aiPicMatting
Remove backgrounds from images stored in cloud storage using AI-powered matting. Specify image path, width, and height parameters for precise extraction.
Instructions
图片处理-抠图
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| objectKey | Yes | 图片在存储桶里的路径 | |
| width | No | 宽度 | |
| height | No | 高度 |
Input Schema (JSON Schema)
{
"$schema": "http://json-schema.org/draft-07/schema#",
"additionalProperties": false,
"properties": {
"height": {
"description": "高度",
"type": "string"
},
"objectKey": {
"description": "图片在存储桶里的路径",
"type": "string"
},
"width": {
"description": "宽度",
"type": "string"
}
},
"required": [
"objectKey"
],
"type": "object"
}
Implementation Reference
- src/server.ts:387-398 (handler)MCP tool handler function that calls the CIAIInstance.aiPicMatting service method and formats the response.async ({ objectKey, width = '5', height = '5' }) => { const res = await CIAIInstance.aiPicMatting(objectKey, width, height); return { content: [ { type: 'text', text: JSON.stringify(res.data, null, 2), }, ], isError: !res.isSuccess, }; },
- src/server.ts:382-386 (schema)Zod schema defining input parameters: objectKey (required), width and height (optional).{ objectKey: z.string().describe('图片在存储桶里的路径'), width: z.string().optional().describe('宽度'), height: z.string().optional().describe('高度'), },
- src/server.ts:379-399 (registration)Registration of the 'aiPicMatting' tool using McpServer.tool(), including description, schema, and handler.server.tool( 'aiPicMatting', '图片处理-抠图', { objectKey: z.string().describe('图片在存储桶里的路径'), width: z.string().optional().describe('宽度'), height: z.string().optional().describe('高度'), }, async ({ objectKey, width = '5', height = '5' }) => { const res = await CIAIInstance.aiPicMatting(objectKey, width, height); return { content: [ { type: 'text', text: JSON.stringify(res.data, null, 2), }, ], isError: !res.isSuccess, }; }, );
- src/services/ci/ai.service.ts:90-151 (helper)Helper method in CIAIService class that performs the actual AI image matting (抠图) using Tencent COS image_process API with AIImageCrop rule.async aiPicMatting(objectKey: string, width: string, height: string) { try { const result = await new Promise((resolve, reject) => { const outPutFileid = generateOutPutFileId(objectKey); this.cos.request( { Bucket: this.bucket, // 存储桶，必须字段 Region: this.region, // 存储桶所在地域，必须字段 如 ap-beijing Key: objectKey, // 对象文件名，例如：folder/document.jpg。 Method: 'POST', // 固定值 Action: 'image_process', // 固定值 Headers: { 'Pic-Operations': JSON.stringify({ rules: [ { fileid: `${outPutFileid}`, rule: 'ci-process=AIImageCrop&width=' + width + '&height=' + height, }, ], }), }, }, function (error, data) { if (error) { // 处理请求失败 reject(error); } else { // 处理请求成功 resolve(data); } }, ); }); // const localPath = "结果.png"; // 填写要写入的本地文件路径 // if (result.Body) { // fs.writeFileSync(localPath, result.Body); // 将图片内容保存本地路径 // } else { // throw new Error("Result body is undefined"); // } // if (!result.Body) { // throw new Error("Result body is undefined"); // } // const base64Image = `data:image/jpeg;base64,${typeof result.Body === "string" ? Buffer.from(result.Body).toString("base64") : result.Body.toString("base64")}`; return { isSuccess: true, message: '图片处理成功', data: result, }; } catch (error) { return { isSuccess: false, message: '图片处理失败', data: error, }; } }