apply_emboss
Adds a 3D embossed effect to images by applying a filter that creates raised or engraved appearance for visual enhancement.
Instructions
应用浮雕滤镜
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image_source | Yes | 图片源，可以是文件路径或base64编码的图片数据 |
Implementation Reference
- tools/filters.py:389-437 (handler)Core handler function that loads the image, applies PIL's ImageFilter.EMBOSS filter, processes output via ImageProcessor, and returns JSON result as TextContent.async def apply_emboss(image_data: str) -> list[TextContent]: """ 应用浮雕滤镜 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) # 应用浮雕滤镜 embossed_image = image.filter(ImageFilter.EMBOSS) # 输出处理后的图片 output_info = processor.output_image(embossed_image, "emboss") result = { "success": True, "message": "浮雕滤镜应用成功", "data": { **output_info, "filter_type": "emboss", "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"浮雕滤镜应用失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]
- main.py:299-310 (registration)MCP tool registration via @mcp.tool() decorator. This wrapper function delegates to the filters.apply_emboss handler using safe_run_async.def apply_emboss( 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: """应用浮雕滤镜""" try: result = safe_run_async(filters_apply_emboss(image_source)) return result[0].text except Exception as e: return json.dumps({ "success": False, "error": f"应用浮雕效果失败: {str(e)}" }, ensure_ascii=False, indent=2)
- tools/filters.py:93-106 (schema)Tool schema definition in get_filter_tools(), specifying input schema for 'image_data' parameter, though primarily used via main.py decorator.Tool( name="apply_emboss", description="应用浮雕滤镜", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" } }, "required": ["image_data"] } ),