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[Other] Refactor js submodule (#415)
* Refactor js submodule * Remove change-log * Update ocr module * Update ocr-detection module * Update ocr-detection module * Remove change-log
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| # Web Demo介绍 | ||
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| - [简介](#0) | ||
| - [1. 快速开始](#1) | ||
| - [2. npm包调用](#2) | ||
| - [3. 模型替换](#3) | ||
| - [4. 自定义前后处理参数](#4) | ||
| - [5. 其他](#5) | ||
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| <a name="0"></a> | ||
| ## 简介 | ||
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| 本项目基于[Paddle.js](https://github.com/PaddlePaddle/Paddle.js)在浏览器中实现目标检测，人像分割，OCR，物品分类等计算机视觉任务。 | ||
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| |demo名称|web demo组件|源码目录|npm包| | ||
| |-|-|-|-| | ||
| |人脸检测|[FaceDetection](./web_demo/src/pages/cv/detection/FaceDetection/)| [facedetect](./package/packages/paddlejs-models/facedetect)|[@paddle-js-models/facedetect](https://www.npmjs.com/package/@paddle-js-models/facedetect)| | ||
| |螺丝钉检测|[ScrewDetection](./web_demo/src/pages/cv/detection/ScrewDetection)| [detect](./package/packages/paddlejs-models/detect)|[@paddle-js-models/detect](https://www.npmjs.com/package/@paddle-js-models/detect)| | ||
| |人像分割背景替换|[HumanSeg](./web_demo/src/pages/cv/segmentation/HumanSeg)|[humanseg](./package/packages/paddlejs-models/humanseg)|[@paddle-js-models/humanseg](https://www.npmjs.com/package/@paddle-js-models/humanseg)| | ||
| |手势识别AI猜丁壳|[GestureRecognition](./web_demo/src/pages/cv/recognition/GestureRecognition)|[gesture](./package/packages/paddlejs-models/gesture)|[@paddle-js-models/gesture](https://www.npmjs.com/package/@paddle-js-models/gesture)| | ||
| |1000种物品识别|[ItemIdentification](./web_demo/src/pages/cv/recognition/ItemIdentification)|[mobilenet](./package/packages/paddlejs-models/mobilenet)|[@paddle-js-models/mobilenet](https://www.npmjs.com/package/@paddle-js-models/mobilenet)| | ||
| |文本检测|[TextDetection](./web_demo/src/pages/cv/ocr/TextDetection)|[ocrdetection](./package/packages/paddlejs-models/ocrdetection)|[@paddle-js-models/ocrdet](https://www.npmjs.com/package/@paddle-js-models/ocrdet)| | ||
| |文本识别|[TextRecognition](./web_demo/src/pages/cv/ocr/TextRecognition)|[ocr](./package/packages/paddlejs-models/ocr)|[@paddle-js-models/ocr](https://www.npmjs.com/package/@paddle-js-models/ocr)| | ||
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| <a name="1"></a> | ||
| ## 1. 快速开始 | ||
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| 本节介绍如何在浏览器中直接运行官方demo。 | ||
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| **1. 安装Node.js** | ||
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| 从`Node.js`官网https://nodejs.org/en/download/ 下载适合自己平台的`Node.js`安装包并安装。 | ||
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| **2. 安装demo依赖并启动** | ||
| 在`./web_demo`目录下执行如下指令： | ||
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| ``` | ||
| # 安装依赖 | ||
| npm install | ||
| # 启动demo | ||
| npm run dev | ||
| ``` | ||
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| 在浏览器中打开网址 `http://localhost:5173/main/index.html` 即可快速体验在浏览器中运行计算机视觉任务。 | ||
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|  | ||
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| <a name="2"></a> | ||
| ## 2. npm包调用 | ||
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| 本节介绍npm包的使用方式，每个demo均提供简单易用的接口，用户只需初始化上传图片即可获得结果，使用步骤如下： | ||
| 1. 调用模块 | ||
| 2. 初始化模型 | ||
| 3. 传入输入，执行预测 | ||
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| 以 OCR 为例，在前端项目中，`@paddle-js-models/ocr`包的使用方式如下： | ||
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| ``` | ||
| // 1. 调用ocr模块 | ||
| import * as ocr from '@paddle-js-models/ocr'; | ||
| // 2. 初始化ocr模型 | ||
| await ocr.init(); | ||
| // 3. 传入HTMLImageElement类型的图像作为输入并获得结果 | ||
| const res = await ocr.recognize(img); | ||
| // 打印OCR模型得到的文本坐标以及文本内容 | ||
| console.log(res.text); | ||
| console.log(res.points); | ||
| ``` | ||
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| <a name="3"></a> | ||
| ## 3. 模型替换 | ||
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| 由于前端环境和计算资源限制，在前端部署深度学习模型时，我们对模型的性能有着更严格的要求，简单来说，模型需要足够轻量化。理论上模型的输入shape越小、模型大小越小，则对应的模型的flops越小，在前端运行也能更流畅。经验总结，使用`Paddle.js`部署的模型存储尽量不超过*5M*，实际情况根据硬件和计算资源情况决定。 | ||
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| 在实际应用中，常常根据垂类的场景定制化模型，官方的demo支持修改传入参数替换模型。 | ||
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| 以OCR demo为例，[ocr.init()函数](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/application/js/package/packages/paddlejs-models/ocr/src/index.ts#L52)中，包含默认初始化的模型链接，如果要替换模型参考下述步骤。 | ||
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| 步骤1：将模型转成js格式： | ||
| ``` | ||
| # 安装paddlejsconverter | ||
| pip3 install paddlejsconverter | ||
| # 转换模型格式，输入模型为inference模型 | ||
| paddlejsconverter --modelPath=./inference.pdmodel --paramPath=./inference.pdiparams --outputDir=./ --useGPUOpt=True | ||
| # 注意：useGPUOpt 选项默认不开启，如果模型用在 gpu backend（webgl/webgpu），则开启 useGPUOpt，如果模型运行在（wasm/plain js）则不要开启。 | ||
| ``` | ||
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| 导出成功后，本地目录下会出现 `model.json chunk_1.dat`等文件，分别是对应js模型的网络结构、模型参数二进制文件。 | ||
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| 步骤2：将导出的js模型上传到支持跨域访问的服务器，服务器的CORS配置参考下图： | ||
|  | ||
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| 步骤3：修改代码替换默认的模型。以OCR demo为例，修改OCR web demo中[模型初始化代码](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/application/js/web_demo/src/pages/cv/ocr/TextRecognition/TextRecognition.vue#L64)，即 | ||
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| ``` | ||
| await ocr.init(); | ||
| 修改为： | ||
| await ocr.init({modelPath: "https://js-models.bj.bcebos.com/PaddleOCR/PP-OCRv3/ch_PP-OCRv3_det_infer_js_960/model.json"}); # 第一个参数传入新的文本检测字典类型参数 | ||
| ``` | ||
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| 重新在demo目录下执行下述命令，即可体验新的模型效果。 | ||
| ``` | ||
| npm run dev | ||
| ``` | ||
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| <a name="4"></a> | ||
| ## 4. 自定义前后处理参数 | ||
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| **自定义前处理参数** | ||
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| 在不同计算机视觉任务中，不同的模型可能有不同的预处理参数，比如mean,std,keep_ratio等参数，替换模型后也需要对预处理参数进行修改。paddle.js发布的npm包中提供了自定义预处理参数的简单方案。只需要在调用模型初始化函数时，传入自定义的参数即可。 | ||
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| ``` | ||
| # 默认参数初始化 | ||
| await model.init(); | ||
| 自定义参数初始化 | ||
| const Config = {mean: [0.5, 0.5, 0.5], std: [0.5, 0.5, 0.5], keepratio: false}; | ||
| await model.init(Config); | ||
| ``` | ||
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| 以OCR文本检测demo为例，修改模型前处理的mean和std参数，只需要在模型初始化时传入自定义的mean和std参数。 | ||
| ``` | ||
| await ocr.init(); | ||
| 修改为： | ||
| const detConfig = {mean: [0.5, 0.5, 0.5], std: [0.5, 0.5, 0.5]}; | ||
| await ocr.init(detConfig); # 第一个参数传入新的文本检测模型链接 | ||
| ``` | ||
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| **自定义后处理参数** | ||
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| 同理，paddle.js发布的npm包也提供了后处理参数的自定义方案。 | ||
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| ``` | ||
| # 默认参数运行 | ||
| await model.predict(); | ||
| # 自定义后处理参数 | ||
| const postConfig = {thresh: 0.5}; | ||
| await model.predict(Config); | ||
| ``` | ||
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| 以OCR文本检测 demo为例，修改文本检测后处理的参数实现扩大文本检测框的效果，修改OCR web demo中执行[模型预测代码](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/application/web_demo/src/pages/cv/ocr/TextRecognition/TextRecognition.vue#L99)，即： | ||
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| ``` | ||
| const res = await ocr.recognize(img, { canvas: canvas.value }); | ||
| 修改为： | ||
| // 定义超参数，将unclip_ratio参数从1.5 增大为3.5 | ||
| const detConfig = {shape: 960, thresh: 0.3, box_thresh: 0.6, unclip_ratio:3.5}; | ||
| const res = await ocr.recognize(img, { canvas: canvas.value }, detConfig); | ||
| ``` | ||
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| 注：不同的任务有不同的后处理参数，详细参数参考npm包中的API。 | ||
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| <a name="5"></a> | ||
| ## 5. 其他 | ||
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| `Paddle.js`转换后的模型不仅支持浏览器中使用，也可以在百度小程序和微信小程序环境下运行。 | ||
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| |名称|目录| | ||
| |-|-| | ||
| |OCR文本检测| [ocrdetecXcx](./mini_program/ocrdetectXcx/) | | ||
| |OCR文本识别| [ocrXcx](./mini_program/ocrXcx/) | | ||
| |目标检测| coming soon | | ||
| |图像分割| coming soon | | ||
| |物品分类| coming soon | |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,41 @@ | ||
| # Paddle.js Model Module介绍 | ||
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| 该部分是基于 Paddle.js 进行开发的模型库，主要提供 Web 端可直接引入使用模型的能力。 | ||
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| | demo名称 | 源码目录 | npm包 | | ||
| | ---------------- | ------------------------------------------------------ | ------------------------------------------------------------ | | ||
| | 人脸检测 | [facedetect](./packages/paddlejs-models/facedetect) | [@paddle-js-models/facedetect](https://www.npmjs.com/package/@paddle-js-models/facedetect) | | ||
| | 螺丝钉检测 | [detect](./packages/paddlejs-models/detect) | [@paddle-js-models/detect](https://www.npmjs.com/package/@paddle-js-models/detect) | | ||
| | 人像分割背景替换 | [humanseg](./packages/paddlejs-models/humanseg) | [@paddle-js-models/humanseg](https://www.npmjs.com/package/@paddle-js-models/humanseg) | | ||
| | 手势识别AI猜丁壳 | [gesture](./packages/paddlejs-models/gesture) | [@paddle-js-models/gesture](https://www.npmjs.com/package/@paddle-js-models/gesture) | | ||
| | 1000种物品识别 | [mobilenet](./packages/paddlejs-models/mobilenet) | [@paddle-js-models/mobilenet](https://www.npmjs.com/package/@paddle-js-models/mobilenet) | | ||
| | 文本检测 | [ocrdetection](./packages/paddlejs-models/ocrdetection) | [@paddle-js-models/ocrdet](https://www.npmjs.com/package/@paddle-js-models/ocrdet) | | ||
| | 文本识别 | [ocr](./packages/paddlejs-models/ocr) | [@paddle-js-models/ocr](https://www.npmjs.com/package/@paddle-js-models/ocr) | | ||
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| ## 开发使用 | ||
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| 该部分是使用 `pnpm` 搭建的 Menorepo | ||
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| ### 安装依赖 | ||
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| ```sh | ||
| pnpm i | ||
| ``` | ||
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| ### 开发 | ||
| 参考 Package.json 使用 `yalc` 进行开发测试。 | ||
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| ```sh | ||
| pnpm run dev:xxx | ||
| ``` | ||
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| ### 整体简介 | ||
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| 1. 使用 rollup 一次性打包生成 commonjs 和 es 规范的代码；同时具有可扩展性；目前由于依赖的cv库有些问题；就没有配置umd打包。 | ||
| 2. 打包时基于 api-extractor 实现 d.ts 文件生成，实现支持 ts 引入生成我们的包 | ||
| 3. 基于 jest 支持测试并显示测试相关覆盖率等 | ||
| 4. 基于 ts 和 eslint 维护代码风格，保证代码更好开发 | ||
| 5. 基于 conventional-changelog-cli 实现自定义关键词生成对应生成changelog | ||
| 6. 基于 yalc 实现本地打包开发测试 | ||
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