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apply_edge_enhance

Enhance image edges to improve clarity and definition by applying an edge enhancement filter to sharpen visual details.

Instructions

应用边缘增强滤镜

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_sourceYes图片源,可以是文件路径或base64编码的图片数据

Implementation Reference

  • Core handler function that loads the image, applies PIL's ImageFilter.EDGE_ENHANCE filter, processes output via ImageProcessor, and returns JSON result.
    async def apply_edge_enhance(image_data: str) -> list[TextContent]: """ 应用边缘增强滤镜 Args: image_data: 图片数据(base64编码) Returns: 应用滤镜后的图片数据 """ try: # 验证参数 if not image_data: raise ValidationError("图片数据不能为空") # 加载图片 image = processor.load_image(image_data) # 应用边缘增强滤镜 enhanced_image = image.filter(ImageFilter.EDGE_ENHANCE) # 输出处理后的图片 output_info = processor.output_image(enhanced_image, "edge_enhance") result = { "success": True, "message": "边缘增强滤镜应用成功", "data": { **output_info, "filter_type": "edge_enhance", "size": image.size } } return [TextContent(type="text", text=json.dumps(result, ensure_ascii=False))] except ValidationError as e: error_result = { "success": False, "error": f"参数验证失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] except Exception as e: error_result = { "success": False, "error": f"边缘增强滤镜应用失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
  • main.py:284-297 (registration)
    Registers the MCP tool 'apply_edge_enhance' with input schema defined via Annotated[str], wraps the call to filters.py implementation using safe_run_async.
    @mcp.tool() def apply_edge_enhance( image_source: Annotated[str, Field(description="图片源,可以是文件路径或base64编码的图片数据")] ) -> str: """应用边缘增强滤镜""" try: result = safe_run_async(filters_apply_edge_enhance(image_source)) return result[0].text except Exception as e: return json.dumps({ "success": False, "error": f"应用边缘增强失败: {str(e)}" }, ensure_ascii=False, indent=2)
  • Explicit JSON schema definition for the tool input, requiring 'image_data' as base64 string, part of get_filter_tools().
    Tool( name="apply_edge_enhance", description="应用边缘增强滤镜", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据(base64编码)" } }, "required": ["image_data"] } ),

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