需求：前端获取到摄像头信息，通过模型来进行判断人像是否在镜头中，镜头是否有被遮挡。

实现步骤：

1、通过video标签来展示摄像头中的内容

2、通过canvas来绘制视频中信息进行展示

3、在拍照时候将canvas的当前帧转成图片

第一步：下载引入必要包

下载依赖

face-api.js是核心依赖必须要下

npm install face-api.js

element-ui为了按钮好看一点（可以不下） ，axios用于请求发送

npm istall element-ui axios -S

 element-ui根据官方文档进行引入使用

import Vue from 'vue';
import ElementUI from 'element-ui';
import 'element-ui/lib/theme-chalk/index.css';
import App from './App.vue';
Vue.use(ElementUI);
new Vue({
  el: '#app',
  render: h => h(App)
});

下载model

下载地址： 模板地址 如果访问出现异常请科学上网

将项目中的model放入VUE中的public文件加下

VUE+faceApi.js实现摄像头拍摄人脸识别

VUE+faceApi.js实现摄像头拍摄人脸识别

第二步：先把HTML写上去 

<template>
    <div>
        <el-button type="primary" @click="useCamera">打开摄像头</el-button>
        <el-button type="plain" @click="photoShoot">拍照</el-button>
        <el-alert
            :title="httpsAlert"
            type="info"
            :closable="false"
            v-show="httpsAlert !== ''">
        </el-alert>
        <div class="videoImage" ref="faceBox">
            <video ref="video" style="display: none;"></video>
            <canvas ref="canvas" width="400" height="400" v-show="videoShow"></canvas>
            <img ref="image" :src="picture" alt="" v-show="pictureShow">
        </div>
    </div>
</template>

第三步 可以开始代码了

首先引入下载好的face-api.js包

import * as faceApi from 'face-api.js'

 以下是需要用到的属性

1、视频和图片不同时出现

videoShow: false,
pictureShow: false,

2、生成图片后用于保存图片路径

picture: '',

3、因为在操作时会用到DOM所以将要用到虚拟DOM保存在data中

canvas: null,
video: null,
image: null,

 4、模型识别时直接传入此属性，在初始化时赋值（可省略，直接卸载逻辑代码中）

options: ''

 5、在人脸识别时对结果进行反馈（识别出人像数量大于1或小于1时给出提示）

noOne: '',
moreThanOne: '',

6、如果用户不是在https下进行使用摄像头调用给出提示

httpsAlert: ''

属性准备好之后就可以开始初始化了

1、初始化模型

2、获取需要用到的虚拟DOM

async init() {
    await faceApi.nets.ssdMobilenetv1.loadFromUri("/models");
    await faceApi.loadFaceLandmarkModel("/models");
    this.options = new faceApi.SsdMobilenetv1Options({
        minConfidence: 0.5, // 0.1 ~ 0.9
    });
    // 视频中识别使用的节点
    this.video = this.$refs.video
    this.canvas = this.$refs.canvas
    this.image = this.$refs.image
}

 调用摄像头

通过navigator.mediaDevices.getUserMedia()

useCamera(){
    this.videoShow = true
    this.pictureShow = false
    this.cameraOptions()
},
cameraOptions(){
    let constraints = {
        video: {
            width: 400,
            height: 400
        }
    }
    // 如果不是通过loacalhost或者通过https访问会将报错捕获并提示
    try{
        let promise = navigator.mediaDevices.getUserMedia(constraints);
        promise.then((MediaStream) => {
            // 返回参数
            this.video.srcObject = MediaStream;
            this.video.play();
            this.recognizeFace()
        }).catch((error) => {
            console.log(error);
        });
    }catch(err){
        this.httpsAlert = `您现在在使用非Https访问，
        请先在chrome://flags/#unsafely-treat-insecure-origin-as-secure中修改配置,
        添将当前链接${window.location.href}添加到列表,
        并且将Insecure origins treated as secure修改为enabled,
        修改完成后请重启浏览器后再次访问！`
    }
}

识别视频中的人像

这里通过递归的方式将视频中的内容用canvas显示

将canvas的节点传入到faceApi的方法中进行识别

通过faceApi返回的数组可以得到当前人脸的识别状况(数组长度0没有识别到人脸，长度1识别到一个人脸...以此类推)

async recognizeFace(){
    if (this.video.paused) return clearTimeout(this.timeout);
    this.canvas.getContext('2d', { willReadFrequently: true }).drawImage(this.video, 0, 0, 400, 400);
    const results = await faceApi.detectAllFaces(this.canvas, this.options).withFaceLandmarks();
    if(results.length === 0){
        if(this.moreThanOne !== ''){
            this.moreThanOne.close()
            this.moreThanOne = ''
        }
        if(this.noOne === ''){
            this.noOne = this.$message({
                message: '未识别到人脸',
                type: 'warning',
                duration: 0
            });
        }
    }else if(results.length > 1){
        if(this.noOne !== ''){
            this.noOne.close()
            this.noOne = ''
        }
        if(this.moreThanOne === ''){
            this.moreThanOne = this.$message({
                message: '检测到镜头中有多个人',
                type: 'warning',
                duration: 0
            });
        }
    }else{
        if(this.noOne !== ''){
            this.noOne.close()
            this.noOne = ''
        }
        if(this.moreThanOne !== ''){
            this.moreThanOne.close()
            this.moreThanOne = ''
        }
    }
    this.timeout = setTimeout(() => {
        return this.recognizeFace()
    });
},

拍照上传 

async photoShoot(){
    // 拿到图片的base64
    let canvas = this.canvas.toDataURL("image/png");
    // 拍照以后将video隐藏
    this.videoShow = false
    this.pictureShow = true
    // 停止摄像头成像
    this.video.srcObject.getTracks()[0].stop()
    this.video.pause()
    if(canvas) {
        // 拍照将base64转为file流文件
        let blob = this.dataURLtoBlob(canvas);
        let file = this.blobToFile(blob, "imgName");
        // 将blob图片转化路径图片
        let image = window.URL.createObjectURL(file)
        this.picture = image
        return
        let formData = new FormData()
        formData.append('file', this.picture)
        axios({
            method: 'post',
            url: '/user/12345',
            data: formData
        }).then(res => {
            console.log(res)
        }).catch(err => {
            console.log(err)
        })
    } else {
        console.log('canvas生成失败')
    }
},

需要用到的图片格式转换方法 

方法1：先将base64转为文件

方法2：设置新的文件中的参数信息

dataURLtoBlob(dataurl) {
    let arr = dataurl.split(','),
        mime = arr[0].match(/:(.*?);/)[1],
        bstr = atob(arr[1]),
        n = bstr.length,
        u8arr = new Uint8Array(n);
    while(n--) {
        u8arr[n] = bstr.charCodeAt(n);
    }
    return new Blob([u8arr], {
        type: mime
    });
},
blobToFile(theBlob, fileName) {
    theBlob.lastModifiedDate = new Date().toLocaleDateString();
    theBlob.name = fileName;
    return theBlob;
},

完整代码

import bingImage from '@/assets/bbt1.jpg';
import BingWallpaper from '@/assets/BingWallpaper.jpg';
import * as faceApi from 'face-api.js'
export default {
    name: 'Recognize',
    data(){
        return{
            videoShow: false,
            pictureShow: false,
            // 图片地址
            picture: '',
            // 用于视频识别的节点
            canvas: null,
            video: null,
            image: null,
            timeout: 0,
            // 模型识别的条件
            options: '',
            // 提示控制
            noOne: '',
            moreThanOne: '',
            // 不是通过Https访问提示
            httpsAlert: '',
        }
    },
    mounted() {
        // 初始化
        this.init()
    },
    beforeDestroy() {
        clearTimeout(this.timeout);
    },
    methods: {
        async init() {
            await faceApi.nets.ssdMobilenetv1.loadFromUri("/models");
            await faceApi.loadFaceLandmarkModel("/models");
            this.options = new faceApi.SsdMobilenetv1Options({
                minConfidence: 0.5, // 0.1 ~ 0.9
            });
            // 视频中识别使用的节点
            this.video = this.$refs.video
            this.canvas = this.$refs.canvas
            this.image = this.$refs.image
        },
        /**
         * 使用视频来成像摄像头
         */
        useCamera(){
            this.videoShow = true
            this.pictureShow = false
            this.cameraOptions()
        },
        /**
         * 使用摄像头
         */
        cameraOptions(){
            let constraints = {
                video: {
                    width: 400,
                    height: 400
                }
            }
            // 如果不是通过loacalhost或者通过https访问会将报错捕获并提示
            try{
                let promise = navigator.mediaDevices.getUserMedia(constraints);
                promise.then((MediaStream) => {
                    // 返回参数
                    this.video.srcObject = MediaStream;
                    this.video.play();
                    this.recognizeFace()
                }).catch((error) => {
                    console.log(error);
                });
            }catch(err){
                this.httpsAlert = `您现在在使用非Https访问，
                请先在chrome://flags/#unsafely-treat-insecure-origin-as-secure中修改配置,
                添将当前链接${window.location.href}添加到列表,
                并且将Insecure origins treated as secure修改为enabled,
                修改完成后请重启浏览器后再次访问！`
            }
        },
        /**
         * 人脸识别方法
         * 通过canvas节点识别
         * 节点对象执行递归识别绘制
         */
        async recognizeFace(){
            if (this.video.paused) return clearTimeout(this.timeout);
            this.canvas.getContext('2d', { willReadFrequently: true }).drawImage(this.video, 0, 0, 400, 400);
            const results = await faceApi.detectAllFaces(this.canvas, this.options).withFaceLandmarks();
            if(results.length === 0){
                if(this.moreThanOne !== ''){
                    this.moreThanOne.close()
                    this.moreThanOne = ''
                }
                if(this.noOne === ''){
                    this.noOne = this.$message({
                        message: '未识别到人脸',
                        type: 'warning',
                        duration: 0
                    });
                }
            }else if(results.length > 1){
                if(this.noOne !== ''){
                    this.noOne.close()
                    this.noOne = ''
                }
                if(this.moreThanOne === ''){
                    this.moreThanOne = this.$message({
                        message: '检测到镜头中有多个人',
                        type: 'warning',
                        duration: 0
                    });
                }
            }else{
                if(this.noOne !== ''){
                    this.noOne.close()
                    this.noOne = ''
                }
                if(this.moreThanOne !== ''){
                    this.moreThanOne.close()
                    this.moreThanOne = ''
                }
            }
            // 通过canvas显示video信息
            this.timeout = setTimeout(() => {
                return this.recognizeFace()
            });
        },
        /**
         * 拍照上传
         */
        async photoShoot(){
            // 拿到图片的base64
            let canvas = this.canvas.toDataURL("image/png");
            // 拍照以后将video隐藏
            this.videoShow = false
            this.pictureShow = true
            // 停止摄像头成像
            this.video.srcObject.getTracks()[0].stop()
            this.video.pause()
            if(canvas) {
                // 拍照将base64转为file流文件
                let blob = this.dataURLtoBlob(canvas);
                console.log(blob)
                let file = this.blobToFile(blob, "imgName");
                console.info(file);
                // 将blob图片转化路径图片
                let image = window.URL.createObjectURL(file)
                this.picture = image
                // 将拍照后的图片发送给后端
                let formData = new FormData()
                formData.append('file', this.picture)
                axios({
                    method: 'post',
                    url: '/user/12345',
                    data: formData
                }).then(res => {
                    console.log(res)
                }).catch(err => {
                    console.log(err)
                })
            } else {
                console.log('canvas生成失败')
            }
        },
        /**
         * 将图片转为blob格式
         * dataurl 拿到的base64的数据
         */
        dataURLtoBlob(dataurl) {
            let arr = dataurl.split(','),
                mime = arr[0].match(/:(.*?);/)[1],
                bstr = atob(arr[1]),
                n = bstr.length,
                u8arr = new Uint8Array(n);
            while(n--) {
                u8arr[n] = bstr.charCodeAt(n);
            }
            return new Blob([u8arr], {
                type: mime
            });
        },
        /**
         * 生成文件信息
         * theBlob 文件
         * fileName 文件名字
         */
        blobToFile(theBlob, fileName) {
            theBlob.lastModifiedDate = new Date().toLocaleDateString();
            theBlob.name = fileName;
            return theBlob;
        },
    }
}
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