agent-app/rag-server/face-feat68/facetf.sdk.js

'use strict'
let os = require("os");
let isWindows = os.platform().startsWith("win");
let path = require("path");
// import nodejs bindings to native tensorflow,
// not required, but will speed up things drastically (python required)
let process = require("process");
process.env.TF_CPP_MIN_LOG_LEVEL=2

const shell = require("shelljs")
if(!isWindows){ // 仅在Linux下启动内存加速和GPU加速
    let nvidiaSmi = shell.exec("nvidia-smi",{silent:true})
    if(nvidiaSmi?.stdout.indexOf("CUDA")>-1){
        console.log("facetf gpu mode")
        require('@tensorflow/tfjs-node-gpu');
    }else{
        console.log("facetf cpu mode")
        require('@tensorflow/tfjs-node');
    }
}
// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
let canvas = require('canvas');

// import * as faceapi from 'face-api.js';
let faceapi = require('@vladmandic/face-api/dist/face-api.node.js');
const baseDir = path.resolve(__dirname, './tmp')  

 // 设置识别参数
    // mokey pathing the faceapi canvas
    const { Canvas, Image, ImageData } = canvas  
    faceapi.env.monkeyPatch({ Canvas, Image, ImageData })

    const faceDetectionNet = faceapi.nets.ssdMobilenetv1 

    // SsdMobilenetv1Options
    const minConfidence = 0.5

    // TinyFaceDetectorOptions
    const inputSize = 408  
    const scoreThreshold = 0.5

    // MtcnnOptions
    const minFaceSize = 50  
    const scaleFactor = 0.8

    const faceDetectionOptions = getFaceDetectorOptions(faceDetectionNet)




var FACE_TFJS_WEIGHTS_LOADED = false;

async function loadModel(){
    // load weights 首次调用加载模型
    if(!FACE_TFJS_WEIGHTS_LOADED){
        await faceDetectionNet.loadFromDisk(path.join(__dirname,'./model'))
        await faceapi.nets.faceLandmark68Net.loadFromDisk(path.join(__dirname,'./model'))
        await faceapi.nets.faceRecognitionNet.loadFromDisk(path.join(__dirname,'./model'))
        FACE_TFJS_WEIGHTS_LOADED = true
    }
}

loadModel()

class FacetfSDK{

    constructor(){

    }

    async getPhotoFeat68(photoUrl){
        let detections = await this.detectFaceWithLandmarksAndDescriptor(photoUrl);
        if(detections?.length>0){
            let feat68 = Array.from(detections[0]?.descriptor);
            return feat68
        }
        return null
    }
    async setReportFeat68(report){
        if(report?.className && !report?.get("feat68")){
            let detections = await this.detectFaceWithLandmarksAndDescriptor(report.get("photo"));
            if(detections?.length>0){
              let feat68 = Array.from(detections[0]?.descriptor);
              report.set("feat68",feat68)
            }
        }
        await report.save();
        return report
    }
    async detectFaceWithLandmarksAndDescriptor(img1){

    await loadModel();
    
    let referenceImage
    try{
        referenceImage = await canvas.loadImage(img1)
    }catch(err){
        throw "图片地址有误无法加载"
    }
    if(!(referenceImage)){
        throw "图片不够清晰请重新上传"
    }

    let detections = await faceapi.detectAllFaces(referenceImage, faceDetectionOptions)
    .withFaceLandmarks()
    .withFaceDescriptors()

    if(!detections){
        throw "图片不够清晰请重新上传"
    }
    if(detections.length==0){
        // openapi.goWrong(response,"照片未上传或不清晰，请重新上传");
        let imgstr = ""
        if(detections.length==0){
            imgstr += "<1>"
        }
        throw `图${imgstr}无法识别人脸`
    }

    // 获取特征向量 68关键点 以及128特征向量
    return detections
    }
}

function getFaceDetectorOptions(net) {  
    return net === faceapi.nets.ssdMobilenetv1
        ? new faceapi.SsdMobilenetv1Options({ minConfidence })
        : (net === faceapi.nets.tinyFaceDetector
            ? new faceapi.TinyFaceDetectorOptions({ inputSize, scoreThreshold })
            : new faceapi.MtcnnOptions({ minFaceSize, scaleFactor })
        )
}

module.exports.FacetfSDK = FacetfSDK