parent.resolve() / "certifications.json"
|
||||
)
|
||||
|
|
@ -709,40 +604,34 @@ class Authenticator:
|
|||
ensure_ascii=False,
|
||||
)
|
||||
|
||||
# 初始化HTTP客户端
|
||||
# 初始化请求客户端
|
||||
self.http_client = HTTPClient()
|
||||
|
||||
def _szkt_get_certification(self) -> tuple[str, float]:
|
||||
"""获取深圳快瞳访问凭证"""
|
||||
|
||||
# 请求深圳快瞳访问凭证获取接口
|
||||
response = self.http_client.get(
|
||||
url="https://ai.inspirvision.cn/s/api/getAccessToken?accessKey=APPID_6Gf78H59D3O2Q81u&accessSecret=947b8829d4d5d55890b304d322ac2d0d"
|
||||
)
|
||||
|
||||
# 若响应非成功则抛出异常
|
||||
# 若非响应成功则抛出异常
|
||||
if not (response["status"] == 200 and response["code"] == 0):
|
||||
raise RuntimeError("获取深圳快瞳访问凭证发生异常")
|
||||
|
||||
# 返回令牌、失效时间戳
|
||||
# 返回访问令牌、失效时间戳
|
||||
# noinspection PyTypeChecker
|
||||
return (
|
||||
response["data"]["access_token"],
|
||||
time.time() + response["data"]["expires_in"],
|
||||
)
|
||||
|
||||
def _hlyj_get_certification(self) -> tuple[str, float]:
|
||||
def _hlyj_get_certification(self) -> Tuple[str, float]:
|
||||
"""获取合力亿捷访问凭证"""
|
||||
|
||||
# 企业访问标识
|
||||
access_key_id = "25938f1c190448829dbdb5d344231e42"
|
||||
|
||||
# 签名秘钥
|
||||
secret_access_key = "44dc0299aff84d68ae27712f8784f173"
|
||||
|
||||
# 时间戳（秒级）
|
||||
timestamp = int(time.time())
|
||||
|
||||
# 签名，企业访问标识、签名秘钥和时间戳拼接后计算的十六进制的HMAC-SHA256
|
||||
signature = hmac.new(
|
||||
secret_access_key.encode("utf-8"),
|
||||
|
|
@ -750,26 +639,23 @@ class Authenticator:
|
|||
hashlib.sha256,
|
||||
).hexdigest()
|
||||
|
||||
# 请求合力亿捷访问凭证获取接口
|
||||
response = self.http_client.get(
|
||||
url=f"https://kms.7x24cc.com/api/v1/corp/auth/token?access_key_id={access_key_id}×tamp={timestamp}&signature={signature}"
|
||||
)
|
||||
|
||||
# 若响应非成功则抛出异常
|
||||
# 若非响应成功则抛出异常
|
||||
if not response["success"]:
|
||||
raise RuntimeError("获取合力亿捷访问凭证发生异常")
|
||||
|
||||
# 返回令牌、失效时间戳
|
||||
# 返回访问令牌、失效时间戳
|
||||
# noinspection PyTypeChecker
|
||||
return (
|
||||
response["data"],
|
||||
time.time() + 3600, # 访问令牌有效期为1小时
|
||||
time.time() + 1 * 60 * 60, # 访问令牌有效期为1小时
|
||||
)
|
||||
|
||||
def _feishu_get_certification(self) -> tuple[str, float]:
|
||||
"""获取飞书访问凭证"""
|
||||
|
||||
# 请求飞书访问凭证获取接口
|
||||
response = self.http_client.post(
|
||||
url="https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal",
|
||||
data={
|
||||
|
|
@ -778,11 +664,11 @@ class Authenticator:
|
|||
},
|
||||
)
|
||||
|
||||
# 若响应非成功则抛出异常
|
||||
# 若非响应成功则抛出异常
|
||||
if not response["code"] == 0:
|
||||
raise RuntimeError("获取飞书访问凭证发生异常")
|
||||
|
||||
# 返回令牌、失效时间戳
|
||||
# 返回访问令牌、失效时间戳
|
||||
# noinspection PyTypeChecker
|
||||
return (
|
||||
response["tenant_access_token"],
|
||||
|
|
@ -790,28 +676,28 @@ class Authenticator:
|
|||
)
|
||||
|
||||
def get_token(self, servicer: str) -> str | None:
|
||||
"""获取访问令牌"""
|
||||
"""
|
||||
:param servicer: 服务商，数据类型为字符串
|
||||
获取访问令牌
|
||||
:param servicer: 服务商，暂仅支持深圳快瞳、合力亿捷和飞书
|
||||
:return token: 访问令牌
|
||||
"""
|
||||
|
||||
with threading.Lock():
|
||||
# 初始化令牌和失效时间戳
|
||||
# 初始化访问令牌和失效时间戳
|
||||
token, expired_timestamp = None, 0
|
||||
try:
|
||||
with open(self.certifications_path, "r", encoding="utf-8") as file:
|
||||
# 读取所有服务商访问凭证
|
||||
# 本地打开并读取所有服务商的访问凭证
|
||||
certifications = json.load(file)
|
||||
# 获取服务商访问凭证
|
||||
# 获取指定服务商的访问凭证
|
||||
certification = certifications.get(servicer, None)
|
||||
# 若服务商访问凭证非NONE则解析令牌和失效时间戳
|
||||
# 若指定服务商的访问凭证非空则解析访问令牌和失效时间戳
|
||||
if certification is not None:
|
||||
# 解析服务商访问令牌
|
||||
# 访问令牌
|
||||
token = certification["token"]
|
||||
# 解析服务商访问令牌失效时间戳
|
||||
# 失效时间戳
|
||||
expired_timestamp = certification["expired_timestamp"]
|
||||
|
||||
# 若JSON反序列化时发生异常则重置访问凭证
|
||||
# 若反序列化发生异常则重置访问凭证储存文件
|
||||
except json.decoder.JSONDecodeError:
|
||||
with open(self.certifications_path, "w", encoding="utf-8") as file:
|
||||
json.dump(
|
||||
|
|
@ -823,21 +709,17 @@ class Authenticator:
|
|||
except Exception:
|
||||
raise RuntimeError("获取访问令牌发生异常")
|
||||
|
||||
# 若当前时间戳大于失效时间戳，则请求服务商获取访问凭证接口
|
||||
if time.time() > expired_timestamp:
|
||||
# noinspection PyUnreachableCode
|
||||
match servicer:
|
||||
# 获取深圳快瞳访问凭证
|
||||
case "szkt":
|
||||
token, expired_timestamp = self._szkt_get_certification()
|
||||
case "feishu":
|
||||
token, expired_timestamp = self._feishu_get_certification()
|
||||
# 获取合力亿捷访问凭证
|
||||
case "hlyj":
|
||||
token, expired_timestamp = self._hlyj_get_certification()
|
||||
case "feishu":
|
||||
token, expired_timestamp = self._feishu_get_certification()
|
||||
case _:
|
||||
raise RuntimeError(f"未设置服务商：({servicer})")
|
||||
|
||||
raise RuntimeError(f"未设置服务商：{servicer}获取访问凭证方法")
|
||||
# 更新服务商访问凭证
|
||||
certifications[servicer] = {
|
||||
"token": token,
|
||||
|
|
@ -855,22 +737,15 @@ class Authenticator:
|
|||
return token
|
||||
|
||||
|
||||
"""
|
||||
封装飞书客户端，实现获取验证码、操作多维表格等
|
||||
"""
|
||||
|
||||
|
||||
class FeishuClinet:
|
||||
class FeishuClient:
|
||||
"""飞书客户端"""
|
||||
|
||||
def __init__(self):
|
||||
|
||||
self.authenticator = Authenticator()
|
||||
|
||||
self.http_client = HTTPClient()
|
||||
|
||||
def _headers(self):
|
||||
"""请求头"""
|
||||
|
||||
# 装配飞书访问凭证
|
||||
return {
|
||||
"Authorization": f"Bearer {self.authenticator.get_token(servicer='feishu')}",
|
||||
|
|
@ -878,67 +753,51 @@ class FeishuClinet:
|
|||
|
||||
@staticmethod
|
||||
def get_verification_code():
|
||||
|
||||
try:
|
||||
|
||||
# 执行时间戳
|
||||
execute_timestamp = time.time()
|
||||
|
||||
# 超时时间戳
|
||||
timeout_timestamp = execute_timestamp + 65
|
||||
|
||||
# 建立加密IMAP连接
|
||||
server = IMAP4_SSL("imap.feishu.cn", 993)
|
||||
|
||||
# 登录
|
||||
server.login("mars@liubiren.cloud", "a2SfPUgbKDmrjPV2")
|
||||
|
||||
while True:
|
||||
|
||||
# 若当前时间戳大于超时时间戳则返回NONE
|
||||
if time.time() <= timeout_timestamp:
|
||||
|
||||
# 等待10秒
|
||||
time.sleep(10)
|
||||
|
||||
# 选择文件夹（邮箱验证码）
|
||||
server.select("&kK57sZqMi8F4AQ-")
|
||||
|
||||
# noinspection PyBroadException
|
||||
try:
|
||||
|
||||
# 获取最后一封邮件索引，server.search()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件索引字节串的列表）；然后，从列表获取字节串并分割取最后一个，作为最后一封邮件索引
|
||||
index = server.search(None, "ALL")[1][0].split()[-1]
|
||||
|
||||
# 获取最后一封邮件内容并解析，server.fetch()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件内容字节串的列表）；然后，从列表获取字节串并解析正文
|
||||
# noinspection PyUnresolvedReferences
|
||||
contents = BytesParser(policy=default).parsebytes(
|
||||
server.fetch(index, "(RFC822)")[1][0][1]
|
||||
)
|
||||
|
||||
# 遍历邮件内容，若正文内容类型为纯文本或HTML则解析发送时间和验证码
|
||||
for content in contents.walk():
|
||||
|
||||
if (
|
||||
content.get_content_type() == "text/plain"
|
||||
or content.get_content_type() == "text/html"
|
||||
):
|
||||
|
||||
# 邮件发送时间戳
|
||||
# noinspection PyUnresolvedReferences
|
||||
send_timestamp = parsedate_to_datetime(
|
||||
content["Date"]
|
||||
).timestamp()
|
||||
|
||||
# 若邮件发送时间戳大于执行时间戳则解析验证码并返回
|
||||
if (
|
||||
execute_timestamp
|
||||
> send_timestamp
|
||||
>= execute_timestamp - 35
|
||||
):
|
||||
|
||||
# 登出
|
||||
server.logout()
|
||||
|
||||
# 解析验证码
|
||||
return re.search(
|
||||
r"【普康健康】您的验证码是：(\d+)",
|
||||
|
|
@ -947,18 +806,14 @@ class FeishuClinet:
|
|||
|
||||
# 若文件夹无邮件则继续
|
||||
except:
|
||||
|
||||
pass
|
||||
|
||||
# 若超时则登出
|
||||
else:
|
||||
|
||||
server.logout()
|
||||
|
||||
return None
|
||||
|
||||
except Exception:
|
||||
|
||||
raise RuntimeError("获取邮箱验证码发生其它异常")
|
||||
|
||||
# 查询多维表格记录，单次最多查询500条记录
|
||||
|
|
@ -970,9 +825,7 @@ class FeishuClinet:
|
|||
field_names: Optional[list[str]] = None,
|
||||
filter_conditions: Optional[dict] = None,
|
||||
) -> pandas.DataFrame:
|
||||
|
||||
# 先查询多维表格记录，在根据字段解析记录
|
||||
|
||||
# 装配多维表格查询记录地址
|
||||
url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/records/search?page_size=20"
|
||||
|
||||
|
|
|
|||
Binary file not shown.
451
票据理赔自动化/main.py
451
票据理赔自动化/main.py
|
|
@ -27,175 +27,6 @@ from utils.client import Authenticator, HTTPClient, CacheClient
|
|||
# from utils.ocr import fuzzy_match
|
||||
|
||||
|
||||
# -------------------------
|
||||
# 封装方法
|
||||
# -------------------------
|
||||
|
||||
|
||||
# noinspection PyShadowingNames
|
||||
def image_read(
|
||||
image_path: Path,
|
||||
) -> Tuple[numpy.ndarray | None, str | None, bytes | None]:
|
||||
"""
|
||||
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
|
||||
"""
|
||||
# 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 = image_path.suffix.lower()
|
||||
|
||||
# 按照影像件格式将影像件数组编码
|
||||
success, image_ndarray_encoded = cv2.imencode(image_format, image_ndarray)
|
||||
if not success or image_ndarray_encoded is 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()
|
||||
|
||||
return image_ndarray, image_format, image_bytes
|
||||
except Exception:
|
||||
return None, None, None
|
||||
|
||||
|
||||
# noinspection PyShadowingNames
|
||||
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
|
||||
|
||||
# 影像件BASE64编码
|
||||
image_base64 = b64encode(image_bytes).decode("utf-8")
|
||||
if len(image_base64) <= image_size_specified:
|
||||
return image_base64
|
||||
|
||||
# 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：通过调整影像件质量实现压缩影像件大小
|
||||
for quality in range(90, 50, -10):
|
||||
image_ndarray_copy = image_ndarray.copy()
|
||||
# 内循环压缩：通过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_ndarray_encoded = cv2.imencode(
|
||||
image_format,
|
||||
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_ndarray_encoded is None:
|
||||
break
|
||||
|
||||
# 影像件BASE64编码
|
||||
image_base64 = b64encode(image_ndarray_encoded.tobytes()).decode("utf-8")
|
||||
if len(image_base64) <= image_size_specified:
|
||||
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(
|
||||
url=(url := "https://ai.inspirvision.cn/s/api/ocr/genalClassify"),
|
||||
# 用于和深圳快瞳联查定位
|
||||
headers={"X-RequestId-Header": image_guid},
|
||||
data={
|
||||
"token": globals()["authenticator"].get_token(
|
||||
servicer="szkt"
|
||||
), # 使用全局变量
|
||||
"imgBase64": f"data:image/{image_format};base64,{image_base64}", # 深圳快瞳要求修饰影像件BASE64编码的DATAURI
|
||||
},
|
||||
guid=hashlib.md5((url + image_guid).encode("utf-8")).hexdigest().upper(),
|
||||
)
|
||||
|
||||
# 若响应非成功，则返回NONE
|
||||
if not (response.get("status") == 200 and response.get("code") == 0):
|
||||
return None, None
|
||||
|
||||
# 根据票据类型和票据二级分类映射影像件类型
|
||||
match (response["data"]["flag"], response["data"]["type"]):
|
||||
case (7, "idcard-front-back"):
|
||||
image_type = "居民身份证(正背面)"
|
||||
case (7, "idcard-front"):
|
||||
image_type = "居民身份证(正面)" # 包含国徽一面
|
||||
case (7, "idcard-back"):
|
||||
image_type = "居民身份证(背面)" # 包含头像一面
|
||||
case (8, _):
|
||||
image_type = "银行卡"
|
||||
case (4, _):
|
||||
image_type = "增值税发票"
|
||||
case (5, _):
|
||||
image_type = "门诊收费票据"
|
||||
case (3, _):
|
||||
image_type = "住院收费票据"
|
||||
case (18, _):
|
||||
image_type = "理赔申请书"
|
||||
case _:
|
||||
return None, None
|
||||
|
||||
# 影像件方向
|
||||
image_orientation = {
|
||||
"0": "0度",
|
||||
"90": "顺时针90度",
|
||||
"180": "180度",
|
||||
"270": "逆时针90度",
|
||||
}.get(response["data"]["angle"], "0度")
|
||||
|
||||
return image_type, image_orientation
|
||||
|
||||
|
||||
def idcard_extraction(**kwargs) -> dict | None:
|
||||
"""居民身份证数据提取"""
|
||||
|
|
@ -1140,29 +971,25 @@ def disease_diagnosis(**kwargs) -> str | None:
|
|||
|
||||
|
||||
# -------------------------
|
||||
# 主逻辑部分
|
||||
# 主逻辑
|
||||
# -------------------------
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# 初始化HTTP客户端
|
||||
http_client = HTTPClient(timeout=300, cache_enabled=True)
|
||||
|
||||
# 初始化认证器
|
||||
# 实例认证器
|
||||
authenticator = Authenticator()
|
||||
# 实例请求客户端
|
||||
http_client = HTTPClient(timeout=300, cache_enabled=True) # 使用缓存
|
||||
|
||||
# 初始化影像件识别规则引擎
|
||||
recognize_decision = decision(Path("rules/影像件是否需要数据提取.json"))
|
||||
|
||||
# 初始化工作目录地址对象
|
||||
directory_path = Path("directory")
|
||||
# 若不存在则创建
|
||||
directory_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 初始化影像件识别规则引擎
|
||||
recognize_decision = decision(Path("rules/影像件是否需要数据提取.json"))
|
||||
|
||||
# 初始化JINJA2环境
|
||||
environment = Environment(loader=FileSystemLoader("."))
|
||||
|
||||
# 添加DATE过滤器
|
||||
environment.filters["date"] = lambda date: (
|
||||
date.strftime("%Y-%m-%d") if date else "长期"
|
||||
|
|
@ -1170,21 +997,212 @@ if __name__ == "__main__":
|
|||
# 加载赔案档案模版
|
||||
template = environment.get_template("template.html")
|
||||
|
||||
# 遍历工作目录中赔案目录，根据赔案创建赔案档案（模拟自动化域就待自动化任务创建理赔档案）
|
||||
|
||||
# -------------------------
|
||||
# 自定义方法
|
||||
# -------------------------
|
||||
# noinspection PyShadowingNames
|
||||
def image_read(
|
||||
image_path: Path,
|
||||
) -> Optional[numpy.ndarray | None]:
|
||||
"""
|
||||
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
|
||||
"""
|
||||
# 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")
|
||||
|
||||
return image_ndarray
|
||||
except Exception:
|
||||
# 若本地打开并读取影像件发生异常则抛出异常（实际作业需从影像件服务器下载并读取影像件，因签收时会转存，故必可下载）
|
||||
raise RuntimeError("影像件打开并读取发生异常")
|
||||
|
||||
|
||||
# noinspection PyShadowingNames
|
||||
def image_serialize(image_format: str, image_ndarray: numpy.ndarray) -> str:
|
||||
"""
|
||||
影像件序列化
|
||||
:param image_format: 影像件格式
|
||||
:param image_ndarray: 影像件数组
|
||||
:return: 影像件唯一标识
|
||||
"""
|
||||
# 按照影像件格式就影像件数组编码
|
||||
success, image_ndarray_encoded = cv2.imencode(image_format, image_ndarray)
|
||||
if not success or image_ndarray_encoded is 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()
|
||||
# 生成影像件唯一标识
|
||||
image_guid = md5(image_bytes).hexdigest().upper()
|
||||
return image_guid
|
||||
|
||||
|
||||
# noinspection PyShadowingNames
|
||||
def images_classify(
|
||||
image_guid: str, image_format: str, image_ndarray: numpy.ndarray
|
||||
) -> Optional[Tuple[str, str, str]]:
|
||||
"""
|
||||
影像件分类并旋正
|
||||
:param image_guid: 影像件唯一标识
|
||||
:param image_format: 影像件格式
|
||||
:param image_ndarray: 影像件数据
|
||||
:return: 压缩后影像件BASE64编码，影像件类型和影像件方向
|
||||
"""
|
||||
|
||||
# noinspection PyShadowingNames
|
||||
def images_compress(
|
||||
image_format, image_ndarray, 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_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
|
||||
|
||||
# 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：通过调整影像件质量实现压缩影像件大小
|
||||
for quality in range(100, 50, -10):
|
||||
image_ndarray_copy = image_ndarray.copy()
|
||||
# 内循环压缩：通过调整影像件尺寸实现压缩影像件大小
|
||||
for i in range(10):
|
||||
# 按照影像件格式和影像件质量将影像件数组编码
|
||||
success, image_ndarray_encoded = cv2.imencode(
|
||||
image_format,
|
||||
image_ndarray_copy,
|
||||
params=(
|
||||
[cv2.IMWRITE_PNG_COMPRESSION, 10 - quality // 10]
|
||||
if image_format == "png"
|
||||
else [cv2.IMWRITE_JPEG_QUALITY, quality]
|
||||
),
|
||||
)
|
||||
# 若编码发生异常则停止循环
|
||||
if not success or image_ndarray_encoded is None:
|
||||
break
|
||||
|
||||
# 影像件BASE64编码
|
||||
image_base64 = b64encode(image_ndarray_encoded.tobytes()).decode("utf-8")
|
||||
if len(image_base64) <= image_size_specified:
|
||||
return image_base64
|
||||
|
||||
# 调整影像件尺寸
|
||||
image_ndarray_copy = cv2.resize(
|
||||
image_ndarray_copy,
|
||||
(
|
||||
int(image_ndarray_copy.shape[0] * 0.95),
|
||||
int(image_ndarray_copy.shape[1] * 0.95),
|
||||
),
|
||||
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
|
||||
|
||||
# 影像件压缩
|
||||
image_base64 = images_compress(image_format, image_ndarray, image_size_specified=2) # 深圳快瞳要求为2兆字节
|
||||
# TODO: 若影像件压缩发生异常则流转至人工处理
|
||||
if image_base64 is None:
|
||||
raise RuntimeError("影像件压缩发生异常")
|
||||
|
||||
# 请求深圳快瞳影像件分类接口
|
||||
response = http_client.post(
|
||||
url=(url := "https://ai.inspirvision.cn/s/api/ocr/genalClassify"),
|
||||
headers={"X-RequestId-Header": image_guid}, # 以影像件唯一标识作为请求唯一标识，用于双方联查
|
||||
data={
|
||||
"token": authenticator.get_token(
|
||||
servicer="szkt"
|
||||
), # 获取深圳快瞳访问令牌
|
||||
"imgBase64": f"data:image/{image_format.lstrip(".")};base64,{image_base64}", # 影像件BASE64编码嵌入数据统一资源标识符
|
||||
},
|
||||
guid=md5((url + image_guid).encode("utf-8")).hexdigest().upper(),
|
||||
)
|
||||
# 若响应非成功则抛出异常
|
||||
# TODO: 若响应非成功则流转至人工处理
|
||||
if not (response.get("status") == 200 and response.get("code") == 0):
|
||||
raise RuntimeError("请求深圳快瞳影像件分类接口发生异常")
|
||||
|
||||
# 解析影像件类型
|
||||
# TODO: 后续完成居民户口簿、中国港澳台地区及境外护照和医疗费用清单
|
||||
# noinspection PyTypeChecker
|
||||
match (response["data"]["flag"], response["data"]["type"]):
|
||||
case (14, _):
|
||||
image_type = "居民户口簿"
|
||||
case (7, "idcard-front-back"):
|
||||
image_type = "居民身份证(国徽、头像面)"
|
||||
case (7, "idcard-front"):
|
||||
image_type = "居民身份证(国徽面)"
|
||||
case (7, "idcard-back"):
|
||||
image_type = "居民身份证(头像面)"
|
||||
case (11, _):
|
||||
image_type = "中国港澳台地区及境外护照"
|
||||
case (8, _):
|
||||
image_type = "银行卡"
|
||||
case (4, _):
|
||||
image_type = "增值税发票"
|
||||
case (1, _):
|
||||
image_type = "医疗费用清单"
|
||||
case (5, _):
|
||||
image_type = "门诊收费票据"
|
||||
case (3, _):
|
||||
image_type = "住院收费票据"
|
||||
case (18, _):
|
||||
image_type = "理赔申请书"
|
||||
case _:
|
||||
image_type = "其它"
|
||||
|
||||
# 解析影像件方向
|
||||
# noinspection PyTypeChecker
|
||||
image_orientation = {
|
||||
"0": "0度",
|
||||
"90": "顺时针90度",
|
||||
"180": "180度",
|
||||
"270": "逆时针90度",
|
||||
}.get(response["data"]["angle"], "0度")
|
||||
# 若影像件方向非0度则旋正
|
||||
if image_orientation != "0度":
|
||||
image_ndarray = cv2.rotate(
|
||||
image_ndarray,
|
||||
{
|
||||
"顺时针90度": cv2.ROTATE_90_COUNTERCLOCKWISE, # 逆时针旋转90度
|
||||
"180度": cv2.ROTATE_180, # 旋转180度
|
||||
"逆时针90度": cv2.ROTATE_90_CLOCKWISE, # 顺时针旋转90度
|
||||
}[image_orientation],
|
||||
)
|
||||
# 旋正后影像件再次压缩
|
||||
image_base64 = images_compress(image_format, image_ndarray, image_size_specified=2)
|
||||
# TODO: 若旋正后影像件再次压缩发生异常则流转至人工处理
|
||||
if image_base64 is None:
|
||||
raise RuntimeError("旋正后影像件再次压缩发生异常")
|
||||
|
||||
return image_base64, image_type, image_orientation
|
||||
|
||||
|
||||
# 遍历工作目录中赔案目录并创建赔案档案（模拟自动化域就待自动化任务创建理赔档案）
|
||||
for case_path in [
|
||||
case_path for case_path in directory_path.iterdir() if case_path.is_dir()
|
||||
x for x in directory_path.iterdir() if x.is_dir()
|
||||
]:
|
||||
# 初始化赔案档案（实际报案层包括保险分公司名称、报案渠道、批次号、报案号和报案时间等）
|
||||
# 报案渠道包括：保司定义，例如中银项目包括总行和各地分行驻场报案和普康宝自助报案等
|
||||
dossier = {
|
||||
"报案层": {
|
||||
"保险分公司": "中银保险有限公司广东分公司", # 设定：保险分公司
|
||||
"保险分公司": "中银保险有限公司广东分公司", # 设定：保险分公司为中银保险有限公司广东分公司
|
||||
"赔案号": (case_number := case_path.stem), # 设定：赔案目录名称为赔案号
|
||||
},
|
||||
"影像件层": [],
|
||||
}
|
||||
|
||||
# 遍历赔案目录中影像件地址
|
||||
# 遍历赔案目录中影像件路径对象
|
||||
for image_index, image_path in enumerate(
|
||||
sorted(
|
||||
[
|
||||
|
|
@ -1201,64 +1219,31 @@ if __name__ == "__main__":
|
|||
"原始影像件": {
|
||||
"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_ndarray, image_format, image_bytes = image_read(image_path)
|
||||
# 若本地打开并读取影像件发生异常则跳过该影像件
|
||||
if image_format is None or image_bytes is None:
|
||||
raise RuntimeError("本地打开并读取影像件发生异常")
|
||||
# 本地打开并读取影像件
|
||||
image_ndarray = image_read(image_path)
|
||||
|
||||
image["原始影像件"]["影像件格式"] = image_format
|
||||
# 生成影像件唯一标识
|
||||
# 影像件序列化
|
||||
# noinspection PyTypeChecker
|
||||
image["影像件唯一标识"] = md5(image_bytes).hexdigest().upper()
|
||||
image["影像件唯一标识"] = (image_guid := image_serialize(image_format, image_ndarray))
|
||||
|
||||
# 影像件分类
|
||||
# 影像件分类并旋正（较初审自动化，无使能检查）
|
||||
image_base64, image_type, image_orientation = images_classify(image_guid, image_format, image_ndarray)
|
||||
image["影像件类型"] = image_type
|
||||
image["影像件方向"] = image_orientation
|
||||
# 将影像件数据添加至影像件层
|
||||
dossier["影像件层"].append(image)
|
||||
|
||||
print(image)
|
||||
exit()
|
||||
# 影像件识别
|
||||
|
||||
|
||||
print(dossier)
|
||||
exit()
|
||||
"""
|
||||
|
||||
# 影像件压缩（输出BASE64编码）
|
||||
image_guid, image_base64 = images_compression()
|
||||
# 若发生异常则跳过该影像件
|
||||
if image_guid is None or image_base64 is None:
|
||||
dossier["影像件层"][-1]["已分类"] = "否，压缩异常"
|
||||
continue
|
||||
|
||||
# 通过请求深圳快瞳影像件分类接口获取影像件类型和方向
|
||||
image_type, image_orientation = images_classification()
|
||||
# 若发生异常则跳过该影像件
|
||||
if image_type is None or image_orientation is None:
|
||||
dossier["影像件层"][-1]["已分类"] = "否，影像件分类异常"
|
||||
continue
|
||||
#
|
||||
dossier["影像件层"].append(
|
||||
{
|
||||
"影像件序号": (image_index := f"{image_index:02d}"),
|
||||
"影像件名称": (image_name := image_path.name),
|
||||
}
|
||||
)
|
||||
# 若影像件方向非0度，则影像件旋正并在此压缩
|
||||
if image_orientation != "0度":
|
||||
# 影像件旋正
|
||||
image = cv2.rotate(
|
||||
image,
|
||||
{
|
||||
"顺时针90度": cv2.ROTATE_90_COUNTERCLOCKWISE, # 逆时针旋转90度
|
||||
"180度": cv2.ROTATE_180, # 旋转180度
|
||||
"逆时针90度": cv2.ROTATE_90_CLOCKWISE, # 顺时针旋转90度
|
||||
}[image_orientation],
|
||||
)
|
||||
# 影像件再次压缩
|
||||
image_guid, image_base64 = images_compression()
|
||||
if image_guid is None or image_base64 is None:
|
||||
dossier["影像件层"][-1]["已分类"] = "否，压缩异常"
|
||||
continue
|
||||
|
||||
dossier["影像件层"][-1].update({"已分类": "是", "影像件类型": image_type})
|
||||
|
||||
# 根据保险总公司和影像件类型评估影像件是否需要数据提取，若无需数据提取则跳过该影像件（例如，中银保险有限公司理赔申请书包含户名、开户银行和银行账号，无需识别银行卡）
|
||||
if not recognize_decision.evaluate(
|
||||
|
|
|
|||
Loading…
Reference in New Issue