Merge branch 'main' of https://gitea.functen.cn/shihao/WechatHookBot
This commit is contained in:
690
utils/image_processor.py
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690
utils/image_processor.py
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@@ -0,0 +1,690 @@
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"""
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图片/视频处理模块
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提供媒体文件的下载、编码和描述生成：
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- 图片下载与 base64 编码
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- 表情包下载与编码
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- 视频下载与编码
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- AI 图片/视频描述生成
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使用示例:
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from utils.image_processor import ImageProcessor, MediaConfig
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config = MediaConfig(
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api_url="https://api.openai.com/v1/chat/completions",
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api_key="sk-xxx",
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model="gpt-4-vision-preview",
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)
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processor = ImageProcessor(config)
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# 下载图片
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image_base64 = await processor.download_image(bot, cdnurl, aeskey)
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# 生成描述
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description = await processor.generate_description(image_base64, "描述这张图片")
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"""
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from __future__ import annotations
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import asyncio
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import base64
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import json
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import uuid
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, Dict, Optional, TYPE_CHECKING
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import aiohttp
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from loguru import logger
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# 可选代理支持
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try:
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from aiohttp_socks import ProxyConnector
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PROXY_SUPPORT = True
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except ImportError:
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PROXY_SUPPORT = False
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if TYPE_CHECKING:
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pass # bot 类型提示
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@dataclass
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class MediaConfig:
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"""媒体处理配置"""
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# API 配置
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api_url: str = "https://api.openai.com/v1/chat/completions"
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api_key: str = ""
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model: str = "gpt-4-vision-preview"
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timeout: int = 120
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max_tokens: int = 1000
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retries: int = 2
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# 代理配置
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proxy_enabled: bool = False
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proxy_type: str = "socks5"
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proxy_host: str = "127.0.0.1"
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proxy_port: int = 7890
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proxy_username: str = ""
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proxy_password: str = ""
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# 视频专用配置
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video_api_url: str = ""
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video_model: str = ""
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video_max_size_mb: int = 20
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video_timeout: int = 360
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video_max_tokens: int = 8192
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# 临时目录
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temp_dir: Optional[Path] = None
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@classmethod
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def from_dict(cls, config: Dict[str, Any]) -> "MediaConfig":
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"""从配置字典创建"""
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api_config = config.get("api", {})
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proxy_config = config.get("proxy", {})
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image_desc_config = config.get("image_description", {})
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video_config = config.get("video_recognition", {})
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return cls(
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api_url=api_config.get("url", "https://api.openai.com/v1/chat/completions"),
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api_key=api_config.get("api_key", ""),
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model=image_desc_config.get("model", api_config.get("model", "gpt-4-vision-preview")),
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timeout=api_config.get("timeout", 120),
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max_tokens=image_desc_config.get("max_tokens", 1000),
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retries=image_desc_config.get("retries", 2),
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proxy_enabled=proxy_config.get("enabled", False),
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proxy_type=proxy_config.get("type", "socks5"),
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proxy_host=proxy_config.get("host", "127.0.0.1"),
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proxy_port=proxy_config.get("port", 7890),
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proxy_username=proxy_config.get("username", ""),
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proxy_password=proxy_config.get("password", ""),
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video_api_url=video_config.get("api_url", ""),
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video_model=video_config.get("model", ""),
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video_max_size_mb=video_config.get("max_size_mb", 20),
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video_timeout=video_config.get("timeout", 360),
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video_max_tokens=video_config.get("max_tokens", 8192),
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)
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@dataclass
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class MediaResult:
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"""媒体处理结果"""
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success: bool = False
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data: str = "" # base64 数据
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description: str = ""
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error: Optional[str] = None
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media_type: str = "image" # image, emoji, video
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class ImageProcessor:
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"""
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图片/视频处理器
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提供统一的媒体处理接口：
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- 下载和编码
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- AI 描述生成
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- 缓存支持
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"""
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def __init__(self, config: MediaConfig, temp_dir: Optional[Path] = None):
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self.config = config
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self.temp_dir = temp_dir or config.temp_dir or Path("temp")
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self.temp_dir.mkdir(exist_ok=True)
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def _get_proxy_connector(self) -> Optional[Any]:
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"""获取代理连接器"""
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if not self.config.proxy_enabled or not PROXY_SUPPORT:
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return None
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proxy_type = self.config.proxy_type.upper()
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if self.config.proxy_username and self.config.proxy_password:
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proxy_url = (
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f"{proxy_type}://{self.config.proxy_username}:"
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f"{self.config.proxy_password}@"
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f"{self.config.proxy_host}:{self.config.proxy_port}"
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)
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else:
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proxy_url = f"{proxy_type}://{self.config.proxy_host}:{self.config.proxy_port}"
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try:
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return ProxyConnector.from_url(proxy_url)
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except Exception as e:
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logger.warning(f"[ImageProcessor] 代理配置失败: {e}")
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return None
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async def download_image(
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self,
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bot,
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cdnurl: str,
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aeskey: str,
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use_cache: bool = True,
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) -> str:
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"""
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下载图片并转换为 base64
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Args:
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bot: WechatHookClient 实例（用于 CDN 下载）
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cdnurl: CDN URL
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aeskey: AES 密钥
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use_cache: 是否使用缓存
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Returns:
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||||
base64 编码的图片数据（带 data URI 前缀）
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||||
"""
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try:
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||||
# 1. 优先从 Redis 缓存获取
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if use_cache:
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from utils.redis_cache import RedisCache, get_cache
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redis_cache = get_cache()
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if redis_cache and redis_cache.enabled:
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media_key = RedisCache.generate_media_key(cdnurl, aeskey)
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if media_key:
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cached_data = redis_cache.get_cached_media(media_key, "image")
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if cached_data:
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logger.debug(f"[ImageProcessor] 图片缓存命中: {media_key[:20]}...")
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return cached_data
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# 2. 缓存未命中，下载图片
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logger.debug(f"[ImageProcessor] 开始下载图片...")
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filename = f"temp_{uuid.uuid4().hex[:8]}.jpg"
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save_path = str((self.temp_dir / filename).resolve())
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# 尝试下载中图，失败则下载原图
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success = await bot.cdn_download(cdnurl, aeskey, save_path, file_type=2)
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if not success:
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success = await bot.cdn_download(cdnurl, aeskey, save_path, file_type=1)
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if not success:
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logger.error("[ImageProcessor] CDN 下载失败")
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return ""
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||||
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||||
# 等待文件写入完成
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import os
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for _ in range(20): # 最多等待10秒
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if os.path.exists(save_path) and os.path.getsize(save_path) > 0:
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break
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await asyncio.sleep(0.5)
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if not os.path.exists(save_path):
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logger.error("[ImageProcessor] 图片文件未生成")
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return ""
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with open(save_path, "rb") as f:
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image_data = base64.b64encode(f.read()).decode()
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base64_result = f"data:image/jpeg;base64,{image_data}"
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# 3. 缓存到 Redis
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if use_cache:
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try:
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from utils.redis_cache import RedisCache, get_cache
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redis_cache = get_cache()
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if redis_cache and redis_cache.enabled:
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media_key = RedisCache.generate_media_key(cdnurl, aeskey)
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if media_key:
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redis_cache.cache_media(media_key, base64_result, "image", ttl=300)
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logger.debug(f"[ImageProcessor] 图片已缓存: {media_key[:20]}...")
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||||
except Exception as e:
|
||||
logger.debug(f"[ImageProcessor] 缓存图片失败: {e}")
|
||||
|
||||
# 清理临时文件
|
||||
try:
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||||
Path(save_path).unlink()
|
||||
except Exception:
|
||||
pass
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||||
|
||||
return base64_result
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||||
|
||||
except Exception as e:
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||||
logger.error(f"[ImageProcessor] 下载图片失败: {e}")
|
||||
return ""
|
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|
||||
async def download_emoji(
|
||||
self,
|
||||
cdn_url: str,
|
||||
max_retries: int = 3,
|
||||
use_cache: bool = True,
|
||||
) -> str:
|
||||
"""
|
||||
下载表情包并转换为 base64
|
||||
|
||||
Args:
|
||||
cdn_url: CDN URL
|
||||
max_retries: 最大重试次数
|
||||
use_cache: 是否使用缓存
|
||||
|
||||
Returns:
|
||||
base64 编码的表情包数据（带 data URI 前缀）
|
||||
"""
|
||||
# 替换 HTML 实体
|
||||
cdn_url = cdn_url.replace("&", "&")
|
||||
|
||||
# 1. 优先从 Redis 缓存获取
|
||||
media_key = None
|
||||
if use_cache:
|
||||
try:
|
||||
from utils.redis_cache import RedisCache, get_cache
|
||||
redis_cache = get_cache()
|
||||
media_key = RedisCache.generate_media_key(cdnurl=cdn_url)
|
||||
if redis_cache and redis_cache.enabled and media_key:
|
||||
cached_data = redis_cache.get_cached_media(media_key, "emoji")
|
||||
if cached_data:
|
||||
logger.debug(f"[ImageProcessor] 表情包缓存命中: {media_key[:20]}...")
|
||||
return cached_data
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 2. 缓存未命中，下载表情包
|
||||
logger.debug(f"[ImageProcessor] 开始下载表情包...")
|
||||
|
||||
last_error = None
|
||||
connector = self._get_proxy_connector()
|
||||
|
||||
for attempt in range(max_retries):
|
||||
try:
|
||||
timeout = aiohttp.ClientTimeout(total=30 + attempt * 15)
|
||||
|
||||
async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
|
||||
async with session.get(cdn_url) as response:
|
||||
if response.status == 200:
|
||||
content = await response.read()
|
||||
|
||||
if len(content) == 0:
|
||||
logger.warning(f"[ImageProcessor] 表情包内容为空，重试 {attempt + 1}/{max_retries}")
|
||||
continue
|
||||
|
||||
image_data = base64.b64encode(content).decode()
|
||||
base64_result = f"data:image/gif;base64,{image_data}"
|
||||
|
||||
logger.debug(f"[ImageProcessor] 表情包下载成功，大小: {len(content)} 字节")
|
||||
|
||||
# 3. 缓存到 Redis
|
||||
if use_cache and media_key:
|
||||
try:
|
||||
from utils.redis_cache import get_cache
|
||||
redis_cache = get_cache()
|
||||
if redis_cache and redis_cache.enabled:
|
||||
redis_cache.cache_media(media_key, base64_result, "emoji", ttl=300)
|
||||
logger.debug(f"[ImageProcessor] 表情包已缓存: {media_key[:20]}...")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return base64_result
|
||||
else:
|
||||
logger.warning(f"[ImageProcessor] 表情包下载失败，状态码: {response.status}")
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
last_error = "请求超时"
|
||||
logger.warning(f"[ImageProcessor] 表情包下载超时，重试 {attempt + 1}/{max_retries}")
|
||||
except aiohttp.ClientError as e:
|
||||
last_error = str(e)
|
||||
logger.warning(f"[ImageProcessor] 表情包下载网络错误: {e}")
|
||||
except Exception as e:
|
||||
last_error = str(e)
|
||||
logger.warning(f"[ImageProcessor] 表情包下载异常: {e}")
|
||||
|
||||
if attempt < max_retries - 1:
|
||||
await asyncio.sleep(1 * (attempt + 1))
|
||||
|
||||
logger.error(f"[ImageProcessor] 表情包下载失败，已重试 {max_retries} 次: {last_error}")
|
||||
return ""
|
||||
|
||||
async def download_video(
|
||||
self,
|
||||
bot,
|
||||
cdnurl: str,
|
||||
aeskey: str,
|
||||
use_cache: bool = True,
|
||||
) -> str:
|
||||
"""
|
||||
下载视频并转换为 base64
|
||||
|
||||
Args:
|
||||
bot: WechatHookClient 实例
|
||||
cdnurl: CDN URL
|
||||
aeskey: AES 密钥
|
||||
use_cache: 是否使用缓存
|
||||
|
||||
Returns:
|
||||
base64 编码的视频数据（带 data URI 前缀）
|
||||
"""
|
||||
try:
|
||||
# 从缓存获取
|
||||
media_key = None
|
||||
if use_cache:
|
||||
try:
|
||||
from utils.redis_cache import RedisCache, get_cache
|
||||
redis_cache = get_cache()
|
||||
if redis_cache and redis_cache.enabled:
|
||||
media_key = RedisCache.generate_media_key(cdnurl, aeskey)
|
||||
if media_key:
|
||||
cached_data = redis_cache.get_cached_media(media_key, "video")
|
||||
if cached_data:
|
||||
logger.debug(f"[ImageProcessor] 视频缓存命中: {media_key[:20]}...")
|
||||
return cached_data
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 下载视频
|
||||
logger.info(f"[ImageProcessor] 开始下载视频...")
|
||||
|
||||
filename = f"video_{uuid.uuid4().hex[:8]}.mp4"
|
||||
save_path = str((self.temp_dir / filename).resolve())
|
||||
|
||||
# file_type=4 表示视频
|
||||
success = await bot.cdn_download(cdnurl, aeskey, save_path, file_type=4)
|
||||
if not success:
|
||||
logger.error("[ImageProcessor] 视频 CDN 下载失败")
|
||||
return ""
|
||||
|
||||
# 等待文件写入完成
|
||||
import os
|
||||
for _ in range(30):
|
||||
if os.path.exists(save_path) and os.path.getsize(save_path) > 0:
|
||||
break
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
if not os.path.exists(save_path):
|
||||
logger.error("[ImageProcessor] 视频文件未生成")
|
||||
return ""
|
||||
|
||||
file_size = os.path.getsize(save_path)
|
||||
logger.info(f"[ImageProcessor] 视频下载完成，大小: {file_size / 1024 / 1024:.2f} MB")
|
||||
|
||||
# 检查文件大小限制
|
||||
max_size_mb = self.config.video_max_size_mb
|
||||
if file_size > max_size_mb * 1024 * 1024:
|
||||
logger.warning(f"[ImageProcessor] 视频文件过大: {file_size / 1024 / 1024:.2f} MB > {max_size_mb} MB")
|
||||
try:
|
||||
Path(save_path).unlink()
|
||||
except Exception:
|
||||
pass
|
||||
return ""
|
||||
|
||||
# 读取并编码
|
||||
with open(save_path, "rb") as f:
|
||||
video_data = base64.b64encode(f.read()).decode()
|
||||
|
||||
video_base64 = f"data:video/mp4;base64,{video_data}"
|
||||
|
||||
# 缓存到 Redis
|
||||
if use_cache and media_key:
|
||||
try:
|
||||
from utils.redis_cache import get_cache
|
||||
redis_cache = get_cache()
|
||||
if redis_cache and redis_cache.enabled:
|
||||
redis_cache.cache_media(media_key, video_base64, "video", ttl=600)
|
||||
logger.debug(f"[ImageProcessor] 视频已缓存: {media_key[:20]}...")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 清理临时文件
|
||||
try:
|
||||
Path(save_path).unlink()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return video_base64
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[ImageProcessor] 下载视频失败: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
return ""
|
||||
|
||||
async def generate_description(
|
||||
self,
|
||||
image_base64: str,
|
||||
prompt: str = "请用一句话简洁地描述这张图片的主要内容。",
|
||||
model: Optional[str] = None,
|
||||
) -> str:
|
||||
"""
|
||||
使用 AI 生成图片描述
|
||||
|
||||
Args:
|
||||
image_base64: 图片的 base64 数据
|
||||
prompt: 描述提示词
|
||||
model: 使用的模型（默认使用配置中的模型）
|
||||
|
||||
Returns:
|
||||
图片描述文本，失败返回空字符串
|
||||
"""
|
||||
description_model = model or self.config.model
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": prompt},
|
||||
{"type": "image_url", "image_url": {"url": image_base64}}
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
payload = {
|
||||
"model": description_model,
|
||||
"messages": messages,
|
||||
"max_tokens": self.config.max_tokens,
|
||||
"stream": True
|
||||
}
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.config.api_key}"
|
||||
}
|
||||
|
||||
max_retries = self.config.retries
|
||||
last_error = None
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
timeout = aiohttp.ClientTimeout(total=self.config.timeout)
|
||||
connector = self._get_proxy_connector()
|
||||
|
||||
async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
|
||||
async with session.post(
|
||||
self.config.api_url,
|
||||
json=payload,
|
||||
headers=headers
|
||||
) as resp:
|
||||
if resp.status != 200:
|
||||
error_text = await resp.text()
|
||||
raise Exception(f"API 返回错误: {resp.status}, {error_text[:200]}")
|
||||
|
||||
# 流式接收响应
|
||||
description = ""
|
||||
async for line in resp.content:
|
||||
line = line.decode('utf-8').strip()
|
||||
if not line or line == "data: [DONE]":
|
||||
continue
|
||||
|
||||
if line.startswith("data: "):
|
||||
try:
|
||||
data = json.loads(line[6:])
|
||||
delta = data.get("choices", [{}])[0].get("delta", {})
|
||||
content = delta.get("content", "")
|
||||
if content:
|
||||
description += content
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
logger.debug(f"[ImageProcessor] 图片描述生成成功: {description[:50]}...")
|
||||
return description.strip()
|
||||
|
||||
except asyncio.CancelledError:
|
||||
raise
|
||||
except (aiohttp.ClientError, asyncio.TimeoutError) as e:
|
||||
last_error = str(e)
|
||||
if attempt < max_retries:
|
||||
logger.warning(f"[ImageProcessor] 图片描述网络错误: {e}，重试 {attempt + 1}/{max_retries}")
|
||||
await asyncio.sleep(1 * (attempt + 1))
|
||||
continue
|
||||
except Exception as e:
|
||||
last_error = str(e)
|
||||
if attempt < max_retries:
|
||||
logger.warning(f"[ImageProcessor] 图片描述生成异常: {e}，重试 {attempt + 1}/{max_retries}")
|
||||
await asyncio.sleep(1 * (attempt + 1))
|
||||
continue
|
||||
|
||||
logger.error(f"[ImageProcessor] 生成图片描述失败，已重试 {max_retries + 1} 次: {last_error}")
|
||||
return ""
|
||||
|
||||
async def analyze_video(
|
||||
self,
|
||||
video_base64: str,
|
||||
prompt: Optional[str] = None,
|
||||
) -> str:
|
||||
"""
|
||||
使用 AI 分析视频内容
|
||||
|
||||
Args:
|
||||
video_base64: 视频的 base64 数据
|
||||
prompt: 分析提示词
|
||||
|
||||
Returns:
|
||||
视频分析描述，失败返回空字符串
|
||||
"""
|
||||
if not self.config.video_api_url or not self.config.video_model:
|
||||
logger.error("[ImageProcessor] 视频分析配置不完整")
|
||||
return ""
|
||||
|
||||
# 去除 data:video/mp4;base64, 前缀（如果有）
|
||||
if video_base64.startswith("data:"):
|
||||
video_base64 = video_base64.split(",", 1)[1]
|
||||
|
||||
default_prompt = """请详细分析这个视频的内容，包括：
|
||||
1. 视频的主要场景和环境
|
||||
2. 出现的人物/物体及其动作
|
||||
3. 视频中的文字、对话或声音（如果有）
|
||||
4. 视频的整体主题或要表达的内容
|
||||
5. 任何值得注意的细节
|
||||
|
||||
请用客观、详细的方式描述，不要加入主观评价。"""
|
||||
|
||||
analyze_prompt = prompt or default_prompt
|
||||
|
||||
full_url = f"{self.config.video_api_url}/{self.config.video_model}:generateContent"
|
||||
|
||||
payload = {
|
||||
"contents": [
|
||||
{
|
||||
"parts": [
|
||||
{"text": analyze_prompt},
|
||||
{
|
||||
"inline_data": {
|
||||
"mime_type": "video/mp4",
|
||||
"data": video_base64
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"generationConfig": {
|
||||
"maxOutputTokens": self.config.video_max_tokens
|
||||
}
|
||||
}
|
||||
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.config.api_key}"
|
||||
}
|
||||
|
||||
timeout = aiohttp.ClientTimeout(total=self.config.video_timeout)
|
||||
max_retries = 2
|
||||
retry_delay = 5
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
logger.info(f"[ImageProcessor] 开始分析视频...{f' (重试 {attempt}/{max_retries})' if attempt > 0 else ''}")
|
||||
|
||||
async with aiohttp.ClientSession(timeout=timeout) as session:
|
||||
async with session.post(full_url, json=payload, headers=headers) as resp:
|
||||
if resp.status in [502, 503, 504]:
|
||||
logger.warning(f"[ImageProcessor] 视频 API 临时错误: {resp.status}")
|
||||
if attempt < max_retries:
|
||||
await asyncio.sleep(retry_delay)
|
||||
continue
|
||||
return ""
|
||||
|
||||
if resp.status != 200:
|
||||
error_text = await resp.text()
|
||||
logger.error(f"[ImageProcessor] 视频 API 错误: {resp.status}, {error_text[:300]}")
|
||||
return ""
|
||||
|
||||
result = await resp.json()
|
||||
|
||||
# 检查安全过滤
|
||||
if "promptFeedback" in result:
|
||||
feedback = result["promptFeedback"]
|
||||
if feedback.get("blockReason"):
|
||||
logger.warning(f"[ImageProcessor] 视频内容被过滤: {feedback.get('blockReason')}")
|
||||
return ""
|
||||
|
||||
# 提取文本
|
||||
if "candidates" in result and result["candidates"]:
|
||||
for candidate in result["candidates"]:
|
||||
if candidate.get("finishReason") == "SAFETY":
|
||||
logger.warning("[ImageProcessor] 视频响应被安全过滤")
|
||||
return ""
|
||||
|
||||
content = candidate.get("content", {})
|
||||
for part in content.get("parts", []):
|
||||
if "text" in part:
|
||||
text = part["text"]
|
||||
logger.info(f"[ImageProcessor] 视频分析完成，长度: {len(text)}")
|
||||
return text
|
||||
|
||||
logger.error(f"[ImageProcessor] 视频分析无有效响应")
|
||||
return ""
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning(f"[ImageProcessor] 视频分析超时{f', 将重试...' if attempt < max_retries else ''}")
|
||||
if attempt < max_retries:
|
||||
await asyncio.sleep(retry_delay)
|
||||
continue
|
||||
return ""
|
||||
except Exception as e:
|
||||
logger.error(f"[ImageProcessor] 视频分析失败: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
return ""
|
||||
|
||||
return ""
|
||||
|
||||
|
||||
# ==================== 便捷函数 ====================
|
||||
|
||||
_default_processor: Optional[ImageProcessor] = None
|
||||
|
||||
|
||||
def get_image_processor(config: Optional[MediaConfig] = None) -> ImageProcessor:
|
||||
"""获取默认图片处理器"""
|
||||
global _default_processor
|
||||
if config:
|
||||
_default_processor = ImageProcessor(config)
|
||||
if _default_processor is None:
|
||||
raise ValueError("ImageProcessor 未初始化，请先传入配置")
|
||||
return _default_processor
|
||||
|
||||
|
||||
def init_image_processor(config_dict: Dict[str, Any], temp_dir: Optional[Path] = None) -> ImageProcessor:
|
||||
"""从配置字典初始化图片处理器"""
|
||||
config = MediaConfig.from_dict(config_dict)
|
||||
if temp_dir:
|
||||
config.temp_dir = temp_dir
|
||||
processor = ImageProcessor(config, temp_dir)
|
||||
global _default_processor
|
||||
_default_processor = processor
|
||||
return processor
|
||||
|
||||
|
||||
# ==================== 导出 ====================
|
||||
|
||||
__all__ = [
|
||||
'MediaConfig',
|
||||
'MediaResult',
|
||||
'ImageProcessor',
|
||||
'get_image_processor',
|
||||
'init_image_processor',
|
||||
]
|
||||
Reference in New Issue
Block a user