![\"img\"](\"https://img.serverlesscloud.cn/202099/1599651083623-v2-74aee01635eec12eea32cf2ae80d4ceb_b.png\")
本文将会分享，如何从零开始搭建一个基于腾讯云 Serverless 的图片艺术化应用！
\n\n\n线上 demo 预览：https://art.x96.xyz/ ，项目已开源，完整代码见文末。
\n
在完整阅读文章后，读者应该能够实现并部署一个相同的应用，这也是本篇文章的目标。
\n项目看点概览：
\n![\"img\"](\"https://img.serverlesscloud.cn/202099/1599651387973-v2-1fde91eae8d502af43bde5912e7a55aa_b.png\")
本项目部署借助了 Serverless component，因此当前开发环境需先全局安装 Serverless 命令行工具
\nnpm install -g serverless本应用的整体需求很简单：图片上传与展示。
\n![\"模块概览\"](\"https://img.serverlesscloud.cn/202099/1599651426241-v2-503298ca435eee806d6ad281505e5db2_b.png\")
![\"上传图片\"](\"https://img.serverlesscloud.cn/202099/1599651443287-v2-1f5d371818671e0122d57650da860765_b.png\")
![\"浏览图片\"](\"https://img.serverlesscloud.cn/202099/1599651463965-v2-a51d31aa252e42d2f073a1ee69cb42d1_b.png\")
在开发之前，我们先创建一个 oss 用于提供图片存储（可以使用你已有的对象存储）
\nmkdir oss在新建的 oss 目录下添加 serverless.yml
\ncomponent: cos\nname: xart-oss\napp: xart\nstage: dev\n\ninputs:\n src:\n src: ./\n exclude:\n - .env # 防止密钥被上传\n bucket: ${name} # 存储桶名称，如若不添加 AppId 后缀，则系统会自动添加，后缀为大写（xart-oss-<你的appid>）\n website: false\n targetDir: /\n protocol: https\n region: ap-guangzhou # 配置区域，尽量配置在和服务同区域内，速度更快\n acl:\n permissions: public-read # 读写配置为，私有写，共有读执行 sls deploy 几秒后，你应该就能看到如下提示，表示新建对象存储成功。
\n![\"新建对象存储\"](\"https://img.serverlesscloud.cn/202099/1599651487608-v2-fb1abe3f51ae4aa8f6246bec14d3a786_b.png\")
这里，我们看到 url https://art-oss-
简单记录一下，在后面服务中会用到，忘记了也不要紧，看看 .env 内 TENCENT_APP_ID 字段（部署后会自动生成 .env）
新建一个目录并初始化
\nmkdir art-api && cd art-api && npm init安装依赖（期望获取 ts 类型提示，请自行安装 @types）
\nnpm i koa @koa/router @koa/cors koa-body typescript ts-node cos-nodejs-sdk-v5 axios dotenv配置 tsconfig.json
{\n \"compilerOptions\": {\n \"target\": \"es2018\",\n \"module\": \"commonjs\",\n \"lib\": [\"es2018\", \"esnext.asynciterable\"],\n \"experimentalDecorators\": true,\n \"emitDecoratorMetadata\": true,\n \"esModuleInterop\": true\n }\n}入口文件 sls.js
require(\"ts-node\").register({ transpileOnly: true }); // 载入 ts 运行时环境，配置忽略类型错误\nmodule.exports = require(\"./app.ts\"); // 直接引入业务逻辑，下面我会和你一起实现补充两个实用知识点：
\nnode -r
\n在入口文件中引入 require("ts-node").register({ transpileOnly: true }) 实际等同于 node -r ts-node/register/transpile-only
所以 node -r 就是在执行之前载入一些特定模块，利用这个能力，能快速实现对一些功能的支持
比如 node -r esm main.js 通过 esm 模块就能在无需 babel、webpack 的情况下快速 import 与 export 进行模块加载与导出
ts 加载路径
\n如果不希望用 ../../../../../ 来加载模块，那么
baseUrl: "."ts-node -r tsconfig-paths/register main.ts 或 require("tsconfig-paths").register()import utils from 'src/utils' 即可愉快地从项目根路径加载模块下面来实现具体逻辑：
\napp.ts
\nrequire(\"dotenv\").config(); // 载入 .env 环境变量，可以将一些密钥配置在环境变量中，并通过 .gitignore 阻止提交\nimport Koa from \"koa\";\nimport Router from \"@koa/router\";\nimport koaBody from \"koa-body\";\nimport cors from '@koa/cors'\nimport util from 'util'\nimport COS from 'cos-nodejs-sdk-v5'\nimport axios from 'axios'\n\nconst app = new Koa();\nconst router = new Router();\n\nvar cos = new COS({\n SecretId: process.env.SecretId // 你的id,\n SecretKey: process.env.SecretKey // 你的key,\n});\n\nconst cosInfo = {\n Bucket: \"xart-oss-<你的appid>\", // 部署oss后获取\n Region: \"ap-guangzhou\",\n}\n\nconst putObjectSync = util.promisify(cos.putObject.bind(cos));\nconst getBucketSync = util.promisify(cos.getBucket.bind(cos));\n\nrouter.get(\"/hello\", async (ctx) => {\n ctx.body = 'hello world!'\n})\n\nrouter.get(\"/api/images\", async (ctx) => {\n const files = await getBucketSync({\n ...cosInfo,\n Prefix: \"result\",\n });\n\n const cosURL = `https://${cosInfo.Bucket}.cos.${cosInfo.Region}.myqcloud.com`;\n ctx.body = files.Contents.map((it) => {\n const [timestamp, size] = it.Key.split(\".jpg\")[0].split(\"__\");\n const [width, height] = size.split(\"_\");\n return {\n url: `${cosURL}/${it.Key}`,\n width,\n height,\n timestamp: Number(timestamp),\n name: it.Key,\n };\n })\n .filter(Boolean)\n .sort((a, b) => b.timestamp - a.timestamp);\n});\n\nrouter.post(\"/api/images/upload\", async (ctx) => {\n const { imgBase64, style } = JSON.parse(ctx.request.body)\n const buf = Buffer.from(imgBase64.replace(/^data:image\\/\\w+;base64,/, \"\"), 'base64')\n // 调用预先提供tensorflow服务加工图片，后面替换成你自己的服务\n const { data } = await axios.post('https://service-edtflvxk-1254074572.gz.apigw.tencentcs.com/release/', {\n imgBase64: buf.toString('base64'),\n style\n })\n if (data.success) {\n const afterImg = await putObjectSync({\n ...cosInfo,\n Key: `result/${Date.now()}__400_200.jpg`,\n Body: Buffer.from(data.data, 'base64'),\n });\n ctx.body = {\n success: true,\n data: 'https://' + afterImg.Location\n }\n }\n});\n\napp.use(cors());\napp.use(koaBody({\n formLimit: \"10mb\",\n jsonLimit: '10mb',\n textLimit: \"10mb\"\n}));\napp.use(router.routes()).use(router.allowedMethods());\n\nconst port = 8080;\napp.listen(port, () => {\n console.log(\"listen in http://localhost:%s\", port);\n});\n\nmodule.exports = app;在代码里可以看到，在图片上传采用了 base64 的形式。这里需要注意，通过 api 网关触发 scf 的时候，网关无法透传 binary，具体上传规则可以参阅官方文档：
\n![](\"https://img.serverlesscloud.cn/202099/1599651515005-v2-a47c5e265c7a5ab449a548ebd3825222_b.png\")
再补充一个知识点：实际我们访问的是 api 网关，然后触发云函数，来获得请求返回结果，所以 debug 时需要关注全链路
\n![](\"https://img.serverlesscloud.cn/202099/1599651534103-v2-d99e3200ff10dc46b0aecdd3ceb5aca7_b.png\")
回归正题，接着配置环境变量 .env
NODE_ENV=development\n\n# 配置 oss 上传所需密钥，需要自行配置，配好了也别告诉我：）\n# 密钥查看地址：https://console.cloud.tencent.com/cam/capi\nSecretId=xxxx\nSecretKey=xxxx以上，server 部分就开发完成了，我们可以通过在本地执行 node sls.js 来验证一下，应该可以看到服务启动的提示了。
\n\nlisten in http://localhost:8080
\n
来简单配置一下 serverless.yml，把服务部署到线上，后面再进一步使用 layer 进行优化
component: koa # 这里填写对应的 component\napp: art\nname: art-api\nstage: dev\n\ninputs:\n src:\n src: ./\n exclude:\n - .env\n functionName: ${name}\n region: ap-guangzhou\n runtime: Nodejs10.15\n functionConf:\n timeout: 60 # 超时时间配置的稍微久一点\n environment:\n variables: # 配置环境变量，同时也可以直接在scf控制台配置\n NODE_ENV: production\n apigatewayConf:\n enableCORS: true\n protocols:\n - https\n - http\n environment: release之后执行部署命令 sls deploy
等待数十秒，应该会得到如下的输出结果（如果是第一次执行，需要平台方授权）
\n![\"img\"](\"https://img.serverlesscloud.cn/202099/1599651558871-v2-2384954cb77cbaf3abf1daaad7efe2c4_b.png\")
其中 url 就是当前服务部署在线上的地址，我们可以试着访问一下看看，是否看到了预设的 hello world。
\n到这里，server 基本上已经部署完成了。如果代码有改动，那就修改后再次执行 sls deploy。官方为代码小于 10M 的项目提供了在线编辑的能力。
但是，随着项目复杂度的增加，deploy 上传会变慢。所以，让我们再优化一下。
\n新建 layer 目录
mkdir layer在 layer 目录下添加 serverless.yml
component: layer\napp: art\nname: art-api-layer\nstage: dev\n\ninputs:\n region: ap-guangzhou\n name: ${name}\n src: ../node_modules # 将 node_modules 打包上传\n runtimes:\n - Nodejs10.15 # 注意配置为相同环境回到项目根目录，调整一下根目录的 serverless.yml
component: koa # 这里填写对应的 component\napp: art\nname: art-api\nstage: dev\n\ninputs:\n src:\n src: ./\n exclude:\n - .env\n - node_modules/** # deploy 时排除 node_modules\n functionName: ${name}\n region: ap-guangzhou\n runtime: Nodejs10.15\n functionConf:\n timeout: 60 # 超时时间配置的稍微久一点\n environment:\n variables: # 配置环境变量，同时也可以直接在 scf 控制台配置\n NODE_ENV: production\n apigatewayConf:\n enableCORS: true\n protocols:\n - https\n - http\n environment: release\n layers:\n - name: ${output:${stage}:${app}:${name}-layer.name} # 配置对应的 layer\n version: ${output:${stage}:${app}:${name}-layer.version} # 配置对应的 layer 版本接着执行命令 sls deploy --target=./layer 部署 layer，然后这次部署看看速度应该已经在 10s 左右了
sls deploy关于 layer 和云函数，补充两个知识点：
\nlayer 的加载与访问
\nlayer 会在函数运行时，将内容解压到 /opt 目录下，如果存在多个 layer，那么会按时间循序进行解压。如果需要访问 layer 内的文件，可以直接通过 /opt/xxx 访问。如果是访问 node_module 则可以直接 import，因为 scf 的 NODE_PATH 环境变量默认已包含 /opt/node_modules 路径。
配额
\n云函数 scf 针对每个用户帐号，均有一定的配额限制：
\n![\"img\"](\"https://img.serverlesscloud.cn/202099/1599651593275-v2-35a92707d47d21e22f162774dbfa9190_b.png\")
其中需要重点关注的就是单个函数代码体积 500mb 的上限。在实际操作中，云函数虽然提供了 500mb。但也存在着一个 deploy 解压上限。
\n关于绕过配额问题：
\nnpm install --production 就能解决问题下面将使用 next.js 来构建一个前端 SSR 服务。
\n新建目录并初始化项目：
\nmkdir art-front && cd art-front && npm init安装依赖：
\nnpm install next react react-dom typescript @types/node swr antd @ant-design/icons dayjs增加 ts 支持（next.js 跑起来会自动配置）：
\ntouch tsconfig.json打开 package.json 文件并添加 scripts 配置段：
\n"scripts": {\n "dev": "next",\n "build": "next build",\n "start": "next start"\n}编写前端业务逻辑（文中仅展示主要逻辑，源码在 GitHub 获取）
\npages/_app.tsx
\nimport React from \"react\";\nimport \"antd/dist/antd.css\";\nimport { SWRConfig } from \"swr\";\n\nexport default function MyApp({ Component, pageProps }) {\n return (\n <SWRConfig\n value={{\n refreshInterval: 2000,\n fetcher: (...args) => fetch(args[0], args[1]).then((res) => res.json()),\n }}\n >\n <Component {...pageProps} />\n </SWRConfig>\n );\n}pages/index.tsx 完整代码
\nimport React from \"react\";\nimport { Card, Upload, message, Radio, Spin, Divider } from \"antd\";\nimport { InboxOutlined } from \"@ant-design/icons\";\nimport dayjs from \"dayjs\";\nimport useSWR from \"swr\";\n\nlet origin = 'http://localhost:8080'\nif (process.env.NODE_ENV === 'production') {\n // 使用你自己的部署的art-api服务地址\n origin = 'https://service-5yyo7qco-1254074572.gz.apigw.tencentcs.com/release' \n}\n\n// 略...\nexport default function Index() {\n const { data } = useSWR(`${origin}/api/images`);\n\n const [img, setImg] = React.useState(\"\");\n const [loading, setLoading] = React.useState(false);\n\n const uploadImg = React.useCallback((file, style) => {\n const reader = new FileReader();\n reader.readAsDataURL(file);\n reader.onload = async () => {\n const res = await fetch(\n `${origin}/api/images/upload`, {\n method: 'POST',\n body: JSON.stringify({\n imgBase64: reader.result,\n style\n }),\n mode: 'cors'\n }\n ).then((res) => res.json());\n\n if (res.success) {\n setImg(res.data);\n } else {\n message.error(res.message);\n }\n setLoading(false);\n }\n }, []);\n\n const [artStyle, setStyle] = React.useState(STYLE_MODE.cube);\n\n return (\n <Dragger\n style={{ padding: 24 }}\n {...{\n name: \"art_img\",\n showUploadList: false,\n action: `${origin}/api/upload`,\n onChange: (info) => {\n const { status } = info.file;\n if (status !== \"uploading\") {\n console.log(info.file, info.fileList);\n }\n if (status === \"done\") {\n setImg(info.file.response);\n message.success(`${info.file.name} 上传成功`);\n setLoading(false);\n } else if (status === \"error\") {\n message.error(`${info.file.name} 上传失败`);\n setLoading(false);\n }\n },\n beforeUpload: (file) => {\n if (\n ![\"image/png\", \"image/jpg\", \"image/jpeg\"].includes(file.type)\n ) {\n message.error(\"图片格式必须是 png、jpg、jpeg\");\n return false;\n }\n const isLt10M = file.size / 1024 / 1024 < 10;\n if (!isLt10M) {\n message.error(\"文件大小超过10M\");\n return false;\n }\n setLoading(true);\n\n uploadImg(file, artStyle);\n return false;\n },\n }}\n // 略...使用 npm run dev 把前端跑起来看看，看到以下提示就是成功了
\n\nready - started server on http://localhost:3000
\n
接着配置 serverless.yml（如果有需要可以参考前文，使用 layer 优化部署体验）
component: nextjs\napp: art\nname: art-front\nstage: dev\n\ninputs:\n src:\n dist: ./\n hook: npm run build\n exclude:\n - .env\n region: ap-guangzhou\n functionName: ${name}\n runtime: Nodejs12.16\n staticConf:\n cosConf:\n bucket: art-front # 将前端静态资源部署到oss，减少scf的调用频次\n apigatewayConf:\n enableCORS: true\n protocols:\n - https\n - http\n environment: release\n # customDomains: # 如果需要，可以自己配置自定义域名\n # - domain: xxxxx \n # certificateId: xxxxx # 证书 ID\n # # 这里将 API 网关的 release 环境映射到根路径\n # isDefaultMapping: false\n # pathMappingSet:\n # - path: /\n # environment: release\n # protocols:\n # - https\n functionConf:\n timeout: 60\n memorySize: 128\n environment:\n variables:\n apiUrl: ${output:${stage}:${app}:art-api.apigw.url} # 此处可以将api通过环境变量注入由于我们额外配置了 oss，所以需要额外配置一下 next.config.js
const isProd = process.env.NODE_ENV === \"production\";\n\nconst STATIC_URL =\n \"https://art-front-<你的appid>.cos.ap-guangzhou.myqcloud.com/\";\n\nmodule.exports = {\n assetPrefix: isProd ? STATIC_URL : \"\",\n};在上面的例子中，我们使用的 Tensorflow，暂时还是调用我预先提供的接口。
\n接着让我们会把它替换成我们自己的服务。
\n基础信息
\nscf 在 python 环境下，默认提供了 tensorflow1.9 依赖包，使用 python 可以用较低的成本直接上手。
\n问题所在
\n但如果你想使用 2.x 版本，或不熟悉 python，想用 node 来跑 tensorflow，那么就会遇到代码包大小的限制的问题。
\n怎么解决 —— 文件存储服务！
\n先看看 CFS 文档的介绍
\n![\"img\"](\"https://img.serverlesscloud.cn/202099/1599651653678-v2-1ddde91871f80f65cf2bc3e44938f489_b.png\")
挂载后，就可以正常使用了，腾讯云提供了一个简单例子。
\nvar fs = requiret('fs');\nexports.main_handler = async (event, context) => {\n await fs.promises.writeFile('/mnt/myfolder/filel.txt', JSON.stringify(event)); \n return event;\n};既然能正常读写，那么就能够正常的载入 npm 包，可以看到我直接加载了 /mnt 目录下的包，同时 model 也放在 /mnt 下
tf = require(\"/mnt/nodelib/node_modules/@tensorflow/tfjs-node\");\n jpeg = require(\"/mnt/nodelib/node_modules/jpeg-js\");\n images = require(\"/mnt/nodelib/node_modules/images\");\n loadModel = async () => tf.node.loadSavedModel(\"/mnt/model\");如果你使用 Python，那么可能会遇到一个问题，那就是 scf 默认环境下提供了 tensorflow 1.9 的依赖包，所以需要使用 insert，提高 /mnt 目录下包的优先级
sys.path.insert(0, \"./mnt/xxx\")上面提供了解决方案，那么具体开发中可能会感觉很麻烦，因为 csf 必须和 scf 配置在同一个子网内，无法挂载到本地进行操作。
\n所以，在实际部署过程中，可以在对应网络下，购置一台按需计费的 ecs 云服务器实例。然后将硬盘挂载后，直接进行操作，最后在云函数成功部署后，销毁实例：）
\nsudo yum install nfs-utils\nmkdir <待挂载目标目录>\nsudo mount -t nfs -o vers=4.0，noresvport <挂载点IP>:/ <待挂载目录>具体业务代码如下：
\nconst fs = require(\"fs\");\nlet tf, jpeg, loadModel, images;\n\nif (process.env.NODE_ENV !== \"production\") {\n tf = require(\"@tensorflow/tfjs-node\");\n jpeg = require(\"jpeg-js\");\n images = require(\"images\");\n loadModel = async () => tf.node.loadSavedModel(\"./model\");\n} else {\n tf = require(\"/mnt/nodelib/node_modules/@tensorflow/tfjs-node\");\n jpeg = require(\"/mnt/nodelib/node_modules/jpeg-js\");\n images = require(\"/mnt/nodelib/node_modules/images\");\n loadModel = async () => tf.node.loadSavedModel(\"/mnt/model\");\n}\n\nexports.main_handler = async (event) => {\n const { imgBase64, style } = JSON.parse(event.body)\n if (!imgBase64 || !style) {\n return { success: false, message: \"需要提供完整的参数imgBase64、style\" };\n }\n time = Date.now();\n console.log(\"解析图片--\");\n const styleImg = tf.node.decodeJpeg(fs.readFileSync(`./imgs/style_${style}.jpeg`));\n const contentImg = tf.node.decodeJpeg(\n images(Buffer.from(imgBase64, 'base64')).size(400).encode(\"jpg\", { operation: 50 }) // 压缩图片尺寸\n );\n const a = styleImg.toFloat().div(tf.scalar(255)).expandDims();\n const b = contentImg.toFloat().div(tf.scalar(255)).expandDims();\n console.log(\"--解析图片 %s ms\", Date.now() - time);\n\n\n time = Date.now();\n console.log(\"载入模型--\");\n const model = await loadModel();\n console.log(\"--载入模型 %s ms\", Date.now() - time);\n\n\n time = Date.now();\n console.log(\"执行模型--\");\n const stylized = tf.tidy(() => {\n const x = model.predict([b, a])[0];\n return x.squeeze();\n });\n console.log(\"--执行模型 %s ms\", Date.now() - time);\n\n time = Date.now();\n\n const imgData = await tf.browser.toPixels(stylized);\n var rawImageData = {\n data: Buffer.from(imgData),\n width: stylized.shape[1],\n height: stylized.shape[0],\n };\n\n const result = images(jpeg.encode(rawImageData, 50).data)\n .draw(\n images(\"./imgs/logo.png\"),\n Math.random() * rawImageData.width * 0.9,\n Math.random() * rawImageData.height * 0.9\n )\n .encode(\"jpg\", { operation: 50 });\n\n return { success: true, data: result.toString('base64') };\n};感谢阅读，以上代码均经过实测，如果发现异常，那就再看一遍：）
\n有其他问题或想法，可以移步原文链接讨论。
\n\n\n源码：jiangqizheng/art，欢迎 star。
\n
欢迎访问：Serverless 中文网，您可以在 最佳实践 里体验更多关于 Serverless 应用的开发！
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