require("@tensorflow/tfjs-node");
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const fs= require ('fs');
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const express = require("express");
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const moment = require("moment");
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// 配置 cors 这个中间件
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const cors = require('cors')
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// const faceapi = require("face-api.js");
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const faceapi = require('@vladmandic/face-api');
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const mongoose = require("mongoose");
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const canvas = require("canvas");
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const { Canvas, Image } = require("canvas");
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const fileUpload = require("express-fileupload");
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const multer = require('multer')
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console.log('----')
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faceapi.env.monkeyPatch({ Canvas, Image });
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const app = express();
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// 设置存放格式
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const storage = multer.diskStorage({
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destination(req, file, cb) {
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cb(null, './uploads')
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},
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filename(req, file, cb) {
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cb(null, Date.now() + '-' + file.originalname)
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}
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})
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// 加载文件存储
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const upload = multer({ storage })
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// app.use(
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// fileUpload({
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// useTempFiles: true,
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// })
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// );
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app.use(cors())
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app.use(express.static('./web'))
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app.use('/uploads', express.static('./uploads'));
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async function LoadModels() {
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await faceapi.nets.faceRecognitionNet.loadFromDisk(__dirname + "/models");
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await faceapi.nets.faceLandmark68Net.loadFromDisk(__dirname + "/models");
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await faceapi.nets.ssdMobilenetv1.loadFromDisk(__dirname + "/models");
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}
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LoadModels();
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const faceSchema = new mongoose.Schema({
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label: {
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type: String,
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required: true,
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unique: true,
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},
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images:{
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type:Array,
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required:true
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},
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descriptions: {
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type: Array,
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required: true,
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},
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});
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const FaceModel = mongoose.model("Face", faceSchema);
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/**
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*
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* @param {图片} images
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* @param {图片标记：张三} label
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* @returns
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*/
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async function uploadLabeledImages(images, label) {
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try {
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let counter = 0;
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const descriptions = [];
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const imgs=[]
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// 循环图片
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for (let i = 0; i < images.length; i++) {
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const img = await canvas.loadImage(images[i]);
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counter = (i / images.length) * 100;
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console.log(`Progress = ${counter}%`);
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// Read each face and save the face descriptions in the descriptions array
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const detections = await faceapi.detectSingleFace(img).withFaceLandmarks().withFaceDescriptor();
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descriptions.push(detections.descriptor);
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imgs.push(images[i])
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}
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//用给定的标签创建一个新的面文档，并将其保存在数据库中
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const createFace = new FaceModel({
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label: label,
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descriptions: descriptions,
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images:imgs
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});
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try{
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await createFace.save();
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console.log('人脸录入成功！')
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return true
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}catch(error){
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return error;
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}
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} catch (error) {
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console.log(error);
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return (error);
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}
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}
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/**
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* 清空 人脸库
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* @param {人物标记} label
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* @returns
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*/
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async function deleteLabelImages(label) {
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try{
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await FaceModel.deleteMany({ label: label })
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return true
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}catch(error){
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return error
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}
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}
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/**
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* 清空 人脸库
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* @param {人物标记} label
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* @returns
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*/
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async function selectLabelImages(label) {
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try{
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await FaceModel.findOne({ label: label })
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return true
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}catch(error){
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return error
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}
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}
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async function getDescriptorsFromDB(image) {
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// 从mongodb获取所有的面部数据，并循环遍历每个面部数据以读取数据
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let faces = await FaceModel.find();
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for (i = 0; i < faces.length; i++) {
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// 将面数据描述符从obiects更改为Float32Array类型
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for (j = 0; j < faces[i].descriptions.length; j++) {
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faces[i].descriptions[j] = new Float32Array(Object.values(faces[i].descriptions[j]));
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}
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// 将DB面文档改为
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faces[i] = new faceapi.LabeledFaceDescriptors(faces[i].label, faces[i].descriptions);
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}
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// 加载面匹配器以查找匹配面
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const faceMatcher = new faceapi.FaceMatcher(faces, 0.45);
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// 使用画布或其他方法读取图像
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const img = await canvas.loadImage(image);
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let temp = faceapi.createCanvasFromMedia(img);
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// 处理模型的图像
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const displaySize = { width: img.width, height: img.height };
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faceapi.matchDimensions(temp, displaySize);
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const currentTime = moment().format("YYYY-MM-DD HH:mm:ss");
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// 查找匹配的人脸
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const detections = await faceapi.detectAllFaces(img).withFaceLandmarks().withFaceDescriptors();
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const currentTime2 = moment().format("YYYY-MM-DD HH:mm:ss");
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const resizedDetections = faceapi.resizeResults(detections, displaySize);
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const results = resizedDetections.map((d) => faceMatcher.findBestMatch(d.descriptor));
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return results;
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}
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/**
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* 上传人脸信息
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*/
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app.post("/post-face",upload.array('files',6),async (req,res)=>{
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let imgs=[]
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for (let i = 0; i < req.files.length; i++) {
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imgs.push(req.files[i].path)
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}
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let label = req.body.label
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console.log('---------------',label)
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let result = await uploadLabeledImages(imgs, label);
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console.log('---------------',result)
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if(result===true){
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res.json({code:200, message:"录入成功"})
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}else{
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// let result = await getDescriptorsFromDB(File1);
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imgs.forEach(e=>{
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fs.unlink(e,(err)=>{
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if(err){
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console.log('删除失败')
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}else{
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console.log('删除成功')
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}
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})
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})
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res.json({code:400, message:"人脸模型录入失败，请重新录入"})
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}
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})
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/**验证人脸对比 */
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app.post("/check-face",upload.single('file'), async (req, res) => {
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console.log(req.file)
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const File1 = req.file.path;
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try {
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let result = await getDescriptorsFromDB(File1);
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fs.unlink(File1,(err)=>{
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if(err){
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console.log('删除失败')
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}else{
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console.log('删除成功')
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}
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})
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res.json({code:200,result });
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} catch (error) {
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res.json({code:200,error });
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}
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});
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// 获取人脸模型
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app.post('/get-face',upload.single('file'),async (req, res) => {
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const label = req.body.label
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const result=await FaceModel.findOne({ "label": label })
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res.json({code:200,result})
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})
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// 清除人脸模型
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app.post("/del-face",upload.single('file'),async(req,res)=>{
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const label = req.body.label
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console.log('删除人脸库',label)
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FaceModel.findOne({ "label": label }).then(re=>{
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console.log('kankan---')
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console.log(re.images)
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if(re.images.length>0){
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for (let i = 0; i < re.images.length; i++) {
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fs.unlink(re.images[i],(err)=>{
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if(err){
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console.log('删除失败')
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}else{
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console.log('删除成功')
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}
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})
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}
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}
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})
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let result= deleteLabelImages(label)
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if(result){
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res.json({message:'success'})
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}else{
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res.json({message:"err"})
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}
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})
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app.post("/test",async(req,res)=>{
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console.log('---测试--------')
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res.json({message:"err"})
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})
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//链接数据库初始化
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mongoose
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.connect(
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`mongodb://localhost/test`,
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{
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useNewUrlParser: true,
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useUnifiedTopology: true,
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useCreateIndex: true,
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}
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)
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.then(() => {
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app.listen(process.env.PORT || 5000);
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console.log("DB connected and server us running.");
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console.log('http-sse-'+5000)
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})
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.catch((err) => {
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console.log(err);
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});
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