batch_resize
Resize multiple images to specified dimensions in bulk using target width and height. Maintain aspect ratio and choose output formats like PNG, JPEG, or WEBP for streamlined image processing.
Instructions
批量调整图片大小
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image_sources | Yes | 图片源列表，每个元素可以是文件路径或base64编码的图片数据 | |
| keep_aspect_ratio | No | 是否保持宽高比 | |
| output_format | No | 输出格式：PNG、JPEG、WEBP 等 | PNG |
| target_height | Yes | 目标高度（像素） | |
| target_width | Yes | 目标宽度（像素） |
Implementation Reference
- tools/advanced.py:326-439 (handler)Core asynchronous handler that performs batch resizing of multiple images using PIL. Supports aspect ratio preservation, various resampling methods, parallel processing with ThreadPoolExecutor, and returns detailed JSON results including successes and failures.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) )]
- main.py:649-673 (registration)MCP tool registration using @mcp.tool() decorator. This wrapper function adapts parameters, calls the core advanced_batch_resize handler, and handles errors for the FastMCP server.@mcp.tool() def batch_resize( image_sources: Annotated[list, Field(description="图片源列表，每个元素可以是文件路径或base64编码的图片数据")], target_width: Annotated[int, Field(description="目标宽度（像素）", ge=1)], target_height: Annotated[int, Field(description="目标高度（像素）", ge=1)], keep_aspect_ratio: Annotated[bool, Field(description="是否保持宽高比", default=True)], output_format: Annotated[str, Field(description="输出格式：PNG、JPEG、WEBP 等", default="PNG")] ) -> str: """批量调整图片大小""" try: arguments = { "image_sources": image_sources, "target_width": target_width, "target_height": target_height, "keep_aspect_ratio": keep_aspect_ratio, "output_format": output_format } result = safe_run_async(advanced_batch_resize(arguments)) return result[0].text except Exception as e: return json.dumps({ "success": False, "error": f"批量调整大小失败: {str(e)}" }, ensure_ascii=False, indent=2)
- tools/advanced.py:35-80 (schema)JSON schema definition for the batch_resize tool input parameters, including validation rules for image sources, dimensions, aspect ratio, resampling method, and output format. Part of get_advanced_tools() function.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"] } ),