require("@tensorflow/tfjs-node");
const fs= require ('fs');
const express = require("express");
const moment = require("moment");
// 配置 cors 这个中间件
const cors = require('cors')
// const faceapi = require("face-api.js");
const faceapi = require('@vladmandic/face-api');
const mongoose = require("mongoose");
const canvas = require("canvas");
const { Canvas, Image } = require("canvas");
const fileUpload = require("express-fileupload");
const multer = require('multer')
console.log('----')
faceapi.env.monkeyPatch({ Canvas, Image });

const app = express();
// 设置存放格式
const storage = multer.diskStorage({
  destination(req, file, cb) {
    cb(null, './uploads')
  },
  filename(req, file, cb) {
    cb(null, Date.now() + '-' + file.originalname)
  }
})
// 加载文件存储
const upload = multer({ storage })
// app.use(
//   fileUpload({
//     useTempFiles: true,
//   })
// );
app.use(cors())
app.use(express.static('./dist'))
app.use('/uploads', express.static('./uploads'));
async function LoadModels() {
  await faceapi.nets.faceRecognitionNet.loadFromDisk(__dirname + "/models");
  await faceapi.nets.faceLandmark68Net.loadFromDisk(__dirname + "/models");
  await faceapi.nets.ssdMobilenetv1.loadFromDisk(__dirname + "/models");
}

LoadModels();

const faceSchema = new mongoose.Schema({
  label: {
    type: String,
    required: true,
    unique: true,
  },
  images:{
    type:Array,
    required:true
  },
  descriptions: {
    type: Array,
    required: true,
  },
});

const FaceModel = mongoose.model("Face", faceSchema);


/**
 * 
 * @param {图片} images 
 * @param {图片标记：张三} label 
 * @returns 
 */
async function uploadLabeledImages(images, label) {
  try {
    let counter = 0;
    const descriptions = [];
    const imgs=[]
    // 循环图片
    for (let i = 0; i < images.length; i++) {
      const img = await canvas.loadImage(images[i]);
      counter = (i / images.length) * 100;
      console.log(`Progress = ${counter}%`);
      // Read each face and save the face descriptions in the descriptions array
      const detections = await faceapi.detectSingleFace(img).withFaceLandmarks().withFaceDescriptor();
      descriptions.push(detections.descriptor);
      imgs.push(images[i])
    }
    //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
    const createFace = new FaceModel({
      label: label,
      descriptions: descriptions,
      images:imgs
    });
    try{
      await createFace.save();
      console.log('人脸录入成功！')
      return true
    }catch(error){
      return error;
    }
  } catch (error) {
    console.log(error);
    return (error);
  }
}
/**
 * 清空 人脸库
 * @param {人物标记} label 
 * @returns 
 */
async function deleteLabelImages(label) {
  try{
    await FaceModel.deleteMany({ label: label })
    return true
  }catch(error){  
    return error
  }
  
}
/**
 * 清空 人脸库
 * @param {人物标记} label 
 * @returns 
 */
async function selectLabelImages(label) {
  try{
    await FaceModel.findOne({ label: label })
    return true
  }catch(error){  
    return error
  }
  
}


async function getDescriptorsFromDB(image) {
  // 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
  let faces = await FaceModel.find();
  for (i = 0; i < faces.length; i++) {
    // 将面数据描述符从obiects更改为Float32Array类型
    for (j = 0; j < faces[i].descriptions.length; j++) {
      faces[i].descriptions[j] = new Float32Array(Object.values(faces[i].descriptions[j]));
    }
    // 将DB面文档改为
    faces[i] = new faceapi.LabeledFaceDescriptors(faces[i].label, faces[i].descriptions);
  }

  // 加载面匹配器以查找匹配面
  const faceMatcher = new faceapi.FaceMatcher(faces, 0.45);
  // 使用画布或其他方法读取图像
  const img = await canvas.loadImage(image);
  let temp = faceapi.createCanvasFromMedia(img);
  // 处理模型的图像
  const displaySize = { width: img.width, height: img.height };
  faceapi.matchDimensions(temp, displaySize);
  const currentTime = moment().format("YYYY-MM-DD HH:mm:ss");
  // 查找匹配的人脸
  const detections = await faceapi.detectAllFaces(img).withFaceLandmarks().withFaceDescriptors();
  const currentTime2 = moment().format("YYYY-MM-DD HH:mm:ss");
  const resizedDetections = faceapi.resizeResults(detections, displaySize);
  const results = resizedDetections.map((d) => faceMatcher.findBestMatch(d.descriptor));
  return results;
}


/**
 * 上传人脸信息
 */
app.post("/post-face",upload.array('files',6),async (req,res)=>{
    let imgs=[]
    for (let i = 0; i < req.files.length; i++) {
      imgs.push(req.files[i].path)
    }
    let label = req.body.label
    let result = await uploadLabeledImages(imgs, label);
    console.log(result)
    if(result===true){ 
        res.json({code:200, message:"录入成功"})
    }else{
        res.json({code:400, message:"人脸模型录入失败，请重新录入"})
        
    }
})
/**验证人脸对比 */
app.post("/check-face",upload.single('file'), async (req, res) => {
  console.log(req.file)
  const File1 = req.file.path;
  let result = await getDescriptorsFromDB(File1);
  fs.unlink(File1,(err)=>{
    if(err){
      console.log('删除失败')
    }else{
      console.log('删除成功')
    }
  })
  res.json({code:200,result });
  
});
// 获取人脸模型
app.post('/get-face',upload.single('file'),async (req, res) => {
  console.log('------------')
  console.log(req.body)
  const label = req.body.label
  const result=await FaceModel.findOne({ "label": label })
  res.json({code:200,result})
})
// 清除人脸模型
app.post("/del-face",upload.single('file'),async(req,res)=>{
  console.log('------------')
  console.log(req.body)
  const label = req.body.label
  console.log('删除人脸库',label)
  let result= deleteLabelImages(label)
  if(result){
    res.json({message:'success'})
  }else{
    res.json({message:"err"})
  }
})
app.post("/test",async(req,res)=>{
  console.log('---测试--------')
  res.json({message:"err"})
  
})



// add your mongo key instead of the ***
mongoose
  .connect(
    `mongodb://localhost/test`,
    {
      useNewUrlParser: true,
      useUnifiedTopology: true,
      useCreateIndex: true,
    }
  )
  .then(() => {
    app.listen(process.env.PORT || 5000);
    console.log("DB connected and server us running.");
  })
  .catch((err) => {
    console.log(err);
  });