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使用相机拍照实现图像识别

首先需要下载节点 node-red-contrib-tfjs-coco-ssd，下载不上的朋友可以根据【Node-Red】最新版coco-ssd 1.0.6安装方法（windows）文章进行安装。

1、智能识别图片

使用本地文件的形式对图像进行识别

• 时间戳（inject）：作为触发点节点
• 文件路径（file in）：写入需要识别的图像路径，例如：D:\node-redPicture\123.jpeg，在输出中选择buffer流
• tf coco ssd：此节点中，Threshold为分数阈值（0-1），也可通过传参msg.scoreThreshold 进行修改；
  Model Url为地址，尽量不要改动，修改为连接不成功的地址后node-red后台会崩溃；
  Passthru：可以选择图片显示模式；
  Box colour：当Passthru中选择为做过标注的图片，那就需要对标注颜色做设定
  
• msg：在msg节点中设置为与调试输出相同来查看完整输出信息
  payload为数组格式，当识别物体为多种时，都可以显示在数组中，在数组中还显示了标注框位置、类型、打分
  image为buffer类型的数组存放图片内容
  classes为识别类型及数量
  

2、将识别信息显示在UI界面

根据如上输出的msg，将classes和image 进行输出。

• base64：需要下载新的节点node-red-node-base64，并将属性改为image，实现对msg.image的buffer转为base64进行输出。
• 识别信息（text）：设置为{{msg.classes}}实现将识别类型和数量进行输出
• template：将保存在msg.image中的base64码的图片进行输出

<img src="data:image/png;base64,{{msg.image}}"/>

3、将使用相机拍摄并智能识别

将文件路径节点换成webcam相机节点即可实现使用相机拍照并智能识别显示在UI界面。

Webcam节点：需要下载node-red-contrib-webcam节点，此节点支持多个相机的选择。

4、源码

包含有使用文件和相机识别的源码，导入后记得file in节点中修改文件路径为自己电脑上的图片哦。

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