Image Processing MCP Server

""" 几何变换工具 包含缩放、裁剪、旋转等几何变换功能 """ from mcp.types import Tool from typing import List def get_transform_tools() -> List[Tool]: """返回几何变换工具列表""" return [ Tool( name="resize_image", description="调整图片大小", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" }, "width": { "type": "integer", "description": "目标宽度", "minimum": 1, "maximum": 4096 }, "height": { "type": "integer", "description": "目标高度", "minimum": 1, "maximum": 4096 }, "keep_aspect_ratio": { "type": "boolean", "description": "是否保持宽高比", "default": True }, "resample": { "type": "string", "description": "重采样方法（NEAREST, BILINEAR, BICUBIC, LANCZOS）", "default": "LANCZOS" } }, "required": ["image_data", "width", "height"] } ), Tool( name="crop_image", description="裁剪图片", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" }, "left": { "type": "integer", "description": "左边界坐标", "minimum": 0 }, "top": { "type": "integer", "description": "上边界坐标", "minimum": 0 }, "right": { "type": "integer", "description": "右边界坐标" }, "bottom": { "type": "integer", "description": "下边界坐标" } }, "required": ["image_data", "left", "top", "right", "bottom"] } ), Tool( name="rotate_image", description="旋转图片", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" }, "angle": { "type": "number", "description": "旋转角度（度，正值为顺时针）" }, "expand": { "type": "boolean", "description": "是否扩展画布以容纳旋转后的图片", "default": True }, "fill_color": { "type": "string", "description": "填充颜色（十六进制格式，如#FFFFFF）", "default": "#FFFFFF" } }, "required": ["image_data", "angle"] } ), Tool( name="flip_image", description="翻转图片", inputSchema={ "type": "object", "properties": { "image_data": { "type": "string", "description": "图片数据（base64编码）" }, "direction": { "type": "string", "description": "翻转方向（horizontal, vertical）", "enum": ["horizontal", "vertical"] } }, "required": ["image_data", "direction"] } ) ] from utils.image_processor import ImageProcessor from utils.validation import ensure_valid_dimensions, validate_crop_coordinates, validate_color_hex, ValidationError from mcp.types import TextContent from PIL import Image import json # 全局图片处理器实例 processor = ImageProcessor() async def resize_image(image_source: str, width: int, height: int, keep_aspect_ratio: bool = True, resample: str = "LANCZOS") -> list[TextContent]: """ 调整图片大小 Args: image_source: 图片源（文件路径或base64编码数据） width: 目标宽度 height: 目标高度 keep_aspect_ratio: 是否保持宽高比 resample: 重采样方法 Returns: 调整后的图片数据 """ try: # 验证参数 if not image_source: raise ValidationError("图片源不能为空") ensure_valid_dimensions(width, height) # 验证重采样方法 resample_methods = { 'NEAREST': Image.NEAREST, 'BILINEAR': Image.BILINEAR, 'BICUBIC': Image.BICUBIC, 'LANCZOS': Image.LANCZOS } if resample.upper() not in resample_methods: raise ValidationError(f"不支持的重采样方法: {resample}") # 加载图片 image = processor.load_image(image_source) original_size = image.size # 计算目标尺寸 if keep_aspect_ratio: # 保持宽高比，计算实际尺寸 aspect_ratio = original_size[0] / original_size[1] target_aspect_ratio = width / height if aspect_ratio > target_aspect_ratio: # 以宽度为准 new_width = width new_height = int(width / aspect_ratio) else: # 以高度为准 new_height = height new_width = int(height * aspect_ratio) else: new_width, new_height = width, height # 调整大小 resized_image = image.resize( (new_width, new_height), resample_methods[resample.upper()] ) # 输出调整后的图片 output_info = processor.output_image(resized_image, f"resize_{new_width}x{new_height}") result = { "success": True, "message": f"图片大小调整成功: {original_size} -> {(new_width, new_height)}", "data": { **output_info, "original_size": original_size, "new_size": (new_width, new_height), "keep_aspect_ratio": keep_aspect_ratio, "resample_method": resample } } 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"图片大小调整失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] async def crop_image(image_data: str, left: int, top: int, right: int, bottom: int) -> list[TextContent]: """ 裁剪图片 Args: image_data: 图片数据（base64编码） left, top, right, bottom: 裁剪坐标 Returns: 裁剪后的图片数据 """ 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) image_width, image_height = image.size # 验证裁剪坐标 if not validate_crop_coordinates(left, top, right, bottom, image_width, image_height): raise ValidationError(f"无效的裁剪坐标: ({left}, {top}, {right}, {bottom}), 图片尺寸: {image_width}x{image_height}") # 裁剪图片 cropped_image = image.crop((left, top, right, bottom)) # 输出裁剪后的图片 output_info = processor.output_image(cropped_image, f"crop_{left}_{top}_{right}_{bottom}") result = { "success": True, "message": f"图片裁剪成功: ({left}, {top}, {right}, {bottom})", "data": { **output_info, "original_size": (image_width, image_height), "crop_box": (left, top, right, bottom), "cropped_size": cropped_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"图片裁剪失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] async def rotate_image(image_data: str, angle: float, expand: bool = True, fill_color: str = "#FFFFFF") -> list[TextContent]: """ 旋转图片 Args: image_data: 图片数据（base64编码） angle: 旋转角度（度，正值为顺时针） expand: 是否扩展画布 fill_color: 填充颜色 Returns: 旋转后的图片数据 """ try: # 验证参数 if not image_data: raise ValidationError("图片数据不能为空") if not validate_color_hex(fill_color): raise ValidationError(f"无效的颜色格式: {fill_color}") # 加载图片 image = processor.load_image(image_data) original_size = image.size # 转换颜色格式 fill_rgb = tuple(int(fill_color[i:i+2], 16) for i in (1, 3, 5)) # 旋转图片 rotated_image = image.rotate( -angle, # PIL中负值为顺时针 expand=expand, fillcolor=fill_rgb ) # 输出旋转后的图片 output_info = processor.output_image(rotated_image, f"rotate_{int(angle)}deg") result = { "success": True, "message": f"图片旋转成功: {angle}度", "data": { **output_info, "original_size": original_size, "rotated_size": rotated_image.size, "angle": angle, "expand": expand, "fill_color": fill_color } } 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"图片旋转失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))] async def flip_image(image_data: str, direction: str) -> list[TextContent]: """ 翻转图片 Args: image_data: 图片数据（base64编码） direction: 翻转方向（horizontal, vertical） Returns: 翻转后的图片数据 """ try: # 验证参数 if not image_data: raise ValidationError("图片数据不能为空") if direction not in ['horizontal', 'vertical']: raise ValidationError(f"无效的翻转方向: {direction}") # 加载图片 image = processor.load_image(image_data) # 翻转图片 if direction == 'horizontal': flipped_image = image.transpose(Image.FLIP_LEFT_RIGHT) direction_desc = "水平翻转" else: # vertical flipped_image = image.transpose(Image.FLIP_TOP_BOTTOM) direction_desc = "垂直翻转" # 输出翻转后的图片 output_info = processor.output_image(flipped_image, f"flip_{direction}") result = { "success": True, "message": f"图片{direction_desc}成功", "data": { **output_info, "direction": direction, "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"图片翻转失败: {str(e)}" } return [TextContent(type="text", text=json.dumps(error_result, ensure_ascii=False))]