fx 完善

konabot/plugins/fx_process/__init__.py

@@ -1,25 +1,17 @@
 from io import BytesIO
-from pathlib import Path
 
 from inspect import signature
 
 from konabot.common.nb.extract_image import DepPILImage
-from konabot.common.nb.match_keyword import match_keyword
-from konabot.plugins.fx_process.fx_handle import ImageFilterUtils
-
-import nonebot
 from nonebot.adapters import Event as BaseEvent
-from nonebot import on_message, logger
-from nonebot_plugin_alconna import Alconna, Args, on_alconna
+from nonebot import on_message
 
 from nonebot_plugin_alconna import (
     UniMessage,
     UniMsg
 )
 
-filter_map = {
-    "模糊": ImageFilterUtils.apply_blur
-}
+from konabot.plugins.fx_process.fx_manager import ImageFilterManager
 
 def is_fx_mentioned(evt: BaseEvent, msg: UniMsg) -> bool:
     txt = msg.extract_plain_text()
@@ -32,17 +24,32 @@ fx_on = on_message(rule=is_fx_mentioned)
 @fx_on.handle()
 async def _(msg: UniMsg, event: BaseEvent, img: DepPILImage):
     args = msg.extract_plain_text().split()
-    if len(args) < 2 or args[1] not in filter_map:
+    if len(args) < 2:
         return
     filter_name = args[1]
-    filter_func = filter_map[filter_name]
+    filter_func = ImageFilterManager.get_filter(filter_name)
+    if not filter_func:
+        return
     # 获取函数最大参数数量
     sig = signature(filter_func)
     max_params = len(sig.parameters) - 1  # 减去第一个参数 image
-    # 从 args 提取参数，不能超界
+    # 从 args 提取参数，并转换为适当类型
     func_args = []
     for i in range(2, min(len(args), max_params + 2)):
-        func_args.append(args[i])
+        # 尝试将参数转换为函数签名中对应的类型
+        param = list(sig.parameters.values())[i - 1]
+        param_type = param.annotation
+        arg_value = args[i]
+        try:
+            if param_type is float:
+                converted_value = float(arg_value)
+            elif param_type is int:
+                converted_value = int(arg_value)
+            else:
+                converted_value = arg_value
+        except Exception:
+            converted_value = arg_value
+        func_args.append(converted_value)
     # 应用滤镜
     out_img = filter_func(img, *func_args)
     output = BytesIO()