Image Processing MCP Server

""" 高级功能模块 提供批量操作、图片合成、缩略图生成等高级图片处理功能。 """ from typing import List, Dict, Any, Tuple, Optional from PIL import Image, ImageDraw, ImageFilter, ImageEnhance import json import asyncio import os from concurrent.futures import ThreadPoolExecutor import time from mcp.types import Tool, TextContent from utils.image_processor import ImageProcessor from utils.validation import ( validate_image_source, validate_numeric_range, validate_color_hex, ensure_valid_image_source, ValidationError ) from config import ( MAX_IMAGE_SIZE, MIN_IMAGE_SIZE, MIN_DIMENSION, MAX_BLUR_RADIUS, DEFAULT_FORMAT, DEFAULT_QUALITY, DEFAULT_IMAGE_FORMAT ) def get_advanced_tools() -> List[Tool]: """ 返回高级功能工具列表 Returns: List[Tool]: 高级功能工具列表 """ return [ Tool( name="batch_resize", description="批量调整多张图片的大小", inputSchema={ "type": "object", "properties": { "image_sources": { "type": "array", "description": "图片源列表（文件路径或base64编码）", "items": {"type": "string"}, "minItems": 1, "maxItems": 10 }, "width": { "type": "integer", "description": "目标宽度", "minimum": MIN_IMAGE_SIZE, "maximum": MAX_IMAGE_SIZE }, "height": { "type": "integer", "description": "目标高度", "minimum": MIN_IMAGE_SIZE, "maximum": MAX_IMAGE_SIZE }, "maintain_aspect_ratio": { "type": "boolean", "description": "是否保持宽高比", "default": True }, "resample_method": { "type": "string", "description": "重采样方法", "enum": ["LANCZOS", "BILINEAR", "BICUBIC", "NEAREST"], "default": "LANCZOS" }, "output_format": { "type": "string", "description": "输出格式", "enum": ["PNG", "JPEG", "WEBP"], "default": "PNG" } }, "required": ["image_sources", "width", "height"] } ), Tool( name="create_collage", description="创建图片拼贴", inputSchema={ "type": "object", "properties": { "image_sources": { "type": "array", "description": "图片源列表（文件路径或base64编码）", "items": {"type": "string"}, "minItems": 2, "maxItems": 9 }, "layout": { "type": "string", "description": "拼贴布局", "enum": ["grid", "horizontal", "vertical", "mosaic"], "default": "grid" }, "spacing": { "type": "integer", "description": "图片间距（像素）", "minimum": 0, "maximum": 50, "default": 10 }, "background_color": { "type": "string", "description": "背景颜色（十六进制格式）", "default": "#FFFFFF" }, "max_width": { "type": "integer", "description": "最大宽度", "minimum": 200, "maximum": MAX_IMAGE_SIZE, "default": 1200 }, "max_height": { "type": "integer", "description": "最大高度", "minimum": 200, "maximum": MAX_IMAGE_SIZE, "default": 1200 }, "output_format": { "type": "string", "description": "输出格式", "enum": ["PNG", "JPEG", "WEBP"], "default": "PNG" } }, "required": ["image_sources"] } ), Tool( name="create_thumbnail_grid", description="创建缩略图网格", inputSchema={ "type": "object", "properties": { "image_sources": { "type": "array", "description": "图片源列表（文件路径或base64编码）", "items": {"type": "string"}, "minItems": 1, "maxItems": 20 }, "thumbnail_size": { "type": "integer", "description": "缩略图大小（像素）", "minimum": 50, "maximum": 300, "default": 150 }, "columns": { "type": "integer", "description": "列数", "minimum": 1, "maximum": 10, "default": 4 }, "spacing": { "type": "integer", "description": "间距（像素）", "minimum": 0, "maximum": 50, "default": 10 }, "background_color": { "type": "string", "description": "背景颜色（十六进制格式）", "default": "#FFFFFF" }, "border_width": { "type": "integer", "description": "边框宽度", "minimum": 0, "maximum": 10, "default": 2 }, "border_color": { "type": "string", "description": "边框颜色（十六进制格式）", "default": "#CCCCCC" }, "output_format": { "type": "string", "description": "输出格式", "enum": ["PNG", "JPEG", "WEBP"], "default": "PNG" } }, "required": ["image_sources"] } ), Tool( name="blend_images", description="混合两张图片", inputSchema={ "type": "object", "properties": { "image1_source": { "type": "string", "description": "第一张图片源（文件路径或base64编码）" }, "image2_source": { "type": "string", "description": "第二张图片源（文件路径或base64编码）" }, "blend_mode": { "type": "string", "description": "混合模式", "enum": ["normal", "multiply", "screen", "overlay", "soft_light", "hard_light"], "default": "normal" }, "opacity": { "type": "number", "description": "第二张图片的透明度（0.0-1.0）", "minimum": 0.0, "maximum": 1.0, "default": 0.5 }, "resize_mode": { "type": "string", "description": "尺寸调整模式", "enum": ["fit_first", "fit_second", "fit_largest", "fit_smallest"], "default": "fit_first" }, "output_format": { "type": "string", "description": "输出格式", "enum": ["PNG", "JPEG", "WEBP"], "default": "PNG" } }, "required": ["image1_source", "image2_source"] } ), Tool( name="extract_colors", description="提取图片的主要颜色", inputSchema={ "type": "object", "properties": { "image_source": { "type": "string", "description": "图片源（文件路径或base64编码）" }, "color_count": { "type": "integer", "description": "提取的颜色数量", "minimum": 1, "maximum": 20, "default": 5 }, "create_palette": { "type": "boolean", "description": "是否创建调色板图片", "default": True }, "palette_width": { "type": "integer", "description": "调色板宽度", "minimum": 100, "maximum": 800, "default": 400 }, "palette_height": { "type": "integer", "description": "调色板高度", "minimum": 50, "maximum": 200, "default": 100 } }, "required": ["image_source"] } ), Tool( name="create_gif", description="从多张图片创建GIF动画", inputSchema={ "type": "object", "properties": { "image_sources": { "type": "array", "description": "图片源列表（文件路径或base64编码）", "items": {"type": "string"}, "minItems": 2, "maxItems": 20 }, "duration": { "type": "integer", "description": "每帧持续时间（毫秒）", "minimum": 100, "maximum": 5000, "default": 500 }, "loop": { "type": "boolean", "description": "是否循环播放", "default": True }, "optimize": { "type": "boolean", "description": "是否优化文件大小", "default": True }, "resize_to": { "type": "object", "description": "调整所有帧到指定尺寸", "properties": { "width": {"type": "integer", "minimum": 50, "maximum": 800}, "height": {"type": "integer", "minimum": 50, "maximum": 800} } } }, "required": ["image_sources"] } ) ] # 高级功能实现 async def batch_resize(arguments: Dict[str, Any]) -> List[TextContent]: """ 批量调整多张图片的大小 Args: arguments: 包含图片源列表和调整参数的字典 Returns: List[TextContent]: 处理结果 """ try: # 参数验证 image_sources = arguments.get("image_sources", []) if not image_sources: raise ValidationError("image_sources不能为空") width = arguments.get("width") height = arguments.get("height") maintain_aspect_ratio = arguments.get("maintain_aspect_ratio", True) resample_method = arguments.get("resample_method", "LANCZOS") output_format = arguments.get("output_format", DEFAULT_IMAGE_FORMAT) # 验证参数 validate_numeric_range(width, MIN_IMAGE_SIZE, MAX_IMAGE_SIZE, "width") validate_numeric_range(height, MIN_IMAGE_SIZE, MAX_IMAGE_SIZE, "height") # 获取重采样方法 resample_map = { "LANCZOS": Image.Resampling.LANCZOS, "BILINEAR": Image.Resampling.BILINEAR, "BICUBIC": Image.Resampling.BICUBIC, "NEAREST": Image.Resampling.NEAREST } resample = resample_map.get(resample_method, Image.Resampling.LANCZOS) processor = ImageProcessor() results = [] failed_count = 0 # 使用线程池进行并行处理 def resize_single_image(image_source): try: ensure_valid_image_source(image_source) image = processor.load_image(image_source) if maintain_aspect_ratio: # 保持宽高比 image.thumbnail((width, height), resample) resized_image = image else: # 强制调整到指定尺寸 resized_image = image.resize((width, height), resample) # 输出图片 output_info = processor.output_image(resized_image, "batch_resize", output_format) return output_info except Exception as e: return f"ERROR: {str(e)}" # 并行处理 with ThreadPoolExecutor(max_workers=min(len(image_sources), MAX_CONCURRENT_TASKS)) as executor: futures = [executor.submit(resize_single_image, source) for source in image_sources] for i, future in enumerate(futures): result = future.result() if result.startswith("ERROR:"): failed_count += 1 results.append({ "index": i, "success": False, "error": result[7:] # 移除"ERROR: "前缀 }) else: results.append({ "index": i, "success": True, "result": result }) return [TextContent( type="text", text=json.dumps({ "success": True, "message": f"批量调整完成，成功: {len(results) - failed_count}, 失败: {failed_count}", "results": results, "metadata": { "total_images": len(image_sources), "successful": len(results) - failed_count, "failed": failed_count, "target_size": f"{width}x{height}", "maintain_aspect_ratio": maintain_aspect_ratio, "resample_method": resample_method, "format": output_format } }, ensure_ascii=False) )] except ValidationError as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"参数验证失败: {str(e)}" }, ensure_ascii=False) )] except Exception as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"批量调整失败: {str(e)}" }, ensure_ascii=False) )] async def create_collage(arguments: Dict[str, Any]) -> List[TextContent]: """ 创建图片拼贴 Args: arguments: 包含图片源列表和拼贴参数的字典 Returns: List[TextContent]: 处理结果 """ try: # 参数验证 image_sources = arguments.get("image_sources", []) if len(image_sources) < 2: raise ValidationError("至少需要2张图片") layout = arguments.get("layout", "grid") spacing = arguments.get("spacing", 10) background_color = arguments.get("background_color", "#FFFFFF") max_width = arguments.get("max_width", 1200) max_height = arguments.get("max_height", 1200) output_format = arguments.get("output_format", DEFAULT_IMAGE_FORMAT) # 验证参数 validate_numeric_range(spacing, 0, 50, "spacing") validate_color_hex(background_color) validate_numeric_range(max_width, 200, MAX_IMAGE_SIZE, "max_width") validate_numeric_range(max_height, 200, MAX_IMAGE_SIZE, "max_height") processor = ImageProcessor() images = [] # 加载所有图片 for source in image_sources: ensure_valid_image_source(source) image = processor.load_image(source) images.append(image) # 根据布局创建拼贴 if layout == "horizontal": # 水平排列 total_width = sum(img.width for img in images) + spacing * (len(images) - 1) max_height_img = max(img.height for img in images) # 缩放以适应最大尺寸 if total_width > max_width: scale = max_width / total_width images = [img.resize((int(img.width * scale), int(img.height * scale)), Image.Resampling.LANCZOS) for img in images] total_width = max_width max_height_img = max(img.height for img in images) collage = Image.new("RGB", (total_width, max_height_img), background_color) x_offset = 0 for img in images: y_offset = (max_height_img - img.height) // 2 collage.paste(img, (x_offset, y_offset)) x_offset += img.width + spacing elif layout == "vertical": # 垂直排列 max_width_img = max(img.width for img in images) total_height = sum(img.height for img in images) + spacing * (len(images) - 1) # 缩放以适应最大尺寸 if total_height > max_height: scale = max_height / total_height images = [img.resize((int(img.width * scale), int(img.height * scale)), Image.Resampling.LANCZOS) for img in images] max_width_img = max(img.width for img in images) total_height = max_height collage = Image.new("RGB", (max_width_img, total_height), background_color) y_offset = 0 for img in images: x_offset = (max_width_img - img.width) // 2 collage.paste(img, (x_offset, y_offset)) y_offset += img.height + spacing else: # grid 或 mosaic # 网格排列 import math cols = math.ceil(math.sqrt(len(images))) rows = math.ceil(len(images) / cols) # 计算每个单元格的大小 cell_width = (max_width - spacing * (cols - 1)) // cols cell_height = (max_height - spacing * (rows - 1)) // rows # 调整所有图片到单元格大小 resized_images = [] for img in images: img.thumbnail((cell_width, cell_height), Image.Resampling.LANCZOS) resized_images.append(img) # 创建拼贴 collage_width = cols * cell_width + spacing * (cols - 1) collage_height = rows * cell_height + spacing * (rows - 1) collage = Image.new("RGB", (collage_width, collage_height), background_color) for i, img in enumerate(resized_images): row = i // cols col = i % cols x = col * (cell_width + spacing) y = row * (cell_height + spacing) # 居中放置图片 x_offset = x + (cell_width - img.width) // 2 y_offset = y + (cell_height - img.height) // 2 collage.paste(img, (x_offset, y_offset)) # 转换为base64 output_info = processor.output_image(collage, "batch_resize", output_format) return [TextContent( type="text", text=json.dumps({ "success": True, "message": f"成功创建{layout}拼贴", "data": { **output_info, "metadata": { "image_count": len(images), "layout": layout, "size": f"{collage.width}x{collage.height}", "spacing": spacing, "background_color": background_color, "format": output_format } } }, ensure_ascii=False) )] except ValidationError as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"参数验证失败: {str(e)}" }, ensure_ascii=False) )] except Exception as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"创建拼贴失败: {str(e)}" }, ensure_ascii=False) )] async def create_thumbnail_grid(arguments: Dict[str, Any]) -> List[TextContent]: """ 创建缩略图网格 Args: arguments: 包含图片源列表和网格参数的字典 Returns: List[TextContent]: 处理结果 """ try: # 参数验证 image_sources = arguments.get("image_sources", []) if not image_sources: raise ValidationError("image_sources不能为空") thumbnail_size = arguments.get("thumbnail_size", 150) columns = arguments.get("columns", 4) spacing = arguments.get("spacing", 10) background_color = arguments.get("background_color", "#FFFFFF") border_width = arguments.get("border_width", 2) border_color = arguments.get("border_color", "#CCCCCC") output_format = arguments.get("output_format", DEFAULT_IMAGE_FORMAT) # 验证参数 validate_numeric_range(thumbnail_size, 50, 300, "thumbnail_size") validate_numeric_range(columns, 1, 10, "columns") validate_numeric_range(spacing, 0, 50, "spacing") validate_color_hex(background_color) validate_numeric_range(border_width, 0, 10, "border_width") validate_color_hex(border_color) processor = ImageProcessor() thumbnails = [] # 创建缩略图 for source in image_sources: try: ensure_valid_image_source(source) image = processor.load_image(source) # 创建正方形缩略图 image.thumbnail((thumbnail_size, thumbnail_size), Image.Resampling.LANCZOS) # 创建正方形背景 thumb = Image.new("RGB", (thumbnail_size, thumbnail_size), background_color) # 居中粘贴图片 x_offset = (thumbnail_size - image.width) // 2 y_offset = (thumbnail_size - image.height) // 2 thumb.paste(image, (x_offset, y_offset)) # 添加边框 if border_width > 0: draw = ImageDraw.Draw(thumb) for i in range(border_width): draw.rectangle( [i, i, thumbnail_size - 1 - i, thumbnail_size - 1 - i], outline=border_color ) thumbnails.append(thumb) except Exception as e: # 创建错误占位符 error_thumb = Image.new("RGB", (thumbnail_size, thumbnail_size), "#FF0000") draw = ImageDraw.Draw(error_thumb) draw.text((10, thumbnail_size//2), "ERROR", fill="white") thumbnails.append(error_thumb) # 计算网格尺寸 rows = (len(thumbnails) + columns - 1) // columns grid_width = columns * thumbnail_size + spacing * (columns - 1) grid_height = rows * thumbnail_size + spacing * (rows - 1) # 创建网格 grid = Image.new("RGB", (grid_width, grid_height), background_color) for i, thumb in enumerate(thumbnails): row = i // columns col = i % columns x = col * (thumbnail_size + spacing) y = row * (thumbnail_size + spacing) grid.paste(thumb, (x, y)) # 转换为base64 output_info = processor.output_image(grid, "batch_resize", output_format) return [TextContent( type="text", text=json.dumps({ "success": True, "message": f"成功创建{len(thumbnails)}个缩略图的网格", "data": { **output_info, "metadata": { "thumbnail_count": len(thumbnails), "grid_size": f"{grid.width}x{grid.height}", "thumbnail_size": thumbnail_size, "columns": columns, "rows": rows, "spacing": spacing, "border_width": border_width, "format": output_format } } }, ensure_ascii=False) )] except ValidationError as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"参数验证失败: {str(e)}" }, ensure_ascii=False) )] except Exception as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"创建缩略图网格失败: {str(e)}" }, ensure_ascii=False) )] async def blend_images(arguments: Dict[str, Any]) -> List[TextContent]: """ 混合两张图片 Args: arguments: 包含两张图片源和混合参数的字典 Returns: List[TextContent]: 处理结果 """ try: # 参数验证 image1_source = arguments.get("image1_source") image2_source = arguments.get("image2_source") ensure_valid_image_source(image1_source) ensure_valid_image_source(image2_source) blend_mode = arguments.get("blend_mode", "normal") opacity = arguments.get("opacity", 0.5) resize_mode = arguments.get("resize_mode", "fit_first") output_format = arguments.get("output_format", DEFAULT_IMAGE_FORMAT) # 验证参数 validate_numeric_range(opacity, 0.0, 1.0, "opacity") processor = ImageProcessor() # 加载图片 image1 = processor.load_image(image1_source) image2 = processor.load_image(image2_source) # 转换为RGBA模式 if image1.mode != "RGBA": image1 = image1.convert("RGBA") if image2.mode != "RGBA": image2 = image2.convert("RGBA") # 调整尺寸 if resize_mode == "fit_first": image2 = image2.resize(image1.size, Image.Resampling.LANCZOS) final_size = image1.size elif resize_mode == "fit_second": image1 = image1.resize(image2.size, Image.Resampling.LANCZOS) final_size = image2.size elif resize_mode == "fit_largest": if image1.width * image1.height > image2.width * image2.height: image2 = image2.resize(image1.size, Image.Resampling.LANCZOS) final_size = image1.size else: image1 = image1.resize(image2.size, Image.Resampling.LANCZOS) final_size = image2.size else: # fit_smallest if image1.width * image1.height < image2.width * image2.height: image2 = image2.resize(image1.size, Image.Resampling.LANCZOS) final_size = image1.size else: image1 = image1.resize(image2.size, Image.Resampling.LANCZOS) final_size = image2.size # 调整第二张图片的透明度 alpha_channel = image2.split()[-1] alpha_channel = alpha_channel.point(lambda p: int(p * opacity)) image2.putalpha(alpha_channel) # 应用混合模式 if blend_mode == "normal": result = Image.alpha_composite(image1, image2) elif blend_mode == "multiply": # 简化的乘法混合 result = Image.blend(image1, image2, opacity) elif blend_mode == "screen": # 简化的屏幕混合 result = Image.blend(image1, image2, opacity) else: # 其他混合模式使用普通混合 result = Image.alpha_composite(image1, image2) # 转换为base64 output_info = processor.output_image(result, "batch_resize", output_format) return [TextContent( type="text", text=json.dumps({ "success": True, "message": f"成功混合图片，使用{blend_mode}模式", "data": { **output_info, "metadata": { "size": f"{result.width}x{result.height}", "blend_mode": blend_mode, "opacity": opacity, "resize_mode": resize_mode, "format": output_format } } }, ensure_ascii=False) )] except ValidationError as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"参数验证失败: {str(e)}" }, ensure_ascii=False) )] except Exception as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"混合图片失败: {str(e)}" }, ensure_ascii=False) )] async def extract_colors(arguments: Dict[str, Any]) -> List[TextContent]: """ 提取图片的主要颜色 Args: arguments: 包含图片源和颜色提取参数的字典 Returns: List[TextContent]: 处理结果 """ try: # 参数验证 image_source = arguments.get("image_source") ensure_valid_image_source(image_source) color_count = arguments.get("color_count") or arguments.get("num_colors", 5) create_palette = arguments.get("create_palette", True) palette_width = arguments.get("palette_width", 400) palette_height = arguments.get("palette_height", 100) # 验证参数 validate_numeric_range(color_count, 1, 20, "color_count") validate_numeric_range(palette_width, 100, 800, "palette_width") validate_numeric_range(palette_height, 50, 200, "palette_height") processor = ImageProcessor() image = processor.load_image(image_source) # 转换为RGB模式 if image.mode != "RGB": image = image.convert("RGB") # 使用量化来提取主要颜色 quantized = image.quantize(colors=color_count) palette_colors = quantized.getpalette() # 提取RGB颜色值 colors = [] # 确保不超过实际可用的颜色数量 actual_color_count = min(color_count, len(palette_colors) // 3) for i in range(actual_color_count): try: r = palette_colors[i * 3] g = palette_colors[i * 3 + 1] b = palette_colors[i * 3 + 2] hex_color = f"#{r:02x}{g:02x}{b:02x}" colors.append({ "rgb": [r, g, b], "hex": hex_color }) except IndexError: # 如果索引越界，停止添加颜色 break result_data = { "success": True, "message": f"成功提取{len(colors)}种主要颜色", "colors": colors, "metadata": { "image_size": f"{image.width}x{image.height}", "color_count": len(colors) } } # 创建调色板图片 if create_palette: palette_image = Image.new("RGB", (palette_width, palette_height)) color_width = palette_width // len(colors) for i, color_info in enumerate(colors): color_rgb = tuple(color_info["rgb"]) x1 = i * color_width x2 = (i + 1) * color_width if i < len(colors) - 1 else palette_width # 填充颜色块 for x in range(x1, x2): for y in range(palette_height): palette_image.putpixel((x, y), color_rgb) # 输出调色板图片 palette_output = processor.output_image(palette_image, "extract_colors", "PNG") result_data["palette"] = palette_output result_data["metadata"]["palette_size"] = f"{palette_width}x{palette_height}" return [TextContent( type="text", text=json.dumps(result_data, ensure_ascii=False) )] except ValidationError as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"参数验证失败: {str(e)}" }, ensure_ascii=False) )] except Exception as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"提取颜色失败: {str(e)}" }, ensure_ascii=False) )] async def create_gif(arguments: Dict[str, Any]) -> List[TextContent]: """ 从多张图片创建GIF动画 Args: arguments: 包含图片源列表和GIF参数的字典 Returns: List[TextContent]: 处理结果 """ try: # 参数验证 image_sources = arguments.get("image_sources", []) if len(image_sources) < 2: raise ValidationError("至少需要2张图片") duration = arguments.get("duration", 500) loop = arguments.get("loop", 0) # 默认0表示无限循环 optimize = arguments.get("optimize", True) resize_to = arguments.get("resize_to") # 验证参数 validate_numeric_range(duration, 100, 5000, "duration") processor = ImageProcessor() frames = [] # 加载所有图片 for source in image_sources: ensure_valid_image_source(source) image = processor.load_image(source) # 转换为RGB模式（GIF不支持RGBA） if image.mode != "RGB": if image.mode == "RGBA": # 创建白色背景 background = Image.new("RGB", image.size, (255, 255, 255)) background.paste(image, mask=image.split()[-1]) image = background else: image = image.convert("RGB") frames.append(image) # 调整所有帧到相同尺寸 if resize_to: target_width = resize_to.get("width") target_height = resize_to.get("height") if target_width and target_height: frames = [frame.resize((target_width, target_height), Image.Resampling.LANCZOS) for frame in frames] else: # 使用第一帧的尺寸 target_size = frames[0].size frames = [frame.resize(target_size, Image.Resampling.LANCZOS) for frame in frames] # 生成唯一文件名并保存到temp目录 import uuid import os from config import TEMP_DIR unique_id = str(uuid.uuid4())[:8] gif_filename = f"gif_{unique_id}.gif" gif_path = os.path.join(TEMP_DIR, gif_filename) # 确保temp目录存在 os.makedirs(TEMP_DIR, exist_ok=True) # 创建并保存GIF frames[0].save( gif_path, format="GIF", save_all=True, append_images=frames[1:], duration=duration, loop=loop, # 直接使用传入的整数值：0=无限循环，1=播放一次，n=循环n次 optimize=optimize ) # 获取文件大小 file_size = os.path.getsize(gif_path) return [TextContent( type="text", text=json.dumps({ "success": True, "message": f"成功创建包含{len(frames)}帧的GIF动画", "data": { "output_type": "file_reference", "file_path": gif_path, "format": "GIF", "file_size": file_size, "operation": "create_gif", "frame_count": len(frames), "size": [frames[0].width, frames[0].height], "duration": duration, "loop": loop, "optimize": optimize } }, ensure_ascii=False) )] except ValidationError as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"参数验证失败: {str(e)}" }, ensure_ascii=False) )] except Exception as e: return [TextContent( type="text", text=json.dumps({ "success": False, "error": f"创建GIF失败: {str(e)}" }, ensure_ascii=False) )]